{ "cells": [ { "cell_type": "markdown", "id": "bebd6fba-d5ea-4765-9a34-fa763315434f", "metadata": {}, "source": [ "## Working with WRF NetCDF Files" ] }, { "cell_type": "markdown", "id": "0f5ad154-d5dd-4459-b6c4-534a2930e1e2", "metadata": {}, "source": [ "This example notebook shows how to use `xarray` and `rioxarray` libraries to convert Weather Research and Forecasting Model (WRF) NetCDF files to GeoTIFF files.\n", "\n", "References\n", "- [WRF data and Xarray](https://gallery.pangeo.io/repos/NCAR/notebook-gallery/notebooks/Run-Anywhere/WRF/wrf_ex.html)\n", "- [GIS4WRF](https://github.com/GIS4WRF/gis4wrf/)" ] }, { "cell_type": "code", "execution_count": 15, "id": "1da4df5f-1ebd-4032-bf2c-c0d5ab2b5e8e", "metadata": {}, "outputs": [], "source": [ "import os\n", "import xarray as xr\n", "import rioxarray as rxr\n", "from osgeo import osr, ogr\n", "from affine import Affine\n", "import rasterio" ] }, { "cell_type": "code", "execution_count": 16, "id": "e9938da1-edc0-4a82-9c31-6c1284a59b0e", "metadata": {}, "outputs": [], "source": [ "file_name = 'wrfout_20221211_12.nc'\n", "file_path = os.path.join('data', file_name)" ] }, { "cell_type": "code", "execution_count": 17, "id": "b4589dab-ed54-47d5-a16c-afeca7de0273", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:                (Time: 1, south_north: 299, west_east: 299,\n",
       "                            bottom_top: 35, bottom_top_stag: 36,\n",
       "                            soil_layers_stag: 4, west_east_stag: 300,\n",
       "                            south_north_stag: 300, seed_dim_stag: 8,\n",
       "                            num_turb_layers: 7)\n",
       "Coordinates:\n",
       "    XLAT                   (Time, south_north, west_east) float32 4.8 ... 25.81\n",
       "    XLONG                  (Time, south_north, west_east) float32 -94.28 ... ...\n",
       "    XTIME                  (Time) datetime64[ns] 2022-12-11T12:00:00\n",
       "    XLAT_U                 (Time, south_north, west_east_stag) float32 4.8 .....\n",
       "    XLONG_U                (Time, south_north, west_east_stag) float32 -94.32...\n",
       "    XLAT_V                 (Time, south_north_stag, west_east) float32 4.764 ...\n",
       "    XLONG_V                (Time, south_north_stag, west_east) float32 -94.28...\n",
       "Dimensions without coordinates: Time, south_north, west_east, bottom_top,\n",
       "                                bottom_top_stag, soil_layers_stag,\n",
       "                                west_east_stag, south_north_stag,\n",
       "                                seed_dim_stag, num_turb_layers\n",
       "Data variables: (12/253)\n",
       "    Times                  (Time) |S19 b'2022-12-11_12:00:00'\n",
       "    LU_INDEX               (Time, south_north, west_east) float32 17.0 ... 17.0\n",
       "    ZNU                    (Time, bottom_top) float32 0.9969 0.9899 ... 0.003772\n",
       "    ZNW                    (Time, bottom_top_stag) float32 1.0 0.9938 ... 0.0\n",
       "    ZS                     (Time, soil_layers_stag) float32 0.05 0.25 0.7 1.5\n",
       "    DZS                    (Time, soil_layers_stag) float32 0.1 0.3 0.6 1.0\n",
       "    ...                     ...\n",
       "    PCB                    (Time, south_north, west_east) float32 0.0 ... 0.0\n",
       "    PC                     (Time, south_north, west_east) float32 0.0 ... 0.0\n",
       "    LANDMASK               (Time, south_north, west_east) float32 0.0 ... 0.0\n",
       "    LAKEMASK               (Time, south_north, west_east) float32 0.0 ... 0.0\n",
       "    SST                    (Time, south_north, west_east) float32 299.4 ... 2...\n",
       "    SST_INPUT              (Time, south_north, west_east) float32 0.0 ... 0.0\n",
       "Attributes: (12/132)\n",
       "    TITLE:                            OUTPUT FROM WRF V4.2.1 MODEL\n",
       "    START_DATE:                      2022-12-09_12:00:00\n",
       "    SIMULATION_START_DATE:           2022-12-09_12:00:00\n",
       "    WEST-EAST_GRID_DIMENSION:        300\n",
       "    SOUTH-NORTH_GRID_DIMENSION:      300\n",
       "    BOTTOM-TOP_GRID_DIMENSION:       36\n",
       "    ...                              ...\n",
       "    ISLAKE:                          21\n",
       "    ISICE:                           15\n",
       "    ISURBAN:                         13\n",
       "    ISOILWATER:                      14\n",
       "    HYBRID_OPT:                      2\n",
       "    ETAC:                            0.2
" ], "text/plain": [ "\n", "Dimensions: (Time: 1, south_north: 299, west_east: 299,\n", " bottom_top: 35, bottom_top_stag: 36,\n", " soil_layers_stag: 4, west_east_stag: 300,\n", " south_north_stag: 300, seed_dim_stag: 8,\n", " num_turb_layers: 7)\n", "Coordinates:\n", " XLAT (Time, south_north, west_east) float32 ...\n", " XLONG (Time, south_north, west_east) float32 ...\n", " XTIME (Time) datetime64[ns] ...\n", " XLAT_U (Time, south_north, west_east_stag) float32 ...\n", " XLONG_U (Time, south_north, west_east_stag) float32 ...\n", " XLAT_V (Time, south_north_stag, west_east) float32 ...\n", " XLONG_V (Time, south_north_stag, west_east) float32 ...\n", "Dimensions without coordinates: Time, south_north, west_east, bottom_top,\n", " bottom_top_stag, soil_layers_stag,\n", " west_east_stag, south_north_stag,\n", " seed_dim_stag, num_turb_layers\n", "Data variables: (12/253)\n", " Times (Time) |S19 ...\n", " LU_INDEX (Time, south_north, west_east) float32 ...\n", " ZNU (Time, bottom_top) float32 ...\n", " ZNW (Time, bottom_top_stag) float32 ...\n", " ZS (Time, soil_layers_stag) float32 ...\n", " DZS (Time, soil_layers_stag) float32 ...\n", " ... ...\n", " PCB (Time, south_north, west_east) float32 ...\n", " PC (Time, south_north, west_east) float32 ...\n", " LANDMASK (Time, south_north, west_east) float32 ...\n", " LAKEMASK (Time, south_north, west_east) float32 ...\n", " SST (Time, south_north, west_east) float32 ...\n", " SST_INPUT (Time, south_north, west_east) float32 ...\n", "Attributes: (12/132)\n", " TITLE: OUTPUT FROM WRF V4.2.1 MODEL\n", " START_DATE: 2022-12-09_12:00:00\n", " SIMULATION_START_DATE: 2022-12-09_12:00:00\n", " WEST-EAST_GRID_DIMENSION: 300\n", " SOUTH-NORTH_GRID_DIMENSION: 300\n", " BOTTOM-TOP_GRID_DIMENSION: 36\n", " ... ...\n", " ISLAKE: 21\n", " ISICE: 15\n", " ISURBAN: 13\n", " ISOILWATER: 14\n", " HYBRID_OPT: 2\n", " ETAC: 0.2" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.open_dataset(file_path)\n", "ds" ] }, { "cell_type": "markdown", "id": "88f682df-c31f-4b76-b2dd-0ced4bf40c7d", "metadata": {}, "source": [ "Select 'T2' variable and convert from K to C." ] }, { "cell_type": "code", "execution_count": 18, "id": "82945b80-b5a2-4064-9792-3d965940f2c4", "metadata": {}, "outputs": [], "source": [ "da = ds.T2.sel(Time=0) -273.15" ] }, { "cell_type": "markdown", "id": "b488590c-02f9-48ff-b682-acab9115d0ae", "metadata": {}, "source": [ "We pre-process the data to match CF conventions." ] }, { "cell_type": "code", "execution_count": 19, "id": "8988eb31-142e-4f8e-b079-673a7af9294e", "metadata": {}, "outputs": [], "source": [ "da_with_latlon = da.assign_coords(lat=da.coords['XLAT'], lon=da.coords['XLONG'])\n", "da_with_latlon_rename = da_with_latlon.rename({'south_north':'y', 'west_east':'x'})\n", "da_with_latlon_drop = da_with_latlon_rename.drop(['XLAT', 'XLONG', 'XTIME'])\n", "da_wrf_cf = da_with_latlon_drop" ] }, { "cell_type": "code", "execution_count": 20, "id": "6d6fdb75-e19f-4d89-9613-32682076e938", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'T2' (y: 299, x: 299)>\n",
       "array([[24.988586, 24.995941, 24.98938 , ..., 24.238098, 24.39441 ,\n",
       "        24.465973],\n",
       "       [24.998016, 24.975922, 24.946411, ..., 24.205078, 24.348206,\n",
       "        24.480438],\n",
       "       [24.987885, 24.968018, 24.917847, ..., 24.132355, 24.332092,\n",
       "        24.46167 ],\n",
       "       ...,\n",
       "       [24.696686, 24.733307, 24.769958, ..., 22.910309, 22.873535,\n",
       "        22.919525],\n",
       "       [24.678864, 24.719208, 24.76773 , ..., 22.932556, 22.871765,\n",
       "        22.88565 ],\n",
       "       [24.64627 , 24.688324, 24.740234, ..., 23.005432, 22.941345,\n",
       "        22.857056]], dtype=float32)\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 4.8 4.8 4.8 4.8 4.8 ... 25.81 25.81 25.81 25.81\n",
       "    lon      (y, x) float32 -94.28 -94.21 -94.14 -94.06 ... -72.51 -72.43 -72.36\n",
       "Dimensions without coordinates: y, x
" ], "text/plain": [ "\n", "array([[24.988586, 24.995941, 24.98938 , ..., 24.238098, 24.39441 ,\n", " 24.465973],\n", " [24.998016, 24.975922, 24.946411, ..., 24.205078, 24.348206,\n", " 24.480438],\n", " [24.987885, 24.968018, 24.917847, ..., 24.132355, 24.332092,\n", " 24.46167 ],\n", " ...,\n", " [24.696686, 24.733307, 24.769958, ..., 22.910309, 22.873535,\n", " 22.919525],\n", " [24.678864, 24.719208, 24.76773 , ..., 22.932556, 22.871765,\n", " 22.88565 ],\n", " [24.64627 , 24.688324, 24.740234, ..., 23.005432, 22.941345,\n", " 22.857056]], dtype=float32)\n", "Coordinates:\n", " lat (y, x) float32 4.8 4.8 4.8 4.8 4.8 ... 25.81 25.81 25.81 25.81\n", " lon (y, x) float32 -94.28 -94.21 -94.14 -94.06 ... -72.51 -72.43 -72.36\n", "Dimensions without coordinates: y, x" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da_wrf_cf" ] }, { "cell_type": "code", "execution_count": 21, "id": "c97792fe-861d-44f8-b909-a70f458e03e7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "da_wrf_cf.plot(x='lon', y='lat')" ] }, { "cell_type": "markdown", "id": "eda899af-994a-41bb-b9de-087534f2c68d", "metadata": {}, "source": [ "To convert the data into a geoereferenced raster, we need to assign it a projection and a transform" ] }, { "cell_type": "markdown", "id": "f351720e-a2c1-4e15-9c47-ed766df77c89", "metadata": {}, "source": [ "WRF datasets store the projection in a dataset attribute named MAP_PROJ. The value is a numeric value that is associated with a different projection. \n", "\n", "```\n", "LAMBERT_CONFORMAL = 1\n", "POLAR_STEREOGRAPHIC = 2\n", "MERCATOR = 3\n", "LAT_LON = 6\n", "```" ] }, { "cell_type": "code", "execution_count": 22, "id": "92b7c2ac-474a-456c-a514-f63fffc632be", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.attrs['MAP_PROJ']" ] }, { "cell_type": "markdown", "id": "b6ed9005-a08d-47cb-ba97-9839264db473", "metadata": {}, "source": [ "This dataset's projection is Mercator, so we extract the parameters required to construct the projection string. The CRS definitions come from [here](https://github.com/GIS4WRF/gis4wrf/blob/master/gis4wrf/core/crs.py)." ] }, { "cell_type": "code", "execution_count": 23, "id": "a7beb3fe-b6c0-44c6-b603-8578a607770f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'+proj=merc +lat_ts=12.0 +lon_0=9.0 +x_0=0 +y_0=0 +datum=WGS84'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "truelat1 = ds.attrs['TRUELAT1']\n", "origin_lon = ds.attrs['STAND_LON']\n", "\n", "proj_str = ('+proj=merc +lat_ts={lat1} +lon_0={lon0} +x_0=0 +y_0=0 +datum=WGS84').format(\n", " lat1=truelat1, lon0=origin_lon)\n", "proj_str" ] }, { "cell_type": "markdown", "id": "74cc0b40-72c8-40cf-be4e-0a5b9d0f2157", "metadata": {}, "source": [ "We have to now compute a transform." ] }, { "cell_type": "code", "execution_count": 24, "id": "59ecf288-3daa-45ff-ab4d-fa71b51dfc0c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Affine(7984.582560670005, 0.0, -11247780.567759313,\n", " 0.0, 7935.42637387715, 519869.30354344024)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lons_u = ds.XLONG\n", "lons_v = ds.XLONG\n", "lats_u = ds.XLAT\n", "lats_v = ds.XLAT\n", "\n", "dim_x = ds.west_east.size\n", "dim_y = ds.south_north.size\n", "\n", "lower_left_u = float(lons_u[0,0,0]), float(lats_u[0,0,0])\n", "lower_right_u = float(lons_u[0,0,-1]), float(lats_u[0,0,-1])\n", "lower_left_v = float(lons_v[0,0,0]), float(lats_v[0,0,0])\n", "upper_left_v = float(lons_v[0,-1,0]), float(lats_v[0,-1,0])\n", "\n", "\n", "\n", "def transform_point(point):\n", " point_geom = ogr.Geometry(ogr.wkbPoint)\n", " point_geom.AddPoint(point[0], point[1])\n", " srs_in = osr.SpatialReference()\n", " srs_in.ImportFromProj4('+proj=latlong +datum=WGS84')\n", " srs_out = osr.SpatialReference()\n", " srs_out.ImportFromProj4(proj_str)\n", " transform = osr.CoordinateTransformation(srs_in, srs_out)\n", " point_geom.Transform(transform)\n", " return point_geom.GetX(), point_geom.GetY()\n", "\n", "lower_left_u_xy = transform_point(lower_left_u)\n", "lower_right_u_xy = transform_point(lower_right_u)\n", "lower_left_v_xy = transform_point(lower_left_v)\n", "upper_left_v_xy = transform_point(upper_left_v)\n", "\n", "dx = (lower_right_u_xy[0] - lower_left_u_xy[0])/dim_x\n", "dy = (upper_left_v_xy[1] - lower_left_v_xy[1])/dim_y\n", "dx, dy\n", "\n", "gdal_transform = (lower_left_u_xy[0], dx, 0, lower_left_v_xy[1], 0, dy)\n", "transform = Affine.from_gdal(*gdal_transform)\n", "transform" ] }, { "cell_type": "markdown", "id": "67615486-8b60-4bed-8078-33ff2d7906f2", "metadata": {}, "source": [ "Apply the transform and CRS." ] }, { "cell_type": "code", "execution_count": 25, "id": "9310d391-1b0e-417e-94e1-0ea2aefb239d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'T2' (y: 299, x: 299)>\n",
       "array([[24.988586, 24.995941, 24.98938 , ..., 24.238098, 24.39441 ,\n",
       "        24.465973],\n",
       "       [24.998016, 24.975922, 24.946411, ..., 24.205078, 24.348206,\n",
       "        24.480438],\n",
       "       [24.987885, 24.968018, 24.917847, ..., 24.132355, 24.332092,\n",
       "        24.46167 ],\n",
       "       ...,\n",
       "       [24.696686, 24.733307, 24.769958, ..., 22.910309, 22.873535,\n",
       "        22.919525],\n",
       "       [24.678864, 24.719208, 24.76773 , ..., 22.932556, 22.871765,\n",
       "        22.88565 ],\n",
       "       [24.64627 , 24.688324, 24.740234, ..., 23.005432, 22.941345,\n",
       "        22.857056]], dtype=float32)\n",
       "Coordinates:\n",
       "    lat          (y, x) float32 4.8 4.8 4.8 4.8 4.8 ... 25.81 25.81 25.81 25.81\n",
       "    lon          (y, x) float32 -94.28 -94.21 -94.14 ... -72.51 -72.43 -72.36\n",
       "    spatial_ref  int64 0\n",
       "Dimensions without coordinates: y, x
" ], "text/plain": [ "\n", "array([[24.988586, 24.995941, 24.98938 , ..., 24.238098, 24.39441 ,\n", " 24.465973],\n", " [24.998016, 24.975922, 24.946411, ..., 24.205078, 24.348206,\n", " 24.480438],\n", " [24.987885, 24.968018, 24.917847, ..., 24.132355, 24.332092,\n", " 24.46167 ],\n", " ...,\n", " [24.696686, 24.733307, 24.769958, ..., 22.910309, 22.873535,\n", " 22.919525],\n", " [24.678864, 24.719208, 24.76773 , ..., 22.932556, 22.871765,\n", " 22.88565 ],\n", " [24.64627 , 24.688324, 24.740234, ..., 23.005432, 22.941345,\n", " 22.857056]], dtype=float32)\n", "Coordinates:\n", " lat (y, x) float32 4.8 4.8 4.8 4.8 4.8 ... 25.81 25.81 25.81 25.81\n", " lon (y, x) float32 -94.28 -94.21 -94.14 ... -72.51 -72.43 -72.36\n", " spatial_ref int64 0\n", "Dimensions without coordinates: y, x" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da_wrf_cf.rio.write_crs(rasterio.crs.CRS().from_proj4(proj_str), inplace=True)\n", "da_wrf_cf.rio.write_transform(transform, inplace=True)" ] }, { "cell_type": "markdown", "id": "de2d88d7-833b-4336-b6a0-35f2b2842f19", "metadata": {}, "source": [ "We now have a array with correct CRS and transform. We drop existing coordinates and reproject it to EPSG:4326" ] }, { "cell_type": "code", "execution_count": 26, "id": "d54431e3-bf05-4cf5-91fd-97081d514f1f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'T2' (y: 293, x: 305)>\n",
       "array([[24.64627 , 24.688324, 24.740234, ..., 23.005432, 22.941345,\n",
       "        22.857056],\n",
       "       [24.678864, 24.719208, 24.76773 , ..., 22.932556, 22.871765,\n",
       "        22.88565 ],\n",
       "       [24.696686, 24.733307, 24.769958, ..., 22.910309, 22.873535,\n",
       "        22.919525],\n",
       "       ...,\n",
       "       [24.987885, 24.968018, 24.917847, ..., 24.132355, 24.332092,\n",
       "        24.46167 ],\n",
       "       [24.998016, 24.975922, 24.946411, ..., 24.205078, 24.348206,\n",
       "        24.480438],\n",
       "       [24.988586, 24.995941, 24.98938 , ..., 24.238098, 24.39441 ,\n",
       "        24.465973]], dtype=float32)\n",
       "Coordinates:\n",
       "    spatial_ref  int64 0\n",
       "Dimensions without coordinates: y, x\n",
       "Attributes:\n",
       "    _FillValue:  3.402823466e+38
" ], "text/plain": [ "\n", "array([[24.64627 , 24.688324, 24.740234, ..., 23.005432, 22.941345,\n", " 22.857056],\n", " [24.678864, 24.719208, 24.76773 , ..., 22.932556, 22.871765,\n", " 22.88565 ],\n", " [24.696686, 24.733307, 24.769958, ..., 22.910309, 22.873535,\n", " 22.919525],\n", " ...,\n", " [24.987885, 24.968018, 24.917847, ..., 24.132355, 24.332092,\n", " 24.46167 ],\n", " [24.998016, 24.975922, 24.946411, ..., 24.205078, 24.348206,\n", " 24.480438],\n", " [24.988586, 24.995941, 24.98938 , ..., 24.238098, 24.39441 ,\n", " 24.465973]], dtype=float32)\n", "Coordinates:\n", " spatial_ref int64 0\n", "Dimensions without coordinates: y, x\n", "Attributes:\n", " _FillValue: 3.402823466e+38" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da_wrf_cf_reprojected = da_wrf_cf.drop(['lat', 'lon']).rio.reproject('epsg:4326')\n", "da_wrf_cf_reprojected" ] }, { "cell_type": "code", "execution_count": 27, "id": "03ce3d2b-56f4-4590-b557-ac084b55b5ce", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ujavalgandhi/opt/anaconda3/envs/python_dataviz/lib/python3.10/site-packages/rioxarray/raster_writer.py:110: UserWarning: The nodata value (3.402823466e+38) has been automatically changed to (3.4028234663852886e+38) to match the dtype of the data.\n", " warnings.warn(\n" ] } ], "source": [ "output_file_name = file_name.replace('.nc', '.tif')\n", "output_file_path = os.path.join('data', output_file_name)\n", "da_wrf_cf_reprojected.rio.to_raster(output_file_path)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.5" } }, "nbformat": 4, "nbformat_minor": 5 }