{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# SWOT Hydrology Dataset Exploration on a local machine\n",
    "\n",
    "## Accessing and Visualizing SWOT Datasets\n",
    "\n",
    "### Requirement:\n",
    "Local compute environment e.g. laptop, server: this tutorial can be run on your local machine.\n",
    "\n",
    "### Learning Objectives:\n",
    "- Access SWOT HR data products (archived in NASA Earthdata Cloud) by downloading to local machine\n",
    "- Visualize accessed data for a quick check\n",
    "\n",
    "#### SWOT Level 2 KaRIn High Rate Version 2.0 Datasets:\n",
    "\n",
    "1. **River Vector Shapefile** - SWOT_L2_HR_RIVERSP_2.0\n",
    "\n",
    "2. **Lake Vector Shapefile** - SWOT_L2_HR_LAKESP_2.0\n",
    "\n",
    "3. **Water Mask Pixel Cloud NetCDF** - SWOT_L2_HR_PIXC_2.0\n",
    "\n",
    "4. **Water Mask Pixel Cloud Vector Attribute NetCDF** - SWOT_L2_HR_PIXCVec_2.0\n",
    "\n",
    "5. **Raster NetCDF** - SWOT_L2_HR_Raster_2.0\n",
    "\n",
    "_Notebook Author: Cassie Nickles, NASA PO.DAAC (Feb 2024) || Other Contributors: Zoe Walschots (PO.DAAC Summer Intern 2023), Catalina Taglialatela (NASA PO.DAAC), Luis Lopez (NASA NSIDC DAAC), Brent Williams (NASA JPL)_\n",
    "\n",
    "_Last updated: 9 July 2024_\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries Needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import h5netcdf\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import contextily as cx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import hvplot.xarray\n",
    "import zipfile\n",
    "import earthaccess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Earthdata Login\n",
    "\n",
    "An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. If you don't already have one, please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use `earthaccess` to authenticate your login credentials below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "auth = earthaccess.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Single File Access\n",
    "\n",
    "#### **1. River Vector Shapefiles**\n",
    "\n",
    "The https access link can be found using `earthaccess` data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the 'Reach' title in the data link.\n",
    "\n",
    "Alternatively, Earthdata Search [(see tutorial)](https://nasa-openscapes.github.io/2021-Cloud-Workshop-AGU/tutorials/01_Earthdata_Search.html) can be used to manually search in a GUI interface.\n",
    "\n",
    "For additional tips on spatial searching of SWOT HR L2 data, see also [PO.DAAC Cookbook - SWOT Chapter tips section](https://podaac.github.io/tutorials/quarto_text/SWOT.html#tips-for-swot-hr-spatial-search).\n",
    "\n",
    "#### Search for the data of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 8\n"
     ]
    }
   ],
   "source": [
    "#Retrieves granule from the day we want, in this case by passing to `earthaccess.search_data` function the data collection shortname, temporal bounds, and filter by wildcards\n",
    "river_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_RIVERSP_2.0', \n",
    "                                        temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time\n",
    "                                        granule_name = '*Reach*_287_NA*') # here we filter by Reach files (not node), pass=287, continent code=NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dowload, unzip, read the data\n",
    "\n",
    "Let's download the first data file! `earthaccess.download` has a list as the input format, so we need to put brackets around the single file we pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Getting 1 granules, approx download size: 0.01 GB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 62.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File SWOT_L2_HR_RiverSP_Reach_010_287_NA_20240204T060400_20240204T060409_PIC0_01.zip already downloaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 996.51it/s]\n",
      "COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['data_downloads\\\\SWOT_L2_HR_RiverSP_Reach_010_287_NA_20240204T060400_20240204T060409_PIC0_01.zip']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthaccess.download([river_results[0]], \"./data_downloads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we must first extract the data to open it. First, we'll programmatically get the filename we just downloaded, and then extract all data to the `data_downloads` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SWOT_L2_HR_RiverSP_Reach_010_287_NA_20240204T060400_20240204T060409_PIC0_01.zip'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = earthaccess.results.DataGranule.data_links(river_results[0], access='external')\n",
    "filename = filename[0].split(\"/\")[-1]\n",
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(f'data_downloads/{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('data_downloads')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the shapefile using `geopandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filename_shp = filename.replace('.zip','.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "1     71224800101 -1.000000e+12 -1.000000e+12               no_data   \n",
       "2     71224800114 -1.000000e+12 -1.000000e+12               no_data   \n",
       "3     71224800123  7.603424e+08  7.603424e+08  2024-02-04T06:13:10Z   \n",
       "4     71224800133  7.603424e+08  7.603424e+08  2024-02-04T06:13:10Z   \n",
       "...           ...           ...           ...                   ...   \n",
       "1058  77127000061  7.603418e+08  7.603419e+08  2024-02-04T06:04:09Z   \n",
       "1059  77127000071 -1.000000e+12 -1.000000e+12               no_data   \n",
       "1060  77127000131  7.603418e+08  7.603419e+08  2024-02-04T06:04:09Z   \n",
       "1061  77127000141 -1.000000e+12 -1.000000e+12               no_data   \n",
       "1062  77127000151 -1.000000e+12 -1.000000e+12               no_data   \n",
       "\n",
       "          p_lat      p_lon river_name           wse         wse_u  \\\n",
       "0     48.724265 -92.406254    no_data -1.000000e+12 -1.000000e+12   \n",
       "1     48.739159 -92.290054    no_data -1.000000e+12 -1.000000e+12   \n",
       "2     48.743344 -92.283320    no_data -1.000000e+12 -1.000000e+12   \n",
       "3     48.751442 -92.242669    no_data  3.585147e+02  2.006910e+00   \n",
       "4     48.762334 -92.189341    no_data  3.579681e+02  1.451600e-01   \n",
       "...         ...        ...        ...           ...           ...   \n",
       "1058  18.050684 -98.761645    no_data  6.529558e+02  1.000896e+02   \n",
       "1059  17.981704 -98.686712    no_data -1.000000e+12 -1.000000e+12   \n",
       "1060  18.102586 -98.771552    no_data  6.576003e+02  1.240586e+02   \n",
       "1061  18.094132 -98.694466    no_data -1.000000e+12 -1.000000e+12   \n",
       "1062  18.097046 -98.657280    no_data -1.000000e+12 -1.000000e+12   \n",
       "\n",
       "           wse_r_u  ...   p_wid_var  p_n_nodes  p_dist_out      p_length  \\\n",
       "0    -1.000000e+12  ...  232341.227         90   47778.423  18013.132474   \n",
       "1    -1.000000e+12  ...     767.700          6   48958.712   1180.288364   \n",
       "2    -1.000000e+12  ...    2911.208          3   49549.648    590.936467   \n",
       "3     2.004890e+00  ...   57688.777         31   55684.066   6134.417666   \n",
       "4     1.138900e-01  ...   20821.463         13   58222.719   2538.653439   \n",
       "...            ...  ...         ...        ...         ...           ...   \n",
       "1058  1.000896e+02  ...     784.041         67  667747.660  13493.202300   \n",
       "1059 -1.000000e+12  ...     824.145         97  687123.984  19376.324005   \n",
       "1060  1.240586e+02  ...     281.012         77  683164.834  15417.173639   \n",
       "1061 -1.000000e+12  ...     414.760         54  693896.634  10731.799933   \n",
       "1062 -1.000000e+12  ...     436.883         54  704624.208  10727.574606   \n",
       "\n",
       "             p_maf  p_dam_id  p_n_ch_max  p_n_ch_mod  p_low_slp  \\\n",
       "0    -1.000000e+12         0           2           1          0   \n",
       "1    -1.000000e+12         0           2           1          0   \n",
       "2    -1.000000e+12     23000           2           1          0   \n",
       "3    -1.000000e+12         0           2           1          0   \n",
       "4    -1.000000e+12         0           3           1          0   \n",
       "...            ...       ...         ...         ...        ...   \n",
       "1058 -1.000000e+12         0           2           1          0   \n",
       "1059 -1.000000e+12         0           3           1          0   \n",
       "1060 -1.000000e+12         0           2           1          0   \n",
       "1061 -1.000000e+12         0           2           1          0   \n",
       "1062 -1.000000e+12         0           2           1          0   \n",
       "\n",
       "                                               geometry  \n",
       "0     LINESTRING (-92.51093 48.70847, -92.51052 48.7...  \n",
       "1     LINESTRING (-92.29723 48.73905, -92.29682 48.7...  \n",
       "2     LINESTRING (-92.28569 48.74125, -92.28495 48.7...  \n",
       "3     LINESTRING (-92.28196 48.74559, -92.28163 48.7...  \n",
       "4     LINESTRING (-92.20553 48.75837, -92.20512 48.7...  \n",
       "...                                                 ...  \n",
       "1058  LINESTRING (-98.81280 18.06539, -98.81280 18.0...  \n",
       "1059  LINESTRING (-98.71239 18.03246, -98.71239 18.0...  \n",
       "1060  LINESTRING (-98.81280 18.06539, -98.81280 18.0...  \n",
       "1061  LINESTRING (-98.71770 18.11625, -98.71764 18.1...  \n",
       "1062  LINESTRING (-98.66628 18.07224, -98.66611 18.0...  \n",
       "\n",
       "[1063 rows x 127 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SWOT_HR_shp1 = gpd.read_file(f'data_downloads/{filename_shp}') \n",
    "\n",
    "#view the attribute table\n",
    "SWOT_HR_shp1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Quickly plot the SWOT river data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple plot\n",
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "SWOT_HR_shp1.plot(ax=ax, color='black')\n",
    "cx.add_basemap(ax, crs=SWOT_HR_shp1.crs, source=cx.providers.OpenTopoMap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Another way to plot geopandas dataframes is with `explore`, which also plots a basemap\n",
    "SWOT_HR_shp1.explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **2. Lake Vector Shapefiles**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lake vector shapefiles can be accessed in the same way as the river shapefiles above.\n",
    "\n",
    "For additional tips on spatial searching of SWOT HR L2 data, see also [PO.DAAC Cookbook - SWOT Chapter tips section](https://podaac.github.io/tutorials/quarto_text/SWOT.html#tips-for-swot-hr-spatial-search)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Search for data of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 8\n"
     ]
    }
   ],
   "source": [
    "lake_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_LAKESP_2.0', \n",
    "                                        temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time\n",
    "                                        granule_name = '*Prior*_287_NA*') # here we filter by files with 'Prior' in the name (This collection has three options: Obs, Unassigned, and Prior), pass 287 and continent code=NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download the first data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Getting 1 granules, approx download size: 0.07 GB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 199.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.zip already downloaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 986.43it/s]\n",
      "COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['data_downloads\\\\SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.zip']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthaccess.download([lake_results[0]], \"./data_downloads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we must first extract the data to open it. First, we'll programmatically get the filename we just downloaded, and then extract all data to the `SWOT_downloads` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.zip'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename2 = earthaccess.results.DataGranule.data_links(lake_results[0], access='external')\n",
    "filename2 = filename2[0].split(\"/\")[-1]\n",
    "filename2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(f'data_downloads/{filename2}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('data_downloads')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the shapefile using `geopandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SWOT_L2_HR_LakeSP_Prior_010_287_NA_20240204T060400_20240204T061541_PIC0_01.shp'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename_shp2 = filename2.replace('.zip','.shp')\n",
    "filename_shp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lake_id</th>\n",
       "      <th>reach_id</th>\n",
       "      <th>obs_id</th>\n",
       "      <th>overlap</th>\n",
       "      <th>n_overlap</th>\n",
       "      <th>time</th>\n",
       "      <th>time_tai</th>\n",
       "      <th>time_str</th>\n",
       "      <th>wse</th>\n",
       "      <th>wse_u</th>\n",
       "      <th>...</th>\n",
       "      <th>lake_name</th>\n",
       "      <th>p_res_id</th>\n",
       "      <th>p_lon</th>\n",
       "      <th>p_lat</th>\n",
       "      <th>p_ref_wse</th>\n",
       "      <th>p_ref_area</th>\n",
       "      <th>p_date_t0</th>\n",
       "      <th>p_ds_t0</th>\n",
       "      <th>p_storage</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7120822822</td>\n",
       "      <td>no_data</td>\n",
       "      <td>712239R999998</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>7.603424e+08</td>\n",
       "      <td>7.603424e+08</td>\n",
       "      <td>2024-02-04T06:13:08Z</td>\n",
       "      <td>5.281870e+02</td>\n",
       "      <td>1.500000e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>-91.557528</td>\n",
       "      <td>47.616292</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>1.038600</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((-91.56583 47.61200, -91.56589 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7120822902</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>-91.623241</td>\n",
       "      <td>47.756499</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>0.113400</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7120822932</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>TONY LAKE</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>-91.635242</td>\n",
       "      <td>47.726123</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>0.017100</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7120822982</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>-91.665522</td>\n",
       "      <td>47.705366</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>0.026100</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7120823182</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>HEART LAKE</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>-91.651807</td>\n",
       "      <td>47.769148</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>0.124200</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65495</th>\n",
       "      <td>7130133552</td>\n",
       "      <td>no_data</td>\n",
       "      <td>713244L999972;713244L000002</td>\n",
       "      <td>73;2</td>\n",
       "      <td>2</td>\n",
       "      <td>7.603424e+08</td>\n",
       "      <td>7.603424e+08</td>\n",
       "      <td>2024-02-04T06:14:01Z</td>\n",
       "      <td>3.926960e+02</td>\n",
       "      <td>5.900000e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>-90.889026</td>\n",
       "      <td>50.669027</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>0.695690</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>POLYGON ((-90.89210 50.67709, -90.89185 50.677...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65496</th>\n",
       "      <td>7130141612</td>\n",
       "      <td>no_data</td>\n",
       "      <td>713245L999974;713245L000001</td>\n",
       "      <td>31;5</td>\n",
       "      <td>2</td>\n",
       "      <td>7.603424e+08</td>\n",
       "      <td>7.603425e+08</td>\n",
       "      <td>2024-02-04T06:14:08Z</td>\n",
       "      <td>3.741850e+02</td>\n",
       "      <td>8.200000e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>LAKE ST JOSEPH;ST JOSEPH</td>\n",
       "      <td>-99999999</td>\n",
       "      <td>-90.750682</td>\n",
       "      <td>51.061383</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>0.256500</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((-90.75251 51.06305, -90.75228 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65497</th>\n",
       "      <td>7420206383</td>\n",
       "      <td>74226000013;74227100043;74227100013;7422710006...</td>\n",
       "      <td>742214L000175;742214L999934;742214L000500;7422...</td>\n",
       "      <td>64;23;0;0</td>\n",
       "      <td>4</td>\n",
       "      <td>7.603421e+08</td>\n",
       "      <td>7.603422e+08</td>\n",
       "      <td>2024-02-04T06:09:01Z</td>\n",
       "      <td>1.875740e+02</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>LAKE TEXOMA</td>\n",
       "      <td>1135</td>\n",
       "      <td>-96.688976</td>\n",
       "      <td>33.901142</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>257.028517</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((-96.70899 33.82534, -96.70885 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65498</th>\n",
       "      <td>7420280413</td>\n",
       "      <td>74246000423;74246000413;74246000404</td>\n",
       "      <td>742218L999996;742218L001654;742219L999885</td>\n",
       "      <td>4;0;0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.603422e+08</td>\n",
       "      <td>7.603422e+08</td>\n",
       "      <td>2024-02-04T06:09:48Z</td>\n",
       "      <td>1.941800e+02</td>\n",
       "      <td>2.100000e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>OOLAGAHL LAKE;OOLOGAH LAKE</td>\n",
       "      <td>1032</td>\n",
       "      <td>-95.593848</td>\n",
       "      <td>36.550604</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>123.796498</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((-95.67217 36.43803, -95.67157 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65499</th>\n",
       "      <td>7710056183</td>\n",
       "      <td>77125000273;77125000263;77125000283;7712500030...</td>\n",
       "      <td>771186L999995;771186L000013;771186L999993</td>\n",
       "      <td>12;1;0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.603419e+08</td>\n",
       "      <td>7.603419e+08</td>\n",
       "      <td>2024-02-04T06:04:22Z</td>\n",
       "      <td>4.586500e+02</td>\n",
       "      <td>3.400000e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>PRESA EL CARACOL</td>\n",
       "      <td>1384</td>\n",
       "      <td>-99.861530</td>\n",
       "      <td>17.975030</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>35.410155</td>\n",
       "      <td>no_data</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>-1.000000e+12</td>\n",
       "      <td>MULTIPOLYGON (((-99.75753 18.02115, -99.75739 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65500 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          lake_id                                           reach_id  \\\n",
       "0      7120822822                                            no_data   \n",
       "1      7120822902                                            no_data   \n",
       "2      7120822932                                            no_data   \n",
       "3      7120822982                                            no_data   \n",
       "4      7120823182                                            no_data   \n",
       "...           ...                                                ...   \n",
       "65495  7130133552                                            no_data   \n",
       "65496  7130141612                                            no_data   \n",
       "65497  7420206383  74226000013;74227100043;74227100013;7422710006...   \n",
       "65498  7420280413                74246000423;74246000413;74246000404   \n",
       "65499  7710056183  77125000273;77125000263;77125000283;7712500030...   \n",
       "\n",
       "                                                  obs_id    overlap n_overlap  \\\n",
       "0                                          712239R999998         99         1   \n",
       "1                                                no_data    no_data   no_data   \n",
       "2                                                no_data    no_data   no_data   \n",
       "3                                                no_data    no_data   no_data   \n",
       "4                                                no_data    no_data   no_data   \n",
       "...                                                  ...        ...       ...   \n",
       "65495                        713244L999972;713244L000002       73;2         2   \n",
       "65496                        713245L999974;713245L000001       31;5         2   \n",
       "65497  742214L000175;742214L999934;742214L000500;7422...  64;23;0;0         4   \n",
       "65498          742218L999996;742218L001654;742219L999885      4;0;0         3   \n",
       "65499          771186L999995;771186L000013;771186L999993     12;1;0         3   \n",
       "\n",
       "               time      time_tai              time_str           wse  \\\n",
       "0      7.603424e+08  7.603424e+08  2024-02-04T06:13:08Z  5.281870e+02   \n",
       "1     -1.000000e+12 -1.000000e+12               no_data -1.000000e+12   \n",
       "2     -1.000000e+12 -1.000000e+12               no_data -1.000000e+12   \n",
       "3     -1.000000e+12 -1.000000e+12               no_data -1.000000e+12   \n",
       "4     -1.000000e+12 -1.000000e+12               no_data -1.000000e+12   \n",
       "...             ...           ...                   ...           ...   \n",
       "65495  7.603424e+08  7.603424e+08  2024-02-04T06:14:01Z  3.926960e+02   \n",
       "65496  7.603424e+08  7.603425e+08  2024-02-04T06:14:08Z  3.741850e+02   \n",
       "65497  7.603421e+08  7.603422e+08  2024-02-04T06:09:01Z  1.875740e+02   \n",
       "65498  7.603422e+08  7.603422e+08  2024-02-04T06:09:48Z  1.941800e+02   \n",
       "65499  7.603419e+08  7.603419e+08  2024-02-04T06:04:22Z  4.586500e+02   \n",
       "\n",
       "              wse_u  ...                   lake_name  p_res_id      p_lon  \\\n",
       "0      1.500000e-02  ...                     no_data -99999999 -91.557528   \n",
       "1     -1.000000e+12  ...                     no_data -99999999 -91.623241   \n",
       "2     -1.000000e+12  ...                   TONY LAKE -99999999 -91.635242   \n",
       "3     -1.000000e+12  ...                     no_data -99999999 -91.665522   \n",
       "4     -1.000000e+12  ...                  HEART LAKE -99999999 -91.651807   \n",
       "...             ...  ...                         ...       ...        ...   \n",
       "65495  5.900000e-02  ...                     no_data -99999999 -90.889026   \n",
       "65496  8.200000e-02  ...    LAKE ST JOSEPH;ST JOSEPH -99999999 -90.750682   \n",
       "65497  1.000000e-03  ...                 LAKE TEXOMA      1135 -96.688976   \n",
       "65498  2.100000e-02  ...  OOLAGAHL LAKE;OOLOGAH LAKE      1032 -95.593848   \n",
       "65499  3.400000e-02  ...            PRESA EL CARACOL      1384 -99.861530   \n",
       "\n",
       "           p_lat     p_ref_wse  p_ref_area  p_date_t0       p_ds_t0  \\\n",
       "0      47.616292 -1.000000e+12    1.038600    no_data -1.000000e+12   \n",
       "1      47.756499 -1.000000e+12    0.113400    no_data -1.000000e+12   \n",
       "2      47.726123 -1.000000e+12    0.017100    no_data -1.000000e+12   \n",
       "3      47.705366 -1.000000e+12    0.026100    no_data -1.000000e+12   \n",
       "4      47.769148 -1.000000e+12    0.124200    no_data -1.000000e+12   \n",
       "...          ...           ...         ...        ...           ...   \n",
       "65495  50.669027 -1.000000e+12    0.695690    no_data -1.000000e+12   \n",
       "65496  51.061383 -1.000000e+12    0.256500    no_data -1.000000e+12   \n",
       "65497  33.901142 -1.000000e+12  257.028517    no_data -1.000000e+12   \n",
       "65498  36.550604 -1.000000e+12  123.796498    no_data -1.000000e+12   \n",
       "65499  17.975030 -1.000000e+12   35.410155    no_data -1.000000e+12   \n",
       "\n",
       "          p_storage                                           geometry  \n",
       "0     -1.000000e+12  MULTIPOLYGON (((-91.56583 47.61200, -91.56589 ...  \n",
       "1     -1.000000e+12                                               None  \n",
       "2     -1.000000e+12                                               None  \n",
       "3     -1.000000e+12                                               None  \n",
       "4     -1.000000e+12                                               None  \n",
       "...             ...                                                ...  \n",
       "65495 -1.000000e+12  POLYGON ((-90.89210 50.67709, -90.89185 50.677...  \n",
       "65496 -1.000000e+12  MULTIPOLYGON (((-90.75251 51.06305, -90.75228 ...  \n",
       "65497 -1.000000e+12  MULTIPOLYGON (((-96.70899 33.82534, -96.70885 ...  \n",
       "65498 -1.000000e+12  MULTIPOLYGON (((-95.67217 36.43803, -95.67157 ...  \n",
       "65499 -1.000000e+12  MULTIPOLYGON (((-99.75753 18.02115, -99.75739 ...  \n",
       "\n",
       "[65500 rows x 51 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SWOT_HR_shp2 = gpd.read_file(f'data_downloads/{filename_shp2}') \n",
    "\n",
    "#view the attribute table\n",
    "SWOT_HR_shp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Quickly plot the SWOT lakes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "SWOT_HR_shp2.plot(ax=ax, color='black')\n",
    "cx.add_basemap(ax, crs=SWOT_HR_shp2.crs, source=cx.providers.OpenTopoMap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing the remaining files is different than the shp files above. We do not need to extract the shapefiles from a zip file because the following SWOT HR collections are stored in **netCDF** files in the cloud. For the rest of the products, we will open via `xarray`, not `geopandas`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### **3. Water Mask Pixel Cloud NetCDF**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Search for data collection and time of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 229\n"
     ]
    }
   ],
   "source": [
    "pixc_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_PIXC_2.0', \n",
    "                                        temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time\n",
    "                                        bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download one data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Getting 1 granules, approx download size: 0.5 GB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 333.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File SWOT_L2_HR_PIXC_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc already downloaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<?, ?it/s]\n",
      "COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['data_downloads\\\\SWOT_L2_HR_PIXC_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthaccess.download([pixc_results[0]], \"./data_downloads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Open data using xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pixel cloud netCDF files are formatted with three groups titled, \"pixel cloud\", \"tvp\", or \"noise\" (more detail [here](https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56411_SWOT_Product_Description_L2_HR_PIXC_20200810.pdf)). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
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       "\n",
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       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
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       "\n",
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       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
       ".xr-var-dims {\n",
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       "\n",
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       "  text-align: right;\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1GB\n",
       "Dimensions:                                (points: 5332824, complex_depth: 2,\n",
       "                                            num_pixc_lines: 3277)\n",
       "Coordinates:\n",
       "    latitude                               (points) float64 43MB dask.array&lt;chunksize=(484803,), meta=np.ndarray&gt;\n",
       "    longitude                              (points) float64 43MB dask.array&lt;chunksize=(484803,), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: points, complex_depth, num_pixc_lines\n",
       "Data variables: (12/61)\n",
       "    azimuth_index                          (points) float64 43MB dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;\n",
       "    range_index                            (points) float64 43MB dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;\n",
       "    interferogram                          (points, complex_depth) float32 43MB dask.array&lt;chunksize=(1333206, 1), meta=np.ndarray&gt;\n",
       "    power_plus_y                           (points) float32 21MB dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;\n",
       "    power_minus_y                          (points) float32 21MB dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;\n",
       "    coherent_power                         (points) float32 21MB dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;\n",
       "    ...                                     ...\n",
       "    pixc_line_qual                         (num_pixc_lines) float64 26kB dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;\n",
       "    pixc_line_to_tvp                       (num_pixc_lines) float32 13kB dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;\n",
       "    data_window_first_valid                (num_pixc_lines) float64 26kB dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;\n",
       "    data_window_last_valid                 (num_pixc_lines) float64 26kB dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;\n",
       "    data_window_first_cross_track          (num_pixc_lines) float32 13kB dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;\n",
       "    data_window_last_cross_track           (num_pixc_lines) float32 13kB dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    description:                 cloud of geolocated interferogram pixels\n",
       "    interferogram_size_azimuth:  3277\n",
       "    interferogram_size_range:    5622\n",
       "    looks_to_efflooks:           1.550384810089747\n",
       "    num_azimuth_looks:           7.0\n",
       "    azimuth_offset:              7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c14b020b-467c-4d8c-9462-90c00b08ca95' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c14b020b-467c-4d8c-9462-90c00b08ca95' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>points</span>: 5332824</li><li><span>complex_depth</span>: 2</li><li><span>num_pixc_lines</span>: 3277</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-336de14a-cdbe-4b39-9af6-f800523e3a76' class='xr-section-summary-in' type='checkbox'  checked><label for='section-336de14a-cdbe-4b39-9af6-f800523e3a76' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(484803,), meta=np.ndarray&gt;</div><input id='attrs-19437380-c07e-4e44-91f6-3ba6c4ec1fb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19437380-c07e-4e44-91f6-3ba6c4ec1fb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e5672e4-0f53-4640-b4a8-71131a16b4e8' class='xr-var-data-in' type='checkbox'><label for='data-2e5672e4-0f53-4640-b4a8-71131a16b4e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude (positive N, negative S)</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-80.0</dd><dt><span>valid_max :</span></dt><dd>80.0</dd><dt><span>comment :</span></dt><dd>Geodetic latitude [-80,80] (degrees north of equator) of the pixel.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 3.70 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (484803,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
       "  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"25\" />\n",
       "  <line x1=\"65\" y1=\"0\" x2=\"65\" y2=\"25\" />\n",
       "  <line x1=\"76\" y1=\"0\" x2=\"76\" y2=\"25\" />\n",
       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
       "  <line x1=\"98\" y1=\"0\" x2=\"98\" y2=\"25\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"109\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5332824</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(484803,), meta=np.ndarray&gt;</div><input id='attrs-14e076ba-c61b-4e95-95cc-a5dcbf3a159b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14e076ba-c61b-4e95-95cc-a5dcbf3a159b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-089c0fa9-f90a-46dd-8f5c-6b6826e97d44' class='xr-var-data-in' type='checkbox'><label for='data-089c0fa9-f90a-46dd-8f5c-6b6826e97d44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude (degrees East)</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>Longitude [-180,180) (east of the Greenwich meridian) of the pixel.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 3.70 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (484803,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b65dc56-9267-44cc-ae71-044cad6b0e5e' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b65dc56-9267-44cc-ae71-044cad6b0e5e' class='xr-section-summary' >Data variables: <span>(61)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>azimuth_index</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-5a6b4a69-af4d-4b0f-9df3-f43a414038d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a6b4a69-af4d-4b0f-9df3-f43a414038d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d627aa60-81d9-447d-97a7-8f33691bf753' class='xr-var-data-in' type='checkbox'><label for='data-d627aa60-81d9-447d-97a7-8f33691bf753' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>rare interferogram azimuth index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Rare interferogram azimuth index (indexed from 0).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 6.78 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range_index</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-8f3b0687-c1d6-4e67-9b0c-b67fa2f932bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f3b0687-c1d6-4e67-9b0c-b67fa2f932bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-675484fe-c658-492b-9c50-ce01dd7e1efd' class='xr-var-data-in' type='checkbox'><label for='data-675484fe-c658-492b-9c50-ce01dd7e1efd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>rare interferogram range index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Rare interferogram range index (indexed from 0).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 6.78 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interferogram</span></div><div class='xr-var-dims'>(points, complex_depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1333206, 1), meta=np.ndarray&gt;</div><input id='attrs-2943c6fc-031b-439f-8884-7fe5eb66dbf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2943c6fc-031b-439f-8884-7fe5eb66dbf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1a53651-dfe8-40b9-bf82-cfbb798b1de8' class='xr-var-data-in' type='checkbox'><label for='data-e1a53651-dfe8-40b9-bf82-cfbb798b1de8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>rare interferogram</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>interferogram_qual</dd><dt><span>valid_min :</span></dt><dd>-1e+20</dd><dt><span>valid_max :</span></dt><dd>1e+20</dd><dt><span>comment :</span></dt><dd>Complex unflattened rare interferogram.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 5.09 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824, 2) </td>\n",
       "                        <td> (1333206, 1) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power_plus_y</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-eeb90200-cc88-4c28-9782-a891efcfc604' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eeb90200-cc88-4c28-9782-a891efcfc604' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a93da0a5-7668-4fb4-a470-84bbcc9c1246' class='xr-var-data-in' type='checkbox'><label for='data-a93da0a5-7668-4fb4-a470-84bbcc9c1246' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>power for plus_y channel</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>interferogram_qual</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1e+20</dd><dt><span>comment :</span></dt><dd>Power for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power_minus_y</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-d356d9b0-707f-4d27-9b09-837fd7ad99f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d356d9b0-707f-4d27-9b09-837fd7ad99f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf70953d-2c93-4134-b054-1dc43127c96a' class='xr-var-data-in' type='checkbox'><label for='data-cf70953d-2c93-4134-b054-1dc43127c96a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>power for minus_y channel</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>interferogram_qual</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1e+20</dd><dt><span>comment :</span></dt><dd>Power for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>coherent_power</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-ebf246f5-7a48-4ba5-918c-b780d258469a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebf246f5-7a48-4ba5-918c-b780d258469a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7537fbaf-96d6-4781-b089-66f8a4e4fb2d' class='xr-var-data-in' type='checkbox'><label for='data-7537fbaf-96d6-4781-b089-66f8a4e4fb2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>coherent power combination of minus_y and plus_y channels</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>interferogram_qual</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1e+20</dd><dt><span>comment :</span></dt><dd>Power computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_factor_plus_y</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-28f5b882-e04c-4295-b992-babd263bad52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28f5b882-e04c-4295-b992-babd263bad52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92803149-b262-4472-b308-390abc3a026e' class='xr-var-data-in' type='checkbox'><label for='data-92803149-b262-4472-b308-390abc3a026e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>X factor for plus_y channel power</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1e+20</dd><dt><span>comment :</span></dt><dd>X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_factor_minus_y</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-02c07c20-516b-4f39-858e-6aa1eb03d11c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02c07c20-516b-4f39-858e-6aa1eb03d11c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76cbf903-980a-4c48-bd6e-a36996e9cabc' class='xr-var-data-in' type='checkbox'><label for='data-76cbf903-980a-4c48-bd6e-a36996e9cabc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>X factor for minus_y channel power</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1e+20</dd><dt><span>comment :</span></dt><dd>X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_frac</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-55fafcb9-761b-4e0c-a547-9762c326b6cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55fafcb9-761b-4e0c-a547-9762c326b6cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a2726c7-deb2-4893-8dec-1f1ff8baddff' class='xr-var-data-in' type='checkbox'><label for='data-3a2726c7-deb2-4893-8dec-1f1ff8baddff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>classification_qual</dd><dt><span>valid_min :</span></dt><dd>-1000.0</dd><dt><span>valid_max :</span></dt><dd>10000.0</dd><dt><span>comment :</span></dt><dd>Noisy estimate of the fraction of the pixel that is water.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_frac_uncert</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-f5fb4942-255f-4d89-89a8-323381f13568' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5fb4942-255f-4d89-89a8-323381f13568' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da6cd5c1-88a2-49a9-ac87-ae4ba6aa06dc' class='xr-var-data-in' type='checkbox'><label for='data-da6cd5c1-88a2-49a9-ac87-ae4ba6aa06dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water fraction uncertainty</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>classification</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2666412,), meta=np.ndarray&gt;</div><input id='attrs-206f159c-7d98-4c8e-8ac5-46988d3767b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-206f159c-7d98-4c8e-8ac5-46988d3767b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-022474b2-fdc0-42e2-8ae4-cf18bf2a4aee' class='xr-var-data-in' type='checkbox'><label for='data-022474b2-fdc0-42e2-8ae4-cf18bf2a4aee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>classification</dd><dt><span>quality_flag :</span></dt><dd>classification_qual</dd><dt><span>flag_meanings :</span></dt><dd>land land_near_water water_near_land open_water dark_water low_coh_water_near_land open_low_coh_water</dd><dt><span>flag_values :</span></dt><dd>[1 2 3 4 5 6 7]</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>7</dd><dt><span>comment :</span></dt><dd>Flags indicating water detection results.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>false_detection_rate</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-233e7473-1ed7-49fb-9d6b-1a421b774eb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-233e7473-1ed7-49fb-9d6b-1a421b774eb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4afa9180-f16b-4239-8ab7-6bd398d03c44' class='xr-var-data-in' type='checkbox'><label for='data-4afa9180-f16b-4239-8ab7-6bd398d03c44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>false detection rate</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>classification_qual</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>comment :</span></dt><dd>Probability of falsely detecting water when there is none.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>missed_detection_rate</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-cb1ef612-fb47-4da8-9479-8364624b518d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb1ef612-fb47-4da8-9479-8364624b518d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7611040-0569-40dd-81f6-8d34a410a9ff' class='xr-var-data-in' type='checkbox'><label for='data-c7611040-0569-40dd-81f6-8d34a410a9ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>missed detection rate</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>classification_qual</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>comment :</span></dt><dd>Probability of falsely detecting no water when there is water.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prior_water_prob</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-5bcb99a0-b20f-4f1d-8229-69dae14d2894' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bcb99a0-b20f-4f1d-8229-69dae14d2894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ad8434e-f31b-48ac-a1e5-3660113d14b4' class='xr-var-data-in' type='checkbox'><label for='data-7ad8434e-f31b-48ac-a1e5-3660113d14b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>prior water probability</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>comment :</span></dt><dd>Prior probability of water occurring.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bright_land_flag</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2666412,), meta=np.ndarray&gt;</div><input id='attrs-c677abc1-bb9d-40ed-8581-207db77dfa9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c677abc1-bb9d-40ed-8581-207db77dfa9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e21ece19-77fe-4bd3-8f1b-03b340d11117' class='xr-var-data-in' type='checkbox'><label for='data-e21ece19-77fe-4bd3-8f1b-03b340d11117' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>bright land flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>not_bright_land bright_land bright_land_or_water</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>comment :</span></dt><dd>Flag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice).  Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layover_impact</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-b9809534-fee7-46bb-b13f-77e666e77d40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9809534-fee7-46bb-b13f-77e666e77d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46a4fc75-7a56-4d89-afff-cf26d4beb121' class='xr-var-data-in' type='checkbox'><label for='data-46a4fc75-7a56-4d89-afff-cf26d4beb121' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>layover impact</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Estimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eff_num_rare_looks</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-f6f20062-5a6b-4ca8-ad16-1960e445a2f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6f20062-5a6b-4ca8-ad16-1960e445a2f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b46f794-3364-459d-8dcb-b61fc3ff67f9' class='xr-var-data-in' type='checkbox'><label for='data-5b46f794-3364-459d-8dcb-b61fc3ff67f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>effective number of rare looks</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Effective number of independent looks taken to form the rare interferogram.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-08425a1d-b07f-4373-b61b-5fd4a631ea0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08425a1d-b07f-4373-b61b-5fd4a631ea0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-780c7139-499c-47d8-9a3b-5b2008e61e4e' class='xr-var-data-in' type='checkbox'><label for='data-780c7139-499c-47d8-9a3b-5b2008e61e4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height above reference ellipsoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-1500.0</dd><dt><span>valid_max :</span></dt><dd>15000.0</dd><dt><span>comment :</span></dt><dd>Height of the pixel above the reference ellipsoid.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cross_track</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-ef33f38e-3aa5-480f-8cc6-4cfad467ecc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef33f38e-3aa5-480f-8cc6-4cfad467ecc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-402f1663-f707-425a-932d-168b14965a7b' class='xr-var-data-in' type='checkbox'><label for='data-402f1663-f707-425a-932d-168b14965a7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>approximate cross-track location</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-75000.0</dd><dt><span>valid_max :</span></dt><dd>75000.0</dd><dt><span>comment :</span></dt><dd>Approximate cross-track location of the pixel.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
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       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pixel_area</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-6165d3fd-09f6-4e14-95c9-41a91832d184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6165d3fd-09f6-4e14-95c9-41a91832d184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0237c902-3d33-4b6a-966f-76c26fc97a6a' class='xr-var-data-in' type='checkbox'><label for='data-0237c902-3d33-4b6a-966f-76c26fc97a6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Pixel area.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inc</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-95be7d78-6e09-4bfb-be31-7c046f843f05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95be7d78-6e09-4bfb-be31-7c046f843f05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddb8094c-01a7-4b92-9ffa-e94483551913' class='xr-var-data-in' type='checkbox'><label for='data-ddb8094c-01a7-4b92-9ffa-e94483551913' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>incidence angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Incidence angle.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                        <td> (5332824,) </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phase_noise_std</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-bb475349-f711-42db-83d1-fdf60c1ff2d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb475349-f711-42db-83d1-fdf60c1ff2d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10399e6c-fbfb-46fb-b1dd-a3eb6f8258d2' class='xr-var-data-in' type='checkbox'><label for='data-10399e6c-fbfb-46fb-b1dd-a3eb6f8258d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>phase noise standard deviation</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Estimate of the phase noise standard deviation.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dlatitude_dphase</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-3d7f8ef4-9965-4b18-a50b-786173124725' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d7f8ef4-9965-4b18-a50b-786173124725' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e9e0ace-bae7-4029-a8b0-219d95748ffa' class='xr-var-data-in' type='checkbox'><label for='data-4e9e0ace-bae7-4029-a8b0-219d95748ffa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensitivity of latitude estimate to interferogram phase</dd><dt><span>units :</span></dt><dd>degrees/radian</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Sensitivity of the latitude estimate to the interferogram phase.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dlongitude_dphase</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-9d2c7f65-15bf-4097-ace6-69217a4290e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d2c7f65-15bf-4097-ace6-69217a4290e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef0439a-2926-4c7b-91d2-0f3c06021665' class='xr-var-data-in' type='checkbox'><label for='data-1ef0439a-2926-4c7b-91d2-0f3c06021665' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensitivity of longitude estimate to interferogram phase</dd><dt><span>units :</span></dt><dd>degrees/radian</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Sensitivity of the longitude estimate to the interferogram phase.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dheight_dphase</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-3197e8c3-f6b9-42ce-902d-d13209bf6850' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3197e8c3-f6b9-42ce-902d-d13209bf6850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02c285a5-c517-442c-ada8-020357e0fcd5' class='xr-var-data-in' type='checkbox'><label for='data-02c285a5-c517-442c-ada8-020357e0fcd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensitivity of height estimate to interferogram phase</dd><dt><span>units :</span></dt><dd>m/radian</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Sensitivity of the height estimate to the interferogram phase.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dheight_droll</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-ec81bb8e-0582-481f-b065-3634781a7c58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec81bb8e-0582-481f-b065-3634781a7c58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4dee876-4958-412c-bffd-c5ce1687fad5' class='xr-var-data-in' type='checkbox'><label for='data-c4dee876-4958-412c-bffd-c5ce1687fad5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensitivity of height estimate to spacecraft roll</dd><dt><span>units :</span></dt><dd>m/degrees</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Sensitivity of the height estimate to the spacecraft roll.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dheight_dbaseline</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-802765cb-468d-48f4-8f0f-a550ff3b59ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-802765cb-468d-48f4-8f0f-a550ff3b59ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5fcb5d6-cd4b-4744-9c12-48bff954735e' class='xr-var-data-in' type='checkbox'><label for='data-b5fcb5d6-cd4b-4744-9c12-48bff954735e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensitivity of height estimate to interferometric baseline</dd><dt><span>units :</span></dt><dd>m/m</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Sensitivity of the height estimate to the interferometric baseline.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dheight_drange</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-bb9a3710-37e9-4f17-aa24-5851d5eb8b12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb9a3710-37e9-4f17-aa24-5851d5eb8b12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-388854be-8fbc-4f03-b9be-f1efb006e4f0' class='xr-var-data-in' type='checkbox'><label for='data-388854be-8fbc-4f03-b9be-f1efb006e4f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensitivity of height estimate to range (delay)</dd><dt><span>units :</span></dt><dd>m/m</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Sensitivity of the height estimate to the range (delay).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                        <td> (888804,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>darea_dheight</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-7282f33f-1c93-4635-82dd-f0c9f71434ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7282f33f-1c93-4635-82dd-f0c9f71434ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed98373a-c70a-42c1-8355-4d4ea6779591' class='xr-var-data-in' type='checkbox'><label for='data-ed98373a-c70a-42c1-8355-4d4ea6779591' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sensitivity of pixel area to reference height</dd><dt><span>units :</span></dt><dd>m^2/m</dd><dt><span>quality_flag :</span></dt><dd>geolocation_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Sensitivity of the pixel area to the reference height.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>illumination_time</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(484803,), meta=np.ndarray&gt;</div><input id='attrs-20f0dabf-14aa-4951-bee6-a18969094505' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20f0dabf-14aa-4951-bee6-a18969094505' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71dcafd0-2281-48c0-b718-24fab1c3c044' class='xr-var-data-in' type='checkbox'><label for='data-71dcafd0-2281-48c0-b718-24fab1c3c044' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of illumination of each pixel (UTC)</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>tai_utc_difference :</span></dt><dd>37.0</dd><dt><span>leap_second :</span></dt><dd>0000-00-00T00:00:00Z</dd><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Data type </th>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>illumination_time_tai</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(484803,), meta=np.ndarray&gt;</div><input id='attrs-a53d51c9-8b52-4753-a82f-50828bdbdbfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a53d51c9-8b52-4753-a82f-50828bdbdbfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-657f8eda-ab73-4219-8714-d639c073720b' class='xr-var-data-in' type='checkbox'><label for='data-657f8eda-ab73-4219-8714-d639c073720b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of illumination of each pixel (TAI)</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 3.70 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (484803,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eff_num_medium_looks</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-969f153a-0260-4dd7-b395-a6a79fc4a3ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-969f153a-0260-4dd7-b395-a6a79fc4a3ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28769c5e-11e6-426f-ad4c-e4213a0b80c3' class='xr-var-data-in' type='checkbox'><label for='data-28769c5e-11e6-426f-ad4c-e4213a0b80c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>effective number of medium looks</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-e38f7805-62a1-4eb3-9d35-54d0a37a75e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e38f7805-62a1-4eb3-9d35-54d0a37a75e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ba5515c-1bfc-4f23-9905-d5e06f785861' class='xr-var-data-in' type='checkbox'><label for='data-1ba5515c-1bfc-4f23-9905-d5e06f785861' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sigma0</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>sig0_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_uncert</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-dd0253d0-d004-4b36-b864-d05033283582' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd0253d0-d004-4b36-b864-d05033283582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4be92e9-e518-42ca-ac13-510ae3d695b8' class='xr-var-data-in' type='checkbox'><label for='data-e4be92e9-e518-42ca-ac13-510ae3d695b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sigma0 uncertainty</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>1-sigma uncertainty in the sig0 measurement.  The value is given as an additive (not multiplicative) linear term (not a term in decibels).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phase_unwrapping_region</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-08520dff-bed8-4251-a1f3-bb15a82884d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08520dff-bed8-4251-a1f3-bb15a82884d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da3b89d3-b3cb-4b0e-a27e-b09e6c57274c' class='xr-var-data-in' type='checkbox'><label for='data-da3b89d3-b3cb-4b0e-a27e-b09e6c57274c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>phase unwrapping region index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>-1</dd><dt><span>valid_max :</span></dt><dd>99999999</dd><dt><span>comment :</span></dt><dd>Phase unwrapping region index.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 6.78 MiB </td>\n",
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       "                        <td> (888804,) </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ambiguity_cost2</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-1535667b-6584-4755-a5d0-b9851ad16a86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1535667b-6584-4755-a5d0-b9851ad16a86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2af318f4-9110-4d73-a4e1-6410a64169f8' class='xr-var-data-in' type='checkbox'><label for='data-2af318f4-9110-4d73-a4e1-6410a64169f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>phase ambiguity 2nd minimum cost</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Phase ambiguity 2nd minimum cost.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instrument_range_cor</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-3c186d6a-bc68-42b8-9c53-bc2f46b3a510' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c186d6a-bc68-42b8-9c53-bc2f46b3a510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fede680f-8bb5-4910-a646-6817008d5fa1' class='xr-var-data-in' type='checkbox'><label for='data-fede680f-8bb5-4910-a646-6817008d5fa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>instrument range correction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Term that incorporates all calibration corrections applied to range before geolocation.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instrument_phase_cor</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-3b205d81-6ccd-4c74-8336-dac590fb087c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b205d81-6ccd-4c74-8336-dac590fb087c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a96d01c-690e-4d21-a704-3f91fb1ea825' class='xr-var-data-in' type='checkbox'><label for='data-8a96d01c-690e-4d21-a704-3f91fb1ea825' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>instrument phase correction</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Term that incorporates all calibration corrections applied to phase before geolocation.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instrument_baseline_cor</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-0bad0c7b-86eb-4691-881f-745d6ddd6fdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bad0c7b-86eb-4691-881f-745d6ddd6fdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-182cb536-c899-43b9-aa89-5dc2395c0216' class='xr-var-data-in' type='checkbox'><label for='data-182cb536-c899-43b9-aa89-5dc2395c0216' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>instrument baseline correction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Term that incorporates all calibration corrections applied to baseline before geolocation.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_cor_atmos_model</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-367dc2f9-f40a-436e-b6fd-e985d5e77f4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-367dc2f9-f40a-436e-b6fd-e985d5e77f4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26e3c237-a604-4650-8f50-17d5a879c1c3' class='xr-var-data-in' type='checkbox'><label for='data-26e3c237-a604-4650-8f50-17d5a879c1c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>two-way atmospheric correction to sigma0 from model</dd><dt><span>source :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>1.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>comment :</span></dt><dd>Atmospheric correction to sigma0 from weather model data as a linear power multiplier (not decibels). sig0_cor_atmos_model is already applied in computing sig0 and x_factor_plus_y and x_factor_minus_y.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_cor_xover</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-a2d4a8f0-96be-4028-ae90-908244a5726b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2d4a8f0-96be-4028-ae90-908244a5726b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34280fe3-df61-4d53-8901-4aa9f1fe741b' class='xr-var-data-in' type='checkbox'><label for='data-34280fe3-df61-4d53-8901-4aa9f1fe741b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height correction from KaRIn crossovers</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>comment :</span></dt><dd>Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_dry_tropo_cor</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-8363584e-8051-4dde-ac89-ced89e8050fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8363584e-8051-4dde-ac89-ced89e8050fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd347b6d-899b-4353-8815-d634764a878c' class='xr-var-data-in' type='checkbox'><label for='data-dd347b6d-899b-4353-8815-d634764a878c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dry troposphere vertical correction</dd><dt><span>source :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-3.0</dd><dt><span>valid_max :</span></dt><dd>-1.5</dd><dt><span>comment :</span></dt><dd>Equivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_wet_tropo_cor</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-118b6a6e-6646-43cc-b60d-a2cccc790300' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-118b6a6e-6646-43cc-b60d-a2cccc790300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57eafee6-addb-4bf9-987b-8c8b12085a8e' class='xr-var-data-in' type='checkbox'><label for='data-57eafee6-addb-4bf9-987b-8c8b12085a8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>wet troposphere vertical correction</dd><dt><span>source :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>Equivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iono_cor_gim_ka</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-5e4bf315-570e-4b2b-86a9-258a36d6d397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e4bf315-570e-4b2b-86a9-258a36d6d397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed91a03f-b70c-4bdd-b818-e96f1b06553b' class='xr-var-data-in' type='checkbox'><label for='data-ed91a03f-b70c-4bdd-b818-e96f1b06553b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ionosphere vertical correction</dd><dt><span>source :</span></dt><dd>Global Ionosphere Maps</dd><dt><span>institution :</span></dt><dd>JPL</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.5</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>Equivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 3.39 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geoid</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-1c37899a-7475-49a9-ac55-ff594e0ccb07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c37899a-7475-49a9-ac55-ff594e0ccb07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8155d219-b5c6-4cdf-b3a9-fcccbe085b51' class='xr-var-data-in' type='checkbox'><label for='data-8155d219-b5c6-4cdf-b3a9-fcccbe085b51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geoid height</dd><dt><span>standard_name :</span></dt><dd>geoid_height_above_reference_ellipsoid</dd><dt><span>source :</span></dt><dd>EGM2008 (Pavlis et al., 2012)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-150.0</dd><dt><span>valid_max :</span></dt><dd>150.0</dd><dt><span>comment :</span></dt><dd>Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).  This value is reported for reference but is not applied to the reported height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solid_earth_tide</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-8c52b23b-8094-42b1-b683-7f3bd914d2e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c52b23b-8094-42b1-b683-7f3bd914d2e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c298e98-9b9b-4b9f-a3d4-f70f2929dc7a' class='xr-var-data-in' type='checkbox'><label for='data-2c298e98-9b9b-4b9f-a3d4-f70f2929dc7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>solid Earth tide height</dd><dt><span>source :</span></dt><dd>Cartwright and Taylor (1971) and Cartwright and Edden (1973)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>comment :</span></dt><dd>Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.  This value is reported for reference but is not applied to the reported height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>load_tide_fes</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-9a71b993-cd4e-4014-8ed0-9e1dec4f73e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a71b993-cd4e-4014-8ed0-9e1dec4f73e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33c8ce78-1a85-44c5-bf9a-28d66aafbab5' class='xr-var-data-in' type='checkbox'><label for='data-33c8ce78-1a85-44c5-bf9a-28d66aafbab5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geocentric load tide height (FES)</dd><dt><span>source :</span></dt><dd>FES2014b (Carrere et al., 2016)</dd><dt><span>institution :</span></dt><dd>LEGOS/CNES</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.2</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>comment :</span></dt><dd>Geocentric load tide height. The effect of the ocean tide loading of the Earth&#x27;s crust. This value is reported for reference but is not applied to the reported height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>load_tide_got</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-b2251ee9-03bf-48be-adba-bd20555d8cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2251ee9-03bf-48be-adba-bd20555d8cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24ef8c85-aa17-47af-8c40-174d8fe7fc8a' class='xr-var-data-in' type='checkbox'><label for='data-24ef8c85-aa17-47af-8c40-174d8fe7fc8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geocentric load tide height (GOT)</dd><dt><span>source :</span></dt><dd>GOT4.10c (Ray, 2013)</dd><dt><span>institution :</span></dt><dd>GSFC</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.2</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>comment :</span></dt><dd>Geocentric load tide height. The effect of the ocean tide loading of the Earth&#x27;s crust. This value is reported for reference but is not applied to the reported height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pole_tide</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-ee2954a6-b195-4eaa-ac51-100898193c27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee2954a6-b195-4eaa-ac51-100898193c27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f9572f8-4cd1-4a58-af75-13275ceb5b1a' class='xr-var-data-in' type='checkbox'><label for='data-4f9572f8-4cd1-4a58-af75-13275ceb5b1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geocentric pole tide height</dd><dt><span>source :</span></dt><dd>Wahr (1985) and Desai et al. (2015)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.2</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>comment :</span></dt><dd>Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth&#x27;s crust).  This value is reported for reference but is not applied to the reported height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ancillary_surface_classification_flag</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2666412,), meta=np.ndarray&gt;</div><input id='attrs-83ba5303-db09-42ba-8c13-2c8b03d6177e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83ba5303-db09-42ba-8c13-2c8b03d6177e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f76345-f69b-448f-9164-50a243cfe5c5' class='xr-var-data-in' type='checkbox'><label for='data-50f76345-f69b-448f-9164-50a243cfe5c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>surface classification</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>source :</span></dt><dd>MODIS/GlobCover</dd><dt><span>institution :</span></dt><dd>European Space Agency</dd><dt><span>flag_meanings :</span></dt><dd>open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3 4 5 6]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>6</dd><dt><span>comment :</span></dt><dd>7-state surface type classification computed from a mask built with MODIS and GlobCover data.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 10.17 MiB </td>\n",
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       "                        <td> (5332824,) </td>\n",
       "                        <td> (2666412,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interferogram_qual</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-07d5daca-3388-4e21-a8ea-c9aba8e6b521' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07d5daca-3388-4e21-a8ea-c9aba8e6b521' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e30bd6d-faee-4aad-b567-443c8a2f8bbc' class='xr-var-data-in' type='checkbox'><label for='data-3e30bd6d-faee-4aad-b567-443c8a2f8bbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>rare_power_suspect rare_phase_suspect tvp_suspect sc_event_suspect small_karin_gap in_air_pixel_degraded specular_ringing_degraded rare_power_bad rare_phase_bad tvp_bad sc_event_bad large_karin_gap</dd><dt><span>flag_masks :</span></dt><dd>[      2048       4096       8192      16384      32768     262144\n",
       "     524288  134217728  268435456  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>4161599488</dd><dt><span>comment :</span></dt><dd>Quality flag for the interferogram quantities in the pixel cloud data</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 40.69 MiB </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>classification_qual</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-508315e3-8955-4225-9c26-7e2a7d3302ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-508315e3-8955-4225-9c26-7e2a7d3302ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-297c2311-0ebc-4f78-8d5d-f687548904a0' class='xr-var-data-in' type='checkbox'><label for='data-297c2311-0ebc-4f78-8d5d-f687548904a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>no_coherent_gain power_close_to_noise_floor detected_water_but_no_prior_water detected_water_but_bright_land water_false_detection_rate_suspect coherent_power_suspect tvp_suspect sc_event_suspect small_karin_gap in_air_pixel_degraded specular_ringing_degraded coherent_power_bad tvp_bad sc_event_bad large_karin_gap</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4          8         16       2048\n",
       "       8192      16384      32768     262144     524288  134217728\n",
       "  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3893159967</dd><dt><span>comment :</span></dt><dd>Quality flag for the classification quantities in the pixel cloud data</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 40.69 MiB </td>\n",
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       "                        <td> (5332824,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolocation_qual</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-cdc22293-2393-40a4-a108-234056b25d65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdc22293-2393-40a4-a108-234056b25d65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9ad7875-83fe-4373-9062-5ed99e90fae2' class='xr-var-data-in' type='checkbox'><label for='data-c9ad7875-83fe-4373-9062-5ed99e90fae2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>layover_significant phase_noise_suspect phase_unwrapping_suspect model_dry_tropo_cor_suspect model_wet_tropo_cor_suspect iono_cor_gim_ka_suspect xovercal_suspect medium_phase_suspect tvp_suspect sc_event_suspect small_karin_gap specular_ringing_degraded model_dry_tropo_cor_missing model_wet_tropo_cor_missing iono_cor_gim_ka_missing xovercal_missing geolocation_is_from_refloc no_geolocation_bad medium_phase_bad tvp_bad sc_event_bad large_karin_gap</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4          8         16         32\n",
       "         64       4096       8192      16384      32768     524288\n",
       "    1048576    2097152    4194304    8388608   16777216  134217728\n",
       "  268435456  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>4193841279</dd><dt><span>comment :</span></dt><dd>Quality flag for the geolocation quantities in the pixel cloud data</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 40.69 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_qual</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(888804,), meta=np.ndarray&gt;</div><input id='attrs-90f5c741-c031-4f8f-9ea6-8056d2208bb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90f5c741-c031-4f8f-9ea6-8056d2208bb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8d78466-e89a-4524-9718-e7af63c7aa40' class='xr-var-data-in' type='checkbox'><label for='data-e8d78466-e89a-4524-9718-e7af63c7aa40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>sig0_uncert_suspect sig0_cor_atmos_suspect noise_power_suspect xfactor_suspect rare_power_suspect tvp_suspect sc_event_suspect small_karin_gap in_air_pixel_degraded specular_ringing_degraded sig0_cor_atmos_missing noise_power_bad xfactor_bad rare_power_bad tvp_bad sc_event_bad large_karin_gap</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4          8       2048       8192\n",
       "      16384      32768     262144     524288    1048576   33554432\n",
       "   67108864  134217728  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3994871823</dd><dt><span>comment :</span></dt><dd>Quality flag for sig0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 6.78 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (888804,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 6 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pixc_line_qual</span></div><div class='xr-var-dims'>(num_pixc_lines)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;</div><input id='attrs-ce23f65b-eb40-4498-9744-4ca072e94a57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce23f65b-eb40-4498-9744-4ca072e94a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2f90131-7f77-43cf-98ff-e3fae723cea2' class='xr-var-data-in' type='checkbox'><label for='data-d2f90131-7f77-43cf-98ff-e3fae723cea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>flag_meanings :</span></dt><dd>not_in_tile tvp_suspect sc_event_suspect small_karin_gap tvp_bad sc_event_bad large_karin_gap</dd><dt><span>flag_masks :</span></dt><dd>[         1       8192      16384      32768  536870912 1073741824\n",
       " 2147483649]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3758153729</dd><dt><span>comment :</span></dt><dd>Quality flag for pixel cloud data per rare-posted interferogram line (similar to slc_qual in the L1B_HR_SLC product)</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 25.60 kiB </td>\n",
       "                        <td> 25.60 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3277,) </td>\n",
       "                        <td> (3277,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pixc_line_to_tvp</span></div><div class='xr-var-dims'>(num_pixc_lines)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;</div><input id='attrs-96eb9a9b-b8ed-45ca-820b-99f5b51bb697' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96eb9a9b-b8ed-45ca-820b-99f5b51bb697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7f64198-8db1-42d8-ac49-cc22c61a3334' class='xr-var-data-in' type='checkbox'><label for='data-d7f64198-8db1-42d8-ac49-cc22c61a3334' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel cloud rare line to tvp index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Pixel cloud rare radar grid line index to tvp index mapping</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 12.80 kiB </td>\n",
       "                        <td> 12.80 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3277,) </td>\n",
       "                        <td> (3277,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_window_first_valid</span></div><div class='xr-var-dims'>(num_pixc_lines)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;</div><input id='attrs-83e84e9d-1e24-4ebf-bcb8-e67309c9cd19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83e84e9d-1e24-4ebf-bcb8-e67309c9cd19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48e194ae-694c-4ba0-ac92-639216ce726d' class='xr-var-data-in' type='checkbox'><label for='data-48e194ae-694c-4ba0-ac92-639216ce726d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel cloud data window starting index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Pixel cloud data window starting index of first valid pixel in the range direction</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 25.60 kiB </td>\n",
       "                        <td> 25.60 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3277,) </td>\n",
       "                        <td> (3277,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_window_last_valid</span></div><div class='xr-var-dims'>(num_pixc_lines)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;</div><input id='attrs-a58e51a4-0110-4c54-88e5-8ad49b7ab549' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a58e51a4-0110-4c54-88e5-8ad49b7ab549' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de78fedb-c2fb-4072-96aa-ca260b2e4290' class='xr-var-data-in' type='checkbox'><label for='data-de78fedb-c2fb-4072-96aa-ca260b2e4290' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel cloud data window ending index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Pixel cloud data window ending index of last valid pixel in the range direction</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 25.60 kiB </td>\n",
       "                        <td> 25.60 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3277,) </td>\n",
       "                        <td> (3277,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_window_first_cross_track</span></div><div class='xr-var-dims'>(num_pixc_lines)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;</div><input id='attrs-5bf995af-a568-4cfa-8d0e-4cd5ab282ac2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf995af-a568-4cfa-8d0e-4cd5ab282ac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53699103-ca29-4f35-be08-08499cd91b40' class='xr-var-data-in' type='checkbox'><label for='data-53699103-ca29-4f35-be08-08499cd91b40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel cloud data window starting cross-track distance</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-75000.0</dd><dt><span>valid_max :</span></dt><dd>75000.0</dd><dt><span>comment :</span></dt><dd>Pixel cloud data window starting cross-track distance in meters of first valid pixel in the range direction</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 12.80 kiB </td>\n",
       "                        <td> 12.80 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3277,) </td>\n",
       "                        <td> (3277,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_window_last_cross_track</span></div><div class='xr-var-dims'>(num_pixc_lines)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3277,), meta=np.ndarray&gt;</div><input id='attrs-63b7b51a-285e-48e3-b974-9213378224f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63b7b51a-285e-48e3-b974-9213378224f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4b11699-5511-4471-bca8-ae3ed0255f18' class='xr-var-data-in' type='checkbox'><label for='data-a4b11699-5511-4471-bca8-ae3ed0255f18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel cloud data window ending cross-track distance</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-75000.0</dd><dt><span>valid_max :</span></dt><dd>75000.0</dd><dt><span>comment :</span></dt><dd>Pixel cloud data window ending cross-track distance in meters of last valid pixel in the range direction</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 12.80 kiB </td>\n",
       "                        <td> 12.80 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3277,) </td>\n",
       "                        <td> (3277,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3277</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d94ecca-8ad5-4997-8d19-2e9e19eabc34' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6d94ecca-8ad5-4997-8d19-2e9e19eabc34' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0c041d8f-c3a6-443e-8ff5-bcd31bb52458' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c041d8f-c3a6-443e-8ff5-bcd31bb52458' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>cloud of geolocated interferogram pixels</dd><dt><span>interferogram_size_azimuth :</span></dt><dd>3277</dd><dt><span>interferogram_size_range :</span></dt><dd>5622</dd><dt><span>looks_to_efflooks :</span></dt><dd>1.550384810089747</dd><dt><span>num_azimuth_looks :</span></dt><dd>7.0</dd><dt><span>azimuth_offset :</span></dt><dd>7</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 1GB\n",
       "Dimensions:                                (points: 5332824, complex_depth: 2,\n",
       "                                            num_pixc_lines: 3277)\n",
       "Coordinates:\n",
       "    latitude                               (points) float64 43MB dask.array<chunksize=(484803,), meta=np.ndarray>\n",
       "    longitude                              (points) float64 43MB dask.array<chunksize=(484803,), meta=np.ndarray>\n",
       "Dimensions without coordinates: points, complex_depth, num_pixc_lines\n",
       "Data variables: (12/61)\n",
       "    azimuth_index                          (points) float64 43MB dask.array<chunksize=(888804,), meta=np.ndarray>\n",
       "    range_index                            (points) float64 43MB dask.array<chunksize=(888804,), meta=np.ndarray>\n",
       "    interferogram                          (points, complex_depth) float32 43MB dask.array<chunksize=(1333206, 1), meta=np.ndarray>\n",
       "    power_plus_y                           (points) float32 21MB dask.array<chunksize=(888804,), meta=np.ndarray>\n",
       "    power_minus_y                          (points) float32 21MB dask.array<chunksize=(888804,), meta=np.ndarray>\n",
       "    coherent_power                         (points) float32 21MB dask.array<chunksize=(888804,), meta=np.ndarray>\n",
       "    ...                                     ...\n",
       "    pixc_line_qual                         (num_pixc_lines) float64 26kB dask.array<chunksize=(3277,), meta=np.ndarray>\n",
       "    pixc_line_to_tvp                       (num_pixc_lines) float32 13kB dask.array<chunksize=(3277,), meta=np.ndarray>\n",
       "    data_window_first_valid                (num_pixc_lines) float64 26kB dask.array<chunksize=(3277,), meta=np.ndarray>\n",
       "    data_window_last_valid                 (num_pixc_lines) float64 26kB dask.array<chunksize=(3277,), meta=np.ndarray>\n",
       "    data_window_first_cross_track          (num_pixc_lines) float32 13kB dask.array<chunksize=(3277,), meta=np.ndarray>\n",
       "    data_window_last_cross_track           (num_pixc_lines) float32 13kB dask.array<chunksize=(3277,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:                 cloud of geolocated interferogram pixels\n",
       "    interferogram_size_azimuth:  3277\n",
       "    interferogram_size_range:    5622\n",
       "    looks_to_efflooks:           1.550384810089747\n",
       "    num_azimuth_looks:           7.0\n",
       "    azimuth_offset:              7"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_PIXC = xr.open_mfdataset(\"data_downloads/SWOT_L2_HR_PIXC_*.nc\", group = 'pixel_cloud', engine='h5netcdf')\n",
    "ds_PIXC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### For plotting PIXC using classification and quality flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask to get good water pixels\n",
    "mask = np.where(np.logical_and(ds_PIXC.classification > 2, ds_PIXC.geolocation_qual <16384))\n",
    "\n",
    "plt.scatter(x=ds_PIXC.longitude[mask], y=ds_PIXC.latitude[mask], c=ds_PIXC.height[mask])\n",
    "plt.colorbar().set_label('Height (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **4. Water Mask Pixel Cloud Vector Attribute NetCDF**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Search for data of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 1\n"
     ]
    }
   ],
   "source": [
    "#Let's plot the same pass and tile as the above\n",
    "pixcvec_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_PIXCVEC_2.0', \n",
    "                                          granule_name = '*010_412_087L*') #The same cycle, pass, and tile as previously downloaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download the first data file! earthaccess.download has a list as the input format, so we need to put brackets around the single file we pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Getting 1 granules, approx download size: 0.39 GB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 125.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File SWOT_L2_HR_PIXCVec_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc already downloaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 996.98it/s]\n",
      "COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['data_downloads\\\\SWOT_L2_HR_PIXCVec_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthaccess.download([pixcvec_results[0]], \"./data_downloads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Open data using xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll programmatically get the filename we just downloaded and then view the file via `xarray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 416MB\n",
       "Dimensions:               (points: 5332824, nchar_reach_id: 11,\n",
       "                           nchar_node_id: 14, nchar_lake_id: 10,\n",
       "                           nchar_obs_id: 13)\n",
       "Dimensions without coordinates: points, nchar_reach_id, nchar_node_id,\n",
       "                                nchar_lake_id, nchar_obs_id\n",
       "Data variables:\n",
       "    azimuth_index         (points) int32 21MB dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;\n",
       "    range_index           (points) int32 21MB dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;\n",
       "    latitude_vectorproc   (points) float64 43MB dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;\n",
       "    longitude_vectorproc  (points) float64 43MB dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;\n",
       "    height_vectorproc     (points) float32 21MB dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;\n",
       "    reach_id              (points, nchar_reach_id) |S1 59MB dask.array&lt;chunksize=(5332824, 11), meta=np.ndarray&gt;\n",
       "    node_id               (points, nchar_node_id) |S1 75MB dask.array&lt;chunksize=(5332824, 14), meta=np.ndarray&gt;\n",
       "    lake_id               (points, nchar_lake_id) |S1 53MB dask.array&lt;chunksize=(5332824, 10), meta=np.ndarray&gt;\n",
       "    obs_id                (points, nchar_obs_id) |S1 69MB dask.array&lt;chunksize=(5332824, 13), meta=np.ndarray&gt;\n",
       "    ice_clim_f            (points) int8 5MB dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;\n",
       "    ice_dyn_f             (points) int8 5MB dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;\n",
       "Attributes: (12/45)\n",
       "    Conventions:                     CF-1.7\n",
       "    title:                           Level 2 KaRIn high rate pixel cloud vect...\n",
       "    short_name:                      L2_HR_PIXCVec\n",
       "    institution:                     CNES\n",
       "    source:                          Level 1B KaRIn High Rate Single Look Com...\n",
       "    history:                         2024-02-12T08:03:34.974012Z: Creation\n",
       "    ...                              ...\n",
       "    xref_prior_river_db_file:        \n",
       "    xref_prior_lake_db_file:         SWOT_LakeDatabase_Nom_412_20000101T00000...\n",
       "    xref_reforbittrack_files:        SWOT_RefOrbitTrackTileBoundary_Nom_20000...\n",
       "    xref_param_l2_hr_laketile_file:  SWOT_Param_L2_HR_LakeTile_20000101T00000...\n",
       "    ellipsoid_semi_major_axis:       6378137.0\n",
       "    ellipsoid_flattening:            0.0033528106647474805</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4b579c8e-e515-45f9-b195-1fa0e770ede6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b579c8e-e515-45f9-b195-1fa0e770ede6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>points</span>: 5332824</li><li><span>nchar_reach_id</span>: 11</li><li><span>nchar_node_id</span>: 14</li><li><span>nchar_lake_id</span>: 10</li><li><span>nchar_obs_id</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9f52ef52-164e-451f-ab61-97632eecb01a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9f52ef52-164e-451f-ab61-97632eecb01a' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-56c6b4e1-fae6-482a-accc-6cd7f9995c23' class='xr-section-summary-in' type='checkbox'  checked><label for='section-56c6b4e1-fae6-482a-accc-6cd7f9995c23' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>azimuth_index</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;</div><input id='attrs-d2be216a-eb3c-4172-b94f-547de22427ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2be216a-eb3c-4172-b94f-547de22427ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b307fe93-7ad0-436f-80f1-1dfbd1f460b8' class='xr-var-data-in' type='checkbox'><label for='data-b307fe93-7ad0-436f-80f1-1dfbd1f460b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>2147483647</dd><dt><span>long_name :</span></dt><dd>rare interferogram azimuth index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Rare interferogram azimuth index (indexed from 0).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (5332824,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range_index</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;</div><input id='attrs-68367323-ef80-440d-aa0e-2d5269ee0721' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68367323-ef80-440d-aa0e-2d5269ee0721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4f45209-61fe-464c-992c-b37842953b52' class='xr-var-data-in' type='checkbox'><label for='data-c4f45209-61fe-464c-992c-b37842953b52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>2147483647</dd><dt><span>long_name :</span></dt><dd>rare interferogram range index</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Rare interferogram range index (indexed from 0).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (5332824,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <!-- Horizontal lines -->\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_vectorproc</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;</div><input id='attrs-9d56446c-4683-4e08-9cc3-bcc0636bbfbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d56446c-4683-4e08-9cc3-bcc0636bbfbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b4be88b-40ff-473e-80b8-92556355b554' class='xr-var-data-in' type='checkbox'><label for='data-4b4be88b-40ff-473e-80b8-92556355b554' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>9.969209968386869e+36</dd><dt><span>long_name :</span></dt><dd>height-constrained geolocation latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-80.0</dd><dt><span>valid_max :</span></dt><dd>80.0</dd><dt><span>comment :</span></dt><dd>Height-constrained geodetic latitude of the pixel. Units are in degrees north of the equator.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (5332824,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_vectorproc</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;</div><input id='attrs-cfef5c53-8067-40df-a208-bf9e281a1393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfef5c53-8067-40df-a208-bf9e281a1393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8923e92e-3dce-4d85-9fad-8e4a84b0acbf' class='xr-var-data-in' type='checkbox'><label for='data-8923e92e-3dce-4d85-9fad-8e4a84b0acbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>9.969209968386869e+36</dd><dt><span>long_name :</span></dt><dd>height-constrained geolocation longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>Height-constrained geodetic longitude of the pixel. Positive=degrees east of the Greenwich meridian. Negative=degrees west of the Greenwich meridian.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                        <td> 40.69 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (5332824,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_vectorproc</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;</div><input id='attrs-392baa4b-2512-496d-8282-8dbce48b9c3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-392baa4b-2512-496d-8282-8dbce48b9c3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e45b46d3-87ca-4a84-bd77-deb27805f9c0' class='xr-var-data-in' type='checkbox'><label for='data-e45b46d3-87ca-4a84-bd77-deb27805f9c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>9.96921e+36</dd><dt><span>long_name :</span></dt><dd>height above reference ellipsoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-1500.0</dd><dt><span>valid_max :</span></dt><dd>15000.0</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Height-constrained height of the pixel above the reference ellipsoid.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                        <td> 20.34 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (5332824,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reach_id</span></div><div class='xr-var-dims'>(points, nchar_reach_id)</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824, 11), meta=np.ndarray&gt;</div><input id='attrs-8bdf5ea7-36f0-4c87-94b6-37a6ee2b6b79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bdf5ea7-36f0-4c87-94b6-37a6ee2b6b79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b21cf6d-488f-4571-9bfa-8185a5628f05' class='xr-var-data-in' type='checkbox'><label for='data-3b21cf6d-488f-4571-9bfa-8185a5628f05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>identifier of the associated prior river reach</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Unique reach identifier from the prior river database. The format of the identifier is CBBBBBRRRRT, where C=continent, B=basin, R=reach, T=type.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 55.94 MiB </td>\n",
       "                        <td> 55.94 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824, 11) </td>\n",
       "                        <td> (5332824, 11) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> |S1 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>node_id</span></div><div class='xr-var-dims'>(points, nchar_node_id)</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824, 14), meta=np.ndarray&gt;</div><input id='attrs-1224f73d-8594-4aa9-a9b2-0c555aaf7f40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1224f73d-8594-4aa9-a9b2-0c555aaf7f40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aee4d143-18e4-400b-a914-617acc94c4ee' class='xr-var-data-in' type='checkbox'><label for='data-aee4d143-18e4-400b-a914-617acc94c4ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>identifier of the associated prior river node</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Unique node identifier from the prior river database. The format of the identifier is CBBBBBRRRRNNNT, where C=continent, B=basin, R=reach, N=node, T=type of water body.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 71.20 MiB </td>\n",
       "                        <td> 71.20 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824, 14) </td>\n",
       "                        <td> (5332824, 14) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> |S1 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lake_id</span></div><div class='xr-var-dims'>(points, nchar_lake_id)</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824, 10), meta=np.ndarray&gt;</div><input id='attrs-07cfc9c7-d4a1-41d6-811e-fcd75a0e7b9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07cfc9c7-d4a1-41d6-811e-fcd75a0e7b9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe8bf393-3d80-4e6b-9cf3-71fd3c642b5b' class='xr-var-data-in' type='checkbox'><label for='data-fe8bf393-3d80-4e6b-9cf3-71fd3c642b5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>identifier of the associated prior lake</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Identifier of the lake from the lake prior database) associated to the pixel. The format of the identifier is CBBNNNNNNT, where C=continent, B=basin, N=counter within the basin, T=type of water body.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 50.86 MiB </td>\n",
       "                        <td> 50.86 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824, 10) </td>\n",
       "                        <td> (5332824, 10) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> |S1 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>obs_id</span></div><div class='xr-var-dims'>(points, nchar_obs_id)</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824, 13), meta=np.ndarray&gt;</div><input id='attrs-4696baa6-e381-4557-847f-16e1e3e351fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4696baa6-e381-4557-847f-16e1e3e351fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6bfb850-5219-47a6-aaa3-011073c26c48' class='xr-var-data-in' type='checkbox'><label for='data-b6bfb850-5219-47a6-aaa3-011073c26c48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>identifier of the observed feature</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Tile-specific identifier of the observed feature associated to the pixel. The format of the identifier is CBBTTTSNNNNNN, where C=continent, B=basin, T=tile number, S=swath side, N=lake counter within the PIXC tile.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 66.12 MiB </td>\n",
       "                        <td> 66.12 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824, 13) </td>\n",
       "                        <td> (5332824, 13) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> |S1 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_clim_f</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;</div><input id='attrs-fcd3dfb9-8d56-4259-89b6-45962261575f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcd3dfb9-8d56-4259-89b6-45962261575f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09ba7ec5-861c-4ed2-9725-bfcc680f4e39' class='xr-var-data-in' type='checkbox'><label for='data-09ba7ec5-861c-4ed2-9725-bfcc680f4e39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>127</dd><dt><span>long_name :</span></dt><dd>climatological ice cover flag</dd><dt><span>flag_meanings :</span></dt><dd>no_ice_cover uncertain_ice_cover full_ice_cover</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>institution :</span></dt><dd>University of North Carolina</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the surface is not ice covered, may or may not be partially or fully ice covered, and fully ice covered, respectively. A value of 127 indicates that this flag is not available.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.09 MiB </td>\n",
       "                        <td> 5.09 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5332824,) </td>\n",
       "                        <td> (5332824,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int8 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_dyn_f</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5332824,), meta=np.ndarray&gt;</div><input id='attrs-f604b479-09e8-4a0c-a4a6-36b2b19e9843' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f604b479-09e8-4a0c-a4a6-36b2b19e9843' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db5f7ad2-aca2-4caa-b40f-486f2bac6a38' class='xr-var-data-in' type='checkbox'><label for='data-db5f7ad2-aca2-4caa-b40f-486f2bac6a38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>127</dd><dt><span>long_name :</span></dt><dd>dynamical ice cover flag</dd><dt><span>flag_meanings :</span></dt><dd>no_ice_cover partial_ice_cover full_ice_cover</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>institution :</span></dt><dd>University of North Carolina</dd><dt><span>coordinates :</span></dt><dd>longitude_vectorproc latitude_vectorproc</dd><dt><span>comment :</span></dt><dd>Dynamic ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.09 MiB </td>\n",
       "                        <td> 5.09 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (5332824,) </td>\n",
       "                        <td> (5332824,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int8 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-55291801-07cc-4f7a-a127-112b79f69af0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55291801-07cc-4f7a-a127-112b79f69af0' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-83237df5-bbef-487a-8946-421d13d78075' class='xr-section-summary-in' type='checkbox'  ><label for='section-83237df5-bbef-487a-8946-421d13d78075' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>title :</span></dt><dd>Level 2 KaRIn high rate pixel cloud vector attribute product</dd><dt><span>short_name :</span></dt><dd>L2_HR_PIXCVec</dd><dt><span>institution :</span></dt><dd>CNES</dd><dt><span>source :</span></dt><dd>Level 1B KaRIn High Rate Single Look Complex Data Product</dd><dt><span>history :</span></dt><dd>2024-02-12T08:03:34.974012Z: Creation</dd><dt><span>platform :</span></dt><dd>SWOT</dd><dt><span>references :</span></dt><dd>SWOT-DD-CDM-0565-CNES_SAS_Design_L2_HR_LakeSP - Revision A - 20220531</dd><dt><span>reference_document :</span></dt><dd>SWOT-TN-CDM-0677-CNES_Product_Description_L2_HR_PIXCVec - Revision A - 20220531</dd><dt><span>product_version :</span></dt><dd>V5.3.0</dd><dt><span>crid :</span></dt><dd>PIC0</dd><dt><span>pge_name :</span></dt><dd>PGE_L2_HR_LakeSP</dd><dt><span>pge_version :</span></dt><dd>V4.3.1</dd><dt><span>contact :</span></dt><dd>SWOT-contact@cnes.fr</dd><dt><span>cycle_number :</span></dt><dd>10</dd><dt><span>pass_number :</span></dt><dd>412</dd><dt><span>tile_number :</span></dt><dd>87</dd><dt><span>swath_side :</span></dt><dd>L</dd><dt><span>tile_name :</span></dt><dd>412_087L</dd><dt><span>continent_id :</span></dt><dd>NA</dd><dt><span>continent_code :</span></dt><dd>7</dd><dt><span>time_granule_start :</span></dt><dd>2024-02-08T16:58:37.458189Z</dd><dt><span>time_granule_end :</span></dt><dd>2024-02-08T16:58:48.549822Z</dd><dt><span>time_coverage_start :</span></dt><dd>2024-02-08T16:58:38.039468Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-02-08T16:58:47.997818Z</dd><dt><span>geospatial_lon_min :</span></dt><dd>-107.47770126563466</dd><dt><span>geospatial_lon_max :</span></dt><dd>-106.51240571517286</dd><dt><span>geospatial_lat_min :</span></dt><dd>38.135671144411134</dd><dt><span>geospatial_lat_max :</span></dt><dd>38.90997153687437</dd><dt><span>inner_first_longitude :</span></dt><dd>-107.47770126563466</dd><dt><span>inner_first_latitude :</span></dt><dd>38.770333283891574</dd><dt><span>inner_last_longitude :</span></dt><dd>-107.36194988851383</dd><dt><span>inner_last_latitude :</span></dt><dd>38.135671144411134</dd><dt><span>outer_first_longitude :</span></dt><dd>-106.67322676829212</dd><dt><span>outer_first_latitude :</span></dt><dd>38.90997153687437</dd><dt><span>outer_last_longitude :</span></dt><dd>-106.51240571517286</dd><dt><span>outer_last_latitude :</span></dt><dd>38.28272700181512</dd><dt><span>xref_l2_hr_pixc_file :</span></dt><dd>SWOT_L2_HR_PIXC_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc</dd><dt><span>xref_l2_hr_pixcvecriver_file :</span></dt><dd>SWOT_L2_HR_PIXCVecRiver_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc</dd><dt><span>xref_prior_river_db_file :</span></dt><dd></dd><dt><span>xref_prior_lake_db_file :</span></dt><dd>SWOT_LakeDatabase_Nom_412_20000101T000000_20991231T235959_20231017T000000_v105.sqlite</dd><dt><span>xref_reforbittrack_files :</span></dt><dd>SWOT_RefOrbitTrackTileBoundary_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt</dd><dt><span>xref_param_l2_hr_laketile_file :</span></dt><dd>SWOT_Param_L2_HR_LakeTile_20000101T000000_20991231T235959_20230922T160000_v411.cfg</dd><dt><span>ellipsoid_semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>ellipsoid_flattening :</span></dt><dd>0.0033528106647474805</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 416MB\n",
       "Dimensions:               (points: 5332824, nchar_reach_id: 11,\n",
       "                           nchar_node_id: 14, nchar_lake_id: 10,\n",
       "                           nchar_obs_id: 13)\n",
       "Dimensions without coordinates: points, nchar_reach_id, nchar_node_id,\n",
       "                                nchar_lake_id, nchar_obs_id\n",
       "Data variables:\n",
       "    azimuth_index         (points) int32 21MB dask.array<chunksize=(5332824,), meta=np.ndarray>\n",
       "    range_index           (points) int32 21MB dask.array<chunksize=(5332824,), meta=np.ndarray>\n",
       "    latitude_vectorproc   (points) float64 43MB dask.array<chunksize=(5332824,), meta=np.ndarray>\n",
       "    longitude_vectorproc  (points) float64 43MB dask.array<chunksize=(5332824,), meta=np.ndarray>\n",
       "    height_vectorproc     (points) float32 21MB dask.array<chunksize=(5332824,), meta=np.ndarray>\n",
       "    reach_id              (points, nchar_reach_id) |S1 59MB dask.array<chunksize=(5332824, 11), meta=np.ndarray>\n",
       "    node_id               (points, nchar_node_id) |S1 75MB dask.array<chunksize=(5332824, 14), meta=np.ndarray>\n",
       "    lake_id               (points, nchar_lake_id) |S1 53MB dask.array<chunksize=(5332824, 10), meta=np.ndarray>\n",
       "    obs_id                (points, nchar_obs_id) |S1 69MB dask.array<chunksize=(5332824, 13), meta=np.ndarray>\n",
       "    ice_clim_f            (points) int8 5MB dask.array<chunksize=(5332824,), meta=np.ndarray>\n",
       "    ice_dyn_f             (points) int8 5MB dask.array<chunksize=(5332824,), meta=np.ndarray>\n",
       "Attributes: (12/45)\n",
       "    Conventions:                     CF-1.7\n",
       "    title:                           Level 2 KaRIn high rate pixel cloud vect...\n",
       "    short_name:                      L2_HR_PIXCVec\n",
       "    institution:                     CNES\n",
       "    source:                          Level 1B KaRIn High Rate Single Look Com...\n",
       "    history:                         2024-02-12T08:03:34.974012Z: Creation\n",
       "    ...                              ...\n",
       "    xref_prior_river_db_file:        \n",
       "    xref_prior_lake_db_file:         SWOT_LakeDatabase_Nom_412_20000101T00000...\n",
       "    xref_reforbittrack_files:        SWOT_RefOrbitTrackTileBoundary_Nom_20000...\n",
       "    xref_param_l2_hr_laketile_file:  SWOT_Param_L2_HR_LakeTile_20000101T00000...\n",
       "    ellipsoid_semi_major_axis:       6378137.0\n",
       "    ellipsoid_flattening:            0.0033528106647474805"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_PIXCVEC = xr.open_mfdataset(\"data_downloads/SWOT_L2_HR_PIXCVec_*.nc\", decode_cf=False,  engine='h5netcdf')\n",
    "ds_PIXCVEC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Simple plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pixcvec_htvals = ds_PIXCVEC.height_vectorproc.compute()\n",
    "pixcvec_latvals = ds_PIXCVEC.latitude_vectorproc.compute()\n",
    "pixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc.compute()\n",
    "\n",
    "#Before plotting, we set all fill values to nan so that the graph shows up better spatially\n",
    "pixcvec_htvals[pixcvec_htvals > 15000] = np.nan\n",
    "pixcvec_latvals[pixcvec_latvals == 0] = np.nan\n",
    "pixcvec_lonvals[pixcvec_lonvals == 0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)\n",
    "plt.colorbar().set_label('Height (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### **5. Raster NetCDF**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Search for data of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 1\n"
     ]
    }
   ],
   "source": [
    "raster_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_Raster_2.0', \n",
    "                                        #temporal = ('2024-02-01 00:00:00', '2024-07-15 23:59:59'), # can also specify by time\n",
    "                                        granule_name = '*100m*010_412_*', #The same cycle and pass as previously downloaded', # here we filter by files with '100m' in the name (This collection has two resolution options: 100m & 250m)\n",
    "                                        bounding_box = (-106.62, 38.809, -106.54, 38.859)) # Lake Travis near Austin, TX\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download one data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Getting 1 granules, approx download size: 0.06 GB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QUEUEING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 199.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File SWOT_L2_HR_Raster_100m_UTM13S_N_x_x_x_010_412_044F_20240208T165837_20240208T165858_PIC0_01.nc already downloaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PROCESSING TASKS | : 100%|██████████| 1/1 [00:00<00:00, 998.64it/s]\n",
      "COLLECTING RESULTS | : 100%|██████████| 1/1 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['data_downloads\\\\SWOT_L2_HR_Raster_100m_UTM13S_N_x_x_x_010_412_044F_20240208T165837_20240208T165858_PIC0_01.nc']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthaccess.download([raster_results[0]], \"./data_downloads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Open data with xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll programmatically get the filename we just downloaded and then view the file via `xarray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       ".xr-icon-file-text2,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
       "Dimensions:                  (y: 3236, x: 3237)\n",
       "Coordinates:\n",
       "  * x                        (x) float64 26kB 2.162e+05 2.163e+05 ... 6.525e+05\n",
       "  * y                        (y) float64 26kB 4.152e+06 4.152e+06 ... 7.101e+06\n",
       "Data variables: (12/39)\n",
       "    crs                      (y, x) object 84MB b&#x27;1&#x27; b&#x27;1&#x27; b&#x27;1&#x27; ... b&#x27;1&#x27; b&#x27;1&#x27;\n",
       "    longitude                (y, x) float64 84MB dask.array&lt;chunksize=(502, 502), meta=np.ndarray&gt;\n",
       "    latitude                 (y, x) float64 84MB dask.array&lt;chunksize=(502, 502), meta=np.ndarray&gt;\n",
       "    wse                      (y, x) float32 42MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "    wse_qual                 (y, x) float32 42MB dask.array&lt;chunksize=(1505, 3237), meta=np.ndarray&gt;\n",
       "    wse_qual_bitwise         (y, x) float64 84MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "    ...                       ...\n",
       "    load_tide_fes            (y, x) float32 42MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "    load_tide_got            (y, x) float32 42MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "    pole_tide                (y, x) float32 42MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "    model_dry_tropo_cor      (y, x) float32 42MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "    model_wet_tropo_cor      (y, x) float32 42MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "    iono_cor_gim_ka          (y, x) float32 42MB dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;\n",
       "Attributes: (12/49)\n",
       "    Conventions:                   CF-1.7\n",
       "    title:                         Level 2 KaRIn High Rate Raster Data Product\n",
       "    source:                        Ka-band radar interferometer\n",
       "    history:                       2024-02-12T10:15:16Z : Creation\n",
       "    platform:                      SWOT\n",
       "    references:                    V1.2.1\n",
       "    ...                            ...\n",
       "    x_min:                         216200.0\n",
       "    x_max:                         366700.0\n",
       "    y_min:                         4151600.0\n",
       "    y_max:                         4302000.0\n",
       "    institution:                   CNES\n",
       "    product_version:               01</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-560f22de-3829-4c15-aa3d-b76019deae06' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-560f22de-3829-4c15-aa3d-b76019deae06' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 3236</li><li><span class='xr-has-index'>x</span>: 3237</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bbc8ddc0-1e1b-4daa-b6e1-db8fb2266601' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbc8ddc0-1e1b-4daa-b6e1-db8fb2266601' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.162e+05 2.163e+05 ... 6.525e+05</div><input id='attrs-210e69d1-3ec6-41a9-9e13-6f3ea9fa18a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-210e69d1-3ec6-41a9-9e13-6f3ea9fa18a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-719d31b4-92f8-4e9c-9488-6698e16a0dc9' class='xr-var-data-in' type='checkbox'><label for='data-719d31b4-92f8-4e9c-9488-6698e16a0dc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-10000000.0</dd><dt><span>valid_max :</span></dt><dd>10000000.0</dd><dt><span>comment :</span></dt><dd>UTM easting coordinate of the pixel.</dd></dl></div><div class='xr-var-data'><pre>array([216200., 216300., 216400., ..., 652300., 652400., 652500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.152e+06 4.152e+06 ... 7.101e+06</div><input id='attrs-0dd1faaf-da69-4c2d-a690-35c8c6af376f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dd1faaf-da69-4c2d-a690-35c8c6af376f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127de042-4597-44ad-a39e-6b4b1a36f244' class='xr-var-data-in' type='checkbox'><label for='data-127de042-4597-44ad-a39e-6b4b1a36f244' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-20000000.0</dd><dt><span>valid_max :</span></dt><dd>20000000.0</dd><dt><span>comment :</span></dt><dd>UTM northing coordinate of the pixel.</dd></dl></div><div class='xr-var-data'><pre>array([4151600., 4151700., 4151800., ..., 7101100., 7101200., 7101300.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f790a38-0ec0-45a7-8599-ae66320392b6' class='xr-section-summary-in' type='checkbox'  ><label for='section-8f790a38-0ec0-45a7-8599-ae66320392b6' class='xr-section-summary' >Data variables: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>b&#x27;1&#x27; b&#x27;1&#x27; b&#x27;1&#x27; ... b&#x27;1&#x27; b&#x27;1&#x27; b&#x27;1&#x27;</div><input id='attrs-6e9c16e1-ccd0-44ab-abee-eede90ed9cf1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e9c16e1-ccd0-44ab-abee-eede90ed9cf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-106c37e1-202f-48b5-91bc-ce81f0dab0f2' class='xr-var-data-in' type='checkbox'><label for='data-106c37e1-202f-48b5-91bc-ce81f0dab0f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>CRS Definition</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 13N</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>WGS_1984</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-105.0</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 13N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32613&quot;]]</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 13N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32613&quot;]]</dd><dt><span>comment :</span></dt><dd>UTM zone coordinate reference system.</dd></dl></div><div class='xr-var-data'><pre>array([[b&#x27;1&#x27;, b&#x27;1&#x27;, b&#x27;1&#x27;, ..., nan, nan, nan],\n",
       "       [b&#x27;1&#x27;, b&#x27;1&#x27;, b&#x27;1&#x27;, ..., nan, nan, nan],\n",
       "       [b&#x27;1&#x27;, b&#x27;1&#x27;, b&#x27;1&#x27;, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., b&#x27;1&#x27;, b&#x27;1&#x27;, b&#x27;1&#x27;],\n",
       "       [nan, nan, nan, ..., b&#x27;1&#x27;, b&#x27;1&#x27;, b&#x27;1&#x27;],\n",
       "       [nan, nan, nan, ..., b&#x27;1&#x27;, b&#x27;1&#x27;, b&#x27;1&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(502, 502), meta=np.ndarray&gt;</div><input id='attrs-0e4125e0-89c2-490c-a93d-b8b79502695b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e4125e0-89c2-490c-a93d-b8b79502695b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17f053ec-47a9-4910-a3e6-c339f3046049' class='xr-var-data-in' type='checkbox'><label for='data-17f053ec-47a9-4910-a3e6-c339f3046049' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude (degrees East)</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>Geodetic longitude [-180,180) (east of the Greenwich meridian) of the pixel.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 4.75 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (577, 1079) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 30 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"169\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(502, 502), meta=np.ndarray&gt;</div><input id='attrs-4772acf5-0715-497a-9d3e-abd5a8a782f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4772acf5-0715-497a-9d3e-abd5a8a782f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7967cc6d-336c-41ee-aab8-3474c0e226c3' class='xr-var-data-in' type='checkbox'><label for='data-7967cc6d-336c-41ee-aab8-3474c0e226c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude (positive N, negative S)</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-80.0</dd><dt><span>valid_max :</span></dt><dd>80.0</dd><dt><span>comment :</span></dt><dd>Geodetic latitude [-80,80] (degrees north of equator) of the pixel.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 4.75 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (577, 1079) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 30 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"169\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"119\" />\n",
       "  <line x1=\"77\" y1=\"0\" x2=\"77\" y2=\"119\" />\n",
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       "  <!-- Text -->\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wse</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-e534ebe4-b2d4-4ae7-a13d-9949239aa45b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e534ebe4-b2d4-4ae7-a13d-9949239aa45b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53bd1b25-bd5a-40a0-b9c5-a8e776224173' class='xr-var-data-in' type='checkbox'><label for='data-53bd1b25-bd5a-40a0-b9c5-a8e776224173' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water surface elevation above geoid</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>quality_flag :</span></dt><dd>wse_qual</dd><dt><span>valid_min :</span></dt><dd>-1500.0</dd><dt><span>valid_max :</span></dt><dd>15000.0</dd><dt><span>comment :</span></dt><dd>Water surface elevation of the pixel above the geoid and after using models to subtract the effects of tides (solid_earth_tide, load_tide_fes, pole_tide).</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wse_qual</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1505, 3237), meta=np.ndarray&gt;</div><input id='attrs-b86c4129-a573-4401-ad74-9d2738ed7334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b86c4129-a573-4401-ad74-9d2738ed7334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ca48f1f-f896-4f90-8a21-ceaf8735f30a' class='xr-var-data-in' type='checkbox'><label for='data-3ca48f1f-f896-4f90-8a21-ceaf8735f30a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>summary quality indicator for the water surface elevation</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>good suspect degraded bad</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3</dd><dt><span>comment :</span></dt><dd>Summary quality indicator for the water surface elevation quantities. A value of 0 indicates a nominal measurement, 1 indicates a suspect measurement, 2 indicates a degraded measurement, and 3 indicates a bad measurement.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (1731, 3237) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wse_qual_bitwise</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-f7c5bf65-4c1e-4256-9ccf-47f4d08dd908' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7c5bf65-4c1e-4256-9ccf-47f4d08dd908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e75156-2fde-4cff-bcc1-500e1b6d28f1' class='xr-var-data-in' type='checkbox'><label for='data-27e75156-2fde-4cff-bcc1-500e1b6d28f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>bitwise quality indicator for the water surface elevation</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>classification_qual_suspect geolocation_qual_suspect large_uncert_suspect bright_land few_pixels far_range_suspect near_range_suspect classification_qual_degraded geolocation_qual_degraded dark_water_degraded low_coherence_water_degraded value_bad outside_data_window no_pixels outside_scene_bounds inner_swath missing_karin_data</dd><dt><span>flag_masks :</span></dt><dd>[         2          4         32        128       4096       8192\n",
       "      16384     262144     524288    1048576    2097152   16777216\n",
       "   67108864  268435456  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>4114378918</dd><dt><span>comment :</span></dt><dd>Bitwise quality indicator for the water surface elevation quantities. If this word is interpreted as an unsigned integer, a value of 0 indicates good data, positive values less than 32768 represent suspect data, values greater than or equal to 32768 but less than 8388608 represent degraded data, and values greater than or equal to 8388608 represent bad data.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 79.92 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wse_uncert</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-6203e06e-e075-4510-88e5-7ffb03e0e98c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6203e06e-e075-4510-88e5-7ffb03e0e98c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9536f88-995d-4664-bcfe-ba75d48fe3b7' class='xr-var-data-in' type='checkbox'><label for='data-a9536f88-995d-4664-bcfe-ba75d48fe3b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty in the water surface elevation</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>1-sigma uncertainty in the water surface elevation.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-37b65566-2bcb-4154-85ba-7645a9533dee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37b65566-2bcb-4154-85ba-7645a9533dee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2d846eb-500a-40cf-a626-4037bb4c8852' class='xr-var-data-in' type='checkbox'><label for='data-f2d846eb-500a-40cf-a626-4037bb4c8852' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water surface area</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>quality_flag :</span></dt><dd>water_area_qual</dd><dt><span>valid_min :</span></dt><dd>-2000000.0</dd><dt><span>valid_max :</span></dt><dd>2000000000.0</dd><dt><span>comment :</span></dt><dd>Surface area of the water pixels.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_area_qual</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1505, 3237), meta=np.ndarray&gt;</div><input id='attrs-1059c6ec-bebe-4270-9bd3-1885d6f7e680' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1059c6ec-bebe-4270-9bd3-1885d6f7e680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-004393ce-ee79-4981-ae7b-5b386f45e5d0' class='xr-var-data-in' type='checkbox'><label for='data-004393ce-ee79-4981-ae7b-5b386f45e5d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>summary quality indicator for the water surface area</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>good suspect degraded bad</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3</dd><dt><span>comment :</span></dt><dd>Summary quality indicator for the water surface area and water fraction quantities. A value of 0 indicates a nominal measurement, 1 indicates a suspect measurement, 2 indicates a degraded measurement, and 3 indicates a bad measurement.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 21.37 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (1731, 3237) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_area_qual_bitwise</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-1308f3a5-9a34-421b-8d57-26133ad20691' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1308f3a5-9a34-421b-8d57-26133ad20691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96db5c97-62b1-4b55-8be0-ea0af8ded1bb' class='xr-var-data-in' type='checkbox'><label for='data-96db5c97-62b1-4b55-8be0-ea0af8ded1bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>bitwise quality indicator for the water surface area</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>classification_qual_suspect geolocation_qual_suspect water_fraction_suspect large_uncert_suspect bright_land low_coherence_water_suspect few_pixels far_range_suspect near_range_suspect classification_qual_degraded geolocation_qual_degraded value_bad outside_data_window no_pixels outside_scene_bounds inner_swath missing_karin_data</dd><dt><span>flag_masks :</span></dt><dd>[         2          4          8         32        128        256\n",
       "       4096       8192      16384     262144     524288   16777216\n",
       "   67108864  268435456  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>4111233454</dd><dt><span>comment :</span></dt><dd>Bitwise quality indicator for the water surface area and water fraction quantities. If this word is interpreted as an unsigned integer, a value of 0 indicates good data, positive values less than 32768 represent suspect data, values greater than or equal to 32768 but less than 8388608 represent degraded data, and values greater than or equal to 8388608 represent bad data.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 79.92 MiB </td>\n",
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       "                    <tr>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_area_uncert</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-2d172f5e-9072-473e-a4bc-335a1aae0f58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d172f5e-9072-473e-a4bc-335a1aae0f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6b9a8c0-9824-44ce-b2c6-d4c783df0fc5' class='xr-var-data-in' type='checkbox'><label for='data-d6b9a8c0-9824-44ce-b2c6-d4c783df0fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty in the water surface area</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>2000000000.0</dd><dt><span>comment :</span></dt><dd>1-sigma uncertainty in the water surface area.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                        <td> 39.96 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_frac</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-cc069b70-39ae-43d1-b643-11fe3ad19c10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc069b70-39ae-43d1-b643-11fe3ad19c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c71efa60-cbbf-4584-8564-c56abb5079fa' class='xr-var-data-in' type='checkbox'><label for='data-c71efa60-cbbf-4584-8564-c56abb5079fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water fraction</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>water_area_qual</dd><dt><span>valid_min :</span></dt><dd>-1000.0</dd><dt><span>valid_max :</span></dt><dd>10000.0</dd><dt><span>comment :</span></dt><dd>Fraction of the pixel that is water.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_frac_uncert</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-98761ea8-a0a3-49d6-9120-c47e8794b4fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98761ea8-a0a3-49d6-9120-c47e8794b4fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed79175-341b-4e88-bb56-d67373e0a45d' class='xr-var-data-in' type='checkbox'><label for='data-aed79175-341b-4e88-bb56-d67373e0a45d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty in the water fraction</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>1-sigma uncertainty in the water fraction.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                        <td> 39.96 MiB </td>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-8a3621e8-447e-43e3-92cb-2dd229d05c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a3621e8-447e-43e3-92cb-2dd229d05c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9dce720-85ff-463b-a8c3-9bce0bbeca91' class='xr-var-data-in' type='checkbox'><label for='data-d9dce720-85ff-463b-a8c3-9bce0bbeca91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sigma0</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>quality_flag :</span></dt><dd>sig0_qual</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_qual</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1505, 3237), meta=np.ndarray&gt;</div><input id='attrs-dd1659fc-91a4-4533-a238-502f15dc377b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd1659fc-91a4-4533-a238-502f15dc377b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac4348e2-638b-4e02-8c26-0056bce7d535' class='xr-var-data-in' type='checkbox'><label for='data-ac4348e2-638b-4e02-8c26-0056bce7d535' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>summary quality indicator for the sigma0</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>good suspect degraded bad</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3</dd><dt><span>comment :</span></dt><dd>Summary quality indicator for the sigma0 quantities. A value of 0 indicates a nominal measurement, 1 indicates a suspect measurement, 2 indicates a degraded measurement, and 3 indicates a bad measurement.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
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       "                        <td> (1731, 3237) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_qual_bitwise</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-74e48925-ffd9-45c9-997f-038a40d63257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74e48925-ffd9-45c9-997f-038a40d63257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a98ffb3-f9e9-44be-a270-f691c2ab8ca6' class='xr-var-data-in' type='checkbox'><label for='data-5a98ffb3-f9e9-44be-a270-f691c2ab8ca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>bitwise quality indicator for the sigma0</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>sig0_qual_suspect classification_qual_suspect geolocation_qual_suspect large_uncert_suspect bright_land low_coherence_water_suspect few_pixels far_range_suspect near_range_suspect sig0_qual_degraded classification_qual_degraded geolocation_qual_degraded value_bad outside_data_window no_pixels outside_scene_bounds inner_swath missing_karin_data</dd><dt><span>flag_masks :</span></dt><dd>[         1          2          4         32        128        256\n",
       "       4096       8192      16384     131072     262144     524288\n",
       "   16777216   67108864  268435456  536870912 1073741824 2147483648]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>4111364519</dd><dt><span>comment :</span></dt><dd>Bitwise quality indicator for the sigma0 quantities. If this word is interpreted as an unsigned integer, a value of 0 indicates good data, positive values less than 32768 represent suspect data, values greater than or equal to 32768 but less than 8388608 represent degraded data, and values greater than or equal to 8388608 represent bad data.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 10.70 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_uncert</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-63988956-2c5c-445a-9a3e-a781978a2f02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63988956-2c5c-445a-9a3e-a781978a2f02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edc67f49-0bec-432e-ae02-904f77feb5af' class='xr-var-data-in' type='checkbox'><label for='data-edc67f49-0bec-432e-ae02-904f77feb5af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty in sigma0</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>1-sigma uncertainty in sigma0. The value is provided in linear units. This value is a one-sigma additive (not multiplicative) uncertainty term, which can be added to or subtracted from sigma0.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-9a076261-f32d-4510-8775-36b4ac92dbeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a076261-f32d-4510-8775-36b4ac92dbeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cce60007-44dc-4ba3-b009-0bffdfbcd82a' class='xr-var-data-in' type='checkbox'><label for='data-cce60007-44dc-4ba3-b009-0bffdfbcd82a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>incidence angle</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>Incidence angle.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    </tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cross_track</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-3170f315-ef7e-4eeb-b842-dcf7cdb117a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3170f315-ef7e-4eeb-b842-dcf7cdb117a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba643fae-153c-4aef-90d8-1303c5229597' class='xr-var-data-in' type='checkbox'><label for='data-ba643fae-153c-4aef-90d8-1303c5229597' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>approximate cross-track location</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-75000.0</dd><dt><span>valid_max :</span></dt><dd>75000.0</dd><dt><span>comment :</span></dt><dd>Approximate cross-track location of the pixel.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
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       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>illumination_time</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(502, 502), meta=np.ndarray&gt;</div><input id='attrs-081d369c-1008-4b9c-ad20-d2647e9a545a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-081d369c-1008-4b9c-ad20-d2647e9a545a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ab60faa-00cf-4dcf-88a9-891a293d0534' class='xr-var-data-in' type='checkbox'><label for='data-5ab60faa-00cf-4dcf-88a9-891a293d0534' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of illumination of each pixel (UTC)</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>tai_utc_difference :</span></dt><dd>37.0</dd><dt><span>leap_second :</span></dt><dd>0000-00-00T00:00:00Z</dd><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 4.75 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (577, 1079) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 30 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>illumination_time_tai</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(502, 502), meta=np.ndarray&gt;</div><input id='attrs-817ec42b-64f0-4200-833d-8b410176dfb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-817ec42b-64f0-4200-833d-8b410176dfb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7136d033-4513-4ae0-9e15-7b13b8354002' class='xr-var-data-in' type='checkbox'><label for='data-7136d033-4513-4ae0-9e15-7b13b8354002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of illumination of each pixel (TAI)</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 4.75 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (577, 1079) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 30 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_wse_pix</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-428f7d44-75cc-45d8-a973-258fa3d1752d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-428f7d44-75cc-45d8-a973-258fa3d1752d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-831a1c5e-5cbb-42fa-9513-b00e1a25d7bd' class='xr-var-data-in' type='checkbox'><label for='data-831a1c5e-5cbb-42fa-9513-b00e1a25d7bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of water surface elevation pixels</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>l</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Number of pixel cloud samples used in water surface elevation aggregation.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 10.70 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_water_area_pix</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-b426fd12-32ac-4e1c-bac5-d344ede88c91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b426fd12-32ac-4e1c-bac5-d344ede88c91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fdc17d4-9e68-465b-ad20-b3dd77b496d0' class='xr-var-data-in' type='checkbox'><label for='data-0fdc17d4-9e68-465b-ad20-b3dd77b496d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of water surface area pixels</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>l</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Number of pixel cloud samples used in water surface area and water fraction aggregation.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 10.70 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_sig0_pix</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-46ebb135-70e3-4255-baf3-260bea55ee02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46ebb135-70e3-4255-baf3-260bea55ee02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a74c4ee-f59e-4ec3-b6c5-d1d4ce16d73f' class='xr-var-data-in' type='checkbox'><label for='data-9a74c4ee-f59e-4ec3-b6c5-d1d4ce16d73f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of sigma0 pixels</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>l</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Number of pixel cloud samples used in sigma0 aggregation.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 10.70 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_other_pix</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-b550c9ce-da78-4d14-b083-be7557aa60ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b550c9ce-da78-4d14-b083-be7557aa60ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daa8df93-a264-4017-9662-c94a4b10efe2' class='xr-var-data-in' type='checkbox'><label for='data-daa8df93-a264-4017-9662-c94a4b10efe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of other pixels</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>l</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>999999</dd><dt><span>comment :</span></dt><dd>Number of pixel cloud samples used in aggregation of quantities not related to water surface elevation, water surface area, water fraction or sigma0.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 79.92 MiB </td>\n",
       "                        <td> 10.70 MiB </td>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark_frac</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-e4cae24e-abc3-4824-a0ff-858acdcc50a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4cae24e-abc3-4824-a0ff-858acdcc50a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c5e3e82-562f-46f9-adfa-44a5e79191ed' class='xr-var-data-in' type='checkbox'><label for='data-9c5e3e82-562f-46f9-adfa-44a5e79191ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>fractional area of dark water</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>l</dd><dt><span>valid_min :</span></dt><dd>-1000.0</dd><dt><span>valid_max :</span></dt><dd>10000.0</dd><dt><span>comment :</span></dt><dd>Fraction of pixel water surface area covered by dark water.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_clim_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1505, 3237), meta=np.ndarray&gt;</div><input id='attrs-522354ef-8b28-47b7-b4e0-ceb1a6721baf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-522354ef-8b28-47b7-b4e0-ceb1a6721baf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03fe497f-c3c4-42f6-904f-6c1b53f164fa' class='xr-var-data-in' type='checkbox'><label for='data-03fe497f-c3c4-42f6-904f-6c1b53f164fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>climatological ice cover flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>source :</span></dt><dd>UNC</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>no_ice_cover uncertain_ice_cover full_ice_cover</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>comment :</span></dt><dd>Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the pixel is likely not ice covered, may or may not be partially or fully ice covered, and likely fully ice covered, respectively.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 21.37 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (1731, 3237) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_dyn_flag</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1505, 3237), meta=np.ndarray&gt;</div><input id='attrs-4783ff7c-6639-4913-95b2-9eeb70d53a50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4783ff7c-6639-4913-95b2-9eeb70d53a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eff8f59-479d-4708-a33d-21ec10fab0fa' class='xr-var-data-in' type='checkbox'><label for='data-1eff8f59-479d-4708-a33d-21ec10fab0fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dynamic ice cover flag</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>source :</span></dt><dd>UNC</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>flag_meanings :</span></dt><dd>no_ice_cover partial_ice_cover full_ice_cover</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>comment :</span></dt><dd>Dynamic ice cover flag indicating whether the surface is ice-covered on the day of the observation based on analysis of external satellite optical data.  Values of 0, 1, and 2 indicate that the pixel is not ice covered, partially ice covered, and fully ice covered, respectively.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 39.96 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (1731, 3237) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layover_impact</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-41021255-e739-4954-b021-c3cf0301f11e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41021255-e739-4954-b021-c3cf0301f11e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eec15355-fbdc-4f1a-b6c0-a1c7a313d5e4' class='xr-var-data-in' type='checkbox'><label for='data-eec15355-fbdc-4f1a-b6c0-a1c7a313d5e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>layover impact</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-999999.0</dd><dt><span>valid_max :</span></dt><dd>999999.0</dd><dt><span>comment :</span></dt><dd>Estimate of the water surface elevation error caused by layover.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_cor_atmos_model</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-f5843d06-9e87-43e5-bdf8-0cc4ce5f17c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5843d06-9e87-43e5-bdf8-0cc4ce5f17c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98619594-aafd-495f-9434-772aa3647dca' class='xr-var-data-in' type='checkbox'><label for='data-98619594-aafd-495f-9434-772aa3647dca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>two-way atmospheric correction to sigma0 from model</dd><dt><span>source :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>1.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>comment :</span></dt><dd>Atmospheric correction to sigma0 from weather model data as a linear power multiplier (not decibels). sig0_cor_atmos_model is already applied in computing sig0.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_cor_xover</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-88d3698a-e2c8-45aa-a03a-63760471c9cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88d3698a-e2c8-45aa-a03a-63760471c9cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd0d531f-0fe1-4cd4-9ad3-3b14d340601e' class='xr-var-data-in' type='checkbox'><label for='data-fd0d531f-0fe1-4cd4-9ad3-3b14d340601e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height correction from KaRIn crossovers</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>comment :</span></dt><dd>Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 39.96 MiB </td>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geoid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-67507361-d5ab-44d6-aad8-393b5e351e13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67507361-d5ab-44d6-aad8-393b5e351e13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90684f5d-146d-4936-a0a5-6c2f81785fad' class='xr-var-data-in' type='checkbox'><label for='data-90684f5d-146d-4936-a0a5-6c2f81785fad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geoid height</dd><dt><span>standard_name :</span></dt><dd>geoid_height_above_reference_ellipsoid</dd><dt><span>source :</span></dt><dd>EGM2008 (Pavlis et al., 2012)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-150.0</dd><dt><span>valid_max :</span></dt><dd>150.0</dd><dt><span>comment :</span></dt><dd>Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 39.96 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solid_earth_tide</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-c9f43e8f-53ea-4de7-a236-c60406b0c5c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9f43e8f-53ea-4de7-a236-c60406b0c5c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af026b00-b2c0-48a7-865d-46b7c8804bb3' class='xr-var-data-in' type='checkbox'><label for='data-af026b00-b2c0-48a7-865d-46b7c8804bb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>solid Earth tide height</dd><dt><span>source :</span></dt><dd>Cartwright and Taylor (1971) and Cartwright and Edden (1973)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd><dt><span>comment :</span></dt><dd>Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>load_tide_fes</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-41d6dff9-91b8-4dc1-a291-abd3b41bdb73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41d6dff9-91b8-4dc1-a291-abd3b41bdb73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29f5a50e-376e-4834-9556-c28e8fa8b1fe' class='xr-var-data-in' type='checkbox'><label for='data-29f5a50e-376e-4834-9556-c28e8fa8b1fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geocentric load tide height (FES)</dd><dt><span>source :</span></dt><dd>FES2014b (Carrere et al., 2016)</dd><dt><span>institution :</span></dt><dd>LEGOS/CNES</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.2</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>comment :</span></dt><dd>Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (866, 1619) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>load_tide_got</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-842f4735-c68f-42f0-a5bb-98e53a81c80d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-842f4735-c68f-42f0-a5bb-98e53a81c80d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e94abf85-312b-4dc4-b332-dddcde4b49d7' class='xr-var-data-in' type='checkbox'><label for='data-e94abf85-312b-4dc4-b332-dddcde4b49d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geocentric load tide height (GOT)</dd><dt><span>source :</span></dt><dd>GOT4.10c (Ray, 2013)</dd><dt><span>institution :</span></dt><dd>GSFC</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.2</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>comment :</span></dt><dd>Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust. This value is reported for reference but is not applied to the reported height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pole_tide</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-8070f2e3-6840-4081-b6a4-c1602e887953' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8070f2e3-6840-4081-b6a4-c1602e887953' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91b1b4c5-e871-4adf-9174-b6619f3f87a8' class='xr-var-data-in' type='checkbox'><label for='data-91b1b4c5-e871-4adf-9174-b6619f3f87a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>geocentric pole tide height</dd><dt><span>source :</span></dt><dd>Wahr (1985) and Desai et al. (2015)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.2</dd><dt><span>valid_max :</span></dt><dd>0.2</dd><dt><span>comment :</span></dt><dd>Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth’s crust).</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_dry_tropo_cor</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-f21b3aca-cd2d-4b86-8557-78fb1fbfa4cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f21b3aca-cd2d-4b86-8557-78fb1fbfa4cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c6e35f3-ad67-461c-a9a9-83dc92a237fe' class='xr-var-data-in' type='checkbox'><label for='data-7c6e35f3-ad67-461c-a9a9-83dc92a237fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dry troposphere vertical correction</dd><dt><span>source :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-3.0</dd><dt><span>valid_max :</span></dt><dd>-1.5</dd><dt><span>comment :</span></dt><dd>Equivalent vertical correction due to dry troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 39.96 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_wet_tropo_cor</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-3d272959-dac8-4522-b9a6-1c09a5ef5eb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d272959-dac8-4522-b9a6-1c09a5ef5eb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65295b22-b2c7-44be-9308-0f6c92183fb5' class='xr-var-data-in' type='checkbox'><label for='data-65295b22-b2c7-44be-9308-0f6c92183fb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>wet troposphere vertical correction</dd><dt><span>source :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>Equivalent vertical correction due to wet troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iono_cor_gim_ka</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(753, 753), meta=np.ndarray&gt;</div><input id='attrs-dcca33fe-ca46-483a-9527-a66fe157da5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcca33fe-ca46-483a-9527-a66fe157da5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c165b54-7318-48f5-8212-8d4412ab43ce' class='xr-var-data-in' type='checkbox'><label for='data-7c165b54-7318-48f5-8212-8d4412ab43ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ionosphere vertical correction</dd><dt><span>source :</span></dt><dd>Global Ionosphere Maps</dd><dt><span>institution :</span></dt><dd>JPL</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-0.5</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>Equivalent vertical correction due to ionosphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 39.96 MiB </td>\n",
       "                        <td> 5.35 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3236, 3237) </td>\n",
       "                        <td> (866, 1619) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 23 graph layers </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-24db735c-3f17-4573-842c-28af80a3153e' class='xr-section-summary-in' type='checkbox'  ><label for='section-24db735c-3f17-4573-842c-28af80a3153e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6a6afafa-7d1b-4838-8a65-22608b3491a7' class='xr-index-data-in' type='checkbox'/><label for='index-6a6afafa-7d1b-4838-8a65-22608b3491a7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([216200.0, 216300.0, 216400.0, 216500.0, 216600.0, 216700.0, 216800.0,\n",
       "       216900.0, 217000.0, 217100.0,\n",
       "       ...\n",
       "       651600.0, 651700.0, 651800.0, 651900.0, 652000.0, 652100.0, 652200.0,\n",
       "       652300.0, 652400.0, 652500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=3237))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd93333d-6c9b-45db-a40d-11fbcb97962d' class='xr-index-data-in' type='checkbox'/><label for='index-bd93333d-6c9b-45db-a40d-11fbcb97962d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4151600.0, 4151700.0, 4151800.0, 4151900.0, 4152000.0, 4152100.0,\n",
       "       4152200.0, 4152300.0, 4152400.0, 4152500.0,\n",
       "       ...\n",
       "       7100400.0, 7100500.0, 7100600.0, 7100700.0, 7100800.0, 7100900.0,\n",
       "       7101000.0, 7101100.0, 7101200.0, 7101300.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=3236))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f97a624a-7e93-4cac-a888-766852935836' class='xr-section-summary-in' type='checkbox'  ><label for='section-f97a624a-7e93-4cac-a888-766852935836' class='xr-section-summary' >Attributes: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>title :</span></dt><dd>Level 2 KaRIn High Rate Raster Data Product</dd><dt><span>source :</span></dt><dd>Ka-band radar interferometer</dd><dt><span>history :</span></dt><dd>2024-02-12T10:15:16Z : Creation</dd><dt><span>platform :</span></dt><dd>SWOT</dd><dt><span>references :</span></dt><dd>V1.2.1</dd><dt><span>reference_document :</span></dt><dd>JPL D-56416 - Revision C - December 8, 2023</dd><dt><span>contact :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>cycle_number :</span></dt><dd>10</dd><dt><span>pass_number :</span></dt><dd>412</dd><dt><span>scene_number :</span></dt><dd>44</dd><dt><span>tile_numbers :</span></dt><dd>[86 87 88 89 86 87 88 89]</dd><dt><span>tile_names :</span></dt><dd>412_086L, 412_087L, 412_088L, 412_089L, 412_086R, 412_087R, 412_088R, 412_089R</dd><dt><span>tile_polarizations :</span></dt><dd>H, H, H, H, V, V, V, V</dd><dt><span>coordinate_reference_system :</span></dt><dd>Universal Transverse Mercator</dd><dt><span>resolution :</span></dt><dd>100.0</dd><dt><span>short_name :</span></dt><dd>L2_HR_Raster</dd><dt><span>descriptor_string :</span></dt><dd>100m_UTM13S_N_x_x_x</dd><dt><span>crid :</span></dt><dd>PIC0</dd><dt><span>pge_name :</span></dt><dd>PGE_L2_HR_RASTER</dd><dt><span>pge_version :</span></dt><dd>5.1.1</dd><dt><span>time_granule_start :</span></dt><dd>2024-02-08T16:58:37.454917Z</dd><dt><span>time_granule_end :</span></dt><dd>2024-02-08T16:58:58.551162Z</dd><dt><span>time_coverage_start :</span></dt><dd>2024-02-08T16:58:37.993156Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-02-08T16:58:58.010275Z</dd><dt><span>geospatial_lon_min :</span></dt><dd>-108.25835799293851</dd><dt><span>geospatial_lon_max :</span></dt><dd>-106.51301198457615</dd><dt><span>geospatial_lat_min :</span></dt><dd>37.47471767618876</dd><dt><span>geospatial_lat_max :</span></dt><dd>38.852446680067644</dd><dt><span>left_first_longitude :</span></dt><dd>-106.82305125034438</dd><dt><span>left_first_latitude :</span></dt><dd>38.852446680067644</dd><dt><span>left_last_longitude :</span></dt><dd>-106.51301198457615</dd><dt><span>left_last_latitude :</span></dt><dd>37.72449612913602</dd><dt><span>right_first_longitude :</span></dt><dd>-108.25835799293851</dd><dt><span>right_first_latitude :</span></dt><dd>38.5968033100428</dd><dt><span>right_last_longitude :</span></dt><dd>-107.92809879302563</dd><dt><span>right_last_latitude :</span></dt><dd>37.47471767618876</dd><dt><span>xref_l2_hr_pixc_files :</span></dt><dd>SWOT_L2_HR_PIXC_010_412_086L_20240208T165827_20240208T165838_PIC0_01.nc, SWOT_L2_HR_PIXC_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc, SWOT_L2_HR_PIXC_010_412_088L_20240208T165847_20240208T165858_PIC0_01.nc, SWOT_L2_HR_PIXC_010_412_089L_20240208T165857_20240208T165908_PIC0_01.nc, SWOT_L2_HR_PIXC_010_412_086R_20240208T165827_20240208T165838_PIC0_01.nc, SWOT_L2_HR_PIXC_010_412_087R_20240208T165837_20240208T165848_PIC0_01.nc, SWOT_L2_HR_PIXC_010_412_088R_20240208T165847_20240208T165858_PIC0_01.nc, SWOT_L2_HR_PIXC_010_412_089R_20240208T165857_20240208T165908_PIC0_01.nc</dd><dt><span>xref_l2_hr_pixcvec_files :</span></dt><dd>SWOT_L2_HR_PIXCVec_010_412_086L_20240208T165827_20240208T165838_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_412_087L_20240208T165837_20240208T165848_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_412_088L_20240208T165847_20240208T165858_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_412_089L_20240208T165857_20240208T165908_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_412_086R_20240208T165827_20240208T165838_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_412_087R_20240208T165837_20240208T165848_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_412_088R_20240208T165847_20240208T165858_PIC0_01.nc, SWOT_L2_HR_PIXCVec_010_412_089R_20240208T165857_20240208T165908_PIC0_01.nc</dd><dt><span>xref_param_l2_hr_raster_file :</span></dt><dd>SWOT_Param_L2_HR_Raster_20000101T000000_21000101T000000_20230817T100000_v302.rdf</dd><dt><span>xref_reforbittrack_files :</span></dt><dd>SWOT_RefOrbitTrackTileBoundary_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt, SWOT_RefOrbitTrack125mPass1_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt, SWOT_RefOrbitTrack125mPass2_Nom_20000101T000000_21000101T000000_20200617T193054_v101.txt</dd><dt><span>utm_zone_num :</span></dt><dd>13</dd><dt><span>mgrs_latitude_band :</span></dt><dd>S</dd><dt><span>x_min :</span></dt><dd>216200.0</dd><dt><span>x_max :</span></dt><dd>366700.0</dd><dt><span>y_min :</span></dt><dd>4151600.0</dd><dt><span>y_max :</span></dt><dd>4302000.0</dd><dt><span>institution :</span></dt><dd>CNES</dd><dt><span>product_version :</span></dt><dd>01</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2GB\n",
       "Dimensions:                  (y: 3236, x: 3237)\n",
       "Coordinates:\n",
       "  * x                        (x) float64 26kB 2.162e+05 2.163e+05 ... 6.525e+05\n",
       "  * y                        (y) float64 26kB 4.152e+06 4.152e+06 ... 7.101e+06\n",
       "Data variables: (12/39)\n",
       "    crs                      (y, x) object 84MB b'1' b'1' b'1' ... b'1' b'1'\n",
       "    longitude                (y, x) float64 84MB dask.array<chunksize=(502, 502), meta=np.ndarray>\n",
       "    latitude                 (y, x) float64 84MB dask.array<chunksize=(502, 502), meta=np.ndarray>\n",
       "    wse                      (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "    wse_qual                 (y, x) float32 42MB dask.array<chunksize=(1505, 3237), meta=np.ndarray>\n",
       "    wse_qual_bitwise         (y, x) float64 84MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "    ...                       ...\n",
       "    load_tide_fes            (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "    load_tide_got            (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "    pole_tide                (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "    model_dry_tropo_cor      (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "    model_wet_tropo_cor      (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "    iono_cor_gim_ka          (y, x) float32 42MB dask.array<chunksize=(753, 753), meta=np.ndarray>\n",
       "Attributes: (12/49)\n",
       "    Conventions:                   CF-1.7\n",
       "    title:                         Level 2 KaRIn High Rate Raster Data Product\n",
       "    source:                        Ka-band radar interferometer\n",
       "    history:                       2024-02-12T10:15:16Z : Creation\n",
       "    platform:                      SWOT\n",
       "    references:                    V1.2.1\n",
       "    ...                            ...\n",
       "    x_min:                         216200.0\n",
       "    x_max:                         366700.0\n",
       "    y_min:                         4151600.0\n",
       "    y_max:                         4302000.0\n",
       "    institution:                   CNES\n",
       "    product_version:               01"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_raster = xr.open_mfdataset(f'data_downloads/SWOT_L2_HR_Raster*', engine='h5netcdf')\n",
    "ds_raster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Quick interactive plot with `hvplot`\n",
    "\n",
    "Note: this is not filtered by quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds_raster.wse.hvplot.image(y='y', x='x')"
   ]
  }
 ],
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