R/s5p_l3_access.ipynb

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    "# Daily Sentinel-5P TROPOMI NO2 data over Europe\n",
    "\n",
    "This notebook searches and downloads Sentinel-5P data by accessing the STAC cataloge from the [S5P-PAL Data Portal](https://data-portal.s5p-pal.com/apidoc).\n",
    "Daily mosaics (level 3) are cropped to Europe and warped and to match an existing 1x1 km grid in EPSG:3035. Finally, daily data is aggregated to monthly and annual means and saved as NetCDF files per year."
   ]
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   "source": [
    "import os\n",
    "import glob\n",
    "import pystac\n",
    "import pystac_client\n",
    "import rasterio\n",
    "import xarray as xr\n",
    "import requests\n",
    "from datetime import datetime\n",
    "import h5netcdf\n",
    "import dask\n",
    "from dask.distributed import Client, LocalCluster, progress\n",
    "\n",
    "download_dir = \"../supplementary/sentinel-5p/download\"\n",
    "nc_dir = \"../supplementary/sentinel-5p/nc\"\n",
    "daily_dir = \"../supplementary/01_daily\"\n",
    "monthly_dir = \"../supplementary/02_monthly\"\n",
    "annual_dir = \"../supplementary/03_annual\"\n",
    "var = \"s5p_no2\"\n",
    "   \n",
    "cat_url = \"https://data-portal.s5p-pal.com/api/s5p-l3\"\n",
    "cat = pystac_client.Client.open(cat_url)\n",
    "\n",
    "clc = xr.open_dataset(\"../supplementary/static/CLC_reclass_8_1km.tif\", engine=\"rasterio\")"
   ]
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "client = Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5afe3dc-e4de-4ad9-b1ee-85d303b1bed9",
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   "source": [
    "def get_daily_items_per_year(y):\n",
    "    stac_items = cat.search(\n",
    "        datetime=f\"{y}\",\n",
    "        filter = \"l3:quantity='no2' and l3:period='day'\",\n",
    "        max_items = 370\n",
    "        ).item_collection()\n",
    "    return stac_items\n",
    "\n",
    "def process_daily_item(item, verbose = False):\n",
    "    start_datetime = item.properties['start_datetime']\n",
    "    d = datetime.strptime(start_datetime, '%Y-%m-%dT%H:%M:%S%z').date()\n",
    "    out = f\"{nc_dir}/S5P_L3_NO2_{d}.nc\"\n",
    "    \n",
    "    if not os.path.isfile(out) or os.path.getsize(out) < 50000:\n",
    "        print(f\"{d} -\", sep=' ', end=' ', flush=True)\n",
    "        product = item.assets[\"product\"]\n",
    "        extra_fields = product.extra_fields\n",
    "\n",
    "        download_url = product.href\n",
    "        product_local_path = f\"{download_dir}/{extra_fields[\"file:local_path\"]}\"\n",
    "        product_size = extra_fields[\"file:size\"]\n",
    "\n",
    "        if verbose: print(\"download -\", sep=' ', end=' ', flush=True)\n",
    "        r = requests.get(download_url)\n",
    "        with open(f\"./{product_local_path}\", \"wb\") as product_file:\n",
    "            product_file.write(r.content)\n",
    "\n",
    "        if verbose: print(\"check size -\", sep=' ', end=' ', flush=True)\n",
    "        file_size = os.path.getsize(f\"./{product_local_path}\")\n",
    "        assert file_size == product_size\n",
    "        \n",
    "        if verbose: print(\"warp -\", sep=' ', end=' ', flush=True)\n",
    "        ds = xr.open_dataset(product_local_path)\n",
    "        ds_wgs = ds.rio.write_crs(rasterio.crs.CRS.from_epsg(4326))\n",
    "        ds_crop = ds_wgs.rio.clip_box(minx=-25, miny=30, maxx=45, maxy=75)\n",
    "        ds_etrs = ds_crop.rio.reproject_match(clc).assign_coords({\n",
    "            \"x\": clc.x,\n",
    "            \"y\": clc.y,\n",
    "            \"time\": ds_crop.datetime_start,   # assign datetime as index for concatenation later\n",
    "        })\n",
    "           \n",
    "        if verbose: print(\"write -\", sep=' ', end=' ', flush=True)\n",
    "        #ds_etrs.NO2_column_number_density.rio.to_raster(out, compress=\"LZW\")\n",
    "        ds_etrs.NO2_column_number_density.rename(\"NO2_TROPOMI\").to_netcdf(out, engine=\"h5netcdf\")\n",
    "        os.remove(product_local_path)\n",
    "        if verbose: print(\"completed.\")\n",
    "    \n",
    "def aggregate_daily_monthly_annual(var, y):\n",
    "    out_d = f\"{daily_dir}/{var}_daily_{y}.nc\"\n",
    "    out_m = f\"{monthly_dir}/{var}_monthly_{y}.nc\"\n",
    "    out_y = f\"{annual_dir}/{var}_annual_{y}.nc\"\n",
    "    if not os.path.isfile(out_d) or os.path.getsize(out_d) < 50000:\n",
    "        var_d = xr.open_mfdataset(f\"{nc_dir}/S5P_L3_NO2_{y}*.nc\", concat_dim = \"time\", combine = \"nested\", chunks = 'auto') \n",
    "        \n",
    "        agg_d = var_d.to_netcdf(out_d, mode=\"w\", engine=\"h5netcdf\", compute=False)\n",
    "        agg_m = var_d.resample(time=\"ME\").mean(skipna=True).to_netcdf(out_m, mode=\"w\", engine=\"h5netcdf\", compute=False)\n",
    "        agg_y = var_d.resample(time=\"YE\").mean(skipna=True).to_netcdf(out_y, mode=\"w\", engine=\"h5netcdf\", compute=False)\n",
    "        return agg_d, agg_m, agg_y\n"
   ]
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   "id": "a102f484-4271-412c-b150-4fa999e9a618",
   "metadata": {
    "tags": []
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   "source": [
    "## Download and process daily data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "184bd8ae-95e0-44e0-a83e-2b47a6102a74",
   "metadata": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year = 2018; N_items (days) = 245\n",
      "year = 2019; N_items (days) = 365\n",
      "year = 2020; N_items (days) = 366\n",
      "year = 2021; N_items (days) = 365\n",
      "year = 2022; N_items (days) = 365\n",
      "year = 2023; N_items (days) = 365\n",
      "CPU times: user 2.29 s, sys: 575 ms, total: 2.86 s\n",
      "Wall time: 27.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "years = range(2018, 2024)\n",
    "\n",
    "for y in years:\n",
    "    \n",
    "    # fetch STAC items\n",
    "    item_list = get_daily_items_per_year(y)\n",
    "    print(f\"year = {y}; N_items (days) = {len(item_list)}\")\n",
    "\n",
    "    # iterate and retrieve data\n",
    "    for i in range(0,len(item_list)):\n",
    "        process_daily_item(item_list[i])            \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd83d366-043d-41ae-8375-59e1800af7c1",
   "metadata": {},
   "source": [
    "## Reorganize or Aggregate daily to monthly and annual data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "846972be-a9a7-4c98-9571-0b3e6231a09b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018 2019 2020 2021 2022 2023 "
     ]
    }
   ],
   "source": [
    "years = range(2018, 2024)\n",
    "lazy_results = []\n",
    "\n",
    "for y in years:\n",
    "    tasks = aggregate_daily_monthly_annual(var, y)\n",
    "    for t in tasks:\n",
    "        lazy_results.append(t)\n",
    "    print(y, sep=' ', end=' ', flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "261b3ea2-cbdf-49a9-aaae-9e54034b1818",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n"
     ]
    },
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    "lazy_results = [x for x in lazy_results if x is not None]\n",
    "print(len(lazy_results))\n",
    "\n",
    "reorg_res = dask.persist(*lazy_results)\n",
    "progress(reorg_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa12ffec-53e5-47b2-bf55-1a7a0ed05a77",
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     "text": [
      "../supplementary/01_daily/s5p_no2_daily_2018.nc: 4025.4 mb\n",
      "../supplementary/01_daily/s5p_no2_daily_2019.nc: 6080.2 mb\n",
      "../supplementary/01_daily/s5p_no2_daily_2020.nc: 6344.8 mb\n",
      "../supplementary/01_daily/s5p_no2_daily_2021.nc: 6173.6 mb\n",
      "../supplementary/01_daily/s5p_no2_daily_2022.nc: 6305.0 mb\n",
      "../supplementary/01_daily/s5p_no2_daily_2023.nc: 6322.9 mb\n"
     ]
    }
   ],
   "source": [
    "fls = glob.glob(f\"{daily_dir}/{var}*\")\n",
    "fls.sort()\n",
    "for f in fls:\n",
    "    print(f\"{f}: { round ((os.path.getsize (f)/(1024*1024)),1)} mb\")"
   ]
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       "\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.Dataset&gt; Size: 44GB\n",
       "Dimensions:      (time: 365, y: 4600, x: 6500)\n",
       "Coordinates:\n",
       "  * x            (x) float64 52kB 9.005e+05 9.015e+05 ... 7.398e+06 7.4e+06\n",
       "  * y            (y) float64 37kB 5.5e+06 5.498e+06 ... 9.015e+05 9.005e+05\n",
       "  * time         (time) datetime64[ns] 3kB 2021-01-01T01:57:54.010999936 ... ...\n",
       "Data variables:\n",
       "    spatial_ref  (time) int64 3kB ...\n",
       "    NO2_TROPOMI  (time, y, x) float32 44GB ...</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-96428c24-50e0-434e-822c-70c4e0599af6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96428c24-50e0-434e-822c-70c4e0599af6' 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'>time</span>: 365</li><li><span class='xr-has-index'>y</span>: 4600</li><li><span class='xr-has-index'>x</span>: 6500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9feb6f60-d3af-4638-9f29-038d616fd4ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9feb6f60-d3af-4638-9f29-038d616fd4ae' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.005e+05 9.015e+05 ... 7.4e+06</div><input id='attrs-7d0e39fb-9aff-45a4-8815-93d41b80ab39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d0e39fb-9aff-45a4-8815-93d41b80ab39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8c9bf72-37cb-485a-9c99-81d2ed6df037' class='xr-var-data-in' type='checkbox'><label for='data-a8c9bf72-37cb-485a-9c99-81d2ed6df037' 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([ 900500.,  901500.,  902500., ..., 7397500., 7398500., 7399500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.5e+06 5.498e+06 ... 9.005e+05</div><input id='attrs-d6b5b0ac-3522-4024-924f-c5a5c0148258' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6b5b0ac-3522-4024-924f-c5a5c0148258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-385407fb-e119-4553-ad6e-5a0b1ca0e618' class='xr-var-data-in' type='checkbox'><label for='data-385407fb-e119-4553-ad6e-5a0b1ca0e618' 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([5499500., 5498500., 5497500., ...,  902500.,  901500.,  900500.])</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'>2021-01-01T01:57:54.010999936 .....</div><input id='attrs-b524193e-9cd0-4043-9817-9b6bd4db7127' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b524193e-9cd0-4043-9817-9b6bd4db7127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55f34cce-7d9c-44dd-a68a-e65663a511dd' class='xr-var-data-in' type='checkbox'><label for='data-55f34cce-7d9c-44dd-a68a-e65663a511dd' 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>description :</span></dt><dd>start time of the measurement</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-01T01:57:54.010999936&#x27;, &#x27;2021-01-02T01:39:24.726000000&#x27;,\n",
       "       &#x27;2021-01-03T01:20:30.120000000&#x27;, ..., &#x27;2021-12-29T00:29:56.704999936&#x27;,\n",
       "       &#x27;2021-12-30T01:51:57.698000000&#x27;, &#x27;2021-12-31T01:33:18.102000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-33e7086f-ec90-429b-bc00-7b4e76523889' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33e7086f-ec90-429b-bc00-7b4e76523889' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57caaa5d-d9dd-4999-b3b4-28b37fb78b2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57caaa5d-d9dd-4999-b3b4-28b37fb78b2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0368e3f2-14d7-4146-b56b-3f7ddcc3ddda' class='xr-var-data-in' type='checkbox'><label for='data-0368e3f2-14d7-4146-b56b-3f7ddcc3ddda' 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>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89-extended / LAEA Europe</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89-extended / LAEA Europe&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Northing&quot;,NORTH],AXIS[&quot;Easting&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;3035&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>900000.0 1000.0 0.0 5500000.0 0.0 -1000.0</dd></dl></div><div class='xr-var-data'><pre>[365 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO2_TROPOMI</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-27902699-cd65-4f15-b09b-04ab6559ee8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27902699-cd65-4f15-b09b-04ab6559ee8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef917ace-1ab1-4bcc-9775-8030fa9cbcbd' class='xr-var-data-in' type='checkbox'><label for='data-ef917ace-1ab1-4bcc-9775-8030fa9cbcbd' 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>description :</span></dt><dd>total vertical column of NO2</dd><dt><span>units :</span></dt><dd>umol/m2</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>[10913500000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7771a66-d11c-4731-aba0-596033c9a3d9' class='xr-section-summary-in' type='checkbox'  ><label for='section-d7771a66-d11c-4731-aba0-596033c9a3d9' class='xr-section-summary' >Indexes: <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-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ad648948-8702-4dfc-84e3-79d7837b27ee' class='xr-index-data-in' type='checkbox'/><label for='index-ad648948-8702-4dfc-84e3-79d7837b27ee' 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([ 900500.0,  901500.0,  902500.0,  903500.0,  904500.0,  905500.0,\n",
       "        906500.0,  907500.0,  908500.0,  909500.0,\n",
       "       ...\n",
       "       7390500.0, 7391500.0, 7392500.0, 7393500.0, 7394500.0, 7395500.0,\n",
       "       7396500.0, 7397500.0, 7398500.0, 7399500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=6500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-15156b53-07ce-4c23-b88a-9ff2fb9d2a9e' class='xr-index-data-in' type='checkbox'/><label for='index-15156b53-07ce-4c23-b88a-9ff2fb9d2a9e' 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([5499500.0, 5498500.0, 5497500.0, 5496500.0, 5495500.0, 5494500.0,\n",
       "       5493500.0, 5492500.0, 5491500.0, 5490500.0,\n",
       "       ...\n",
       "        909500.0,  908500.0,  907500.0,  906500.0,  905500.0,  904500.0,\n",
       "        903500.0,  902500.0,  901500.0,  900500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=4600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-49a09ae5-bb5e-4421-88c9-293447ab036e' class='xr-index-data-in' type='checkbox'/><label for='index-49a09ae5-bb5e-4421-88c9-293447ab036e' 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;2021-01-01 01:57:54.010999936&#x27;,\n",
       "                  &#x27;2021-01-02 01:39:24.726000&#x27;,\n",
       "                  &#x27;2021-01-03 01:20:30.120000&#x27;,\n",
       "                  &#x27;2021-01-04 01:01:35.992000&#x27;,\n",
       "                  &#x27;2021-01-05 00:42:22.184000&#x27;,\n",
       "                  &#x27;2021-01-06 02:04:43.320000&#x27;,\n",
       "               &#x27;2021-01-07 01:45:50.749000064&#x27;,\n",
       "                  &#x27;2021-01-08 01:26:57.578000&#x27;,\n",
       "                  &#x27;2021-01-09 01:08:04.766000&#x27;,\n",
       "               &#x27;2021-01-10 00:49:17.832999936&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-12-22 01:02:03.901000064&#x27;,\n",
       "               &#x27;2021-12-23 00:41:35.233000064&#x27;,\n",
       "               &#x27;2021-12-24 02:04:04.069000064&#x27;,\n",
       "                  &#x27;2021-12-25 01:45:37.678000&#x27;,\n",
       "               &#x27;2021-12-26 01:27:21.722999936&#x27;,\n",
       "               &#x27;2021-12-27 01:07:46.611000064&#x27;,\n",
       "                  &#x27;2021-12-28 00:48:00.538000&#x27;,\n",
       "               &#x27;2021-12-29 00:29:56.704999936&#x27;,\n",
       "                  &#x27;2021-12-30 01:51:57.698000&#x27;,\n",
       "                  &#x27;2021-12-31 01:33:18.102000&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-383820b6-520c-46e8-8be9-7bd14c90604d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-383820b6-520c-46e8-8be9-7bd14c90604d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 44GB\n",
       "Dimensions:      (time: 365, y: 4600, x: 6500)\n",
       "Coordinates:\n",
       "  * x            (x) float64 52kB 9.005e+05 9.015e+05 ... 7.398e+06 7.4e+06\n",
       "  * y            (y) float64 37kB 5.5e+06 5.498e+06 ... 9.015e+05 9.005e+05\n",
       "  * time         (time) datetime64[ns] 3kB 2021-01-01T01:57:54.010999936 ... ...\n",
       "Data variables:\n",
       "    spatial_ref  (time) int64 3kB ...\n",
       "    NO2_TROPOMI  (time, y, x) float32 44GB ..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s5p = xr.open_dataset(fls[3])\n",
    "s5p"
   ]
  }
 ],
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