{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Solution 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{hint} \n",
    "<a href='https://dust.ltpy.adamplatform.eu/hub/user-redirect/lab/tree/03_practical_case_study/solutions/02_exercise_solution.ipynb' target='_blank'>Execute the notebook on the training platform >></a>\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## About"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Let us have a closer look at the forecast data from both models for one observation station in Tenerife (Canary Islands). Let us plot the time-series of the CAMS and MONARCH forecasts together in one plot.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**1. Download and animate the CAMS global forecast for 21 Feb 2020**\n",
    "  * Download the CAMS global atmospheric composition forecast for 21 February 2020, with the following specifications:\n",
    "    > Variable on single levels: `Dust aerosol optical depth at 550 nm` <br>\n",
    "    > Date (Start and end): `2020-02-21` <br>\n",
    "    > Time: `12:00` <br>\n",
    "    > Leadtime hour: every three hours from `0 to 90` <br>\n",
    "    > Type: `Forecast` <br>\n",
    "    > Restricted area: `N: 67, W: -30, E: 71, S: -3` <br>\n",
    "    > Format: `Zipped netCDF` <br>\n",
    "  * **Hint** \n",
    "    * [CAMS global atmospheric composition forecasts - Example notebook](./cams_global.ipynb)\n",
    "    * <a href='https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts' target='_blank'>Data access</a>\n",
    "\n",
    "**2. Get the coordinates of the AERONET station *Santa Cruz, Tenerife***\n",
    "  * **Hint**\n",
    "    * You can see an overview of all available AERONET Site Names <a href='https://aeronet.gsfc.nasa.gov/cgi-bin/draw_map_display_aod_v3?long1=-180&long2=180&lat1=-90&lat2=90&multiplier=2&what_map=4&nachal=1&formatter=0&level=3&place_code=10&place_limit=0' target='_blank'>here</a>\n",
    "\n",
    "**3. Select the time-series for CAMS global atmospheric composition forecasts for Santa Cruz, Tenerife**\n",
    "  * **Hint**\n",
    "    * With the xarray function `sel()` and keyword argument `method='nearest'` you can select data based on coordinate information\n",
    "    * We also recommend you to transform your xarray.DataArray into a pandas.DataFrame with the function `to_dataframe()` and save it as `csv` with the function `to_csv()`\n",
    "\n",
    "**4. Load the MONARCH dust forecasts and select time-series for Santa Cruz, Tenerife**\n",
    "  * **Hint**\n",
    "    * With the xarray function `sel()` and keyword argument `method='nearest'` you can select data based on coordinate information\n",
    "    * We also recommend you to transform your xarray.DataArray into a pandas.DataFrame with the function `to_dataframe()` and save it as `csv` with the function `to_csv()`\n",
    "\n",
    "**5. Visualize both time-series of CAMS and MONARCH forecasts together in one plot**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "**Load required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "from matplotlib.cm import get_cmap\n",
    "from matplotlib import animation\n",
    "from matplotlib.axes import Axes\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.mpl.geoaxes import GeoAxes\n",
    "GeoAxes._pcolormesh_patched = Axes.pcolormesh\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action = \"ignore\", category = RuntimeWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load helper functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ../../functions.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1. Download and animate the CAMS global forecast for 21 Feb 2020 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we have to download the CAMS global atmospheric composition forecast data from the [Copernicus Atmosphere Data Store](https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts?tab=form) with the following specifications:\n",
    "* Variable on single levels: `Dust aerosol optical depth at 550 nm`\n",
    "* Date (Start and end): `2020-02-21`\n",
    "* Time: `12:00`\n",
    "* Leadtime hour: every three hours from `0 to 90`\n",
    "* Type: `Forecast`\n",
    "* Restricted area: `N: 67, W: -30, E: 71, S: -3`\n",
    "* Format: `Zipped netCDF`\n",
    "\n",
    "See the `CDSAPI` request below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'https://ads.atmosphere.copernicus.eu/api/v2'\n",
    "KEY = '######################'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cdsapi\n",
    "\n",
    "c = cdsapi.Client(url=URL, key=KEY)\n",
    "\n",
    "c.retrieve(\n",
    "    'cams-global-atmospheric-composition-forecasts',\n",
    "    {\n",
    "        'variable': 'dust_aerosol_optical_depth_550nm',\n",
    "        'date': '2020-02-21/2020-02-21',\n",
    "        'time': '12:00',\n",
    "        'leadtime_hour': [\n",
    "            '0', '12', '15',\n",
    "            '18', '21', '24',\n",
    "            '27', '3', '30',\n",
    "            '33', '36', '39',\n",
    "            '42', '45', '48',\n",
    "            '51', '54', '57',\n",
    "            '6', '60', '63',\n",
    "            '66', '69', '72',\n",
    "            '75', '78', '81',\n",
    "            '84', '87', '9',\n",
    "            '90',\n",
    "        ],\n",
    "        'type': 'forecast',\n",
    "        'area': [\n",
    "            67, -30, -3,\n",
    "            71,\n",
    "        ],\n",
    "        'format': 'netcdf_zip',\n",
    "    },\n",
    "    '../../eodata/case_study/cams/20210221_dustAOD.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to unzip file from the zipped archive downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "with zipfile.ZipFile('../../eodata/case_study/cams/20200221_dustAOD.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall('../../')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can open the netCDF file with the xarray function `open_dataset()`. We see that the data has three dimensions (`latitude`, `longitude`, `time`) and one data variable:\n",
    "* `duaod550`: Dust Aerosol Optical Depth at 550nm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (longitude: 253, latitude: 176, time: 31)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -30.0 -29.6 -29.2 -28.8 ... 70.0 70.4 70.8\n",
       "  * latitude   (latitude) float32 67.0 66.6 66.2 65.8 ... -1.8 -2.2 -2.6 -3.0\n",
       "  * time       (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-25T06:00:00\n",
       "Data variables:\n",
       "    duaod550   (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2021-11-02 14:50:00 GMT by grib_to_netcdf-2.23.0: /opt/ecmw...</pre><div class='xr-wrap' hidden><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-5893328b-4e3e-471c-aa13-f026486879a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5893328b-4e3e-471c-aa13-f026486879a2' 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'>longitude</span>: 253</li><li><span class='xr-has-index'>latitude</span>: 176</li><li><span class='xr-has-index'>time</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fdad3a56-4f28-4667-86e3-47627f46fd3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fdad3a56-4f28-4667-86e3-47627f46fd3a' class='xr-section-summary' >Coordinates: <span>(3)</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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-30.0 -29.6 -29.2 ... 70.4 70.8</div><input id='attrs-bcb03130-49f4-46b9-bce4-71e3d486e78a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcb03130-49f4-46b9-bce4-71e3d486e78a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bedf199b-2b15-4f32-9d7a-f4ebe0064318' class='xr-var-data-in' type='checkbox'><label for='data-bedf199b-2b15-4f32-9d7a-f4ebe0064318' 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>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-30. , -29.6, -29.2, ...,  70. ,  70.4,  70.8], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>67.0 66.6 66.2 ... -2.2 -2.6 -3.0</div><input id='attrs-ccf46969-3604-48e6-8d7b-b0145786c358' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccf46969-3604-48e6-8d7b-b0145786c358' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c862f68-ce36-4fa9-8e23-2496419ee1ee' class='xr-var-data-in' type='checkbox'><label for='data-2c862f68-ce36-4fa9-8e23-2496419ee1ee' 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>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([67. , 66.6, 66.2, 65.8, 65.4, 65. , 64.6, 64.2, 63.8, 63.4, 63. , 62.6,\n",
       "       62.2, 61.8, 61.4, 61. , 60.6, 60.2, 59.8, 59.4, 59. , 58.6, 58.2, 57.8,\n",
       "       57.4, 57. , 56.6, 56.2, 55.8, 55.4, 55. , 54.6, 54.2, 53.8, 53.4, 53. ,\n",
       "       52.6, 52.2, 51.8, 51.4, 51. , 50.6, 50.2, 49.8, 49.4, 49. , 48.6, 48.2,\n",
       "       47.8, 47.4, 47. , 46.6, 46.2, 45.8, 45.4, 45. , 44.6, 44.2, 43.8, 43.4,\n",
       "       43. , 42.6, 42.2, 41.8, 41.4, 41. , 40.6, 40.2, 39.8, 39.4, 39. , 38.6,\n",
       "       38.2, 37.8, 37.4, 37. , 36.6, 36.2, 35.8, 35.4, 35. , 34.6, 34.2, 33.8,\n",
       "       33.4, 33. , 32.6, 32.2, 31.8, 31.4, 31. , 30.6, 30.2, 29.8, 29.4, 29. ,\n",
       "       28.6, 28.2, 27.8, 27.4, 27. , 26.6, 26.2, 25.8, 25.4, 25. , 24.6, 24.2,\n",
       "       23.8, 23.4, 23. , 22.6, 22.2, 21.8, 21.4, 21. , 20.6, 20.2, 19.8, 19.4,\n",
       "       19. , 18.6, 18.2, 17.8, 17.4, 17. , 16.6, 16.2, 15.8, 15.4, 15. , 14.6,\n",
       "       14.2, 13.8, 13.4, 13. , 12.6, 12.2, 11.8, 11.4, 11. , 10.6, 10.2,  9.8,\n",
       "        9.4,  9. ,  8.6,  8.2,  7.8,  7.4,  7. ,  6.6,  6.2,  5.8,  5.4,  5. ,\n",
       "        4.6,  4.2,  3.8,  3.4,  3. ,  2.6,  2.2,  1.8,  1.4,  1. ,  0.6,  0.2,\n",
       "       -0.2, -0.6, -1. , -1.4, -1.8, -2.2, -2.6, -3. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-21T12:00:00 ... 2020-02-...</div><input id='attrs-d12e87a4-cf40-4e9f-acca-2c8138dd3225' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d12e87a4-cf40-4e9f-acca-2c8138dd3225' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-839db4bc-3bdc-4182-aa2b-5a2a733d5587' class='xr-var-data-in' type='checkbox'><label for='data-839db4bc-3bdc-4182-aa2b-5a2a733d5587' 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</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-21T12:00:00.000000000&#x27;, &#x27;2020-02-21T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-21T18:00:00.000000000&#x27;, &#x27;2020-02-21T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-22T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T06:00:00.000000000&#x27;, &#x27;2020-02-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T12:00:00.000000000&#x27;, &#x27;2020-02-22T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T18:00:00.000000000&#x27;, &#x27;2020-02-22T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T00:00:00.000000000&#x27;, &#x27;2020-02-23T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T06:00:00.000000000&#x27;, &#x27;2020-02-23T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T12:00:00.000000000&#x27;, &#x27;2020-02-23T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T18:00:00.000000000&#x27;, &#x27;2020-02-23T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-24T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T06:00:00.000000000&#x27;, &#x27;2020-02-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T12:00:00.000000000&#x27;, &#x27;2020-02-24T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T18:00:00.000000000&#x27;, &#x27;2020-02-24T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-25T00:00:00.000000000&#x27;, &#x27;2020-02-25T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-25T06:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcf98dae-7a10-42f7-915d-79cc588f6014' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcf98dae-7a10-42f7-915d-79cc588f6014' class='xr-section-summary' >Data variables: <span>(1)</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>duaod550</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f371ce1-625f-4c08-b5ef-171a60a0a090' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f371ce1-625f-4c08-b5ef-171a60a0a090' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-594ef40b-5e12-4312-8aed-e989b74736d4' class='xr-var-data-in' type='checkbox'><label for='data-594ef40b-5e12-4312-8aed-e989b74736d4' 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>units :</span></dt><dd>~</dd><dt><span>long_name :</span></dt><dd>Dust Aerosol Optical Depth at 550nm</dd></dl></div><div class='xr-var-data'><pre>[1380368 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1daef7d-a2c3-4eeb-bb10-5dc3c22630e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1daef7d-a2c3-4eeb-bb10-5dc3c22630e8' class='xr-section-summary' >Attributes: <span>(2)</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-1.6</dd><dt><span>history :</span></dt><dd>2021-11-02 14:50:00 GMT by grib_to_netcdf-2.23.0: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/tmp/b8f9e27f-7176-4f09-b29d-111ed06138e8-adaptor.mars_constrained.external-1635864600.6827648-13866-24-tmp.nc /cache/tmp/b8f9e27f-7176-4f09-b29d-111ed06138e8-adaptor.mars_constrained.external-1635864600.6122434-13866-23-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (longitude: 253, latitude: 176, time: 31)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -30.0 -29.6 -29.2 -28.8 ... 70.0 70.4 70.8\n",
       "  * latitude   (latitude) float32 67.0 66.6 66.2 65.8 ... -1.8 -2.2 -2.6 -3.0\n",
       "  * time       (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-25T06:00:00\n",
       "Data variables:\n",
       "    duaod550   (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2021-11-02 14:50:00 GMT by grib_to_netcdf-2.23.0: /opt/ecmw..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = xr.open_dataset('../../eodata/case_study/cams/data.nc')\n",
    "file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now store the data variable `Dust Aerosol Optical Depth (AOD) at 550nm` as `xarray.DataArray` with the name `du_aod`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;duaod550&#x27; (time: 31, latitude: 176, longitude: 253)&gt;\n",
       "[1380368 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -30.0 -29.6 -29.2 -28.8 ... 70.0 70.4 70.8\n",
       "  * latitude   (latitude) float32 67.0 66.6 66.2 65.8 ... -1.8 -2.2 -2.6 -3.0\n",
       "  * time       (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-25T06:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Dust Aerosol Optical Depth at 550nm</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'duaod550'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li><li><span class='xr-has-index'>latitude</span>: 176</li><li><span class='xr-has-index'>longitude</span>: 253</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d6c2603f-9797-46bb-8a06-c66d831da44a' class='xr-array-in' type='checkbox' checked><label for='section-d6c2603f-9797-46bb-8a06-c66d831da44a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1380368 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-0c81101f-7c6e-4d9a-824d-6e4477f434e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c81101f-7c6e-4d9a-824d-6e4477f434e7' class='xr-section-summary' >Coordinates: <span>(3)</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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-30.0 -29.6 -29.2 ... 70.4 70.8</div><input id='attrs-2a0b6910-1ed7-4397-a451-350f1a53405b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a0b6910-1ed7-4397-a451-350f1a53405b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e919e6a-2961-4dcb-8b5e-b5f9fbde2751' class='xr-var-data-in' type='checkbox'><label for='data-4e919e6a-2961-4dcb-8b5e-b5f9fbde2751' 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>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-30. , -29.6, -29.2, ...,  70. ,  70.4,  70.8], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>67.0 66.6 66.2 ... -2.2 -2.6 -3.0</div><input id='attrs-ea2b1c6c-fa80-4976-8eb4-74e864178397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea2b1c6c-fa80-4976-8eb4-74e864178397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fe935cf-4949-4697-9eae-f448edb24a71' class='xr-var-data-in' type='checkbox'><label for='data-8fe935cf-4949-4697-9eae-f448edb24a71' 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>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([67. , 66.6, 66.2, 65.8, 65.4, 65. , 64.6, 64.2, 63.8, 63.4, 63. , 62.6,\n",
       "       62.2, 61.8, 61.4, 61. , 60.6, 60.2, 59.8, 59.4, 59. , 58.6, 58.2, 57.8,\n",
       "       57.4, 57. , 56.6, 56.2, 55.8, 55.4, 55. , 54.6, 54.2, 53.8, 53.4, 53. ,\n",
       "       52.6, 52.2, 51.8, 51.4, 51. , 50.6, 50.2, 49.8, 49.4, 49. , 48.6, 48.2,\n",
       "       47.8, 47.4, 47. , 46.6, 46.2, 45.8, 45.4, 45. , 44.6, 44.2, 43.8, 43.4,\n",
       "       43. , 42.6, 42.2, 41.8, 41.4, 41. , 40.6, 40.2, 39.8, 39.4, 39. , 38.6,\n",
       "       38.2, 37.8, 37.4, 37. , 36.6, 36.2, 35.8, 35.4, 35. , 34.6, 34.2, 33.8,\n",
       "       33.4, 33. , 32.6, 32.2, 31.8, 31.4, 31. , 30.6, 30.2, 29.8, 29.4, 29. ,\n",
       "       28.6, 28.2, 27.8, 27.4, 27. , 26.6, 26.2, 25.8, 25.4, 25. , 24.6, 24.2,\n",
       "       23.8, 23.4, 23. , 22.6, 22.2, 21.8, 21.4, 21. , 20.6, 20.2, 19.8, 19.4,\n",
       "       19. , 18.6, 18.2, 17.8, 17.4, 17. , 16.6, 16.2, 15.8, 15.4, 15. , 14.6,\n",
       "       14.2, 13.8, 13.4, 13. , 12.6, 12.2, 11.8, 11.4, 11. , 10.6, 10.2,  9.8,\n",
       "        9.4,  9. ,  8.6,  8.2,  7.8,  7.4,  7. ,  6.6,  6.2,  5.8,  5.4,  5. ,\n",
       "        4.6,  4.2,  3.8,  3.4,  3. ,  2.6,  2.2,  1.8,  1.4,  1. ,  0.6,  0.2,\n",
       "       -0.2, -0.6, -1. , -1.4, -1.8, -2.2, -2.6, -3. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-21T12:00:00 ... 2020-02-...</div><input id='attrs-69e34d10-3bf3-47ae-9762-105ce096ebc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69e34d10-3bf3-47ae-9762-105ce096ebc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f98b8bec-4220-4e8c-868f-9b1887757895' class='xr-var-data-in' type='checkbox'><label for='data-f98b8bec-4220-4e8c-868f-9b1887757895' 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</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-21T12:00:00.000000000&#x27;, &#x27;2020-02-21T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-21T18:00:00.000000000&#x27;, &#x27;2020-02-21T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-22T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T06:00:00.000000000&#x27;, &#x27;2020-02-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T12:00:00.000000000&#x27;, &#x27;2020-02-22T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T18:00:00.000000000&#x27;, &#x27;2020-02-22T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T00:00:00.000000000&#x27;, &#x27;2020-02-23T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T06:00:00.000000000&#x27;, &#x27;2020-02-23T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T12:00:00.000000000&#x27;, &#x27;2020-02-23T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T18:00:00.000000000&#x27;, &#x27;2020-02-23T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-24T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T06:00:00.000000000&#x27;, &#x27;2020-02-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T12:00:00.000000000&#x27;, &#x27;2020-02-24T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T18:00:00.000000000&#x27;, &#x27;2020-02-24T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-25T00:00:00.000000000&#x27;, &#x27;2020-02-25T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-25T06:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50beb80f-8892-4d1e-9da6-24bb6149d26c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50beb80f-8892-4d1e-9da6-24bb6149d26c' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>~</dd><dt><span>long_name :</span></dt><dd>Dust Aerosol Optical Depth at 550nm</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'duaod550' (time: 31, latitude: 176, longitude: 253)>\n",
       "[1380368 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -30.0 -29.6 -29.2 -28.8 ... 70.0 70.4 70.8\n",
       "  * latitude   (latitude) float32 67.0 66.6 66.2 65.8 ... -1.8 -2.2 -2.6 -3.0\n",
       "  * time       (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-25T06:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Dust Aerosol Optical Depth at 550nm"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "du_aod = file.duaod550\n",
    "du_aod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, you see that the variable `du_aod` has two attributes, `units` and `long_name`. Let us define variables for those attributes. The variables can be used for visualizing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_name = du_aod.long_name\n",
    "units = du_aod.units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us do the same for the coordinates `longitude` and `latitude`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "latitude = du_aod.latitude\n",
    "longitude = du_aod.longitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualize one forecast step of *Dust Aerosol Optical Depth at 550nm***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to visualize the dataset. You can use the function `visualize_pcolormesh`, which makes use of matploblib's function `pcolormesh` and the [Cartopy](https://scitools.org.uk/cartopy/docs/latest/) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1440x720 with 2 Axes>,\n",
       " <GeoAxesSubplot:title={'center':'Dust Aerosol Optical Depth at 550nm 2020-02-21T12:00:00.000000000'}>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_pcolormesh(data_array=du_aod[0,:,:],\n",
    "                     longitude=longitude,\n",
    "                     latitude=latitude,\n",
    "                     projection=ccrs.PlateCarree(),\n",
    "                     color_scale='afmhot_r',\n",
    "                     unit=units,\n",
    "                     long_name=long_name + ' ' + str(du_aod[0,:,:].time.data),\n",
    "                     vmin=0, \n",
    "                     vmax=1.5,\n",
    "                     set_global=False,\n",
    "                    lonmin=du_aod.longitude.min(),\n",
    "                    lonmax=du_aod.longitude.max(),\n",
    "                    latmin=du_aod.latitude.min(),\n",
    "                    latmax=du_aod.latitude.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can also animate the forecast. The animation function consists of 4 parts:\n",
    "- **Setting the initial state:**<br>\n",
    " Here, you define the general plot your animation shall use to initialise the animation. You can also define the number of frames (time steps) your animation shall have.\n",
    " \n",
    " \n",
    "- **Functions to animate:**<br>\n",
    " An animation consists of three functions: `draw()`, `init()` and `animate()`. `draw()` is the function where individual frames are passed on and the figure is returned as image. In this example, the function redraws the plot for each time step. `init()` returns the figure you defined for the initial state. `animate()` returns the `draw()` function and animates the function over the given number of frames (time steps).\n",
    " \n",
    " \n",
    "- **Create a `animate.FuncAnimation` object:** <br>\n",
    " The functions defined before are now combined to build an `animate.FuncAnimation` object.\n",
    " \n",
    " \n",
    "- **Play the animation as video:**<br>\n",
    " As a final step, you can integrate the animation into the notebook with the `HTML` class. You take the generate animation object and convert it to a HTML5 video with the `to_html5_video` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the initial state:\n",
    "# 1. Define figure for initial plot\n",
    "fig, ax = visualize_pcolormesh(data_array=du_aod[0,:,:],\n",
    "                               longitude=du_aod.longitude, \n",
    "                               latitude=du_aod.latitude,\n",
    "                               projection=ccrs.PlateCarree(), \n",
    "                               color_scale='afmhot_r', \n",
    "                               unit='-',\n",
    "                               long_name=long_name + ' '+ str(du_aod.time[0].data),\n",
    "                               vmin=0,\n",
    "                               vmax=1, \n",
    "                                lonmin=du_aod.longitude.min(),\n",
    "                                lonmax=du_aod.longitude.max(),\n",
    "                                latmin=du_aod.latitude.min(),\n",
    "                                latmax=du_aod.latitude.max(),\n",
    "                               set_global=False)\n",
    "\n",
    "frames = 30\n",
    "\n",
    "def draw(i):\n",
    "    img = plt.pcolormesh(du_aod.longitude, \n",
    "                         du_aod.latitude, \n",
    "                         du_aod[i,:,:], \n",
    "                         cmap='afmhot_r', \n",
    "                         transform=ccrs.PlateCarree(),\n",
    "                         vmin=0,\n",
    "                         vmax=1,\n",
    "                         shading='auto')\n",
    "    \n",
    "    ax.set_title(long_name + ' '+ str(du_aod.time[i].data), fontsize=20, pad=20.0)\n",
    "    return img\n",
    "\n",
    "\n",
    "def init():\n",
    "    return fig\n",
    "\n",
    "\n",
    "def animate(i):\n",
    "    return draw(i)\n",
    "\n",
    "ani = animation.FuncAnimation(fig, animate, frames, interval=800, blit=False,\n",
    "                              init_func=init, repeat=True)\n",
    "\n",
    "HTML(ani.to_html5_video())\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Play the animation video as HTML5 video**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2. Select latitude / longitude values for Santa Cruz, Tenerife"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see an overview of all available AERONET Site Names [here](https://aeronet.gsfc.nasa.gov/cgi-bin/draw_map_display_aod_v3?long1=-180&long2=180&lat1=-90&lat2=90&multiplier=2&what_map=4&nachal=1&formatter=0&level=3&place_code=10&place_limit=0). Let's look up the latitude and longitude information for the station `Santa_Cruz_Tenerife` and define the coordinate information as variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = 28.473\n",
    "lon = -16.247"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Select the time-series for CAMS global atmospheric composition forecasts for Santa Cruz, Tenerife"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the loaded xarray data array `du_aod`, we can now select the values for one specific point location. We can select coordinate information with the function `sel()`. We have to make sure to set the keyword argument `method='nearest'`. With this keyword argument, the closest grid location in the data array is used for the time-series retrieval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;duaod550&#x27; (time: 31)&gt;\n",
       "array([5.676746e-04, 5.676746e-04, 1.460314e-03, 2.421856e-03, 5.305767e-03,\n",
       "       1.128006e-02, 8.077216e-02, 2.031391e-01, 3.358061e-01, 5.017774e-01,\n",
       "       5.811578e-01, 7.555066e-01, 1.102213e+00, 1.210434e+00, 1.024824e+00,\n",
       "       9.059588e-01, 7.769997e-01, 6.033378e-01, 5.451070e-01, 4.923698e-01,\n",
       "       3.647155e-01, 2.810775e-01, 1.702471e-01, 2.191389e-01, 3.551019e-01,\n",
       "       5.156484e-01, 6.221528e-01, 8.869376e-01, 1.008824e+00, 7.652575e-01,\n",
       "       6.062905e-01], dtype=float32)\n",
       "Coordinates:\n",
       "    longitude  float32 -16.4\n",
       "    latitude   float32 28.6\n",
       "  * time       (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-25T06:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Dust Aerosol Optical Depth at 550nm</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'duaod550'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 31</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d49c2feb-3f87-4bc2-968b-73a54c852b75' class='xr-array-in' type='checkbox' checked><label for='section-d49c2feb-3f87-4bc2-968b-73a54c852b75' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0005677 0.0005677 0.00146 0.002422 ... 0.8869 1.009 0.7653 0.6063</span></div><div class='xr-array-data'><pre>array([5.676746e-04, 5.676746e-04, 1.460314e-03, 2.421856e-03, 5.305767e-03,\n",
       "       1.128006e-02, 8.077216e-02, 2.031391e-01, 3.358061e-01, 5.017774e-01,\n",
       "       5.811578e-01, 7.555066e-01, 1.102213e+00, 1.210434e+00, 1.024824e+00,\n",
       "       9.059588e-01, 7.769997e-01, 6.033378e-01, 5.451070e-01, 4.923698e-01,\n",
       "       3.647155e-01, 2.810775e-01, 1.702471e-01, 2.191389e-01, 3.551019e-01,\n",
       "       5.156484e-01, 6.221528e-01, 8.869376e-01, 1.008824e+00, 7.652575e-01,\n",
       "       6.062905e-01], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-b0ae5c5c-db40-4c65-877a-63f196474d68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0ae5c5c-db40-4c65-877a-63f196474d68' class='xr-section-summary' >Coordinates: <span>(3)</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>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-16.4</div><input id='attrs-3ac4cb3c-39fc-4c4d-a374-8c5e0a4b23ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ac4cb3c-39fc-4c4d-a374-8c5e0a4b23ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a27bca40-4bb1-4a7b-9d78-24e2803b358b' class='xr-var-data-in' type='checkbox'><label for='data-a27bca40-4bb1-4a7b-9d78-24e2803b358b' 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>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array(-16.4, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>28.6</div><input id='attrs-bb3a8cca-0aa5-4954-b5ed-cff1fd24e77b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb3a8cca-0aa5-4954-b5ed-cff1fd24e77b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0225ea1-3209-4792-8d67-5b571811649b' class='xr-var-data-in' type='checkbox'><label for='data-e0225ea1-3209-4792-8d67-5b571811649b' 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>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array(28.6, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-21T12:00:00 ... 2020-02-...</div><input id='attrs-373180da-ac26-4ea8-9b78-54ec9cffdeff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-373180da-ac26-4ea8-9b78-54ec9cffdeff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43d134a6-2425-41e0-8275-f4642d0acbed' class='xr-var-data-in' type='checkbox'><label for='data-43d134a6-2425-41e0-8275-f4642d0acbed' 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</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-21T12:00:00.000000000&#x27;, &#x27;2020-02-21T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-21T18:00:00.000000000&#x27;, &#x27;2020-02-21T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-22T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T06:00:00.000000000&#x27;, &#x27;2020-02-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T12:00:00.000000000&#x27;, &#x27;2020-02-22T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T18:00:00.000000000&#x27;, &#x27;2020-02-22T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T00:00:00.000000000&#x27;, &#x27;2020-02-23T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T06:00:00.000000000&#x27;, &#x27;2020-02-23T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T12:00:00.000000000&#x27;, &#x27;2020-02-23T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T18:00:00.000000000&#x27;, &#x27;2020-02-23T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-24T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T06:00:00.000000000&#x27;, &#x27;2020-02-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T12:00:00.000000000&#x27;, &#x27;2020-02-24T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T18:00:00.000000000&#x27;, &#x27;2020-02-24T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-25T00:00:00.000000000&#x27;, &#x27;2020-02-25T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-25T06:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d04ba542-685b-4e95-8e36-be5b8cb57d9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d04ba542-685b-4e95-8e36-be5b8cb57d9d' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>~</dd><dt><span>long_name :</span></dt><dd>Dust Aerosol Optical Depth at 550nm</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'duaod550' (time: 31)>\n",
       "array([5.676746e-04, 5.676746e-04, 1.460314e-03, 2.421856e-03, 5.305767e-03,\n",
       "       1.128006e-02, 8.077216e-02, 2.031391e-01, 3.358061e-01, 5.017774e-01,\n",
       "       5.811578e-01, 7.555066e-01, 1.102213e+00, 1.210434e+00, 1.024824e+00,\n",
       "       9.059588e-01, 7.769997e-01, 6.033378e-01, 5.451070e-01, 4.923698e-01,\n",
       "       3.647155e-01, 2.810775e-01, 1.702471e-01, 2.191389e-01, 3.551019e-01,\n",
       "       5.156484e-01, 6.221528e-01, 8.869376e-01, 1.008824e+00, 7.652575e-01,\n",
       "       6.062905e-01], dtype=float32)\n",
       "Coordinates:\n",
       "    longitude  float32 -16.4\n",
       "    latitude   float32 28.6\n",
       "  * time       (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-25T06:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Dust Aerosol Optical Depth at 550nm"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cams_ts = du_aod.sel(longitude=lon, latitude=lat, method='nearest')\n",
    "cams_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time-series information is better to handle via the Python library [Pandas](https://pandas.pydata.org/). You can use the function `to_dataframe()` to convert a xarray.DataArray into a pandas.DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-16.4</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-25 06:00:00</th>\n",
       "      <td>-16.4</td>\n",
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      ],
      "text/plain": [
       "                     longitude  latitude  duaod550\n",
       "time                                              \n",
       "2020-02-21 12:00:00      -16.4      28.6  0.000568\n",
       "2020-02-21 15:00:00      -16.4      28.6  0.000568\n",
       "2020-02-21 18:00:00      -16.4      28.6  0.001460\n",
       "2020-02-21 21:00:00      -16.4      28.6  0.002422\n",
       "2020-02-22 00:00:00      -16.4      28.6  0.005306\n",
       "2020-02-22 03:00:00      -16.4      28.6  0.011280\n",
       "2020-02-22 06:00:00      -16.4      28.6  0.080772\n",
       "2020-02-22 09:00:00      -16.4      28.6  0.203139\n",
       "2020-02-22 12:00:00      -16.4      28.6  0.335806\n",
       "2020-02-22 15:00:00      -16.4      28.6  0.501777\n",
       "2020-02-22 18:00:00      -16.4      28.6  0.581158\n",
       "2020-02-22 21:00:00      -16.4      28.6  0.755507\n",
       "2020-02-23 00:00:00      -16.4      28.6  1.102213\n",
       "2020-02-23 03:00:00      -16.4      28.6  1.210434\n",
       "2020-02-23 06:00:00      -16.4      28.6  1.024824\n",
       "2020-02-23 09:00:00      -16.4      28.6  0.905959\n",
       "2020-02-23 12:00:00      -16.4      28.6  0.777000\n",
       "2020-02-23 15:00:00      -16.4      28.6  0.603338\n",
       "2020-02-23 18:00:00      -16.4      28.6  0.545107\n",
       "2020-02-23 21:00:00      -16.4      28.6  0.492370\n",
       "2020-02-24 00:00:00      -16.4      28.6  0.364715\n",
       "2020-02-24 03:00:00      -16.4      28.6  0.281078\n",
       "2020-02-24 06:00:00      -16.4      28.6  0.170247\n",
       "2020-02-24 09:00:00      -16.4      28.6  0.219139\n",
       "2020-02-24 12:00:00      -16.4      28.6  0.355102\n",
       "2020-02-24 15:00:00      -16.4      28.6  0.515648\n",
       "2020-02-24 18:00:00      -16.4      28.6  0.622153\n",
       "2020-02-24 21:00:00      -16.4      28.6  0.886938\n",
       "2020-02-25 00:00:00      -16.4      28.6  1.008824\n",
       "2020-02-25 03:00:00      -16.4      28.6  0.765257\n",
       "2020-02-25 06:00:00      -16.4      28.6  0.606290"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cams_ts_df = cams_ts.to_dataframe()\n",
    "cams_ts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last step is now to safe the pandas dataframe as csv file. This allows us to easily load the time-series again later. You can use the function `to_csv()` to save a pandas.DataFrame as csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cams_ts_df.to_csv(\"../../cams_ts.csv\", index_label='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Load MONARCH dust forecasts and select time-series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to load a `MONARCH` forecast file. The data is disseminated in the `netCDF` format on a daily basis, with the forecast initialisation at 12:00 UTC. Load the `MONARCH` dust forecast of 21 February 2020. You can use the function `open_dataset()` from the xarray Python library.\n",
    "\n",
    "Once loaded, you see that the data has three dimensions: `lat`, `lon` and `time`; and offers two data variables `od550_dust` and `sconc_dust`."
   ]
  },
  {
   "cell_type": "code",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (lon: 307, lat: 211, time: 25)\n",
       "Coordinates:\n",
       "  * lon         (lon) float64 -31.0 -30.67 -30.33 -30.0 ... 70.33 70.67 71.0\n",
       "  * lat         (lat) float64 -3.0 -2.667 -2.333 -2.0 ... 66.0 66.33 66.67 67.0\n",
       "  * time        (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-24T12:0...\n",
       "Data variables:\n",
       "    od550_dust  (time, lat, lon) float32 ...\n",
       "    sconc_dust  (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:                       Climate Data Interface version 1.5.4 (http://c...\n",
       "    Conventions:               CF-1.2\n",
       "    history:                   Fri Feb 21 23:50:54 2020: cdo remapbil,regular...\n",
       "    _FillValue:                -32767.0\n",
       "    missing_value:             -32767.0\n",
       "    title:                     Regional Reanalysis 0.5x0.5 deg NMMB-BSC-Dust ...\n",
       "    History:                   Fri Feb 21 22:12:45 2020: ncrcat -F -O pout_re...\n",
       "    Grid_type:                 B-grid: vectors interpolated to scalar positions\n",
       "    Map_Proj:                  Rotated latitude longitude\n",
       "    NCO:                       4.0.8\n",
       "    nco_openmp_thread_number:  1\n",
       "    CDO:                       Climate Data Operators version 1.5.4 (http://c...</pre><div class='xr-wrap' hidden><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-5e28433d-a8be-4e01-bfa6-3d70fffde8b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e28433d-a8be-4e01-bfa6-3d70fffde8b5' 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'>lon</span>: 307</li><li><span class='xr-has-index'>lat</span>: 211</li><li><span class='xr-has-index'>time</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dd47e2f2-3b48-4ba1-8c8c-16fa6966ec37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd47e2f2-3b48-4ba1-8c8c-16fa6966ec37' class='xr-section-summary' >Coordinates: <span>(3)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-31.0 -30.67 -30.33 ... 70.67 71.0</div><input id='attrs-d5ddfaa2-7142-4842-a0fc-2de6ba07431c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5ddfaa2-7142-4842-a0fc-2de6ba07431c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d03cb073-22f3-496d-9931-c0528486123b' class='xr-var-data-in' type='checkbox'><label for='data-d03cb073-22f3-496d-9931-c0528486123b' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-31.      , -30.666667, -30.333333, ...,  70.333323,  70.666657,\n",
       "        70.99999 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.0 -2.667 -2.333 ... 66.67 67.0</div><input id='attrs-da2547e9-ca11-41f6-b726-eb74e4c5dbf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da2547e9-ca11-41f6-b726-eb74e4c5dbf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01b7118e-5366-4c02-830a-d2b53a9b8e00' class='xr-var-data-in' type='checkbox'><label for='data-01b7118e-5366-4c02-830a-d2b53a9b8e00' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-3.      , -2.666667, -2.333333, ..., 66.333326, 66.66666 , 66.999993])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-21T12:00:00 ... 2020-02-...</div><input id='attrs-6aa4132d-a44e-4c5a-95ab-e90adbb8bb14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6aa4132d-a44e-4c5a-95ab-e90adbb8bb14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3adbf450-6e3b-4296-baf3-4bdf6d5e4a71' class='xr-var-data-in' type='checkbox'><label for='data-3adbf450-6e3b-4296-baf3-4bdf6d5e4a71' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-21T12:00:00.000000000&#x27;, &#x27;2020-02-21T15:00:00.000000000&#x27;,\n",
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       "       &#x27;2020-02-22T12:00:00.000000000&#x27;, &#x27;2020-02-22T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T18:00:00.000000000&#x27;, &#x27;2020-02-22T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T00:00:00.000000000&#x27;, &#x27;2020-02-23T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T06:00:00.000000000&#x27;, &#x27;2020-02-23T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T12:00:00.000000000&#x27;, &#x27;2020-02-23T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T18:00:00.000000000&#x27;, &#x27;2020-02-23T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-24T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T06:00:00.000000000&#x27;, &#x27;2020-02-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a22ea331-4d50-4b09-8a05-1084814bec3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a22ea331-4d50-4b09-8a05-1084814bec3e' class='xr-section-summary' >Data variables: <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>od550_dust</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f268d096-c2dc-4644-9e15-27969ceb6560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f268d096-c2dc-4644-9e15-27969ceb6560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f30f941-88aa-430a-b60b-5480cda572bf' class='xr-var-data-in' type='checkbox'><label for='data-1f30f941-88aa-430a-b60b-5480cda572bf' 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>dust optical depth at 550 nm</dd><dt><span>units :</span></dt><dd> </dd><dt><span>title :</span></dt><dd>dust optical depth at 550 nm</dd></dl></div><div class='xr-var-data'><pre>[1619425 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sconc_dust</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f0551c2-a9ec-4f83-9477-47c49f258700' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f0551c2-a9ec-4f83-9477-47c49f258700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80ab320e-42ad-4f57-92e2-e31d417efd5b' class='xr-var-data-in' type='checkbox'><label for='data-80ab320e-42ad-4f57-92e2-e31d417efd5b' 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>dust 10m concentration</dd><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>title :</span></dt><dd>dust 10m concentration</dd></dl></div><div class='xr-var-data'><pre>[1619425 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba9b4f4d-5fdd-401d-b308-48068e1c7523' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba9b4f4d-5fdd-401d-b308-48068e1c7523' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.5.4 (http://code.zmaw.de/projects/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.2</dd><dt><span>history :</span></dt><dd>Fri Feb 21 23:50:54 2020: cdo remapbil,regularlatlongrid out3.nc ../../archive/SDS-WAS/2020022112_3H_NMMB-BSC.nc\n",
       "Fri Feb 21 23:50:54 2020: cdo -r settaxis,2020-02-21,12:00:00,10800 out2.nc out3.nc</dd><dt><span>_FillValue :</span></dt><dd>-32767.0</dd><dt><span>missing_value :</span></dt><dd>-32767.0</dd><dt><span>title :</span></dt><dd>Regional Reanalysis 0.5x0.5 deg NMMB-BSC-Dust 1979-2010</dd><dt><span>History :</span></dt><dd>Fri Feb 21 22:12:45 2020: ncrcat -F -O pout_regional_pressure_at_t_000.nc pout_regional_pressure_at_t_003.nc pout_regional_pressure_at_t_006.nc pout_regional_pressure_at_t_009.nc pout_regional_pressure_at_t_012.nc pout_regional_pressure_at_t_015.nc pout_regional_pressure_at_t_018.nc pout_regional_pressure_at_t_021.nc pout_regional_pressure_at_t_024.nc pout_regional_pressure_at_t_027.nc pout_regional_pressure_at_t_030.nc pout_regional_pressure_at_t_033.nc pout_regional_pressure_at_t_036.nc pout_regional_pressure_at_t_039.nc pout_regional_pressure_at_t_042.nc pout_regional_pressure_at_t_045.nc pout_regional_pressure_at_t_048.nc pout_regional_pressure_at_t_051.nc pout_regional_pressure_at_t_054.nc pout_regional_pressure_at_t_057.nc pout_regional_pressure_at_t_060.nc pout_regional_pressure_at_t_063.nc pout_regional_pressure_at_t_066.nc pout_regional_pressure_at_t_069.nc pout_regional_pressure_at_t_072.nc umo20022112reg_complete.nc\n",
       "Fri Feb 21 22:12:44 2020: ncks -A tmp1.nc tmp2.nc\n",
       "Fri Feb 21 22:12:44 2020: ncks -v time pout_regional_pressure_at_t_000.nc tmp1.nc\n",
       "Carlos Perez Garcia-Pando 2010-06</dd><dt><span>Grid_type :</span></dt><dd>B-grid: vectors interpolated to scalar positions</dd><dt><span>Map_Proj :</span></dt><dd>Rotated latitude longitude</dd><dt><span>NCO :</span></dt><dd>4.0.8</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.5.4 (http://code.zmaw.de/projects/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (lon: 307, lat: 211, time: 25)\n",
       "Coordinates:\n",
       "  * lon         (lon) float64 -31.0 -30.67 -30.33 -30.0 ... 70.33 70.67 71.0\n",
       "  * lat         (lat) float64 -3.0 -2.667 -2.333 -2.0 ... 66.0 66.33 66.67 67.0\n",
       "  * time        (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-24T12:0...\n",
       "Data variables:\n",
       "    od550_dust  (time, lat, lon) float32 ...\n",
       "    sconc_dust  (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:                       Climate Data Interface version 1.5.4 (http://c...\n",
       "    Conventions:               CF-1.2\n",
       "    history:                   Fri Feb 21 23:50:54 2020: cdo remapbil,regular...\n",
       "    _FillValue:                -32767.0\n",
       "    missing_value:             -32767.0\n",
       "    title:                     Regional Reanalysis 0.5x0.5 deg NMMB-BSC-Dust ...\n",
       "    History:                   Fri Feb 21 22:12:45 2020: ncrcat -F -O pout_re...\n",
       "    Grid_type:                 B-grid: vectors interpolated to scalar positions\n",
       "    Map_Proj:                  Rotated latitude longitude\n",
       "    NCO:                       4.0.8\n",
       "    nco_openmp_thread_number:  1\n",
       "    CDO:                       Climate Data Operators version 1.5.4 (http://c..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = '../../eodata/case_study/sds_was/2020022112_3H_NMMB-BSC.nc'\n",
    "file = xr.open_dataset(filepath)\n",
    "file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us then retrieve the data variable `od550_dust`, which is the dust optical depth at 550 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;od550_dust&#x27; (time: 25, lat: 211, lon: 307)&gt;\n",
       "[1619425 values with dtype=float32]\n",
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       "  * lat      (lat) float64 -3.0 -2.667 -2.333 -2.0 ... 66.0 66.33 66.67 67.0\n",
       "  * time     (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-24T12:00:00\n",
       "Attributes:\n",
       "    long_name:  dust optical depth at 550 nm\n",
       "    units:       \n",
       "    title:      dust optical depth at 550 nm</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'od550_dust'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 25</li><li><span class='xr-has-index'>lat</span>: 211</li><li><span class='xr-has-index'>lon</span>: 307</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f075c697-27b9-4728-87a6-50836b07da8f' class='xr-array-in' type='checkbox' checked><label for='section-f075c697-27b9-4728-87a6-50836b07da8f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1619425 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-5814c06c-1c6e-49a6-9177-50ba07711080' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5814c06c-1c6e-49a6-9177-50ba07711080' class='xr-section-summary' >Coordinates: <span>(3)</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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-31.0 -30.67 -30.33 ... 70.67 71.0</div><input id='attrs-b628a54d-7e63-4643-879b-22e3a5891199' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b628a54d-7e63-4643-879b-22e3a5891199' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbf4e8c0-0cc8-40b2-8d6f-aba03be47960' class='xr-var-data-in' type='checkbox'><label for='data-bbf4e8c0-0cc8-40b2-8d6f-aba03be47960' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-31.      , -30.666667, -30.333333, ...,  70.333323,  70.666657,\n",
       "        70.99999 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.0 -2.667 -2.333 ... 66.67 67.0</div><input id='attrs-58041435-0c0e-4dd5-9cfe-6ddb9231fc96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58041435-0c0e-4dd5-9cfe-6ddb9231fc96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc1cf01b-fdfd-407e-8543-33d5b12e3c26' class='xr-var-data-in' type='checkbox'><label for='data-fc1cf01b-fdfd-407e-8543-33d5b12e3c26' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-3.      , -2.666667, -2.333333, ..., 66.333326, 66.66666 , 66.999993])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-21T12:00:00 ... 2020-02-...</div><input id='attrs-e91f44a5-a502-457e-9b2f-21bb49620d44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e91f44a5-a502-457e-9b2f-21bb49620d44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe0caae4-cf09-47d8-87c7-864d67a9cda9' class='xr-var-data-in' type='checkbox'><label for='data-fe0caae4-cf09-47d8-87c7-864d67a9cda9' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-21T12:00:00.000000000&#x27;, &#x27;2020-02-21T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-21T18:00:00.000000000&#x27;, &#x27;2020-02-21T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-22T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T06:00:00.000000000&#x27;, &#x27;2020-02-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T12:00:00.000000000&#x27;, &#x27;2020-02-22T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T18:00:00.000000000&#x27;, &#x27;2020-02-22T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T00:00:00.000000000&#x27;, &#x27;2020-02-23T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T06:00:00.000000000&#x27;, &#x27;2020-02-23T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T12:00:00.000000000&#x27;, &#x27;2020-02-23T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T18:00:00.000000000&#x27;, &#x27;2020-02-23T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-24T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T06:00:00.000000000&#x27;, &#x27;2020-02-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67b0bd70-aa7d-45ee-ae76-d54d915c8abd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67b0bd70-aa7d-45ee-ae76-d54d915c8abd' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dust optical depth at 550 nm</dd><dt><span>units :</span></dt><dd> </dd><dt><span>title :</span></dt><dd>dust optical depth at 550 nm</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'od550_dust' (time: 25, lat: 211, lon: 307)>\n",
       "[1619425 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -31.0 -30.67 -30.33 -30.0 ... 70.0 70.33 70.67 71.0\n",
       "  * lat      (lat) float64 -3.0 -2.667 -2.333 -2.0 ... 66.0 66.33 66.67 67.0\n",
       "  * time     (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-24T12:00:00\n",
       "Attributes:\n",
       "    long_name:  dust optical depth at 550 nm\n",
       "    units:       \n",
       "    title:      dust optical depth at 550 nm"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "od_dust_sdswas = file['od550_dust']\n",
    "od_dust_sdswas"
   ]
  },
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    "<br>"
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    "Now, we can also select the time-series for the location *Santa Cruz, Tenerife* from the WMO SDS-WAS forecast data. We again use the function `sel()` together with the keyword argument `method='nearest'` to select the forecast time-series of the closest grid point."
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;od550_dust&#x27; (time: 25)&gt;\n",
       "array([5.792578e-05, 1.870866e-05, 2.691938e-05, 2.069314e-04, 8.895606e-04,\n",
       "       1.751463e-03, 9.110953e-03, 5.093248e-02, 2.034178e-01, 3.637045e-01,\n",
       "       4.338350e-01, 1.095499e+00, 2.165373e+00, 2.052835e+00, 8.611195e-01,\n",
       "       7.533937e-01, 4.669310e-01, 3.542736e-01, 3.206273e-01, 2.312253e-01,\n",
       "       1.795649e-01, 2.214468e-01, 4.775718e-01, 7.005243e-01, 1.151538e+00],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    lon      float64 -16.33\n",
       "    lat      float64 28.33\n",
       "  * time     (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-24T12:00:00\n",
       "Attributes:\n",
       "    long_name:  dust optical depth at 550 nm\n",
       "    units:       \n",
       "    title:      dust optical depth at 550 nm</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'od550_dust'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 25</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6fa392f4-27e7-44ea-8875-f9c14690591d' class='xr-array-in' type='checkbox' checked><label for='section-6fa392f4-27e7-44ea-8875-f9c14690591d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.793e-05 1.871e-05 2.692e-05 0.0002069 ... 0.2214 0.4776 0.7005 1.152</span></div><div class='xr-array-data'><pre>array([5.792578e-05, 1.870866e-05, 2.691938e-05, 2.069314e-04, 8.895606e-04,\n",
       "       1.751463e-03, 9.110953e-03, 5.093248e-02, 2.034178e-01, 3.637045e-01,\n",
       "       4.338350e-01, 1.095499e+00, 2.165373e+00, 2.052835e+00, 8.611195e-01,\n",
       "       7.533937e-01, 4.669310e-01, 3.542736e-01, 3.206273e-01, 2.312253e-01,\n",
       "       1.795649e-01, 2.214468e-01, 4.775718e-01, 7.005243e-01, 1.151538e+00],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f00d4b9b-7d87-4324-908c-e5c5390cf48e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f00d4b9b-7d87-4324-908c-e5c5390cf48e' class='xr-section-summary' >Coordinates: <span>(3)</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>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-16.33</div><input id='attrs-39189ec0-c985-45d2-becc-4ffb1111920a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39189ec0-c985-45d2-becc-4ffb1111920a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7202681f-ae60-47c2-a246-b941d18d8635' class='xr-var-data-in' type='checkbox'><label for='data-7202681f-ae60-47c2-a246-b941d18d8635' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(-16.3333348)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.33</div><input id='attrs-4a0e2e3e-f9c2-4301-a34c-af6d1c867565' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a0e2e3e-f9c2-4301-a34c-af6d1c867565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1050d0c9-c3f9-4195-bacf-85b94bcf3faa' class='xr-var-data-in' type='checkbox'><label for='data-1050d0c9-c3f9-4195-bacf-85b94bcf3faa' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(28.3333302)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-21T12:00:00 ... 2020-02-...</div><input id='attrs-b441b300-44d3-4078-b4d0-f82a2977c3c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b441b300-44d3-4078-b4d0-f82a2977c3c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7487bd-4dcd-4dad-a3d0-0264bc8b1731' class='xr-var-data-in' type='checkbox'><label for='data-1a7487bd-4dcd-4dad-a3d0-0264bc8b1731' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-21T12:00:00.000000000&#x27;, &#x27;2020-02-21T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-21T18:00:00.000000000&#x27;, &#x27;2020-02-21T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-22T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T06:00:00.000000000&#x27;, &#x27;2020-02-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T12:00:00.000000000&#x27;, &#x27;2020-02-22T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-22T18:00:00.000000000&#x27;, &#x27;2020-02-22T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T00:00:00.000000000&#x27;, &#x27;2020-02-23T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T06:00:00.000000000&#x27;, &#x27;2020-02-23T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T12:00:00.000000000&#x27;, &#x27;2020-02-23T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-23T18:00:00.000000000&#x27;, &#x27;2020-02-23T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-24T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T06:00:00.000000000&#x27;, &#x27;2020-02-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-24T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b351a4a-ff22-4da9-9732-0236f1ecaa14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b351a4a-ff22-4da9-9732-0236f1ecaa14' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dust optical depth at 550 nm</dd><dt><span>units :</span></dt><dd> </dd><dt><span>title :</span></dt><dd>dust optical depth at 550 nm</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'od550_dust' (time: 25)>\n",
       "array([5.792578e-05, 1.870866e-05, 2.691938e-05, 2.069314e-04, 8.895606e-04,\n",
       "       1.751463e-03, 9.110953e-03, 5.093248e-02, 2.034178e-01, 3.637045e-01,\n",
       "       4.338350e-01, 1.095499e+00, 2.165373e+00, 2.052835e+00, 8.611195e-01,\n",
       "       7.533937e-01, 4.669310e-01, 3.542736e-01, 3.206273e-01, 2.312253e-01,\n",
       "       1.795649e-01, 2.214468e-01, 4.775718e-01, 7.005243e-01, 1.151538e+00],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    lon      float64 -16.33\n",
       "    lat      float64 28.33\n",
       "  * time     (time) datetime64[ns] 2020-02-21T12:00:00 ... 2020-02-24T12:00:00\n",
       "Attributes:\n",
       "    long_name:  dust optical depth at 550 nm\n",
       "    units:       \n",
       "    title:      dust optical depth at 550 nm"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sds_was_ts = od_dust_sdswas.sel(lon=lon, lat=lat, method='nearest')\n",
    "sds_was_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now, we also want to save the MONARCH forecast time-series as pandas.DataFrame in a csv file. You can combine both functions (`to_dataframe()` and `to_csv`) in one line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sds_was_ts.to_dataframe().to_csv(\"../../sdswas_ts.csv\", index_label='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Visualize time-series of CAMS and MONARCH forecasts together in one plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last step is to visualize both pandas.DataFrame objects (`sds_was_ts` and `cams_ts_df`) as line plots. You can use the generic `plot()` function from matplotlib to visualize a simple line plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0673a41760>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initiate a figure\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "ax = plt.subplot()\n",
    "\n",
    "# Define the plotting function\n",
    "ax.plot(sds_was_ts.time, sds_was_ts, 'o-', color='darkred',  label='SDS-WAS dust forecast')\n",
    "ax.plot(cams_ts_df.index, cams_ts, 'o-', color='blue', label='CAMS dust forecast')\n",
    "\n",
    "# Customize the title and axes lables\n",
    "ax.set_title('\\n'+cams_ts.long_name+' - Santa Cruz Tenerife\\n', fontsize=20)\n",
    "ax.set_ylabel(cams_ts.units, fontsize=14)\n",
    "ax.set_xlabel('\\nForecast hour', fontsize=14)\n",
    "\n",
    "# Customize the fontsize of the axes tickes\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# Add a gridline to the plot\n",
    "ax.grid(linestyle='--')\n",
    "\n",
    "plt.legend(fontsize=16, loc=2)"
   ]
  }
 ],
 "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": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}