2021-09-CAMS-Air-Quality-Data-Access.ipynb

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   "cell_type": "markdown",
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   "source": [
    "<img src='./img/logoline_CAMS_flag_text2.png' align='center' width='100%'></img>"
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   "source": [
    "# CAMS Air Quality Data Access\n",
    "In this tutorial we will access and view data from the Copernicus Atmosphere Monitoring Service (CAMS). The tutorial includes the following steps:\n",
    "\n",
    "1. [Prepare environment](#prepare): Prepare your environment including registering with the Atmosphere Data Store (ADS) of CAMS, and obtaining an Application Programming Interface (API) key.\n",
    "\n",
    "2. [CAMS global atmospheric composition forecasts](#forecast): Download CAMS global atmospheric composition forecasts data for Organic Matter Aerosol Optical Depth from the ADS. View an animation of forecast data for the beginning of August 2021.\n",
    "\n",
    "3. [CAMS global reanalysis (EAC4) monthly averaged fields](#reanalysis): Download EAC global Carbon Monoxide data for August 2021 at all pressure levels. View animation of CO at different pressure levels."
   ]
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    "## <a id='prepare'></a>Prepare environment\n",
    "\n",
    "Before we begin we must prepare our environment. You will not need to install any software for the training, but if you want to adapt the exercise to your own needs you will need to install the ADS API (`pip install cdsapi`), in addition to Jupyter and Python. You will also need various Python libraries, in particular Xarray http://xarray.pydata.org/en/stable/. Given that most of the Python libraries we will need for this tutorial are included in the Anaconda distribution, if you do wish to set-up your own environment, it is recommended to install Anaconda https://docs.anaconda.com/anaconda/install/.\n",
    "\n",
    "### Register with ADS\n",
    "\n",
    "To register with the ADS visit https://ads.atmosphere.copernicus.eu and select **\"Login/Register\"**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='./img/ADS_login.png' align='left' width='60%'></img>"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### Obtain ADS API key\n",
    "\n",
    "Before you can request data from the ADS, you will need to register on the ADS website and obtain a User ID and API Key. In order to do so, please follow the steps at this link: https://ads.atmosphere.copernicus.eu/api-how-to\n",
    "\n",
    "Once you have a User ID and API Key for the ADS, insert these into the variables in the cell below. I.e. substitute `ADS_UID` with your ADS User ID and `ADS_API_KEY` with your ADS API key."
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ADS_URL = \"https://ads.atmosphere.copernicus.eu/api/v2\"\n",
    "\n",
    "# enter your ADS authentication key:\n",
    "ADS_KEY = \"ADS_UID:ADS_API_KEY\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries\n",
    "\n",
    "We now need to import the libraries needed for our analyses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CDS API\n",
    "import cdsapi\n",
    "\n",
    "# Libraries for working with multidimensional arrays\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "# Libraries for plotting and visualising data\n",
    "%matplotlib inline\n",
    "import matplotlib.path as mpath\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Others\n",
    "from zipfile import ZipFile\n",
    "from IPython.display import clear_output\n",
    "from time import sleep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we specify a data directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = './data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='forecast'></a>CAMS global atmospheric composition forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for data on the ADS\n",
    "\n",
    "To search for data, visit the ADS website: https://ads.atmosphere.copernicus.eu To facilitate your search you can use keywords, or apply various filters.\n",
    "\n",
    "The data we are going to use in this part of the exercise is the CAMS global atmospheric composition forecasts data. Using the filters, or keywords, search for the dataset `CAMS global atmospheric composition forecasts`.\n",
    "\n",
    "<img src='./img/ADS_search.png' align='left' width='45%'></img> <img src='./img/ADS_result_forecast.png' align='right' width='45%'></img> "
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having selected the correct dataset, we now need to specify what product type, variables, temporal and geographic coverage we are interested in. These can all be selected in the **\"Download data\"** tab. In this tab a form appears in which we will select the following parameters to download:\n",
    "\n",
    "- Variable (Single level): `Organic matter aerosol optical depth at 550nm`\n",
    "- Date: Start: `2021-08-01`, End: `2021-08-08`\n",
    "- Time: `00:00` (default)\n",
    "- Leadtime hour: `0, 6, 12, 18`\n",
    "- Type: `Forecast` (default)\n",
    "- Area: Restricted area: `North: 80`, `East: -50`, `South: 25`, `West: -150` \n",
    "- Format: `Zipped netCDF (experimental)`\n",
    "\n",
    "At the end of the download form, select **\"Show API request\"**. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook (see cell below)...\n",
    "\n",
    "### Download data using ADS API\n",
    "\n",
    "Having copied the API request into the cell below, running this will retrieve and download the data you requested into your specified directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\cxcs\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py:979: InsecureRequestWarning: Unverified HTTPS request is being made to host 'ads.atmosphere.copernicus.eu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  warnings.warn(\n",
      "2021-09-15 14:37:01,774 INFO Welcome to the CDS\n",
      "2021-09-15 14:37:01,778 INFO Sending request to https://ads.atmosphere.copernicus.eu/api/v2/resources/cams-global-atmospheric-composition-forecasts\n",
      "C:\\Users\\cxcs\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py:979: InsecureRequestWarning: Unverified HTTPS request is being made to host 'ads.atmosphere.copernicus.eu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  warnings.warn(\n",
      "2021-09-15 14:37:01,842 INFO Request is completed\n",
      "2021-09-15 14:37:01,846 INFO Downloading https://download-0000.copernicus-atmosphere.eu/cache-compute-0000/cache/data5/adaptor.mars_constrained.internal-1629130472.331898-14316-15-bf208880-3d10-4fda-b4c4-60733dd5484b.zip to ./data/2021-08_NAmerica_organicMatterAOD.zip (2.1M)\n",
      "C:\\Users\\cxcs\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py:979: InsecureRequestWarning: Unverified HTTPS request is being made to host 'download-0000.copernicus-atmosphere.eu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  warnings.warn(\n",
      "2021-09-15 14:37:02,169 INFO Download rate 6.6M/s                                                                      \n"
     ]
    },
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      "text/plain": [
       "Result(content_length=2219756,content_type=application/zip,location=https://download-0000.copernicus-atmosphere.eu/cache-compute-0000/cache/data5/adaptor.mars_constrained.internal-1629130472.331898-14316-15-bf208880-3d10-4fda-b4c4-60733dd5484b.zip)"
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     "execution_count": 37,
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   ],
   "source": [
    "c = cdsapi.Client(url=ADS_URL, key=ADS_KEY)\n",
    "c.retrieve(\n",
    "    'cams-global-atmospheric-composition-forecasts',\n",
    "    {\n",
    "        'date': '2021-08-01/2021-08-08',\n",
    "        'type': 'forecast',\n",
    "        'format': 'netcdf_zip',\n",
    "        'variable': 'organic_matter_aerosol_optical_depth_550nm',\n",
    "        'time': '00:00',\n",
    "        'leadtime_hour': [\n",
    "            '0', '12', '18',\n",
    "            '6',\n",
    "        ],\n",
    "        'area': [\n",
    "            80, -150, 25,\n",
    "            -50,\n",
    "        ],\n",
    "    },\n",
    "    f'{DATADIR}/2021-08_NAmerica_organicMatterAOD.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data\n",
    "\n",
    "Now that we have downloaded the data, we can read, plot and analyse it...\n",
    "\n",
    "We have requested the data in NetCDF format. This is a commonly used format for gridded (array-based) scientific data. \n",
    "\n",
    "To read and process this data we will make use of the Xarray library. Xarray is an open source project and Python package that makes working with labelled multi-dimensional arrays simple and efficient. We will read the data from our NetCDF file into an Xarray **\"dataset\"**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we extract the downloaded zip file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a ZipFile Object and load zip file in it\n",
    "with ZipFile(f'{DATADIR}/2021-08_NAmerica_organicMatterAOD.zip', 'r') as zipObj:\n",
    "   # Extract all the contents of zip file in current directory\n",
    "   zipObj.extractall(path=f'{DATADIR}/2021-08_NAmerica_organicMatterAOD/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we create a variable with the name of our downloaded file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = f'{DATADIR}/2021-08_NAmerica_organicMatterAOD/data.nc'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can read the data into an Xarray dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Xarray Dataset\n",
    "ds = xr.open_dataset(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how this looks by querying our newly created Xarray dataset ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "Dimensions:    (latitude: 138, longitude: 251, time: 32)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -150.0 -149.6 -149.2 ... -50.8 -50.4 -50.0\n",
       "  * latitude   (latitude) float32 79.8 79.4 79.0 78.6 ... 26.2 25.8 25.4 25.0\n",
       "  * time       (time) datetime64[ns] 2021-08-01 ... 2021-08-08T18:00:00\n",
       "Data variables:\n",
       "    omaod550   (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2021-08-16 16:14:32 GMT by grib_to_netcdf-2.20.0: /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-ecd1b511-1b2f-4779-941f-8e320e86fac4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ecd1b511-1b2f-4779-941f-8e320e86fac4' 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'>latitude</span>: 138</li><li><span class='xr-has-index'>longitude</span>: 251</li><li><span class='xr-has-index'>time</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c655e4a0-a006-4399-a78d-2071305c7186' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c655e4a0-a006-4399-a78d-2071305c7186' 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'>-150.0 -149.6 ... -50.4 -50.0</div><input id='attrs-a290eb0b-90f9-4879-acbc-4e2a0630d9a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a290eb0b-90f9-4879-acbc-4e2a0630d9a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2778022-cf8b-47ef-9e82-6b5846c4bc0f' class='xr-var-data-in' type='checkbox'><label for='data-a2778022-cf8b-47ef-9e82-6b5846c4bc0f' 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([-150. , -149.6, -149.2, ...,  -50.8,  -50.4,  -50. ], 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'>79.8 79.4 79.0 ... 25.8 25.4 25.0</div><input id='attrs-8ec28a62-02e8-42c3-9e8c-fa49382d6bbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ec28a62-02e8-42c3-9e8c-fa49382d6bbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8640de27-0118-492e-973a-a514fbfdf8fe' class='xr-var-data-in' type='checkbox'><label for='data-8640de27-0118-492e-973a-a514fbfdf8fe' 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([79.8, 79.4, 79. , 78.6, 78.2, 77.8, 77.4, 77. , 76.6, 76.2, 75.8, 75.4,\n",
       "       75. , 74.6, 74.2, 73.8, 73.4, 73. , 72.6, 72.2, 71.8, 71.4, 71. , 70.6,\n",
       "       70.2, 69.8, 69.4, 69. , 68.6, 68.2, 67.8, 67.4, 67. , 66.6, 66.2, 65.8,\n",
       "       65.4, 65. , 64.6, 64.2, 63.8, 63.4, 63. , 62.6, 62.2, 61.8, 61.4, 61. ,\n",
       "       60.6, 60.2, 59.8, 59.4, 59. , 58.6, 58.2, 57.8, 57.4, 57. , 56.6, 56.2,\n",
       "       55.8, 55.4, 55. , 54.6, 54.2, 53.8, 53.4, 53. , 52.6, 52.2, 51.8, 51.4,\n",
       "       51. , 50.6, 50.2, 49.8, 49.4, 49. , 48.6, 48.2, 47.8, 47.4, 47. , 46.6,\n",
       "       46.2, 45.8, 45.4, 45. , 44.6, 44.2, 43.8, 43.4, 43. , 42.6, 42.2, 41.8,\n",
       "       41.4, 41. , 40.6, 40.2, 39.8, 39.4, 39. , 38.6, 38.2, 37.8, 37.4, 37. ,\n",
       "       36.6, 36.2, 35.8, 35.4, 35. , 34.6, 34.2, 33.8, 33.4, 33. , 32.6, 32.2,\n",
       "       31.8, 31.4, 31. , 30.6, 30.2, 29.8, 29.4, 29. , 28.6, 28.2, 27.8, 27.4,\n",
       "       27. , 26.6, 26.2, 25.8, 25.4, 25. ], 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'>2021-08-01 ... 2021-08-08T18:00:00</div><input id='attrs-51a0dd6f-1759-4137-aa89-b472966b56ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51a0dd6f-1759-4137-aa89-b472966b56ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15996574-a4a8-4a27-bfa4-0ce974c377cb' class='xr-var-data-in' type='checkbox'><label for='data-15996574-a4a8-4a27-bfa4-0ce974c377cb' 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;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-08-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-01T12:00:00.000000000&#x27;, &#x27;2021-08-01T18:00:00.000000000&#x27;,\n",
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       "       &#x27;2021-08-03T00:00:00.000000000&#x27;, &#x27;2021-08-03T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-03T12:00:00.000000000&#x27;, &#x27;2021-08-03T18:00:00.000000000&#x27;,\n",
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       "       &#x27;2021-08-04T12:00:00.000000000&#x27;, &#x27;2021-08-04T18:00:00.000000000&#x27;,\n",
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       "       &#x27;2021-08-06T00:00:00.000000000&#x27;, &#x27;2021-08-06T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-06T12:00:00.000000000&#x27;, &#x27;2021-08-06T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-07T00:00:00.000000000&#x27;, &#x27;2021-08-07T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-07T12:00:00.000000000&#x27;, &#x27;2021-08-07T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-08T00:00:00.000000000&#x27;, &#x27;2021-08-08T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-08T12:00:00.000000000&#x27;, &#x27;2021-08-08T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d99f735-ae1e-4adc-9731-2b3851e7cbb9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d99f735-ae1e-4adc-9731-2b3851e7cbb9' 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>omaod550</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-5f8cb4bd-dac0-4843-a4be-51406b614df1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f8cb4bd-dac0-4843-a4be-51406b614df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f62e781-3a5d-434d-9a76-f8d9def13497' class='xr-var-data-in' type='checkbox'><label for='data-3f62e781-3a5d-434d-9a76-f8d9def13497' 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>Organic Matter Aerosol Optical Depth at 550nm</dd></dl></div><div class='xr-var-data'><pre>[1108416 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-349c87c5-c18c-48b9-aadd-2f85e48d947b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-349c87c5-c18c-48b9-aadd-2f85e48d947b' 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>2021-08-16 16:14:32 GMT by grib_to_netcdf-2.20.0: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/tmp/bf208880-3d10-4fda-b4c4-60733dd5484b-adaptor.mars_constrained.internal-1629130472.0054545-14316-48-tmp.nc /cache/tmp/bf208880-3d10-4fda-b4c4-60733dd5484b-adaptor.mars_constrained.internal-1629130471.904321-14316-47-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 138, longitude: 251, time: 32)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -150.0 -149.6 -149.2 ... -50.8 -50.4 -50.0\n",
       "  * latitude   (latitude) float32 79.8 79.4 79.0 78.6 ... 26.2 25.8 25.4 25.0\n",
       "  * time       (time) datetime64[ns] 2021-08-01 ... 2021-08-08T18:00:00\n",
       "Data variables:\n",
       "    omaod550   (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2021-08-16 16:14:32 GMT by grib_to_netcdf-2.20.0: /opt/ecmw..."
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the dataset has one variable called **\"omaod550\"**, which stands for \"Organic Matter Aerosol Optical Depth at 550nm\", and three coordinates of **longitude**, **latitude** and **time**.\n",
    "\n",
    "While an Xarray **dataset** may contain multiple variables, an Xarray **data array** holds a single multi-dimensional variable and its coordinates. To make the processing of the **omaod550** data easier, we convert in into an Xarray data array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Xarray Data Array\n",
    "da = ds['omaod550']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;omaod550&#x27; (time: 32, latitude: 138, longitude: 251)&gt;\n",
       "[1108416 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -150.0 -149.6 -149.2 ... -50.8 -50.4 -50.0\n",
       "  * latitude   (latitude) float32 79.8 79.4 79.0 78.6 ... 26.2 25.8 25.4 25.0\n",
       "  * time       (time) datetime64[ns] 2021-08-01 ... 2021-08-08T18:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Organic Matter 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'>'omaod550'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 32</li><li><span class='xr-has-index'>latitude</span>: 138</li><li><span class='xr-has-index'>longitude</span>: 251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-184abd0e-ebf3-4c95-9bb7-ed89ee250e5a' class='xr-array-in' type='checkbox' checked><label for='section-184abd0e-ebf3-4c95-9bb7-ed89ee250e5a' 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>[1108416 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-da919073-225c-426a-865b-a704c96e2178' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da919073-225c-426a-865b-a704c96e2178' 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'>-150.0 -149.6 ... -50.4 -50.0</div><input id='attrs-072b716f-ad83-4dab-9025-ad7a2034fcc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-072b716f-ad83-4dab-9025-ad7a2034fcc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac651633-77a7-49b3-800e-db8124e086c2' class='xr-var-data-in' type='checkbox'><label for='data-ac651633-77a7-49b3-800e-db8124e086c2' 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([-150. , -149.6, -149.2, ...,  -50.8,  -50.4,  -50. ], 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'>79.8 79.4 79.0 ... 25.8 25.4 25.0</div><input id='attrs-89cf2c3b-5827-47dc-b263-4ffe2ccc1ee9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89cf2c3b-5827-47dc-b263-4ffe2ccc1ee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58ae1bc8-d925-4e77-870c-65db9d28055b' class='xr-var-data-in' type='checkbox'><label for='data-58ae1bc8-d925-4e77-870c-65db9d28055b' 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([79.8, 79.4, 79. , 78.6, 78.2, 77.8, 77.4, 77. , 76.6, 76.2, 75.8, 75.4,\n",
       "       75. , 74.6, 74.2, 73.8, 73.4, 73. , 72.6, 72.2, 71.8, 71.4, 71. , 70.6,\n",
       "       70.2, 69.8, 69.4, 69. , 68.6, 68.2, 67.8, 67.4, 67. , 66.6, 66.2, 65.8,\n",
       "       65.4, 65. , 64.6, 64.2, 63.8, 63.4, 63. , 62.6, 62.2, 61.8, 61.4, 61. ,\n",
       "       60.6, 60.2, 59.8, 59.4, 59. , 58.6, 58.2, 57.8, 57.4, 57. , 56.6, 56.2,\n",
       "       55.8, 55.4, 55. , 54.6, 54.2, 53.8, 53.4, 53. , 52.6, 52.2, 51.8, 51.4,\n",
       "       51. , 50.6, 50.2, 49.8, 49.4, 49. , 48.6, 48.2, 47.8, 47.4, 47. , 46.6,\n",
       "       46.2, 45.8, 45.4, 45. , 44.6, 44.2, 43.8, 43.4, 43. , 42.6, 42.2, 41.8,\n",
       "       41.4, 41. , 40.6, 40.2, 39.8, 39.4, 39. , 38.6, 38.2, 37.8, 37.4, 37. ,\n",
       "       36.6, 36.2, 35.8, 35.4, 35. , 34.6, 34.2, 33.8, 33.4, 33. , 32.6, 32.2,\n",
       "       31.8, 31.4, 31. , 30.6, 30.2, 29.8, 29.4, 29. , 28.6, 28.2, 27.8, 27.4,\n",
       "       27. , 26.6, 26.2, 25.8, 25.4, 25. ], 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'>2021-08-01 ... 2021-08-08T18:00:00</div><input id='attrs-6099c0e5-b125-46a4-8722-e214ca595c36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6099c0e5-b125-46a4-8722-e214ca595c36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7df8b55-c910-4101-8105-2137ba0209a0' class='xr-var-data-in' type='checkbox'><label for='data-e7df8b55-c910-4101-8105-2137ba0209a0' 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;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-08-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-01T12:00:00.000000000&#x27;, &#x27;2021-08-01T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-02T00:00:00.000000000&#x27;, &#x27;2021-08-02T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-02T12:00:00.000000000&#x27;, &#x27;2021-08-02T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-03T00:00:00.000000000&#x27;, &#x27;2021-08-03T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-03T12:00:00.000000000&#x27;, &#x27;2021-08-03T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-04T00:00:00.000000000&#x27;, &#x27;2021-08-04T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-04T12:00:00.000000000&#x27;, &#x27;2021-08-04T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-05T00:00:00.000000000&#x27;, &#x27;2021-08-05T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-05T12:00:00.000000000&#x27;, &#x27;2021-08-05T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-06T00:00:00.000000000&#x27;, &#x27;2021-08-06T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-06T12:00:00.000000000&#x27;, &#x27;2021-08-06T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-07T00:00:00.000000000&#x27;, &#x27;2021-08-07T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-07T12:00:00.000000000&#x27;, &#x27;2021-08-07T18:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-08T00:00:00.000000000&#x27;, &#x27;2021-08-08T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-08T12:00:00.000000000&#x27;, &#x27;2021-08-08T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eec99fec-b5fe-4aef-b58d-139a63f38448' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eec99fec-b5fe-4aef-b58d-139a63f38448' 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>Organic Matter Aerosol Optical Depth at 550nm</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'omaod550' (time: 32, latitude: 138, longitude: 251)>\n",
       "[1108416 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -150.0 -149.6 -149.2 ... -50.8 -50.4 -50.0\n",
       "  * latitude   (latitude) float32 79.8 79.4 79.0 78.6 ... 26.2 25.8 25.4 25.0\n",
       "  * time       (time) datetime64[ns] 2021-08-01 ... 2021-08-08T18:00:00\n",
       "Attributes:\n",
       "    units:      ~\n",
       "    long_name:  Organic Matter Aerosol Optical Depth at 550nm"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise this data we will create an animation, with each frame corresponding to a time step. These include forecasts for each day at lead times of 0, 6, 12 and 18 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = da.time.shape[0] # Number of frames in animation (= number of time-steps)\n",
    "\n",
    "for i in range(n):\n",
    "    fig = plt.figure(figsize=(10, 5)) # Define the figure and specify size\n",
    "    ax = plt.subplot(1,1,1, projection=ccrs.PlateCarree()) # Specify plot area & projection\n",
    "    ax.set_extent([-150,-50,25,80], crs=ccrs.PlateCarree()) # Specify geographical extent\n",
    "    ax.coastlines(color='black') # Add coastlines\n",
    "    ax.gridlines(draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--') # Add lat/lon grid\n",
    "    ax.set_title(f'Total AOD at 550nm, {da.time[i].values}', fontsize=12) # Set figure title\n",
    "    im = plt.pcolormesh(da.longitude, da.latitude, da[i,:,:], cmap='YlOrRd', vmin=0, vmax=2) # Plot the data, iterating over time\n",
    "    cbar = plt.colorbar(im,fraction=0.046, pad=0.04) # Specify the colourbar\n",
    "    cbar.set_label('AOD at 550nm') # Define the colourbar label\n",
    "    plt.show() # Display the figure\n",
    "    sleep(0.01) # Specify time delay for each iteration (frame) of the animation\n",
    "    clear_output(wait=True) # Clear the output (figure) before the next iteration (frame) of the animation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The animation clearly shows the high values of organic matter aerosol optical depth originating from the many different wildfires burning across North America in this period. This includes the Dixie fire, which, by August 6, had grown to become the largest single (i.e. non-complex) wildfire in California's history, and the second-largest wildfire overall. \n",
    "\n",
    "The animation also shows these high values crossing the continent to the east coast where it still affects local air quality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='reanalysis'></a>CAMS global reanalysis (EAC4) monthly averaged fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part of the tutorial we will look at another CAMS dataset, the fourth generation ECMWF global reanalysis of atmospheric composition (EAC4). Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry.\n",
    "\n",
    "We will use the monthly averaged version of this data to analyse the distribution of Carbon Monoxide globally and at different vertical layers in the atmosphere for August 2020.\n",
    "\n",
    "This dataset is listed in the ADS as `CAMS global reanalysis (EAC4) monthly averaged fields`. Search for this dataset using the filters, or keywords."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API request below shows the download parameters that we will select:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cxcs\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py:979: InsecureRequestWarning: Unverified HTTPS request is being made to host 'ads.atmosphere.copernicus.eu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  warnings.warn(\n",
      "2021-09-15 14:47:55,700 INFO Welcome to the CDS\n",
      "2021-09-15 14:47:55,704 INFO Sending request to https://ads.atmosphere.copernicus.eu/api/v2/resources/cams-global-reanalysis-eac4-monthly\n",
      "C:\\Users\\cxcs\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py:979: InsecureRequestWarning: Unverified HTTPS request is being made to host 'ads.atmosphere.copernicus.eu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  warnings.warn(\n",
      "2021-09-15 14:47:55,753 INFO Request is completed\n",
      "2021-09-15 14:47:55,758 INFO Downloading https://download-0000.copernicus-atmosphere.eu/cache-compute-0000/cache/data7/adaptor.mars.internal-1631631457.4789374-19369-18-45e81635-d460-44f8-82cc-6092e7bdc213.zip to ./data/EAC4_2020-08_CO.zip (3.5M)\n",
      "C:\\Users\\cxcs\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py:979: InsecureRequestWarning: Unverified HTTPS request is being made to host 'download-0000.copernicus-atmosphere.eu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  warnings.warn(\n",
      "2021-09-15 14:47:56,168 INFO Download rate 8.7M/s                                                                      \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=3710246,content_type=application/zip,location=https://download-0000.copernicus-atmosphere.eu/cache-compute-0000/cache/data7/adaptor.mars.internal-1631631457.4789374-19369-18-45e81635-d460-44f8-82cc-6092e7bdc213.zip)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cdsapi.Client(url=ADS_URL, key=ADS_KEY)\n",
    "c.retrieve(\n",
    "    'cams-global-reanalysis-eac4-monthly',\n",
    "    {\n",
    "        'variable': 'carbon_monoxide',\n",
    "        'pressure_level': [\n",
    "            '100', '150', '200',\n",
    "            '250', '300', '400',\n",
    "            '500', '600', '700',\n",
    "            '800', '850', '900',\n",
    "            '925', '950', '1000',\n",
    "        ],\n",
    "        'model_level': '60',\n",
    "        'year': '2020',\n",
    "        'month': '08',\n",
    "        'product_type': 'monthly_mean',\n",
    "        'format': 'netcdf',\n",
    "    },\n",
    "    f'{DATADIR}/EAC4_2020-08_CO.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the file, open it as an Xarray dataset, and inspect the contents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "with ZipFile(f'{DATADIR}/EAC4_2020-08_CO.zip', 'r') as zipObj:\n",
    "   zipObj.extractall(path=f'{DATADIR}/EAC4_2020-08_CO/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "    Conventions:  CF-1.6\n",
       "    history:      2021-09-14 14:57:36 GMT by grib_to_netcdf-2.20.0: /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-e7e47716-d9cc-4ac8-8fc1-5560f0f9d981' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e7e47716-d9cc-4ac8-8fc1-5560f0f9d981' 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'>latitude</span>: 241</li><li><span class='xr-has-index'>level</span>: 15</li><li><span class='xr-has-index'>longitude</span>: 480</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6e4a6a6a-907e-4f86-991c-7ade4f866c31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e4a6a6a-907e-4f86-991c-7ade4f866c31' class='xr-section-summary' >Coordinates: <span>(4)</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'>0.0 0.75 1.5 ... 358.5 359.25</div><input id='attrs-9d9c8564-0d1d-4247-98b3-fb02e64d65a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d9c8564-0d1d-4247-98b3-fb02e64d65a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b042290c-735d-4ea9-9df4-e462f390175d' class='xr-var-data-in' type='checkbox'><label for='data-b042290c-735d-4ea9-9df4-e462f390175d' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], 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'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-43638b3c-0e75-44a8-ae3b-34a979ce9ca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43638b3c-0e75-44a8-ae3b-34a979ce9ca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aaf4636-b2d6-450a-9de7-8676130169bb' class='xr-var-data-in' type='checkbox'><label for='data-0aaf4636-b2d6-450a-9de7-8676130169bb' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>100 150 200 250 ... 925 950 1000</div><input id='attrs-4d6c54f7-1a5a-4d5c-a20a-914d41cede42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d6c54f7-1a5a-4d5c-a20a-914d41cede42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51cd1d87-6c1a-4666-b589-0121744d6e7d' class='xr-var-data-in' type='checkbox'><label for='data-51cd1d87-6c1a-4666-b589-0121744d6e7d' 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>millibars</dd><dt><span>long_name :</span></dt><dd>pressure_level</dd></dl></div><div class='xr-var-data'><pre>array([ 100,  150,  200,  250,  300,  400,  500,  600,  700,  800,  850,  900,\n",
       "        925,  950, 1000])</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'>2020-08-01</div><input id='attrs-b8000d77-9713-4957-a2a4-183b55e693be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8000d77-9713-4957-a2a4-183b55e693be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d889cf3-7b6d-4f64-b3f0-adff770b944e' class='xr-var-data-in' type='checkbox'><label for='data-7d889cf3-7b6d-4f64-b3f0-adff770b944e' 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;2020-08-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b9b7a46f-d90d-4db3-af7f-de39fad2520c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b9b7a46f-d90d-4db3-af7f-de39fad2520c' 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>co</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b41dc875-1c9e-4c02-bfb6-d0a3f88c0667' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b41dc875-1c9e-4c02-bfb6-d0a3f88c0667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd0ee8b0-8d0b-4c09-b3c4-98cae9c5f43e' class='xr-var-data-in' type='checkbox'><label for='data-dd0ee8b0-8d0b-4c09-b3c4-98cae9c5f43e' 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>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Carbon monoxide</dd><dt><span>standard_name :</span></dt><dd>mass_fraction_of_carbon_monoxide_in_air</dd></dl></div><div class='xr-var-data'><pre>[1735200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9845af3b-62b1-42b0-8d91-ac1b4678f988' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9845af3b-62b1-42b0-8d91-ac1b4678f988' 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>2021-09-14 14:57:36 GMT by grib_to_netcdf-2.20.0: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/tmp/45e81635-d460-44f8-82cc-6092e7bdc213-adaptor.mars.internal-1631631456.7243633-19369-32-tmp.nc /cache/tmp/45e81635-d460-44f8-82cc-6092e7bdc213-adaptor.mars.internal-1631631455.8798268-19369-30-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 241, level: 15, longitude: 480, time: 1)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.75 358.5 359.25\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * level      (level) int32 100 150 200 250 300 400 ... 850 900 925 950 1000\n",
       "  * time       (time) datetime64[ns] 2020-08-01\n",
       "Data variables:\n",
       "    co         (time, level, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2021-09-14 14:57:36 GMT by grib_to_netcdf-2.20.0: /opt/ecmw..."
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = f'{DATADIR}/EAC4_2020-08_CO/levtype_pl.nc'\n",
    "ds_co = xr.open_dataset(fn)\n",
    "ds_co"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;co&#x27; (time: 1, level: 15, latitude: 241, longitude: 480)&gt;\n",
       "[1735200 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.75 358.5 359.25\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * level      (level) int32 100 150 200 250 300 400 ... 850 900 925 950 1000\n",
       "  * time       (time) datetime64[ns] 2020-08-01\n",
       "Attributes:\n",
       "    units:          kg kg**-1\n",
       "    long_name:      Carbon monoxide\n",
       "    standard_name:  mass_fraction_of_carbon_monoxide_in_air</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'co'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>level</span>: 15</li><li><span class='xr-has-index'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e1e2bdb2-9184-4fae-a544-d388a099bf3c' class='xr-array-in' type='checkbox' checked><label for='section-e1e2bdb2-9184-4fae-a544-d388a099bf3c' 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>[1735200 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-d697128e-1fef-4688-8d69-9c3fb1505a27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d697128e-1fef-4688-8d69-9c3fb1505a27' class='xr-section-summary' >Coordinates: <span>(4)</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'>0.0 0.75 1.5 ... 358.5 359.25</div><input id='attrs-f56adc54-388a-48a4-abb5-98b03ede8a39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f56adc54-388a-48a4-abb5-98b03ede8a39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ded71e48-4ab4-4bbd-86a3-fd581b0fd015' class='xr-var-data-in' type='checkbox'><label for='data-ded71e48-4ab4-4bbd-86a3-fd581b0fd015' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], 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'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-19649096-31d7-490d-800b-f0fa65738462' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19649096-31d7-490d-800b-f0fa65738462' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b99fa3e1-9645-4308-b5dd-8837142755d5' class='xr-var-data-in' type='checkbox'><label for='data-b99fa3e1-9645-4308-b5dd-8837142755d5' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>100 150 200 250 ... 925 950 1000</div><input id='attrs-aa127add-7b7d-4845-8164-294bbf3d6476' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa127add-7b7d-4845-8164-294bbf3d6476' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7bbfaae-af04-4dd6-876f-8e48a6eed2cb' class='xr-var-data-in' type='checkbox'><label for='data-a7bbfaae-af04-4dd6-876f-8e48a6eed2cb' 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>millibars</dd><dt><span>long_name :</span></dt><dd>pressure_level</dd></dl></div><div class='xr-var-data'><pre>array([ 100,  150,  200,  250,  300,  400,  500,  600,  700,  800,  850,  900,\n",
       "        925,  950, 1000])</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'>2020-08-01</div><input id='attrs-e1fe8c86-0529-48fb-b992-2a3da4013631' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1fe8c86-0529-48fb-b992-2a3da4013631' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ca4bc5f-4bfc-40bc-b330-bada4c341415' class='xr-var-data-in' type='checkbox'><label for='data-2ca4bc5f-4bfc-40bc-b330-bada4c341415' 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;2020-08-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37536ac2-3995-47e2-a20a-325834fa16b2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37536ac2-3995-47e2-a20a-325834fa16b2' 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>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Carbon monoxide</dd><dt><span>standard_name :</span></dt><dd>mass_fraction_of_carbon_monoxide_in_air</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'co' (time: 1, level: 15, latitude: 241, longitude: 480)>\n",
       "[1735200 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.75 358.5 359.25\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * level      (level) int32 100 150 200 250 300 400 ... 850 900 925 950 1000\n",
       "  * time       (time) datetime64[ns] 2020-08-01\n",
       "Attributes:\n",
       "    units:          kg kg**-1\n",
       "    long_name:      Carbon monoxide\n",
       "    standard_name:  mass_fraction_of_carbon_monoxide_in_air"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_co = ds_co['co']\n",
    "da_co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create meridional mean plot\n",
    "We will now create a meridional mean plot to visualise the changing concentrations of CO at different longitudes and at various levels of the atmosphere. We will focus on the northern hemisphere and average over the latitudinal axis. In this way we can visualise a two dimensional plot of CO concentrations by longitude and altitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise this plot we first need to prepare our data. This includes filtering to keep only data over the northern hemisphere and taking the mean over the latitudinal axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the data to include only the northern hemisphere \n",
    "north = da_co.where((da_co.latitude >= 0), drop=True)\n",
    "\n",
    "# Average (mean) over the latitudinal axis\n",
    "co = north.mean(dim=\"latitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot and visualise our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The y axis (pressure levels) is a non-linear scale.\n",
    "# To ensure equal spacing between the values, we create a regular scale.\n",
    "y = np.arange(co.level.shape[0])+1\n",
    "y = y[::-1]\n",
    "\n",
    "# Define the figure and specify size\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "# Set x and y axis tickmarks, labels and figure title\n",
    "ax.yaxis.set_ticks(y)\n",
    "ax.yaxis.set_ticklabels(co.level.values)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Atmospheric pressure level (millibars)')\n",
    "ax.set_title('CO in Aug 2020 in Northern Hemisphere', fontsize=12)\n",
    "\n",
    "# As the presure levels (25) are much less than the longitudes (480),\n",
    "# we need to ensure the plot fits into the size of the figure.\n",
    "ax.set_aspect('auto')\n",
    "\n",
    "# Plot the figure and specify a colourbar\n",
    "im = plt.pcolormesh(co.longitude-180, y, co[0,:,:], cmap='jet')\n",
    "cbar = plt.colorbar(im,fraction=0.046, pad=0.04)\n",
    "cbar.set_label('CO (kg kg**-1)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the concentration and longitudinal distribution of CO varies at different levels of the atmosphere!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This code is just to create a colour map similar to that used in CAMS products.\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "matrix = np.array([[256, 256, 256],\n",
    "                   [210, 214, 234],\n",
    "                   [167, 174, 214],\n",
    "                   [135, 145, 190],\n",
    "                   [162, 167, 144],\n",
    "                   [189, 188, 101],\n",
    "                   [215, 209, 57],\n",
    "                   [242, 230, 11],\n",
    "                   [243, 197, 10],\n",
    "                   [245, 164, 8],\n",
    "                   [247, 131, 6],\n",
    "                   [248, 98, 5],\n",
    "                   [250, 65, 3],\n",
    "                   [252, 32, 1],\n",
    "                   [254, 0, 0]])\n",
    "n = 17\n",
    "cams = np.ones((253, 4))\n",
    "for i in range(matrix.shape[0]):\n",
    "    cams[(i*n):((i+1)*n),:] = np.array([matrix[i,0]/256, matrix[i,1]/256, matrix[i,2]/256, 1])\n",
    "camscmp = ListedColormap(cams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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