access_ocean_color.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NASA ocean color data \n",
    "\n",
    "\n",
    "This notebook demonstrates how we can access Ocean Color data from NASA.\n",
    "\n",
    "\n",
    "The main repository for NASAs ocean color data is: https://oceandata.sci.gsfc.nasa.gov/opendap/\n",
    "\n",
    "We will look at data from the MODIS-Aqua (MODIS-A) satellite, and in particular we will look at the level 3 product, which is data that has gone through the highest level of processing and nicely gridded.\n",
    "\n",
    "\n",
    "NASA organizes data by year and year day. You can see this structure by clicking through the OpenDAP server. The file used in this example contains the mapped chlorophyll-a data for July 28 (year day 210), 2019.\n",
    "\n",
    "\n",
    "### credit:\n",
    "this lession borrowed from Tom Connolly: https://github.com/tompc35/oceanography-notebooks/blob/master/ocean_color_netcdf.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import statements\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "\n",
    "import xarray as xr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# use `xr.open_dataset()` to access the data\n",
    "\n",
    "the data for yearday 210 of 2019 is located at:\n",
    "```python\n",
    "url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/MODISA/L3SMI/2019/210/A2019210.L3m_DAY_CHL_chlor_a_4km.nc'\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (eightbitcolor: 256, lat: 4320, lon: 8640, rgb: 3)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 89.979164 89.9375 89.89583 ... -89.93751 -89.97918\n",
       "  * lon      (lon) float32 -179.97917 -179.9375 ... 179.93752 179.97917\n",
       "Dimensions without coordinates: eightbitcolor, rgb\n",
       "Data variables:\n",
       "    palette  (rgb, eightbitcolor) int8 ...\n",
       "    chlor_a  (lat, lon) float32 ...\n",
       "Attributes:\n",
       "    _NCProperties:                     version=1|netcdflibversion=4.4.1.1|hdf...\n",
       "    product_name:                      A2019210.L3m_DAY_CHL_chlor_a_4km.nc\n",
       "    instrument:                        MODIS\n",
       "    title:                             MODISA Level-3 Standard Mapped Image\n",
       "    project:                           Ocean Biology Processing Group (NASA/G...\n",
       "    platform:                          Aqua\n",
       "    temporal_range:                    day\n",
       "    processing_version:                2018.0\n",
       "    date_created:                      2019-09-05T22:57:04.000Z\n",
       "    history:                           l3mapgen par=A2019210.L3m_DAY_CHL_chlo...\n",
       "    l2_flag_names:                     ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,...\n",
       "    time_coverage_start:               2019-07-29T00:10:01.000Z\n",
       "    time_coverage_end:                 2019-07-30T02:49:59.000Z\n",
       "    start_orbit_number:                91668\n",
       "    end_orbit_number:                  91684\n",
       "    map_projection:                    Equidistant Cylindrical\n",
       "    latitude_units:                    degrees_north\n",
       "    longitude_units:                   degrees_east\n",
       "    northernmost_latitude:             90.0\n",
       "    southernmost_latitude:             -90.0\n",
       "    westernmost_longitude:             -180.0\n",
       "    easternmost_longitude:             180.0\n",
       "    geospatial_lat_max:                90.0\n",
       "    geospatial_lat_min:                -90.0\n",
       "    geospatial_lon_max:                180.0\n",
       "    geospatial_lon_min:                -180.0\n",
       "    latitude_step:                     0.041666668\n",
       "    longitude_step:                    0.041666668\n",
       "    sw_point_latitude:                 -89.979164\n",
       "    sw_point_longitude:                -179.97917\n",
       "    geospatial_lon_resolution:         4.6383123\n",
       "    geospatial_lat_resolution:         4.6383123\n",
       "    geospatial_lat_units:              degrees_north\n",
       "    geospatial_lon_units:              degrees_east\n",
       "    spatialResolution:                 4.64 km\n",
       "    number_of_lines:                   4320\n",
       "    number_of_columns:                 8640\n",
       "    measure:                           Mean\n",
       "    suggested_image_scaling_minimum:   0.01\n",
       "    suggested_image_scaling_maximum:   20.0\n",
       "    suggested_image_scaling_type:      LOG\n",
       "    suggested_image_scaling_applied:   No\n",
       "    _lastModified:                     2019-09-05T22:57:04.000Z\n",
       "    Conventions:                       CF-1.6 ACDD-1.3\n",
       "    institution:                       NASA Goddard Space Flight Center, Ocea...\n",
       "    standard_name_vocabulary:          CF Standard Name Table v36\n",
       "    naming_authority:                  gov.nasa.gsfc.sci.oceandata\n",
       "    id:                                A2019210.L3b_DAY_CHL.nc/L3/A2019210.L3...\n",
       "    license:                           http://science.nasa.gov/earth-science/...\n",
       "    creator_name:                      NASA/GSFC/OBPG\n",
       "    publisher_name:                    NASA/GSFC/OBPG\n",
       "    creator_email:                     data@oceancolor.gsfc.nasa.gov\n",
       "    publisher_email:                   data@oceancolor.gsfc.nasa.gov\n",
       "    creator_url:                       http://oceandata.sci.gsfc.nasa.gov\n",
       "    publisher_url:                     http://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                  L3 Mapped\n",
       "    cdm_data_type:                     grid\n",
       "    identifier_product_doi_authority:  http://dx.doi.org\n",
       "    identifier_product_doi:            10.5067/AQUA/MODIS/L3M/CHL/2018\n",
       "    keywords:                          Earth Science > Oceans > Ocean Chemist...\n",
       "    keywords_vocabulary:               NASA Global Change Master Directory (G...\n",
       "    data_bins:                         Attribute edlided: Unsupported attribu...\n",
       "    data_minimum:                      0.008464555\n",
       "    data_maximum:                      99.794235"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/MODISA/L3SMI/2019/210/A2019210.L3m_DAY_CHL_chlor_a_4km.nc'\n",
    "data = xr.open_dataset(url)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subset the data\n",
    "\n",
    "Again, let's just grab the mid-atlantic bight.\n",
    "\n",
    "Note, that for some reason I don't understand, the `lat` `coords` are listed from high to low, so when you slice, you need to reverse the order, i.e. use `sel( lat=slice(41, 38))` *not* `sel( lat=slice( 38, 41)`. This is a mystery.\n",
    "\n",
    "fill in the blanks to get a subset of the data that covers the MAB (the lat boundaries at 38 to 41 degrees, and lon boundaries are -76 to -71):\n",
    "\n",
    "```python\n",
    "data_mab_nj = data.___( lat = ___, lon = ___)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.036096\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (eightbitcolor: 256, lat: 72, lon: 120, rgb: 3)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 40.979168 40.9375 40.895832 ... 38.0625 38.020832\n",
       "  * lon      (lon) float32 -75.979164 -75.93749 ... -71.06249 -71.02083\n",
       "Dimensions without coordinates: eightbitcolor, rgb\n",
       "Data variables:\n",
       "    palette  (rgb, eightbitcolor) int8 ...\n",
       "    chlor_a  (lat, lon) float32 ...\n",
       "Attributes:\n",
       "    _NCProperties:                     version=1|netcdflibversion=4.4.1.1|hdf...\n",
       "    product_name:                      A2019210.L3m_DAY_CHL_chlor_a_4km.nc\n",
       "    instrument:                        MODIS\n",
       "    title:                             MODISA Level-3 Standard Mapped Image\n",
       "    project:                           Ocean Biology Processing Group (NASA/G...\n",
       "    platform:                          Aqua\n",
       "    temporal_range:                    day\n",
       "    processing_version:                2018.0\n",
       "    date_created:                      2019-09-05T22:57:04.000Z\n",
       "    history:                           l3mapgen par=A2019210.L3m_DAY_CHL_chlo...\n",
       "    l2_flag_names:                     ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,...\n",
       "    time_coverage_start:               2019-07-29T00:10:01.000Z\n",
       "    time_coverage_end:                 2019-07-30T02:49:59.000Z\n",
       "    start_orbit_number:                91668\n",
       "    end_orbit_number:                  91684\n",
       "    map_projection:                    Equidistant Cylindrical\n",
       "    latitude_units:                    degrees_north\n",
       "    longitude_units:                   degrees_east\n",
       "    northernmost_latitude:             90.0\n",
       "    southernmost_latitude:             -90.0\n",
       "    westernmost_longitude:             -180.0\n",
       "    easternmost_longitude:             180.0\n",
       "    geospatial_lat_max:                90.0\n",
       "    geospatial_lat_min:                -90.0\n",
       "    geospatial_lon_max:                180.0\n",
       "    geospatial_lon_min:                -180.0\n",
       "    latitude_step:                     0.041666668\n",
       "    longitude_step:                    0.041666668\n",
       "    sw_point_latitude:                 -89.979164\n",
       "    sw_point_longitude:                -179.97917\n",
       "    geospatial_lon_resolution:         4.6383123\n",
       "    geospatial_lat_resolution:         4.6383123\n",
       "    geospatial_lat_units:              degrees_north\n",
       "    geospatial_lon_units:              degrees_east\n",
       "    spatialResolution:                 4.64 km\n",
       "    number_of_lines:                   4320\n",
       "    number_of_columns:                 8640\n",
       "    measure:                           Mean\n",
       "    suggested_image_scaling_minimum:   0.01\n",
       "    suggested_image_scaling_maximum:   20.0\n",
       "    suggested_image_scaling_type:      LOG\n",
       "    suggested_image_scaling_applied:   No\n",
       "    _lastModified:                     2019-09-05T22:57:04.000Z\n",
       "    Conventions:                       CF-1.6 ACDD-1.3\n",
       "    institution:                       NASA Goddard Space Flight Center, Ocea...\n",
       "    standard_name_vocabulary:          CF Standard Name Table v36\n",
       "    naming_authority:                  gov.nasa.gsfc.sci.oceandata\n",
       "    id:                                A2019210.L3b_DAY_CHL.nc/L3/A2019210.L3...\n",
       "    license:                           http://science.nasa.gov/earth-science/...\n",
       "    creator_name:                      NASA/GSFC/OBPG\n",
       "    publisher_name:                    NASA/GSFC/OBPG\n",
       "    creator_email:                     data@oceancolor.gsfc.nasa.gov\n",
       "    publisher_email:                   data@oceancolor.gsfc.nasa.gov\n",
       "    creator_url:                       http://oceandata.sci.gsfc.nasa.gov\n",
       "    publisher_url:                     http://oceandata.sci.gsfc.nasa.gov\n",
       "    processing_level:                  L3 Mapped\n",
       "    cdm_data_type:                     grid\n",
       "    identifier_product_doi_authority:  http://dx.doi.org\n",
       "    identifier_product_doi:            10.5067/AQUA/MODIS/L3M/CHL/2018\n",
       "    keywords:                          Earth Science > Oceans > Ocean Chemist...\n",
       "    keywords_vocabulary:               NASA Global Change Master Directory (G...\n",
       "    data_bins:                         Attribute edlided: Unsupported attribu...\n",
       "    data_minimum:                      0.008464555\n",
       "    data_maximum:                      99.794235"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mab_nj = data.sel( lat=slice(41, 38), lon=slice(-76,-71))\n",
    "\n",
    "print(data_mab_nj.nbytes/10**6)\n",
    "\n",
    "data_mab_nj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot the chlorophyll a\n",
    "\n",
    "for the mid atlantic bight subset using the built-in xarray plotting routine. i.e. fill in the blanks, and remember we want to just plot the variable `chlor_a`:\n",
    "```python\n",
    "data_mab_nj.___.___()\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x31f6d4eb8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_mab_nj.chlor_a.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chla should be plotted on a log scale\n",
    "\n",
    "Let's make the same plot with matplotlib, and use `np.log10()` to plot the data on a logarythmic scale:\n",
    "\n",
    "```python\n",
    "plt.pcolor( data_mab_nj.___, data_mab_nj.___, np.log10(data_mab_nj.___))\n",
    "# add a colorbar below:\n",
    "plt.___\n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0xd23236908>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.Mercator())\n",
    "\n",
    "plt.pcolor(data_mab_nj.lon, data_mab_nj.lat, np.log10(data_mab_nj.chlor_a), transform=ccrs.PlateCarree())\n",
    "plt.colorbar()\n",
    "ax.coastlines('50m')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# try to open multiple days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "base_url = 'https://oceandata.sci.gsfc.nasa.gov:443/opendap/MODISA/L3SMI/2019'\n",
    "\n",
    "# pick out days, the syntax is start day, stop day, steps between\n",
    "days = np.arange(1, 300, 100)\n",
    "\n",
    "files = [f'{base_url}/{str(day).zfill(3)}/A2019{str(day).zfill(3)}.L3m_DAY_CHL_chlor_a_4km.nc' for day in days]\n",
    "\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only concat the chla, then only grab it, and subset to MAB:\n",
    "\n",
    "ds = xr.open_mfdataset(files, concat_dim = days, data_vars=['chlor_a']).sel( lat=slice(41, 38), lon=slice(-76,-71))\n",
    "ds = ds.rename({'concat_dim': 'days'})\n",
    "print('data size:',ds.nbytes/1e6, 'MB')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the data coverage in time:\n",
    "\n",
    "nobs = ds.count('lat').count('lon')\n",
    "# nobs.plot()\n",
    "\n",
    "nobs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}