{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GOES netCDF Reader Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Supported Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GOES 8-15 Imager (GVAR_IMG) data in netCDF format distributed by NOAA CLASS. \n",
    "\n",
    "**Important**: Select 16 bits/pixel in your order! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Creating a Mosaic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from satpy import Scene\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "channels = ['00_7', '03_9', '06_5', '10_7', '13_3']\n",
    "filenames = glob.glob('goes15.2018.248.0000??.BAND_??.nc')\n",
    "scene = Scene(filenames=filenames, reader='goes-imager_nc')\n",
    "\n",
    "for ch in channels:\n",
    "    scene.load([ch])\n",
    "    scene.save_dataset(ch, filename=ch+'.png')\n",
    "    del scene[ch]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a mosaic from the *.png files:\n",
    "\n",
    "```\n",
    "montage ??_?.png -geometry 256x256+2-55 -background black goes-imager_nc_mosaic.jpg\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"goes-imager_nc_mosaic.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GOES Imager oversamples the viewed scene in E-W direction by a factor of 1.75: IR/VIS pixels are 112/28 urad on a side, but the instruments samples every 64/16 urad in E-W direction. This explains the elliptical shape of the images. An area definition with uniform sampling is stored in the scene's attributes to facilitate resampling (full disc images only!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from satpy import Scene\n",
    "\n",
    "\n",
    "# Load data\n",
    "filenames = glob.glob('goes15.2018.248.0000??.BAND_04.nc')\n",
    "scene = Scene(filenames=filenames, reader='goes-imager_nc')\n",
    "scene.load(['10_7'])\n",
    "\n",
    "# Resample to area definition with uniform sampling\n",
    "area_def = scene['10_7'].attrs['area_def_uniform_sampling']  # full disc images only\n",
    "resampled = scene.resample(area_def)\n",
    "\n",
    "# Display results\n",
    "crs = area_def.to_cartopy_crs()\n",
    "ax = plt.axes(projection=crs)\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "plt.imshow(resampled['10_7'], transform=crs, extent=crs.bounds, origin='upper')\n",
    "plt.colorbar(label=resampled['10_7'].attrs['units'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Partial Scans\n",
    "\n",
    "Partial scans are supported, but the accompanying set of metadata will be reduced due to limitations of the netCDF files. For example satellite position and area definition with uniform sampling cannot be provided. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Notes on calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Reflectance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calibrating the visible channel to reflectance does not (yet) take into account the angle of incident radiation and the annual variation of the earth-sun distance. A value of 100% corresponds to the radiance of a perfectly reflecting diffuse surface\n",
    "illuminated at normal incidence when the sun is at its annual-average distance from the Earth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Calibration Error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GOES Imager records multiple scanlines simultaneously in one sweep using multiple detectors (8 in VIS and 1-2 in IR channels). The calibration coefficients of the detectors in a channel vary slightly. Unfortunately there is no information in the netCDF files about which scanline was recorded by which detector. That is why the ``goes-imager_nc`` reader uses the **average calibration coefficients** over all detectors in a channel to calibrate the raw image. \n",
    "\n",
    "Of course this approximation introduces an error. A worst case estimate of the error can be obtained by calibrating all possible counts with both the minimum and the maximum calibration coefficients and computing the difference. This is shown in the figures below. The table summarizes the worst case maximum errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"goes-imager_nc_ir_calib.png\">\n",
    "<center><figcaption>Fig.1: IR calibration error</figcaption></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"goes-imager_nc_vis_calib.png\" width=\"450\" height=\"450\">\n",
    "<center><figcaption>Fig.2: VIS calibration error</figcaption></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Platform | Channel | Error \n",
    "---------|---------|-------\n",
    "GOES-8   |    00_7 | 0.0   % \n",
    "         |    03_9 | 0.187 K \n",
    "         |    06_8 | 0.0   K \n",
    "         |    10_7 | 0.106 K \n",
    "         |    12_0 | 0.036 K \n",
    "GOES-9   |    00_7 | 0.0   % \n",
    "         |    03_9 | 0.0   K \n",
    "         |    06_8 | 0.0   K \n",
    "         |    10_7 | 0.021 K\n",
    "         |    12_0 | 0.006 K \n",
    "GOES-10  |    00_7 | 1.05  %\n",
    "         |    03_9 | 0.0   K\n",
    "         |    06_8 | 0.0   K\n",
    "         |    10_7 | 0.013 K \n",
    "         |    12_0 | 0.004 K\n",
    "GOES-11  |    00_7 | 1.25  %\n",
    "         |    03_9 | 0.0   K\n",
    "         |    06_8 | 0.0   K\n",
    "         |    10_7 | 0.0   K\n",
    "         |    12_0 | 0.065 K\n",
    "GOES-12  |    00_7 | 0.8   %\n",
    "         |    03_9 | 0.0   K\n",
    "         |    06_5 | 0.044 K\n",
    "         |    10_7 | 0.0   K\n",
    "         |    13_3 | 0.0   K\n",
    "GOES-13  |    00_7 | 1.31  %\n",
    "         |    03_9 | 0.0   K\n",
    "         |    06_5 | 0.085 K\n",
    "         |    10_7 | 0.008 K\n",
    "         |    13_3 | 0.0   K\n",
    "GOES-14  |    00_7 | 0.66  %\n",
    "         |    03_9 | 0.0   K\n",
    "         |    06_5 | 0.043 K\n",
    "         |    10_7 | 0.006 K\n",
    "         |    13_3 | 0.003 K\n",
    "GOES-15  |    00_7 | 0.86  %\n",
    "         |    03_9 | 0.0   K\n",
    "         |    06_5 | 0.02  K\n",
    "         |    10_7 | 0.009 K\n",
    "         |    13_3 | 0.008 K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
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