vis-profiles.ipynb

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    "![logo](./img/LogoLine_horizon_CAMS.png)"
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    "# Profile Plots and Zonal Means\n",
    "\n",
    "This tutorial demonstrates how to visualise [Copernicus Atmosphere Monitoring Service (CAMS)](https://atmosphere.copernicus.eu/) data of different atmospheric levels in the form of profile plots and zonal mean plots. The data used in this tutorial includes CO from the Northern Hemisphere during the summer (peak fire season) from 2003 to 2021."
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    "<style>\n",
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    "<table align=\"left\">\n",
    "  <tr>\n",
    "    <th>Run the tutorial via free cloud platforms: </th>\n",
    "    <th><a href=\"https://mybinder.org/v2/gh/ecmwf-projects/copernicus-training-cams/master?labpath=vis-profiles.ipynb\">\n",
    "        <img src = \"https://mybinder.org/badge.svg\" alt = \"Binder\"></th>\n",
    "    <th><a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ecmwf-projects/copernicus-training-cams/blob/master/vis-profiles.ipynb\">\n",
    "        <img src = \"https://kaggle.com/static/images/open-in-kaggle.svg\" alt = \"Kaggle\"></th>\n",
    "    <th><a href=\"https://colab.research.google.com/github/ecmwf-projects/copernicus-training-cams/blob/master/vis-profiles.ipynb\">\n",
    "        <img src = \"https://colab.research.google.com/assets/colab-badge.svg\" alt = \"Colab\"></th>\n",
    "  </tr>\n",
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   "source": [
    "<br>"
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Install and import packages"
   ]
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   "source": [
    "!pip install cdsapi"
   ]
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   "source": [
    "# CDS API\n",
    "import cdsapi\n",
    "\n",
    "# Libraries for reading and 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.pyplot as plt\n",
    "\n",
    "# Disable warnings for data download via API\n",
    "import urllib3 \n",
    "urllib3.disable_warnings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68c43ab",
   "metadata": {},
   "source": [
    "## Data access and preprocessing"
   ]
  },
  {
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   "id": "be3be551",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Download CAMS global reanalysis data\n",
    "\n",
    "Copy your API key into the code cell below, replacing `#######` with your key. (Remember, to access data from the ADS, you will need first to register/login https://ads-beta.atmosphere.copernicus.eu/ and obtain an API key from https://ads-beta.atmosphere.copernicus.eu/how-to-api.)"
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   "source": [
    "URL = 'https://ads-beta.atmosphere.copernicus.eu/api'\n",
    "\n",
    "# Replace the hashtags with your key:\n",
    "KEY = '#################################'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e21616",
   "metadata": {
    "tags": []
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   "source": [
    "Here we specify a data directory into which we will download our data and all output files that we will generate:"
   ]
  },
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   "execution_count": 4,
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   "source": [
    "DATADIR = '.'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3480bf01",
   "metadata": {},
   "source": [
    "For this tutorial, we will use CAMS Global Reanalysis (EAC4) data. The code below shows the subset characteristics that we will extract from this dataset as an API request."
   ]
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   "source": [
    "```{note}\n",
    "Before running this code, ensure that you have **accepted the terms and conditions**. This is something you only need to do once for each CAMS dataset. You will find the option to do this by selecting the dataset in the ADS, then scrolling to the end of the *Download data* tab.\n",
    "```"
   ]
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     "text": [
      "2024-09-12 14:27:56,029 INFO Request ID is d917161b-fe2e-4e8c-9cf7-7ffb41e0855e\n",
      "2024-09-12 14:27:56,215 INFO status has been updated to accepted\n",
      "2024-09-12 14:27:58,031 INFO status has been updated to running\n",
      "2024-09-12 14:29:50,536 INFO Creating download object as as_source with files:\n",
      "['data_allhours_plev.nc']\n",
      "2024-09-12 14:29:50,538 INFO status has been updated to successful\n"
     ]
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       "4d7fda9863851e189ea094742d844eff.nc:   0%|          | 0.00/236M [00:00<?, ?B/s]"
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      "text/plain": [
       "'./CO_NHemis_JJA_2003-2023.nc'"
      ]
     },
     "execution_count": 5,
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    "dataset = \"cams-global-reanalysis-eac4-monthly\"\n",
    "request = {\n",
    "    'variable': ['carbon_monoxide'],\n",
    "    'pressure_level': ['100', '150', '200', '250', \n",
    "                       '300', '400', '500', '600', \n",
    "                       '700', '800', '850', '900', \n",
    "                       '925', '950', '1000'],\n",
    "    'year': ['2003', '2004', '2005', '2006', \n",
    "             '2007', '2008', '2009', '2010', \n",
    "             '2011', '2012', '2013', '2014', \n",
    "             '2015', '2016', '2017', '2018', \n",
    "             '2019', '2020', '2021', '2022', \n",
    "             '2023'],\n",
    "    'month': ['06', '07', '08'],\n",
    "    'product_type': ['monthly_mean'],\n",
    "    'data_format': 'netcdf'\n",
    "}\n",
    "\n",
    "client = cdsapi.Client(url=URL, key=KEY)\n",
    "client.retrieve(dataset, request).download(\n",
    "    f'{DATADIR}/CO_NHemis_JJA_2003-2023.nc')"
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   "source": [
    "### Read and inspect data"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
    "tags": []
   },
   "source": [
    "Read the data into an Xarray dataset:"
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       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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,\n",
       ".xr-attrs 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",
       ".xr-no-icon {\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.Dataset&gt; Size: 437MB\n",
       "Dimensions:         (valid_time: 63, pressure_level: 15, latitude: 241,\n",
       "                     longitude: 480)\n",
       "Coordinates:\n",
       "  * valid_time      (valid_time) datetime64[ns] 504B 2003-06-01 ... 2023-08-01\n",
       "  * pressure_level  (pressure_level) float64 120B 1e+03 950.0 ... 150.0 100.0\n",
       "  * latitude        (latitude) float64 2kB 90.0 89.25 88.5 ... -89.25 -90.0\n",
       "  * longitude       (longitude) float64 4kB 0.0 0.75 1.5 ... 357.8 358.5 359.2\n",
       "Data variables:\n",
       "    co              (valid_time, pressure_level, latitude, longitude) float32 437MB ...\n",
       "Attributes:\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-09-12T14:29 GRIB to CDM+CF via cfgrib-0.9.1...</pre><div class='xr-wrap' style='display:none'><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-de7dc49c-ebc5-4890-a7ff-b3268a96503b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de7dc49c-ebc5-4890-a7ff-b3268a96503b' 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'>valid_time</span>: 63</li><li><span class='xr-has-index'>pressure_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><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c3f75c66-659e-40e3-97a8-292bc595b801' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3f75c66-659e-40e3-97a8-292bc595b801' 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'>valid_time</span></div><div class='xr-var-dims'>(valid_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2003-06-01 ... 2023-08-01</div><input id='attrs-b60f86c9-e025-46dd-98f0-13e4f09ecb33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b60f86c9-e025-46dd-98f0-13e4f09ecb33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-037cf6d0-f38c-4e24-8462-c29981c5f1a0' class='xr-var-data-in' type='checkbox'><label for='data-037cf6d0-f38c-4e24-8462-c29981c5f1a0' 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><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2003-06-01T00:00:00.000000000&#x27;, &#x27;2003-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-08-01T00:00:00.000000000&#x27;, &#x27;2004-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-07-01T00:00:00.000000000&#x27;, &#x27;2004-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-06-01T00:00:00.000000000&#x27;, &#x27;2005-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-01T00:00:00.000000000&#x27;, &#x27;2006-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-07-01T00:00:00.000000000&#x27;, &#x27;2006-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-06-01T00:00:00.000000000&#x27;, &#x27;2007-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-08-01T00:00:00.000000000&#x27;, &#x27;2008-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-07-01T00:00:00.000000000&#x27;, &#x27;2008-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-06-01T00:00:00.000000000&#x27;, &#x27;2009-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-08-01T00:00:00.000000000&#x27;, &#x27;2010-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-07-01T00:00:00.000000000&#x27;, &#x27;2010-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2012-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-07-01T00:00:00.000000000&#x27;, &#x27;2012-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2014-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-07-01T00:00:00.000000000&#x27;, &#x27;2014-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2016-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-01T00:00:00.000000000&#x27;, &#x27;2016-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-01T00:00:00.000000000&#x27;, &#x27;2018-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-01T00:00:00.000000000&#x27;, &#x27;2020-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2022-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-01T00:00:00.000000000&#x27;, &#x27;2022-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-08-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_level</span></div><div class='xr-var-dims'>(pressure_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 950.0 925.0 ... 150.0 100.0</div><input id='attrs-ac4ae894-ad51-4cec-8193-54ca7792dcb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac4ae894-ad51-4cec-8193-54ca7792dcb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d837ce4a-3547-41dd-b9c2-ae031276b554' class='xr-var-data-in' type='checkbox'><label for='data-d837ce4a-3547-41dd-b9c2-ae031276b554' 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>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  950.,  925.,  900.,  850.,  800.,  700.,  600.,  500.,  400.,\n",
       "        300.,  250.,  200.,  150.,  100.])</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'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-b501cae2-cdb6-44e0-ab2f-52bb7a63fcf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b501cae2-cdb6-44e0-ab2f-52bb7a63fcf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c882c4b9-d258-4b8e-9fdd-57523c9c4be0' class='xr-var-data-in' type='checkbox'><label for='data-c882c4b9-d258-4b8e-9fdd-57523c9c4be0' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ])</pre></div></li><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'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-910a483a-62c1-479c-94ef-0664eeccaf11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-910a483a-62c1-479c-94ef-0664eeccaf11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7908557d-7cea-4e96-a2bf-7f4dd6ed8dd0' class='xr-var-data-in' type='checkbox'><label for='data-7908557d-7cea-4e96-a2bf-7f4dd6ed8dd0' 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>standard_name :</span></dt><dd>longitude</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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b5be82f-0ce7-4001-96c7-bb43d75e6246' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6b5be82f-0ce7-4001-96c7-bb43d75e6246' 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'>(valid_time, pressure_level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-30535980-be2d-4a36-84a8-e7ee3de708ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30535980-be2d-4a36-84a8-e7ee3de708ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c03457e4-7db0-4556-b581-7801014b3a4e' class='xr-var-data-in' type='checkbox'><label for='data-c03457e4-7db0-4556-b581-7801014b3a4e' 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>GRIB_paramId :</span></dt><dd>210123</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>115680</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>avgua</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>480</dd><dt><span>GRIB_Ny :</span></dt><dd>241</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_carbon_monoxide_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>co</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.75</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.75</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.25</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Carbon monoxide mass mixing ratio</dd><dt><span>GRIB_shortName :</span></dt><dd>co</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Carbon monoxide mass mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>standard_name :</span></dt><dd>mass_fraction_of_carbon_monoxide_in_air</dd><dt><span>GRIB_number :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[109317600 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17fac36b-5580-4583-bb28-e2497a32c3f2' class='xr-section-summary-in' type='checkbox'  ><label for='section-17fac36b-5580-4583-bb28-e2497a32c3f2' class='xr-section-summary' >Indexes: <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-index-name'><div>valid_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8407c7c-8a92-4dc6-b0bd-709601520ee5' class='xr-index-data-in' type='checkbox'/><label for='index-f8407c7c-8a92-4dc6-b0bd-709601520ee5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2003-06-01&#x27;, &#x27;2003-07-01&#x27;, &#x27;2003-08-01&#x27;, &#x27;2004-06-01&#x27;,\n",
       "               &#x27;2004-07-01&#x27;, &#x27;2004-08-01&#x27;, &#x27;2005-06-01&#x27;, &#x27;2005-07-01&#x27;,\n",
       "               &#x27;2005-08-01&#x27;, &#x27;2006-06-01&#x27;, &#x27;2006-07-01&#x27;, &#x27;2006-08-01&#x27;,\n",
       "               &#x27;2007-06-01&#x27;, &#x27;2007-07-01&#x27;, &#x27;2007-08-01&#x27;, &#x27;2008-06-01&#x27;,\n",
       "               &#x27;2008-07-01&#x27;, &#x27;2008-08-01&#x27;, &#x27;2009-06-01&#x27;, &#x27;2009-07-01&#x27;,\n",
       "               &#x27;2009-08-01&#x27;, &#x27;2010-06-01&#x27;, &#x27;2010-07-01&#x27;, &#x27;2010-08-01&#x27;,\n",
       "               &#x27;2011-06-01&#x27;, &#x27;2011-07-01&#x27;, &#x27;2011-08-01&#x27;, &#x27;2012-06-01&#x27;,\n",
       "               &#x27;2012-07-01&#x27;, &#x27;2012-08-01&#x27;, &#x27;2013-06-01&#x27;, &#x27;2013-07-01&#x27;,\n",
       "               &#x27;2013-08-01&#x27;, &#x27;2014-06-01&#x27;, &#x27;2014-07-01&#x27;, &#x27;2014-08-01&#x27;,\n",
       "               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2016-06-01&#x27;,\n",
       "               &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;,\n",
       "               &#x27;2017-08-01&#x27;, &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;,\n",
       "               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2020-06-01&#x27;,\n",
       "               &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;,\n",
       "               &#x27;2021-08-01&#x27;, &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;,\n",
       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;valid_time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>pressure_level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-58566252-48fd-4e62-be17-2287390bfe73' class='xr-index-data-in' type='checkbox'/><label for='index-58566252-48fd-4e62-be17-2287390bfe73' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  950.0,  925.0,  900.0,  850.0,  800.0,  700.0,  600.0,  500.0,\n",
       "        400.0,  300.0,  250.0,  200.0,  150.0,  100.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;pressure_level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ec12ea98-63ef-41f9-b870-6aeb3788012b' class='xr-index-data-in' type='checkbox'/><label for='index-ec12ea98-63ef-41f9-b870-6aeb3788012b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  90.0,  89.25,   88.5,  87.75,   87.0,  86.25,   85.5,  84.75,   84.0,\n",
       "        83.25,\n",
       "       ...\n",
       "       -83.25,  -84.0, -84.75,  -85.5, -86.25,  -87.0, -87.75,  -88.5, -89.25,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0f35bf05-e916-40cb-b550-42f749124332' class='xr-index-data-in' type='checkbox'/><label for='index-0f35bf05-e916-40cb-b550-42f749124332' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.75,    1.5,   2.25,    3.0,   3.75,    4.5,   5.25,    6.0,\n",
       "         6.75,\n",
       "       ...\n",
       "        352.5, 353.25,  354.0, 354.75,  355.5, 356.25,  357.0, 357.75,  358.5,\n",
       "       359.25],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=480))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc2a2f62-0d7d-4159-9ee7-0d5d5aa019c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc2a2f62-0d7d-4159-9ee7-0d5d5aa019c9' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2024-09-12T14:29 GRIB to CDM+CF via cfgrib-0.9.14.0/ecCodes-2.36.0 with {&quot;source&quot;: &quot;data.grib&quot;, &quot;filter_by_keys&quot;: {&quot;marsStream&quot;: &quot;moda&quot;, &quot;typeOfLevel&quot;: &quot;isobaricInhPa&quot;}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 437MB\n",
       "Dimensions:         (valid_time: 63, pressure_level: 15, latitude: 241,\n",
       "                     longitude: 480)\n",
       "Coordinates:\n",
       "  * valid_time      (valid_time) datetime64[ns] 504B 2003-06-01 ... 2023-08-01\n",
       "  * pressure_level  (pressure_level) float64 120B 1e+03 950.0 ... 150.0 100.0\n",
       "  * latitude        (latitude) float64 2kB 90.0 89.25 88.5 ... -89.25 -90.0\n",
       "  * longitude       (longitude) float64 4kB 0.0 0.75 1.5 ... 357.8 358.5 359.2\n",
       "Data variables:\n",
       "    co              (valid_time, pressure_level, latitude, longitude) float32 437MB ...\n",
       "Attributes:\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-09-12T14:29 GRIB to CDM+CF via cfgrib-0.9.1..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = f'{DATADIR}/CO_NHemis_JJA_2003-2023.nc'\n",
    "ds = xr.open_dataset(fn)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227fcf72",
   "metadata": {},
   "source": [
    "### Representation of pressure levels\n",
    "\n",
    "Our CO dataset represents 15 levels of the atmosphere, from 1000 (surface) to 100 hectopascals (hPa). This can be best visualised in a logarithmic scale, or if converted to altitude."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b6cbcd",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Pressure levels in logarithmic scale\n",
    "\n",
    "Here we create a new coordinate of our Data Array with the logarithm of the pressure levels, to facilitate visualisation in our plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9bc5ace4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:31:46.853262Z",
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     "shell.execute_reply": "2024-09-12T14:31:46.860389Z",
     "shell.execute_reply.started": "2024-09-12T14:31:46.853204Z"
    }
   },
   "outputs": [],
   "source": [
    "ds['level_log10'] = np.log10(ds['pressure_level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd8807aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:32:09.747035Z",
     "iopub.status.busy": "2024-09-12T14:32:09.746510Z",
     "iopub.status.idle": "2024-09-12T14:32:09.754516Z",
     "shell.execute_reply": "2024-09-12T14:32:09.752755Z",
     "shell.execute_reply.started": "2024-09-12T14:32:09.746992Z"
    }
   },
   "outputs": [],
   "source": [
    "ds = ds.set_coords('level_log10')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0bdf42",
   "metadata": {},
   "source": [
    "#### Pressure levels to altitude\n",
    "\n",
    "Here we create a new coordinate with altitude. The altitude in meters can be calculated with the international barometric formula:\n",
    "\n",
    "H = 44330 * [1 - (P/p0)^(1/5.255) ]\n",
    "\n",
    "Where H = altitude (m), P = measured pressure (Pa) from the sensor, and p0 = reference pressure at sea level (e.g. 1000 hPa)\n",
    "\n",
    "Here we shall assume that the reference pressure at sea level is 1000 hPa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8304a3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:32:25.359876Z",
     "iopub.status.busy": "2024-09-12T14:32:25.358698Z",
     "iopub.status.idle": "2024-09-12T14:32:25.368798Z",
     "shell.execute_reply": "2024-09-12T14:32:25.367285Z",
     "shell.execute_reply.started": "2024-09-12T14:32:25.359788Z"
    }
   },
   "outputs": [],
   "source": [
    "ds['height'] = 44330. * (1 - ((ds['pressure_level']/1000.)**(1/5.255)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee896fc4",
   "metadata": {},
   "source": [
    "We will convert the heights from meters to kilometers:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "f20b27f1",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2024-09-12T14:32:35.406000Z",
     "iopub.status.idle": "2024-09-12T14:32:35.413678Z",
     "shell.execute_reply": "2024-09-12T14:32:35.412297Z",
     "shell.execute_reply.started": "2024-09-12T14:32:35.406380Z"
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   "outputs": [],
   "source": [
    "ds['height'] = ds['height']/1000."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c690f2",
   "metadata": {},
   "source": [
    "Here we set the heights as a coordinate in our Xarray Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c513e89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:32:38.981483Z",
     "iopub.status.busy": "2024-09-12T14:32:38.980933Z",
     "iopub.status.idle": "2024-09-12T14:32:38.988784Z",
     "shell.execute_reply": "2024-09-12T14:32:38.986920Z",
     "shell.execute_reply.started": "2024-09-12T14:32:38.981436Z"
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   "outputs": [],
   "source": [
    "ds = ds.set_coords('height')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4aae93a",
   "metadata": {},
   "source": [
    "### Convert xarray Dataset into Data Array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abacee22",
   "metadata": {},
   "source": [
    "To facilitate later processing, we will now convert this xarray Dataset into an Xarray Data Array object. This is an object which allows more options for processing data of only one variable, in this case CO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7a106da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:32:43.645668Z",
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     "shell.execute_reply.started": "2024-09-12T14:32:43.645618Z"
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   },
   "outputs": [],
   "source": [
    "da = ds['co']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f3a84813",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-09-12T14:32:50.426794Z"
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       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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,\n",
       ".xr-attrs 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",
       ".xr-no-icon {\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; (valid_time: 63, pressure_level: 15, latitude: 241,\n",
       "                        longitude: 480)&gt; Size: 437MB\n",
       "[109317600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * valid_time      (valid_time) datetime64[ns] 504B 2003-06-01 ... 2023-08-01\n",
       "  * pressure_level  (pressure_level) float64 120B 1e+03 950.0 ... 150.0 100.0\n",
       "  * latitude        (latitude) float64 2kB 90.0 89.25 88.5 ... -89.25 -90.0\n",
       "  * longitude       (longitude) float64 4kB 0.0 0.75 1.5 ... 357.8 358.5 359.2\n",
       "    level_log10     (pressure_level) float64 120B 3.0 2.978 2.966 ... 2.176 2.0\n",
       "    height          (pressure_level) float64 120B 0.0 0.4306 ... 13.43 15.73\n",
       "Attributes: (12/32)\n",
       "    GRIB_paramId:                             210123\n",
       "    GRIB_dataType:                            an\n",
       "    GRIB_numberOfPoints:                      115680\n",
       "    GRIB_typeOfLevel:                         isobaricInhPa\n",
       "    GRIB_stepUnits:                           1\n",
       "    GRIB_stepType:                            avgua\n",
       "    ...                                       ...\n",
       "    GRIB_totalNumber:                         0\n",
       "    GRIB_units:                               kg kg**-1\n",
       "    long_name:                                Carbon monoxide mass mixing ratio\n",
       "    units:                                    kg kg**-1\n",
       "    standard_name:                            mass_fraction_of_carbon_monoxid...\n",
       "    GRIB_number:                              0</pre><div class='xr-wrap' style='display:none'><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'>valid_time</span>: 63</li><li><span class='xr-has-index'>pressure_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-04787bbd-f7bb-4854-89d2-e956614004b0' class='xr-array-in' type='checkbox' checked><label for='section-04787bbd-f7bb-4854-89d2-e956614004b0' 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>[109317600 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-c8d358bc-180d-48f4-a11a-d49c89f6090d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8d358bc-180d-48f4-a11a-d49c89f6090d' class='xr-section-summary' >Coordinates: <span>(6)</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'>valid_time</span></div><div class='xr-var-dims'>(valid_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2003-06-01 ... 2023-08-01</div><input id='attrs-8f3aec81-241e-48f6-8d81-d6ed1703c481' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f3aec81-241e-48f6-8d81-d6ed1703c481' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853c3804-e58c-43c0-ba16-c32003d4898e' class='xr-var-data-in' type='checkbox'><label for='data-853c3804-e58c-43c0-ba16-c32003d4898e' 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><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2003-06-01T00:00:00.000000000&#x27;, &#x27;2003-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-08-01T00:00:00.000000000&#x27;, &#x27;2004-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-07-01T00:00:00.000000000&#x27;, &#x27;2004-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-06-01T00:00:00.000000000&#x27;, &#x27;2005-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-01T00:00:00.000000000&#x27;, &#x27;2006-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-07-01T00:00:00.000000000&#x27;, &#x27;2006-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-06-01T00:00:00.000000000&#x27;, &#x27;2007-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-08-01T00:00:00.000000000&#x27;, &#x27;2008-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-07-01T00:00:00.000000000&#x27;, &#x27;2008-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-06-01T00:00:00.000000000&#x27;, &#x27;2009-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-08-01T00:00:00.000000000&#x27;, &#x27;2010-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-07-01T00:00:00.000000000&#x27;, &#x27;2010-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2012-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-07-01T00:00:00.000000000&#x27;, &#x27;2012-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2014-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-07-01T00:00:00.000000000&#x27;, &#x27;2014-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2016-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-01T00:00:00.000000000&#x27;, &#x27;2016-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-01T00:00:00.000000000&#x27;, &#x27;2018-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-01T00:00:00.000000000&#x27;, &#x27;2020-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2022-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-07-01T00:00:00.000000000&#x27;, &#x27;2022-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-08-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_level</span></div><div class='xr-var-dims'>(pressure_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 950.0 925.0 ... 150.0 100.0</div><input id='attrs-af632a8d-9ffd-4393-a014-573f863e2d93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af632a8d-9ffd-4393-a014-573f863e2d93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-538c4927-a066-44a3-9eb8-f3524c7b2a0e' class='xr-var-data-in' type='checkbox'><label for='data-538c4927-a066-44a3-9eb8-f3524c7b2a0e' 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>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  950.,  925.,  900.,  850.,  800.,  700.,  600.,  500.,  400.,\n",
       "        300.,  250.,  200.,  150.,  100.])</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'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-6db05e4c-cf14-4bd6-ad32-3ba8bb168d5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6db05e4c-cf14-4bd6-ad32-3ba8bb168d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9acb09b9-7f36-486d-8ac7-4f3d8b949dc9' class='xr-var-data-in' type='checkbox'><label for='data-9acb09b9-7f36-486d-8ac7-4f3d8b949dc9' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ])</pre></div></li><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'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-1c920797-7364-4601-9526-ba4a00ac310c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c920797-7364-4601-9526-ba4a00ac310c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b068356-f145-42ba-bfc4-434b3f0870ed' class='xr-var-data-in' type='checkbox'><label for='data-5b068356-f145-42ba-bfc4-434b3f0870ed' 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>standard_name :</span></dt><dd>longitude</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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>level_log10</span></div><div class='xr-var-dims'>(pressure_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 2.978 2.966 ... 2.301 2.176 2.0</div><input id='attrs-e5f94aef-b02b-48d2-a56b-edb7ae892f46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5f94aef-b02b-48d2-a56b-edb7ae892f46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba9cb6d-d714-4667-a3fa-dd095bbb7a6d' class='xr-var-data-in' type='checkbox'><label for='data-3ba9cb6d-d714-4667-a3fa-dd095bbb7a6d' 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>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([3.        , 2.97772361, 2.96614173, 2.95424251, 2.92941893,\n",
       "       2.90308999, 2.84509804, 2.77815125, 2.69897   , 2.60205999,\n",
       "       2.47712125, 2.39794001, 2.30103   , 2.17609126, 2.        ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>(pressure_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.4306 0.6528 ... 13.43 15.73</div><input id='attrs-ca5d80f6-d9aa-47e1-96c2-27b7879a0a78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca5d80f6-d9aa-47e1-96c2-27b7879a0a78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae2d3ce-aab3-4b82-ba80-6c2b226cdd9e' class='xr-var-data-in' type='checkbox'><label for='data-dae2d3ce-aab3-4b82-ba80-6c2b226cdd9e' 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'></dl></div><div class='xr-var-data'><pre>array([ 0.        ,  0.43059381,  0.65281163,  0.87994689,  1.34999034,\n",
       "        1.8429826 ,  2.90899143,  4.10639113,  5.47801243,  7.09325125,\n",
       "        9.07696251, 10.27908779, 11.69472593, 13.43330308, 15.72758036])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8c8714a-29f1-442f-b3d1-9d88d8e235cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-a8c8714a-29f1-442f-b3d1-9d88d8e235cc' class='xr-section-summary' >Indexes: <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-index-name'><div>valid_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ff5b087-aff4-451a-92c9-f1e2154e0d66' class='xr-index-data-in' type='checkbox'/><label for='index-6ff5b087-aff4-451a-92c9-f1e2154e0d66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2003-06-01&#x27;, &#x27;2003-07-01&#x27;, &#x27;2003-08-01&#x27;, &#x27;2004-06-01&#x27;,\n",
       "               &#x27;2004-07-01&#x27;, &#x27;2004-08-01&#x27;, &#x27;2005-06-01&#x27;, &#x27;2005-07-01&#x27;,\n",
       "               &#x27;2005-08-01&#x27;, &#x27;2006-06-01&#x27;, &#x27;2006-07-01&#x27;, &#x27;2006-08-01&#x27;,\n",
       "               &#x27;2007-06-01&#x27;, &#x27;2007-07-01&#x27;, &#x27;2007-08-01&#x27;, &#x27;2008-06-01&#x27;,\n",
       "               &#x27;2008-07-01&#x27;, &#x27;2008-08-01&#x27;, &#x27;2009-06-01&#x27;, &#x27;2009-07-01&#x27;,\n",
       "               &#x27;2009-08-01&#x27;, &#x27;2010-06-01&#x27;, &#x27;2010-07-01&#x27;, &#x27;2010-08-01&#x27;,\n",
       "               &#x27;2011-06-01&#x27;, &#x27;2011-07-01&#x27;, &#x27;2011-08-01&#x27;, &#x27;2012-06-01&#x27;,\n",
       "               &#x27;2012-07-01&#x27;, &#x27;2012-08-01&#x27;, &#x27;2013-06-01&#x27;, &#x27;2013-07-01&#x27;,\n",
       "               &#x27;2013-08-01&#x27;, &#x27;2014-06-01&#x27;, &#x27;2014-07-01&#x27;, &#x27;2014-08-01&#x27;,\n",
       "               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2016-06-01&#x27;,\n",
       "               &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;,\n",
       "               &#x27;2017-08-01&#x27;, &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;,\n",
       "               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2020-06-01&#x27;,\n",
       "               &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;,\n",
       "               &#x27;2021-08-01&#x27;, &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;,\n",
       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;valid_time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>pressure_level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-73c888a6-92bf-4b8b-a46e-f468da93712a' class='xr-index-data-in' type='checkbox'/><label for='index-73c888a6-92bf-4b8b-a46e-f468da93712a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1000.0,  950.0,  925.0,  900.0,  850.0,  800.0,  700.0,  600.0,  500.0,\n",
       "        400.0,  300.0,  250.0,  200.0,  150.0,  100.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;pressure_level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4dfc492d-6c6a-4e74-86fd-83ceff809bfe' class='xr-index-data-in' type='checkbox'/><label for='index-4dfc492d-6c6a-4e74-86fd-83ceff809bfe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  90.0,  89.25,   88.5,  87.75,   87.0,  86.25,   85.5,  84.75,   84.0,\n",
       "        83.25,\n",
       "       ...\n",
       "       -83.25,  -84.0, -84.75,  -85.5, -86.25,  -87.0, -87.75,  -88.5, -89.25,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=241))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-15c58a9b-1edc-459b-9a11-aa50b67019d3' class='xr-index-data-in' type='checkbox'/><label for='index-15c58a9b-1edc-459b-9a11-aa50b67019d3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.75,    1.5,   2.25,    3.0,   3.75,    4.5,   5.25,    6.0,\n",
       "         6.75,\n",
       "       ...\n",
       "        352.5, 353.25,  354.0, 354.75,  355.5, 356.25,  357.0, 357.75,  358.5,\n",
       "       359.25],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=480))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e76d8793-75a2-43bf-a8de-129308e29ce1' class='xr-section-summary-in' type='checkbox'  ><label for='section-e76d8793-75a2-43bf-a8de-129308e29ce1' class='xr-section-summary' >Attributes: <span>(32)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>210123</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>115680</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>avgua</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>480</dd><dt><span>GRIB_Ny :</span></dt><dd>241</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_carbon_monoxide_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>co</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.75</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.75</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.25</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Carbon monoxide mass mixing ratio</dd><dt><span>GRIB_shortName :</span></dt><dd>co</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Carbon monoxide mass mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>standard_name :</span></dt><dd>mass_fraction_of_carbon_monoxide_in_air</dd><dt><span>GRIB_number :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'co' (valid_time: 63, pressure_level: 15, latitude: 241,\n",
       "                        longitude: 480)> Size: 437MB\n",
       "[109317600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * valid_time      (valid_time) datetime64[ns] 504B 2003-06-01 ... 2023-08-01\n",
       "  * pressure_level  (pressure_level) float64 120B 1e+03 950.0 ... 150.0 100.0\n",
       "  * latitude        (latitude) float64 2kB 90.0 89.25 88.5 ... -89.25 -90.0\n",
       "  * longitude       (longitude) float64 4kB 0.0 0.75 1.5 ... 357.8 358.5 359.2\n",
       "    level_log10     (pressure_level) float64 120B 3.0 2.978 2.966 ... 2.176 2.0\n",
       "    height          (pressure_level) float64 120B 0.0 0.4306 ... 13.43 15.73\n",
       "Attributes: (12/32)\n",
       "    GRIB_paramId:                             210123\n",
       "    GRIB_dataType:                            an\n",
       "    GRIB_numberOfPoints:                      115680\n",
       "    GRIB_typeOfLevel:                         isobaricInhPa\n",
       "    GRIB_stepUnits:                           1\n",
       "    GRIB_stepType:                            avgua\n",
       "    ...                                       ...\n",
       "    GRIB_totalNumber:                         0\n",
       "    GRIB_units:                               kg kg**-1\n",
       "    long_name:                                Carbon monoxide mass mixing ratio\n",
       "    units:                                    kg kg**-1\n",
       "    standard_name:                            mass_fraction_of_carbon_monoxid...\n",
       "    GRIB_number:                              0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a24ca7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Unit conversion\n",
    "\n",
    "Most of the chemical species in the CAMS Global data, including our CO data, is archived as mass mixing ratios (MMR, kg of gas / kg of air).\n",
    "\n",
    "We will convert this to Volume Mixing Ratio (VMR, in units parts per billion, or ppbv)\n",
    "\n",
    "To convert data from MMR to VMR you only need the molar masses of dry air and of the atmospheric species. For CO the formula is: VMR = 28.9644 / 28.0101 * 1e9 * MMR. The 1e9 in the formulae gives parts per billion (ppbv). If we wanted parts per million (ppmv) we would use 1e6. See [more details on unit conversions here](https://confluence.ecmwf.int/pages/viewpage.action?pageId=153391710)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecf9675a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:32:56.134415Z",
     "iopub.status.busy": "2024-09-12T14:32:56.133969Z",
     "iopub.status.idle": "2024-09-12T14:32:59.117461Z",
     "shell.execute_reply": "2024-09-12T14:32:59.116222Z",
     "shell.execute_reply.started": "2024-09-12T14:32:56.134372Z"
    }
   },
   "outputs": [],
   "source": [
    "da = (28.9644 / 28.0101) * 1e9 * da"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782898e3",
   "metadata": {},
   "source": [
    "Here we update the attributes of our dataset with the new units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39799120",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:33:05.476684Z",
     "iopub.status.busy": "2024-09-12T14:33:05.476250Z",
     "iopub.status.idle": "2024-09-12T14:33:05.482296Z",
     "shell.execute_reply": "2024-09-12T14:33:05.480961Z",
     "shell.execute_reply.started": "2024-09-12T14:33:05.476645Z"
    }
   },
   "outputs": [],
   "source": [
    "da.attrs['long_name'] = 'CO Volume Mixing Ratio'\n",
    "da.attrs['units'] = 'ppbv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59c6ffc",
   "metadata": {},
   "source": [
    "### Subset and aggregation\n",
    "\n",
    "We would like to visualise CO profiles over the northern hemisphere, so we will take a subset including only northern latitudes. Also, to facilitate visualisation of CO vertical profiles, we are going to average over the temporal and spatial dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8bc4a8f",
   "metadata": {},
   "source": [
    "#### Spatial subset\n",
    "\n",
    "Here we subset the data to keep only northern latitudes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4024f71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:33:11.514081Z",
     "iopub.status.busy": "2024-09-12T14:33:11.513644Z",
     "iopub.status.idle": "2024-09-12T14:33:11.928296Z",
     "shell.execute_reply": "2024-09-12T14:33:11.926908Z",
     "shell.execute_reply.started": "2024-09-12T14:33:11.514043Z"
    }
   },
   "outputs": [],
   "source": [
    "da_north = da.where(da.latitude > 0, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4eab20d",
   "metadata": {},
   "source": [
    "#### Temporal aggregation\n",
    "\n",
    "The purpose of the temporal aggregation is to average June, July and August in each year, resulting in one summer mean value at each year of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a567093",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:33:37.263532Z",
     "iopub.status.busy": "2024-09-12T14:33:37.261953Z",
     "iopub.status.idle": "2024-09-12T14:33:37.834947Z",
     "shell.execute_reply": "2024-09-12T14:33:37.833612Z",
     "shell.execute_reply.started": "2024-09-12T14:33:37.263471Z"
    }
   },
   "outputs": [],
   "source": [
    "summer = da_north.groupby('valid_time.year').mean(keep_attrs=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c45da0",
   "metadata": {},
   "source": [
    "#### Spatial aggregation\n",
    "\n",
    "To visualise the data in a profile plot, we will aggregate over the spatial dimensions. For the latitudinal dimension, we need to take into account the variation in area as a function of latitude, which we will do by taking the cosine of the latitude as a proxy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "280550ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:33:44.408407Z",
     "iopub.status.busy": "2024-09-12T14:33:44.407746Z",
     "iopub.status.idle": "2024-09-12T14:33:44.424120Z",
     "shell.execute_reply": "2024-09-12T14:33:44.422292Z",
     "shell.execute_reply.started": "2024-09-12T14:33:44.408356Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "weights = np.cos(np.deg2rad(summer.latitude))\n",
    "weights.name = \"weights\"\n",
    "weighted = summer.weighted(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7951e528",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:33:49.358993Z",
     "iopub.status.busy": "2024-09-12T14:33:49.358487Z",
     "iopub.status.idle": "2024-09-12T14:33:49.577907Z",
     "shell.execute_reply": "2024-09-12T14:33:49.576641Z",
     "shell.execute_reply.started": "2024-09-12T14:33:49.358949Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Average (mean) over the latitudinal axis\n",
    "co = weighted.mean(dim=[\"latitude\", \"longitude\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea41b378",
   "metadata": {},
   "source": [
    "### Calculate quantiles of climatology\n",
    "\n",
    "Here we calculate the 5th, 50th and 95th quantile of CO climatology from 2003 to 2023."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ccf4633a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:34:15.799689Z",
     "iopub.status.busy": "2024-09-12T14:34:15.799149Z",
     "iopub.status.idle": "2024-09-12T14:34:15.823567Z",
     "shell.execute_reply": "2024-09-12T14:34:15.822051Z",
     "shell.execute_reply.started": "2024-09-12T14:34:15.799642Z"
    }
   },
   "outputs": [],
   "source": [
    "co95 = co.quantile(0.95, dim='year')\n",
    "co05 = co.quantile(0.05, dim='year')\n",
    "co50 = co.quantile(0.5, dim='year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce494e4e",
   "metadata": {},
   "source": [
    "## Plot summer climatology of CO in Northern Hemisphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5477a72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:37:20.458051Z",
     "iopub.status.busy": "2024-09-12T14:37:20.457411Z",
     "iopub.status.idle": "2024-09-12T14:37:21.080258Z",
     "shell.execute_reply": "2024-09-12T14:37:21.078822Z",
     "shell.execute_reply.started": "2024-09-12T14:37:20.457999Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (6, 9))\n",
    "\n",
    "ax.set_title('CO in JJA in Northern Hemisphere', fontsize=12)\n",
    "ax.set_ylabel('Height [Kilometers]')\n",
    "ax.set_xlabel('CO [ppbv]')\n",
    "ax.plot(co50, co50.height, color='black', linestyle='--', label='2003-2023 median')\n",
    "ax.fill_betweenx(co50.height, co05, co95, alpha=0.1, color='black', label='2003-2023 5th to 95th quantile range')\n",
    "ax.plot(co[20,:], co.height, color='red', linestyle='-', label='2023')\n",
    "ax.plot(co[19,:], co.height, color='green', linestyle='-', label='2022')\n",
    "ax.plot(co[18,:], co.height, color='blue', linestyle='-', label='2021')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)\n",
    "\n",
    "fig.savefig(f'{DATADIR}/CO_JJA_NHem_2003-2023_profile.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52745c12",
   "metadata": {},
   "source": [
    "Note the distribution of CO mainly in the lower part of the atmosphere, and the high values in 2021 and 2023, due mainly to the significant wildfire activity those summers. See the [BAMS State of the Climate reports](https://www.ncei.noaa.gov/bams-state-of-climate) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7310b0e2",
   "metadata": {},
   "source": [
    "## Create zonal mean plot of CO summer climatology in Northern Hemisphere"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b993d7f0",
   "metadata": {},
   "source": [
    "### Data prep\n",
    "\n",
    "We again aggregate over time (June, July and Aug of each year), and over space. This time however the spatial aggregation is only over the longitudinal dimension, as we want to visualise data over all latitudinal zones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6ab135af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:41:03.327094Z",
     "iopub.status.busy": "2024-09-12T14:41:03.326141Z",
     "iopub.status.idle": "2024-09-12T14:41:04.239026Z",
     "shell.execute_reply": "2024-09-12T14:41:04.237506Z",
     "shell.execute_reply.started": "2024-09-12T14:41:03.327027Z"
    }
   },
   "outputs": [],
   "source": [
    "jja = da.groupby('valid_time.year').mean(keep_attrs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7d012f6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:41:09.455899Z",
     "iopub.status.busy": "2024-09-12T14:41:09.455379Z",
     "iopub.status.idle": "2024-09-12T14:41:09.606149Z",
     "shell.execute_reply": "2024-09-12T14:41:09.604921Z",
     "shell.execute_reply.started": "2024-09-12T14:41:09.455853Z"
    }
   },
   "outputs": [],
   "source": [
    "zonal = jja.mean(dim=[\"longitude\", \"year\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d38e3e5",
   "metadata": {},
   "source": [
    "### Create zonal mean plot\n",
    "\n",
    "Now we can plot our data. Before we do this however, we define the min, max and step of contours that we will use in a contour plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac9ee5df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:41:20.567183Z",
     "iopub.status.busy": "2024-09-12T14:41:20.566668Z",
     "iopub.status.idle": "2024-09-12T14:41:20.575232Z",
     "shell.execute_reply": "2024-09-12T14:41:20.573025Z",
     "shell.execute_reply.started": "2024-09-12T14:41:20.567138Z"
    }
   },
   "outputs": [],
   "source": [
    "vdiv = 10\n",
    "vmin = 50\n",
    "vmax = 200+vdiv\n",
    "clevs = np.arange(vmin,vmax,vdiv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0afaeb4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-12T14:41:54.075929Z",
     "iopub.status.busy": "2024-09-12T14:41:54.075443Z",
     "iopub.status.idle": "2024-09-12T14:41:54.937568Z",
     "shell.execute_reply": "2024-09-12T14:41:54.936041Z",
     "shell.execute_reply.started": "2024-09-12T14:41:54.075885Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the figure and specify size\n",
    "fig, ax = plt.subplots(1, 1, figsize = (9, 6))\n",
    "\n",
    "# Configure the axes and figure title\n",
    "ax.set_xlabel('Latitude [degrees]')\n",
    "ax.set_ylabel('Altitude [km]')\n",
    "ax.set_title('Zonal mean of CO in N. Hemisphere for JJA climatology (2003 to 2023)', fontsize=12)\n",
    "\n",
    "# As the altitudes (15) are much less than the latitudes (180),\n",
    "# we need to ensure the plot fits into the size of the figure.\n",
    "ax.set_aspect('auto')\n",
    "\n",
    "# Plot the data as a contour plot\n",
    "contour = ax.contourf(zonal.latitude, zonal.height, zonal, levels=clevs, cmap='YlOrRd', extend='max')\n",
    "\n",
    "# Specify the colourbar\n",
    "cbar = plt.colorbar(contour,fraction=0.046, pad=0.04)\n",
    "cbar.set_label('CO [ppbv]')\n",
    "\n",
    "# Save the plot\n",
    "fig.savefig(f'{DATADIR}/CO_JJA_NHem_2003-2023_zonal_mean.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31dcc9e9",
   "metadata": {},
   "source": [
    "Note the greater abundance of CO in the northern hemisphere, and in the lower part of the atmosphere."
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [],
   "dockerImageVersionId": 30761,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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",
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  }
 },
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}