datasets/era5/era5-example.ipynb

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    "## ERA5 data on the Planetary Computer\n",
    "\n",
    "The [ERA5 dataset](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) from the ECMWF, converted to Zarr by [PlanetOS](https://planetos.com/) data on weather."
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    "### Query using the STAC API\n",
    "\n",
    "The data assets are a collection of Zarr groups, which can be opened with libraries like [xarray](https://xarray.pydata.org/).\n",
    "\n",
    "Each STAC item covers a single month and the entire globe. You'll likely query on the `datetime`."
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   "source": [
    "import pystac_client\n",
    "\n",
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1/\"\n",
    ")\n",
    "search = catalog.search(collections=[\"era5-pds\"], datetime=\"1980-01\")\n",
    "items = search.get_all_items()\n",
    "\n",
    "len(items)"
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       "[<Item id=era5-pds-1980-01-fc>, <Item id=era5-pds-1980-01-an>]"
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    "list(items)"
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    "There will be two items per month, depending on the kind of variables within: `fc` (or \"forecast\") and `an` (or \"analysis\").\n",
    "\n",
    "* An **analysis**, of the atmospheric conditions, is a blend of observations\n",
    "  with a previous forecast. An analysis can only provide\n",
    "  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n",
    "  parameters (parameters valid at a specific time, e.g temperature at 12:00),\n",
    "  but not accumulated parameters, mean rates or min/max parameters.\n",
    "* A **forecast** starts with an analysis at a specific time (the 'initialization\n",
    "  time'), and a model computes the atmospheric conditions for a number of\n",
    "  'forecast steps', at increasing 'validity times', into the future. A forecast\n",
    "  can provide\n",
    "  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n",
    "  parameters, accumulated parameters, mean rates, and min/max parameters.\n",
    "\n",
    "You can narrow your search to a specific kind using the `era5:kind` property."
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    "import pystac_client\n",
    "\n",
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1/\"\n",
    ")\n",
    "search = catalog.search(\n",
    "    collections=[\"era5-pds\"], datetime=\"1980-01\", query={\"era5:kind\": {\"eq\": \"an\"}}\n",
    ")\n",
    "items = search.get_all_items()\n",
    "\n",
    "print(len(items))\n",
    "item = items[0]"
   ]
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   "source": [
    "There are several assets available, one for each data variable. We can build up a dataset with all the variables using `xarray.open_dataset` and `combine_by_coords`."
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                            (time: 744, lat: 721, lon: 1440)\n",
       "Coordinates:\n",
       "  * lat                                (lat) float32 90.0 89.75 ... -89.75 -90.0\n",
       "  * lon                                (lon) float32 0.0 0.25 ... 359.5 359.8\n",
       "  * time                               (time) datetime64[ns] 1980-01-01 ... 1...\n",
       "Data variables:\n",
       "    air_pressure_at_mean_sea_level     (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    air_temperature_at_2_metres        (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    dew_point_temperature_at_2_metres  (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    eastward_wind_at_100_metres        (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    eastward_wind_at_10_metres         (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    northward_wind_at_100_metres       (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    northward_wind_at_10_metres        (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    sea_surface_temperature            (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "    surface_air_pressure               (time, lat, lon) float32 dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    institution:  ECMWF\n",
       "    source:       Reanalysis\n",
       "    tilte:        ERA5 forecasts</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-22fd1302-6cfd-497c-b181-89fb2cd77553' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22fd1302-6cfd-497c-b181-89fb2cd77553' 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>: 744</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-17461a60-7424-4674-8e79-b964d9e3d018' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17461a60-7424-4674-8e79-b964d9e3d018' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-a52b75a4-f58a-4576-8020-6f932e7ec8dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a52b75a4-f58a-4576-8020-6f932e7ec8dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a28fce4-7f41-4ae7-ae90-d03b43f51afe' class='xr-var-data-in' type='checkbox'><label for='data-6a28fce4-7f41-4ae7-ae90-d03b43f51afe' 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>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-6fa34fc6-addb-4604-86e7-cf1d398c8617' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fa34fc6-addb-4604-86e7-cf1d398c8617' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dd5443a-f282-417e-b2bd-36eb3a7e3516' class='xr-var-data-in' type='checkbox'><label for='data-6dd5443a-f282-417e-b2bd-36eb3a7e3516' 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>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 1980-01-31T23:00:00</div><input id='attrs-88b29b53-5564-4963-9bcb-fa9e1ef8ca30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88b29b53-5564-4963-9bcb-fa9e1ef8ca30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9707fb1-6b15-45bf-b6c3-098a7bd828a5' class='xr-var-data-in' type='checkbox'><label for='data-a9707fb1-6b15-45bf-b6c3-098a7bd828a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-01T02:00:00.000000000&#x27;, ..., &#x27;1980-01-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-31T22:00:00.000000000&#x27;, &#x27;1980-01-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-296a024f-777e-4bd7-837e-9b07ad6e456f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-296a024f-777e-4bd7-837e-9b07ad6e456f' class='xr-section-summary' >Data variables: <span>(9)</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>air_pressure_at_mean_sea_level</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;</div><input id='attrs-65bb7077-c7f0-4501-a7df-943770244014' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65bb7077-c7f0-4501-a7df-943770244014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42479953-2765-40a1-bf75-28d11721344a' class='xr-var-data-in' type='checkbox'><label for='data-42479953-2765-40a1-bf75-28d11721344a' 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>Mean sea level pressure</dd><dt><span>nameCDM :</span></dt><dd>Mean_sea_level_pressure_surface</dd><dt><span>nameECMWF :</span></dt><dd>Mean sea level pressure</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>msl</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_air_pressure</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(372, 150, 150), meta=np.ndarray&gt;</div><input id='attrs-44ffd5e7-75ac-4e09-910e-c8cfb991febc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44ffd5e7-75ac-4e09-910e-c8cfb991febc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c50f1309-2170-4bbe-863d-2879f1555d59' class='xr-var-data-in' type='checkbox'><label for='data-c50f1309-2170-4bbe-863d-2879f1555d59' 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>Surface pressure</dd><dt><span>nameCDM :</span></dt><dd>Surface_pressure_surface</dd><dt><span>nameECMWF :</span></dt><dd>Surface pressure</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>sp</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                            (time: 744, lat: 721, lon: 1440)\n",
       "Coordinates:\n",
       "  * lat                                (lat) float32 90.0 89.75 ... -89.75 -90.0\n",
       "  * lon                                (lon) float32 0.0 0.25 ... 359.5 359.8\n",
       "  * time                               (time) datetime64[ns] 1980-01-01 ... 1...\n",
       "Data variables:\n",
       "    air_pressure_at_mean_sea_level     (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    air_temperature_at_2_metres        (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    dew_point_temperature_at_2_metres  (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    eastward_wind_at_100_metres        (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    eastward_wind_at_10_metres         (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    northward_wind_at_100_metres       (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    northward_wind_at_10_metres        (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    sea_surface_temperature            (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "    surface_air_pressure               (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>\n",
       "Attributes:\n",
       "    institution:  ECMWF\n",
       "    source:       Reanalysis\n",
       "    tilte:        ERA5 forecasts"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import planetary_computer\n",
    "import xarray as xr\n",
    "\n",
    "signed_item = planetary_computer.sign(item)\n",
    "datasets = [\n",
    "    xr.open_dataset(asset.href, **asset.extra_fields[\"xarray:open_kwargs\"])\n",
    "    for asset in signed_item.assets.values()\n",
    "]\n",
    "\n",
    "ds = xr.combine_by_coords(datasets, join=\"exact\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2fda08e-5513-496d-8ce5-65fbad4efa1e",
   "metadata": {},
   "source": [
    "Now we can plot timeseries for the month, averaged over space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a795fd49-8904-418d-897b-c13ea8d121ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-era5-era5-example.ipynb/5.png\"/>"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "ds[\"sea_surface_temperature\"].mean(dim=[\"lon\", \"lat\"]).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15c37cfa-4b18-4050-97c5-c3210a213358",
   "metadata": {},
   "source": [
    "Or make a map of some variable on a single date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d8beee6-1a5a-4198-b8b3-3f35194a2046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-era5-era5-example.ipynb/6.png\"/>"
      ],
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds[\"sea_surface_temperature\"].isel(time=0).plot(size=8);"
   ]
  }
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