lecture2_droughts.ipynb

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    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import ipywidgets as widgets\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.io.img_tiles import OSM\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import sampling\n",
    "from scipy.stats import theilslopes\n",
    "from ismn.interface import ISMN_Interface\n",
    "from pytesmo.df_metrics import ubrmsd\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "%matplotlib inline"
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    "# Data sets for this lecture\n",
    "\n",
    "## Summary\n",
    "\n",
    "In this lecture we will compute the following soil moisture anomaly based drought indices using monthly soil moisture data from C3S.\n",
    "\n",
    "* Soil Moisture anomalies\n",
    "* Anomaly Quantiles\n",
    "* Z-Scores\n",
    "* Soil Moisture Anomaly Percentage Index (SMAPI)\n",
    "\n",
    "In addition we will use ECMWF ERA-5 monthly precipitation data to compute\n",
    "* Standardised Precipitation Index (SPI)\n",
    "\n",
    "Finally we will use soil moisture and vod time series to assess temporal lags between them."
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    "First we load our soil moisture stack. In this case use the same stack as the one from the previous lecture on satellite soil moisture retrieval. The data is produced as part of the Copernicus Climate Change Service (C3S), which combines retrievals from multiple passive sensors. The used subset contains monthly mean values for Europe from 1978 to September 2022.\n",
    "More information on this data can be found in the [C3S Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-soil-moisture?tab=overview)."
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 160, lon: 240, time: 527)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 69.88 69.62 69.38 69.12 ... 30.88 30.62 30.38 30.12\n",
       "  * lon      (lon) float32 -14.88 -14.62 -14.38 -14.12 ... 44.38 44.62 44.88\n",
       "  * time     (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2022-09-01\n",
       "Data variables:\n",
       "    nobs     (time, lat, lon) float32 ...\n",
       "    sm       (time, lat, lon) float32 ...\n",
       "    sm_anom  (time, lat, lon) float32 ...\n",
       "Attributes: (12/43)\n",
       "    title:                      C3S Surface Soil Moisture merged PASSIVE Product\n",
       "    institution:                EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)\n",
       "    contact:                    C3S_SM_Science@eodc.eu\n",
       "    source:                     LPRMv6/SMMR/Nimbus 7 L3 Surface Soil Moisture...\n",
       "    platform:                   Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1...\n",
       "    sensor:                     SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMO...\n",
       "    ...                         ...\n",
       "    time_coverage_start:        1978-10-31T12:00:00Z\n",
       "    time_coverage_end:          1978-11-30T12:00:00Z\n",
       "    time_coverage_duration:     P1M\n",
       "    NCO:                        netCDF Operators version 4.7.5 (Homepage = ht...\n",
       "    nco_openmp_thread_number:   1\n",
       "    history_of_appended_files:  Mon May  3 22:50:40 2021: Appended file /home...</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-214fa773-da95-4f65-b99a-a7e3d7f3e10c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-214fa773-da95-4f65-b99a-a7e3d7f3e10c' 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'>lat</span>: 160</li><li><span class='xr-has-index'>lon</span>: 240</li><li><span class='xr-has-index'>time</span>: 527</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7ed4071a-cf14-40ae-b3c6-9e6cbf338497' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ed4071a-cf14-40ae-b3c6-9e6cbf338497' 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'>69.88 69.62 69.38 ... 30.38 30.12</div><input id='attrs-98796475-e8b3-44f4-89b7-ed9fdd5de48f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98796475-e8b3-44f4-89b7-ed9fdd5de48f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ec1349-8d07-4860-bb95-4b67ce48eaf9' class='xr-var-data-in' type='checkbox'><label for='data-92ec1349-8d07-4860-bb95-4b67ce48eaf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[-90.  90.]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([69.875, 69.625, 69.375, 69.125, 68.875, 68.625, 68.375, 68.125, 67.875,\n",
       "       67.625, 67.375, 67.125, 66.875, 66.625, 66.375, 66.125, 65.875, 65.625,\n",
       "       65.375, 65.125, 64.875, 64.625, 64.375, 64.125, 63.875, 63.625, 63.375,\n",
       "       63.125, 62.875, 62.625, 62.375, 62.125, 61.875, 61.625, 61.375, 61.125,\n",
       "       60.875, 60.625, 60.375, 60.125, 59.875, 59.625, 59.375, 59.125, 58.875,\n",
       "       58.625, 58.375, 58.125, 57.875, 57.625, 57.375, 57.125, 56.875, 56.625,\n",
       "       56.375, 56.125, 55.875, 55.625, 55.375, 55.125, 54.875, 54.625, 54.375,\n",
       "       54.125, 53.875, 53.625, 53.375, 53.125, 52.875, 52.625, 52.375, 52.125,\n",
       "       51.875, 51.625, 51.375, 51.125, 50.875, 50.625, 50.375, 50.125, 49.875,\n",
       "       49.625, 49.375, 49.125, 48.875, 48.625, 48.375, 48.125, 47.875, 47.625,\n",
       "       47.375, 47.125, 46.875, 46.625, 46.375, 46.125, 45.875, 45.625, 45.375,\n",
       "       45.125, 44.875, 44.625, 44.375, 44.125, 43.875, 43.625, 43.375, 43.125,\n",
       "       42.875, 42.625, 42.375, 42.125, 41.875, 41.625, 41.375, 41.125, 40.875,\n",
       "       40.625, 40.375, 40.125, 39.875, 39.625, 39.375, 39.125, 38.875, 38.625,\n",
       "       38.375, 38.125, 37.875, 37.625, 37.375, 37.125, 36.875, 36.625, 36.375,\n",
       "       36.125, 35.875, 35.625, 35.375, 35.125, 34.875, 34.625, 34.375, 34.125,\n",
       "       33.875, 33.625, 33.375, 33.125, 32.875, 32.625, 32.375, 32.125, 31.875,\n",
       "       31.625, 31.375, 31.125, 30.875, 30.625, 30.375, 30.125], 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'>-14.88 -14.62 ... 44.62 44.88</div><input id='attrs-eedf1439-31ae-43d6-89e3-361ebe7b35d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eedf1439-31ae-43d6-89e3-361ebe7b35d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b6cd83-bcd3-4b4d-bc46-efc722d17fd8' class='xr-var-data-in' type='checkbox'><label for='data-24b6cd83-bcd3-4b4d-bc46-efc722d17fd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([-14.875, -14.625, -14.375, ...,  44.375,  44.625,  44.875],\n",
       "      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'>1978-11-01 ... 2022-09-01</div><input id='attrs-5e344374-ddd8-4cb3-9c5f-8af7b7a3bb38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e344374-ddd8-4cb3-9c5f-8af7b7a3bb38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d132a1c6-fd91-4371-b91d-1b0daafa86eb' class='xr-var-data-in' type='checkbox'><label for='data-d132a1c6-fd91-4371-b91d-1b0daafa86eb' 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><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-11-01T00:00:00.000000000&#x27;, &#x27;1978-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T00:00:00.000000000&#x27;, ..., &#x27;2022-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e32e2bb4-a728-429c-8aa1-901961b8c062' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e32e2bb4-a728-429c-8aa1-901961b8c062' class='xr-section-summary' >Data variables: <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>nobs</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7fcf6bf9-9451-4c0c-81b4-b8fbf0c9f745' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fcf6bf9-9451-4c0c-81b4-b8fbf0c9f745' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63ebffcc-a5cf-4397-bd51-fa0c7dc4dbc0' class='xr-var-data-in' type='checkbox'><label for='data-63ebffcc-a5cf-4397-bd51-fa0c7dc4dbc0' 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>Number of valid observation</dd><dt><span>CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><pre>[20236800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sm</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d9af230-81b7-4f33-a27d-b602dca84fb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d9af230-81b7-4f33-a27d-b602dca84fb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61bf4973-de95-4f11-92dd-1a228de2b1ff' class='xr-var-data-in' type='checkbox'><label for='data-61bf4973-de95-4f11-92dd-1a228de2b1ff' 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>dtype :</span></dt><dd>float32</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>valid_range :</span></dt><dd>[0. 1.]</dd><dt><span>long_name :</span></dt><dd>Volumetric Soil Moisture</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd></dl></div><div class='xr-var-data'><pre>[20236800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sm_anom</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b134c9b5-bcb5-45cd-96f2-eb2e3f2c2cd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b134c9b5-bcb5-45cd-96f2-eb2e3f2c2cd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3e578cb-ea9f-4119-9225-95ef15d4c353' class='xr-var-data-in' type='checkbox'><label for='data-c3e578cb-ea9f-4119-9225-95ef15d4c353' 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>dtype :</span></dt><dd>float32</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd><dt><span>valid_range :</span></dt><dd>[-1.  1.]</dd><dt><span>long_name :</span></dt><dd>Soil Moisture Anomaly</dd></dl></div><div class='xr-var-data'><pre>[20236800 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7be43e6-82b2-4f55-b2fc-4a04f51e0edb' class='xr-section-summary-in' type='checkbox'  ><label for='section-a7be43e6-82b2-4f55-b2fc-4a04f51e0edb' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-40ca95d2-a1dd-44fd-b193-6402303a1a68' class='xr-index-data-in' type='checkbox'/><label for='index-40ca95d2-a1dd-44fd-b193-6402303a1a68' 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(Float64Index([69.875, 69.625, 69.375, 69.125, 68.875, 68.625, 68.375, 68.125,\n",
       "              67.875, 67.625,\n",
       "              ...\n",
       "              32.375, 32.125, 31.875, 31.625, 31.375, 31.125, 30.875, 30.625,\n",
       "              30.375, 30.125],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=160))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0dfcf48d-42b8-487e-b9c0-c32991ec98b2' class='xr-index-data-in' type='checkbox'/><label for='index-0dfcf48d-42b8-487e-b9c0-c32991ec98b2' 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(Float64Index([-14.875, -14.625, -14.375, -14.125, -13.875, -13.625, -13.375,\n",
       "              -13.125, -12.875, -12.625,\n",
       "              ...\n",
       "               42.625,  42.875,  43.125,  43.375,  43.625,  43.875,  44.125,\n",
       "               44.375,  44.625,  44.875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=240))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a29c5f23-802b-4e52-93ef-08296d48960a' class='xr-index-data-in' type='checkbox'/><label for='index-a29c5f23-802b-4e52-93ef-08296d48960a' 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;1978-11-01&#x27;, &#x27;1978-12-01&#x27;, &#x27;1979-01-01&#x27;, &#x27;1979-02-01&#x27;,\n",
       "               &#x27;1979-03-01&#x27;, &#x27;1979-04-01&#x27;, &#x27;1979-05-01&#x27;, &#x27;1979-06-01&#x27;,\n",
       "               &#x27;1979-07-01&#x27;, &#x27;1979-08-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;, &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;,\n",
       "               &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;,\n",
       "               &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=527, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1ef43b9-0f17-45ee-8ec4-6807c5add8e3' class='xr-section-summary-in' type='checkbox'  ><label for='section-b1ef43b9-0f17-45ee-8ec4-6807c5add8e3' class='xr-section-summary' >Attributes: <span>(43)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>C3S Surface Soil Moisture merged PASSIVE Product</dd><dt><span>institution :</span></dt><dd>EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)</dd><dt><span>contact :</span></dt><dd>C3S_SM_Science@eodc.eu</dd><dt><span>source :</span></dt><dd>LPRMv6/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv6/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv6/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv6/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv6/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv6/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv6/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 &amp; RE04; LPRMv6/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC;</dd><dt><span>platform :</span></dt><dd>Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP</dd><dt><span>sensor :</span></dt><dd>SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer</dd><dt><span>references :</span></dt><dd>https://climate.copernicus.eu/; Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F.,  Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001; Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019; Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. https://doi.org/10.1109/TGRS.2017.2734070</dd><dt><span>product_version :</span></dt><dd>v202012</dd><dt><span>tracking_id :</span></dt><dd>8d9a8409-063f-47e8-bd1a-314ec169d4bc</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>The data set was produced with funding from the Copernicus Climate Change Service.</dd><dt><span>keywords :</span></dt><dd>Soil Moisture/Water Content</dd><dt><span>naming_authority :</span></dt><dd>EODC GmbH</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>These data were produced as part of the Copernicus Climate Change Service. Service Contract No 2018/C3S_312b_LOT4_EODC/SC2</dd><dt><span>creator_name :</span></dt><dd>Earth Observation Data Center (EODC)</dd><dt><span>creator_url :</span></dt><dd>https://www.eodc.eu</dd><dt><span>creator_email :</span></dt><dd>C3S_SM_Science@eodc.eu</dd><dt><span>project :</span></dt><dd>Copernicus Climate Change Service.</dd><dt><span>license :</span></dt><dd>Copernicus Data License</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25 degree</dd><dt><span>spatial_resolution :</span></dt><dd>25km</dd><dt><span>id :</span></dt><dd>C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-19781101000000-TCDR-v202012.0.0.nc</dd><dt><span>history :</span></dt><dd>Mon Nov  7 00:01:10 2022: ncrcat -O /GEO/RADAR/datapool1/C3S/02_processed/v202012/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY.nc /GEO/RADAR/datapool1/C3S/02_processed/v202012/IMAGE_STACKS/ICDR/STACK_C3S-SOILMOISTURE_v202012_ICDR_PASSIVE_MONTHLY.nc /GEO/RADAR/datapool1/C3S/02_processed/v202012/IMAGE_STACKS/STACK_C3S-SOILMOISTURE_v202012_PASSIVE_MONTHLY.nc\n",
       "Mon May  3 22:50:40 2021: ncks -A /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY_ANOMALY.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY.nc\n",
       "Mon May  3 22:50:34 2021: ncrename -v sm,sm_anom /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY_ANOMALY.nc\n",
       "Mon May  3 22:49:36 2021: ncrcat -O /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1979-01-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1979-02-TCDR_v202012.0.0.nc 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/home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-03-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-04-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-05-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-06-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-07-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-08-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-09-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-10-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2017-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-01-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-02-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-03-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-04-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-05-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-06-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-07-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-08-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-09-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-10-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-01-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-02-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-03-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-04-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-05-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-06-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-07-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-08-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-09-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-10-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-01-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-02-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-03-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-04-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-05-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-06-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-07-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-08-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-09-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-10-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY_ANOMALY.nc\n",
       "Mon May  3 22:16:30 2021: ncatted -O -a valid_range,sm,m,f,-1,1 -a long_name,sm,m,c,Soil Moisture Anomaly /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/066_anomalies/baseline_1991_2010/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-11-TCDR_v202012.0.0.nc\n",
       "Mon May  3 22:16:30 2021: ncdiff /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/062_monthly_images/passive/1978/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-19781101000000-TCDR-v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/065_climatologies/baseline_1991_2010/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-CLIM-MEAN-1991-2010-11-TCDR-v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/066_anomalies/baseline_1991_2010/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-11-TCDR_v202012.0.0.nc -v sm\n",
       "2021-03-29T13:41:01.581700 mean calculated</dd><dt><span>date_created :</span></dt><dd>2021-03-29T13:41:01Z</dd><dt><span>time_coverage_start :</span></dt><dd>1978-10-31T12:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>1978-11-30T12:00:00Z</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>history_of_appended_files :</span></dt><dd>Mon May  3 22:50:40 2021: Appended file /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY_ANOMALY.nc had following &quot;history&quot; attribute:\n",
       "Mon May  3 22:50:34 2021: ncrename -v sm,sm_anom /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY_ANOMALY.nc\n",
       "Mon May  3 22:49:36 2021: ncrcat -O /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1979-01-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1979-02-TCDR_v202012.0.0.nc 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/home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-07-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-08-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-09-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-10-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2018-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-01-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-02-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-03-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-04-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-05-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-06-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-07-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-08-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-09-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-10-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2019-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-01-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-02-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-03-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-04-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-05-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-06-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-07-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-08-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-09-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-10-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-11-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/TEMP_ANOM_PASSIVE_MONTHLY_TCDR_v202012/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-2020-12-TCDR_v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/IMAGE_STACKS/TCDR/STACK_C3S-SOILMOISTURE_v202012_TCDR_PASSIVE_MONTHLY_ANOMALY.nc\n",
       "Mon May  3 22:16:30 2021: ncatted -O -a valid_range,sm,m,f,-1,1 -a long_name,sm,m,c,Soil Moisture Anomaly /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/066_anomalies/baseline_1991_2010/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-11-TCDR_v202012.0.0.nc\n",
       "Mon May  3 22:16:30 2021: ncdiff /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/062_monthly_images/passive/1978/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-19781101000000-TCDR-v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/065_climatologies/baseline_1991_2010/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-CLIM-MEAN-1991-2010-11-TCDR-v202012.0.0.nc /home/wpreimes/shares/radar/Projects/C3S_312b/07_data/v202012_TCDR/066_anomalies/baseline_1991_2010/C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-ANOMALY-1978-11-TCDR_v202012.0.0.nc -v sm\n",
       "2021-03-29T13:41:01.581700 mean calculated\n",
       "</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 160, lon: 240, time: 527)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 69.88 69.62 69.38 69.12 ... 30.88 30.62 30.38 30.12\n",
       "  * lon      (lon) float32 -14.88 -14.62 -14.38 -14.12 ... 44.38 44.62 44.88\n",
       "  * time     (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2022-09-01\n",
       "Data variables:\n",
       "    nobs     (time, lat, lon) float32 ...\n",
       "    sm       (time, lat, lon) float32 ...\n",
       "    sm_anom  (time, lat, lon) float32 ...\n",
       "Attributes: (12/43)\n",
       "    title:                      C3S Surface Soil Moisture merged PASSIVE Product\n",
       "    institution:                EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)\n",
       "    contact:                    C3S_SM_Science@eodc.eu\n",
       "    source:                     LPRMv6/SMMR/Nimbus 7 L3 Surface Soil Moisture...\n",
       "    platform:                   Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1...\n",
       "    sensor:                     SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMO...\n",
       "    ...                         ...\n",
       "    time_coverage_start:        1978-10-31T12:00:00Z\n",
       "    time_coverage_end:          1978-11-30T12:00:00Z\n",
       "    time_coverage_duration:     P1M\n",
       "    NCO:                        netCDF Operators version 4.7.5 (Homepage = ht...\n",
       "    nco_openmp_thread_number:   1\n",
       "    history_of_appended_files:  Mon May  3 22:50:40 2021: Appended file /home..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c3s_europe = xr.open_dataset('./LTC_DATA/STACK_C3S-SOILMOISTURE_EUROPE_v202012_PASSIVE_MONTHLY.nc')\n",
    "display(c3s_europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0f856e-0b54-4d65-98a6-decb4c60489c",
   "metadata": {},
   "source": [
    "In addition we load precipitation data from ECMWF's ERA5 reanalysis (called `total precipitation` or `tp`). We have downloaded monthly averages for Europe in a similar time range as the soil moisture data. We sum up the (average) rainfall amount for each month. More background information on how to correctly sum-up precipitation data is available in the [support portal](https://confluence.ecmwf.int/pages/viewpage.action?pageId=197702790). \n",
    "ERA5 monthly means are available through the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0da2468e-701d-4bb9-be6b-160c0a114523",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (longitude: 241, latitude: 161, time: 528)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -15.0 -14.75 -14.5 -14.25 ... 44.5 44.75 45.0\n",
       "  * latitude   (latitude) float32 70.0 69.75 69.5 69.25 ... 30.5 30.25 30.0\n",
       "  * time       (time) datetime64[ns] 1978-01-01 1978-02-01 ... 2021-12-01\n",
       "Data variables:\n",
       "    tp         (time, latitude, longitude) float64 57.02 57.56 ... 5.79 5.437\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2022-11-20 15:35:31 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...</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-51a8ec80-fc7a-4035-ad62-448f1ae614af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-51a8ec80-fc7a-4035-ad62-448f1ae614af' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>longitude</span>: 241</li><li><span class='xr-has-index'>latitude</span>: 161</li><li><span class='xr-has-index'>time</span>: 528</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-03434d78-9d39-4442-9e65-6a891dd84448' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03434d78-9d39-4442-9e65-6a891dd84448' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-15.0 -14.75 -14.5 ... 44.75 45.0</div><input id='attrs-faf9a28d-5457-47f2-8e5f-94ad71e07e86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faf9a28d-5457-47f2-8e5f-94ad71e07e86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09343f56-34ab-417e-859e-110fd161c086' class='xr-var-data-in' type='checkbox'><label for='data-09343f56-34ab-417e-859e-110fd161c086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-15.  , -14.75, -14.5 , ...,  44.5 ,  44.75,  45.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 69.75 69.5 ... 30.5 30.25 30.0</div><input id='attrs-0d9fec87-57e3-4998-b6d6-4696e7853776' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d9fec87-57e3-4998-b6d6-4696e7853776' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eea61f06-dbf3-4efa-a7cf-6e97c74edbb2' class='xr-var-data-in' type='checkbox'><label for='data-eea61f06-dbf3-4efa-a7cf-6e97c74edbb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([70.  , 69.75, 69.5 , 69.25, 69.  , 68.75, 68.5 , 68.25, 68.  , 67.75,\n",
       "       67.5 , 67.25, 67.  , 66.75, 66.5 , 66.25, 66.  , 65.75, 65.5 , 65.25,\n",
       "       65.  , 64.75, 64.5 , 64.25, 64.  , 63.75, 63.5 , 63.25, 63.  , 62.75,\n",
       "       62.5 , 62.25, 62.  , 61.75, 61.5 , 61.25, 61.  , 60.75, 60.5 , 60.25,\n",
       "       60.  , 59.75, 59.5 , 59.25, 59.  , 58.75, 58.5 , 58.25, 58.  , 57.75,\n",
       "       57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75, 55.5 , 55.25,\n",
       "       55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25, 53.  , 52.75,\n",
       "       52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75, 50.5 , 50.25,\n",
       "       50.  , 49.75, 49.5 , 49.25, 49.  , 48.75, 48.5 , 48.25, 48.  , 47.75,\n",
       "       47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25, 46.  , 45.75, 45.5 , 45.25,\n",
       "       45.  , 44.75, 44.5 , 44.25, 44.  , 43.75, 43.5 , 43.25, 43.  , 42.75,\n",
       "       42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25, 41.  , 40.75, 40.5 , 40.25,\n",
       "       40.  , 39.75, 39.5 , 39.25, 39.  , 38.75, 38.5 , 38.25, 38.  , 37.75,\n",
       "       37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25, 36.  , 35.75, 35.5 , 35.25,\n",
       "       35.  , 34.75, 34.5 , 34.25, 34.  , 33.75, 33.5 , 33.25, 33.  , 32.75,\n",
       "       32.5 , 32.25, 32.  , 31.75, 31.5 , 31.25, 31.  , 30.75, 30.5 , 30.25,\n",
       "       30.  ], 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'>1978-01-01 ... 2021-12-01</div><input id='attrs-019defd5-5cff-4d66-b6ef-78cf5b1b0bfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-019defd5-5cff-4d66-b6ef-78cf5b1b0bfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-826670e5-1d1e-432d-b0a3-d0496fa45b8f' class='xr-var-data-in' type='checkbox'><label for='data-826670e5-1d1e-432d-b0a3-d0496fa45b8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-01-01T00:00:00.000000000&#x27;, &#x27;1978-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-afbc629a-a136-4856-910d-bee6ca250d0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-afbc629a-a136-4856-910d-bee6ca250d0c' 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>tp</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>57.02 57.56 58.13 ... 5.79 5.437</div><input id='attrs-6fe05103-eea3-4c08-b2b2-99e1d3acdea5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fe05103-eea3-4c08-b2b2-99e1d3acdea5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9653f97-c442-40f3-b9e4-9413684dea89' class='xr-var-data-in' type='checkbox'><label for='data-f9653f97-c442-40f3-b9e4-9413684dea89' 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>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><pre>array([[[5.70219005e+01, 5.75594203e+01, 5.81276312e+01, ...,\n",
       "         5.44265113e+01, 5.69144136e+01, 6.00166020e+01],\n",
       "        [5.94176447e+01, 5.93101578e+01, 6.00166020e+01, ...,\n",
       "         3.13136582e+01, 3.22504644e+01, 3.27265468e+01],\n",
       "        [6.35027184e+01, 6.33184319e+01, 6.31187868e+01, ...,\n",
       "         2.98547221e+01, 3.03000857e+01, 3.07454492e+01],\n",
       "        ...,\n",
       "        [5.34589882e+01, 5.76822658e+01, 6.78795583e+01, ...,\n",
       "         2.16538273e+00, 3.54755297e+00, 4.11579199e+00],\n",
       "        [4.73928447e+01, 5.04335957e+01, 6.04619638e+01, ...,\n",
       "         2.48787180e+00, 3.39395925e+00, 4.92972322e+00],\n",
       "        [4.61181600e+01, 4.75924860e+01, 5.69758215e+01, ...,\n",
       "         3.60899046e+00, 4.22324985e+00, 5.65149821e+00]],\n",
       "\n",
       "       [[8.46418705e+01, 8.42673559e+01, 8.37957497e+01, ...,\n",
       "         6.08805590e+01, 6.33357620e+01, 6.63874273e+01],\n",
       "        [8.80125885e+01, 8.67780542e+01, 8.59319067e+01, ...,\n",
       "         4.05315585e+01, 4.14470701e+01, 4.23486829e+01],\n",
       "        [8.72080641e+01, 8.59319067e+01, 8.46418705e+01, ...,\n",
       "         3.72302313e+01, 3.81873360e+01, 3.90889487e+01],\n",
       "...\n",
       "        [2.87281913e+01, 2.71825397e+01, 2.48343498e+01, ...,\n",
       "         5.79636544e-01, 1.78366899e-01, 2.22902745e-01],\n",
       "        [3.54160559e+01, 3.37663865e+01, 3.23842371e+01, ...,\n",
       "         8.02539289e-01, 4.60725278e-01, 2.22902745e-01],\n",
       "        [3.53566289e+01, 3.15222430e+01, 2.78661954e+01, ...,\n",
       "         2.82358378e-01, 3.41814011e-01, 2.82358378e-01]],\n",
       "\n",
       "       [[5.88340755e+01, 5.92487203e+01, 5.91258749e+01, ...,\n",
       "         8.26533432e+01, 8.31908593e+01, 8.42505109e+01],\n",
       "        [5.93101578e+01, 5.65304561e+01, 5.49333142e+01, ...,\n",
       "         8.26072900e+01, 8.15936916e+01, 8.18854873e+01],\n",
       "        [5.75901372e+01, 5.47490017e+01, 5.19078921e+01, ...,\n",
       "         7.71554105e+01, 7.75700517e+01, 7.79232886e+01],\n",
       "        ...,\n",
       "        [2.52475166e+01, 2.63071682e+01, 2.66603755e+01, ...,\n",
       "         1.09958672e+01, 8.09334964e+00, 7.44831376e+00],\n",
       "        [2.37731887e+01, 2.06403111e+01, 1.83827430e+01, ...,\n",
       "         9.75193083e+00, 6.20437786e+00, 4.91436385e+00],\n",
       "        [2.15770877e+01, 1.41902119e+01, 1.31305307e+01, ...,\n",
       "         8.87656212e+00, 5.78973256e+00, 5.43652475e+00]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc4ca3f6-66e3-4097-afb6-6304e1c3987b' class='xr-section-summary-in' type='checkbox'  ><label for='section-cc4ca3f6-66e3-4097-afb6-6304e1c3987b' 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>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bfd1cc07-de14-456b-a91d-ed9a975a4d94' class='xr-index-data-in' type='checkbox'/><label for='index-bfd1cc07-de14-456b-a91d-ed9a975a4d94' 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(Float64Index([ -15.0, -14.75,  -14.5, -14.25,  -14.0, -13.75,  -13.5, -13.25,\n",
       "               -13.0, -12.75,\n",
       "              ...\n",
       "               42.75,   43.0,  43.25,   43.5,  43.75,   44.0,  44.25,   44.5,\n",
       "               44.75,   45.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=241))</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-d4afbe8f-8bb7-43c4-aba0-fa428296bba4' class='xr-index-data-in' type='checkbox'/><label for='index-d4afbe8f-8bb7-43c4-aba0-fa428296bba4' 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(Float64Index([ 70.0, 69.75,  69.5, 69.25,  69.0, 68.75,  68.5, 68.25,  68.0,\n",
       "              67.75,\n",
       "              ...\n",
       "              32.25,  32.0, 31.75,  31.5, 31.25,  31.0, 30.75,  30.5, 30.25,\n",
       "               30.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=161))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a4947a2-7ce6-4336-94eb-e7e3d05be012' class='xr-index-data-in' type='checkbox'/><label for='index-3a4947a2-7ce6-4336-94eb-e7e3d05be012' 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;1978-01-01&#x27;, &#x27;1978-02-01&#x27;, &#x27;1978-03-01&#x27;, &#x27;1978-04-01&#x27;,\n",
       "               &#x27;1978-05-01&#x27;, &#x27;1978-06-01&#x27;, &#x27;1978-07-01&#x27;, &#x27;1978-08-01&#x27;,\n",
       "               &#x27;1978-09-01&#x27;, &#x27;1978-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;, &#x27;2021-06-01&#x27;,\n",
       "               &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;, &#x27;2021-10-01&#x27;,\n",
       "               &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=528, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6bb1221-7181-4264-806d-a8daef97b051' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6bb1221-7181-4264-806d-a8daef97b051' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2022-11-20 15:35:31 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data6/adaptor.mars.internal-1668958530.0056255-19750-10-0f2762c4-a5e1-4407-81a3-e979daac49ec.nc /cache/tmp/0f2762c4-a5e1-4407-81a3-e979daac49ec-adaptor.mars.internal-1668958339.1349316-19750-13-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (longitude: 241, latitude: 161, time: 528)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -15.0 -14.75 -14.5 -14.25 ... 44.5 44.75 45.0\n",
       "  * latitude   (latitude) float32 70.0 69.75 69.5 69.25 ... 30.5 30.25 30.0\n",
       "  * time       (time) datetime64[ns] 1978-01-01 1978-02-01 ... 2021-12-01\n",
       "Data variables:\n",
       "    tp         (time, latitude, longitude) float64 57.02 57.56 ... 5.79 5.437\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2022-11-20 15:35:31 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "era5_europe = xr.open_dataset('./LTC_DATA/era5_tp.nc').sel(time=slice('1978-01-01',None))\n",
    "\n",
    "days_in_month = pd.DatetimeIndex(era5_europe.time).days_in_month.values\n",
    "era5_europe['tp'].values = era5_europe['tp'].values * 1000 * days_in_month.reshape(len(days_in_month), 1, 1)\n",
    "display(era5_europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d2fa28-e3c6-4435-b676-7fee22fa4b90",
   "metadata": {},
   "source": [
    "# Loading data for the following examples\n",
    "Droughts are extreme events in time. We therefore use time series as inputs to compute the indices. The following example allows you to extract the soil moisture and precipitation data for a chosen point in Europe. The left plot shows the location of the extracted time series.\n",
    "We extract a time series for both data sets from our stacks and combine them in a pandas DataFrame for further processing. The figure on the right shows the extracted values for the chosen location and time period.\n",
    "\n",
    "**Try:**\n",
    "\n",
    "* **Changing the coordinates and see what a time series for a different location than the default one looks like. Find a suitable time series for the rest of the lecture or keep the default one in N-W Italy.**\n",
    "\n",
    "You can always reset the interactive controls by re-running the cell.\n",
    "\n",
    "<font color='red'>**Note: The data you finally choose here will be used in the rest of the notebook**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03b15f81-2126-4cfa-affe-455c9e3ccd16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a9a82782300437e91e60aea22ab9875",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntRangeSlider(value=(1991, 2021), continuous_update=False, description='Time series ran…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "extracted_ts = dict()\n",
    "\n",
    "slider=widgets.IntRangeSlider(min=1979, max=2021, value=[1991, 2021], step=1, description='Time series range (year from, year to)', \n",
    "                              continuous_update=False, style={'description_width': 'initial'}, layout=widgets.Layout(width='30%'))\n",
    "@widgets.interact(period=slider, Longitude=\"8.625\", Latitude=\"44.625\")\n",
    "def extract_ts(period, Longitude, Latitude):\n",
    "    lon, lat = float(Longitude), float(Latitude)\n",
    "    \n",
    "    # Extract a pandas time series at location\n",
    "    ts_sm = c3s_europe.sel(lon=lon, lat=lat, method='nearest') \\\n",
    "                   .to_pandas().rename(columns={'sm': 'soil_moisture'}) \\\n",
    "                   .loc[f\"{period[0]}-01-01\":f\"{period[1]}-12-31\", ['soil_moisture']]\n",
    "    \n",
    "    ts_tp = era5_europe.sel(longitude=lon, latitude=lat, method='nearest') \\\n",
    "               .to_pandas().rename(columns={'tp': 'precipitation'}) \\\n",
    "               .loc[f\"{period[0]}-01-01\":f\"{period[1]}-12-31\", ['precipitation']]\n",
    "    \n",
    "    ts = pd.concat([ts_sm, ts_tp], axis=1).resample('MS').mean()\n",
    "\n",
    "    # Set up figures\n",
    "    fig = plt.figure(figsize=(15,4), constrained_layout=True)\n",
    "    gs = fig.add_gridspec(1, 3)\n",
    "    map_ax = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())\n",
    "    ts_ax = fig.add_subplot(gs[0, 1:])\n",
    "    bar_ax = ts_ax.twinx()\n",
    "\n",
    "    # Plot overview map\n",
    "    map_ax.set_extent([-14, 46, 29, 71])\n",
    "    map_ax.add_feature(cartopy.feature.LAND, zorder=0)\n",
    "    map_ax.add_feature(cartopy.feature.BORDERS)\n",
    "    map_ax.coastlines()\n",
    "    map_ax.plot([lon], [lat], 'ro', transform=ccrs.PlateCarree())\n",
    "    map_ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)\n",
    "    \n",
    "    # Plot time series\n",
    "    ts['soil_moisture'].plot(style='-o', ax=ts_ax, label='soil moisture')\n",
    "    ts['precipitation'].plot.area(ax=bar_ax, alpha=0.25, label='precipitation', linewidth=0)\n",
    "    \n",
    "    ts_ax.set_xlabel('Time [Year]')\n",
    "    ts_ax.set_ylabel(f'Soil moisture $[m³/m³]$')\n",
    "    bar_ax.set_ylabel(f'Precipitation $[mm]$')\n",
    "    ts_ax.set_title('Satellite Soil Moisture and ERA5 Precipitation')\n",
    "    \n",
    "    ts_ax.legend(loc='upper left')\n",
    "    bar_ax.legend(loc='upper right')\n",
    "    \n",
    "    # Make data available for the next example\n",
    "    global extracted_ts\n",
    "    extracted_ts = dict(data=ts.copy(), lon=lon, lat=lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815bc02a-29fe-4421-9a72-7633784d9e0d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Compute anomalies for the chosen location\n",
    "Here we use the time series that was extracted in the previous example.\n",
    "\n",
    "We now compute the average soil moisture conditions for each month using data from all years within the selected reference period (called **Climatology**, resp. $\\overline{SM}$). \n",
    "The deviation of the absolute values ($SM$) from this climatological reference are the soil moisture **Anomalies**. Values close to 0 therefore indicate normal conditions, while negative anomalies indicate drier than usual conditions (droughts). In case of strong precipitation events, positive anomalies are found.\n",
    "\n",
    "$\\Large SMA = SM_{k,i} - \\overline{SM_i}$\n",
    "\n",
    "**Try:**\n",
    "    \n",
    "* **Changing the baseline / reference period to see if / how it affects the climatology computation.**\n",
    "* **Changing the amount of smoothing applied to the climatology.**\n",
    "\n",
    "*Note: You can always reset the interactive controls by re-running the cell.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8339eec9-932b-4abc-85c9-82700c61a184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79f0ac11cf1e44818bc55ee78b0a64b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntRangeSlider(value=(1991, 2020), description='Reference period (year from, year to):',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anomaly_ts = None\n",
    "\n",
    "@widgets.interact(baseline=widgets.IntRangeSlider(min=1991, max=2021, value=[1991, 2020], step=1, style={'description_width': 'initial'}, \n",
    "                                                  description='Reference period (year from, year to):', layout=widgets.Layout(width='30%')), \n",
    "                  smoothing=widgets.Dropdown(options=['strong', 'medium', 'none'], value='medium', style={'description_width': 'initial'}, description='Smoothing of climatology'))\n",
    "def plot_components(baseline, smoothing):\n",
    "    \"\"\"\n",
    "    Compute climatology and anomalies for the loaded soil moisture time series\n",
    "    \"\"\"\n",
    "    loc = f\"Lon: {extracted_ts['lon']}°, Lat: {extracted_ts['lat']}°\" \n",
    "    \n",
    "    fig, axs = plt.subplots(3, 1, figsize=(10, 7))\n",
    "    ts = extracted_ts['data'].copy().loc['1991-01-01':,:]\n",
    "    \n",
    "    clim_data = ts['soil_moisture'].loc[f'{baseline[0]}-01-01':f'{baseline[1]}-12-31']\n",
    "    clim_std = pd.Series(clim_data.groupby(clim_data.index.month).std(), name='climatology_std')      \n",
    "    clim_mean = pd.Series(clim_data.groupby(clim_data.index.month).mean(), name='climatology')\n",
    "    \n",
    "    if smoothing.lower() == 'medium':\n",
    "        window = 3\n",
    "    elif smoothing.lower() == 'strong':\n",
    "        window = 5\n",
    "    else:\n",
    "        window = 0\n",
    "    \n",
    "    if window != 0:\n",
    "        clim_mean = clim_mean.rolling(window, min_periods=1).mean()              \n",
    "    \n",
    "    ts = ts.join(on=ts.index.month, other=clim_mean)\n",
    "    ts['climatology_std'] = ts.join(on=ts.index.month, other=clim_std)['climatology_std']\n",
    "    ts['anomaly'] = ts['soil_moisture'] - ts['climatology']\n",
    "    \n",
    "    ts['soil_moisture'].plot(ax=axs[0], title=f'Soil Moisture (absolute) at {loc}', ylabel=f'SM $[m³/m³]$', xlabel='Time [Year]')\n",
    "    for i, g in clim_data.groupby(clim_data.index.year):\n",
    "        axs[1].plot(range(1,13), g.values, alpha=0.2)\n",
    "        \n",
    "    clim_mean.plot(ax=axs[1], color='blue', title=f'Climatology at {loc}', ylabel='SM $[m³/m³]$', xlabel='Time [Month]', label='mean')\n",
    "    clim_std.plot(ax=axs[1], label='$\\sigma$')\n",
    "    axs[1].legend()\n",
    "    \n",
    "    global anomaly_ts\n",
    "    anomaly_ts = ts.copy()\n",
    "    \n",
    "    axs[2].axhline(0, color='k')\n",
    "    axs[2].fill_between(ts['anomaly'].index,ts['anomaly'].values,where=ts['anomaly'].values>=0, color='blue')\n",
    "    axs[2].fill_between(ts['anomaly'].index,ts['anomaly'].values,where=ts['anomaly'].values<0, color='red')\n",
    "    axs[2].set_ylabel('SM $[m³/m³]$')\n",
    "    axs[2].set_xlabel('Time [Year]')\n",
    "    axs[2].set_title(f\"SM Anomalies at {loc}\")\n",
    "    plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ed3492-c3ca-4756-8d1c-8b4a28092768",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Mapped Quantiles\n",
    "\n",
    "A simple way to classify potential droughts is by applying thresholds based on quantiles of anomalies in a given time series. Extremely low anomaly values therefore correspond to droughts, while anomalies around and above zero do not. Finding meaningful thresholds is difficult only meaningful when long enough time series are available.\n",
    "The default values are taken from literature.\n",
    "\n",
    "**Try:**\n",
    "\n",
    "* **Changing the theshold values for the quantile-based discretization function and find a classification for the time series you have chosen above.**\n",
    "\n",
    "*Note: You can always reset the interactive controls by re-running the cell.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56cfa419-ba67-4483-aab0-67c1a8da5890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2561e29ae1d43e1ac6aec20c287b2ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.2, description='Mild Drought [quantile]', layout=Layout(width='30%')…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = anomaly_ts.copy()\n",
    "\n",
    "@widgets.interact(\n",
    "mild=widgets.FloatSlider(value=0.2, min=0, max=0.5, step=0.01, description='Mild Drought [quantile]', style={'description_width': 'initial'},layout=widgets.Layout(width='30%')),\n",
    "moderate=widgets.FloatSlider(value=0.1, min=0, max=0.5, step=0.01, description='Moderate Drought [quantile]', style={'description_width': 'initial'},layout=widgets.Layout(width='30%')),\n",
    "significant=widgets.FloatSlider(value=0.05, min=0, max=0.5, step=0.01, description='Significant Drought [quantile]', style={'description_width': 'initial'},layout=widgets.Layout(width='30%')),\n",
    "severe=widgets.FloatSlider(value=0.02, min=0, max=0.5, step=0.01, description='Severe Drought [quantile]', style={'description_width': 'initial'},layout=widgets.Layout(width='30%')),\n",
    "extreme=widgets.FloatSlider(value=0.01, min=0, max=0.5, step=0.01, description='Extreme Drought [quantile]', style={'description_width': 'initial'}, layout=widgets.Layout(width='30%')),\n",
    "continuous_update=False)\n",
    "def plot(mild=0.2, moderate=0.1, significant=0.05, severe=0.02, extreme=0.01):\n",
    "    bins = df['anomaly'].quantile([0, extreme, severe, significant, moderate, mild, 1.]).values\n",
    "\n",
    "    fig, ax = plt.subplots(1,1, figsize=(15, 3))\n",
    "    ax.axhline(0, color='k', linestyle='--')\n",
    "    ax.axhspan(bins[4], bins[5], color='#fee5d9', alpha=0.5, label='mild')\n",
    "    ax.axhspan(bins[3], bins[4], color='#fcbba1', alpha=0.5, label='moderate')\n",
    "    ax.axhspan(bins[2], bins[3], color='#fc9272', alpha=0.5, label='significant')\n",
    "    ax.axhspan(bins[1], bins[2], color='#fb6a4a', alpha=0.5, label='severe')\n",
    "    ax.axhspan(bins[0], bins[1], color='#de2d26', alpha=0.5, label='extreme')\n",
    "    \n",
    "    p = df['anomaly'].plot(marker='o', color='black', ax=ax, ylabel='SM Anomaly $[m³/m³]$', \n",
    "                           title=f\"Soil Moisture Anomaly drought classification at Lon: {extracted_ts['lon']}°, Lat: {extracted_ts['lat']}°\")\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594a0d04-122d-4869-9574-993afb464005",
   "metadata": {},
   "source": [
    "## Z-Scores\n",
    "Z-scores are a way of standardising values from different normal distributions. Z-scores show the number of standard deviations from the mean of the sample.\n",
    "\n",
    "$\\Large Z = \\frac{x - \\mu}{\\sigma}$\n",
    "\n",
    "In the case of soil moisture anomalies, the distributions of values within a year can vary, e.g. due to differences in data coverage (i.e. a different distribution for values in summer than for those in winter). This can affect the computed climatology and therefore the derived anomalies. By taking into account the standard deviation of samples used to compute the climatologies, we can normalize the anomalies and reduce intra-annual biases.\n",
    "\n",
    "$\\Large Z_{k,i} = \\frac{SM_{k,i} - \\overline{SM_i}}{\\sigma_{i}}$\n",
    "\n",
    "The default values in the example are taken from literature.\n",
    "\n",
    "**Try:**\n",
    "\n",
    "* **Compare the Z-score time series to the anomaly time series above.**\n",
    "* **Change the threshold values for the quantile-based discretization function find a meaningful classification for the time series you have chosen above.**\n",
    "* **Extract a time series in areas where data coverage varies strongly within a year and compare the output.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43b4a52f-8aff-4c70-85ff-c46ea98d2517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85778a646bdc4c999b9aa31f43e3376f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.0, description='Moderate Drought $[\\\\sigma]$', max=10.0, step=0.25, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = anomaly_ts.copy()\n",
    "\n",
    "@widgets.interact(\n",
    "moderate=widgets.FloatSlider(value=1, min=0, max=10, step=0.25, description='Moderate Drought $[\\sigma]$', style={'description_width': 'initial'},),\n",
    "extreme=widgets.FloatSlider(value=2, min=0, max=10, step=0.25, description='Extreme Drought $[\\sigma]$', style={'description_width': 'initial'},),\n",
    "continuous_update=False)\n",
    "def plot(moderate, extreme):\n",
    "    df.loc[:, 'zscore'] = (df['soil_moisture'] - df['climatology']) / df['climatology_std']\n",
    "    bins = [df['zscore'].min(), -extreme, -moderate, 0]\n",
    "\n",
    "    fig, ax = plt.subplots(1,1, figsize=(15, 3))\n",
    "    ax.axhline(0, color='k', linestyle='--')\n",
    "    ax.axhspan(bins[1], bins[2], color='#fc9272', alpha=0.5, label='moderate')\n",
    "    ax.axhspan(bins[0], bins[1], color='#de2d26', alpha=0.5, label='extreme')\n",
    "    df['zscore'].plot(marker='o', color='black', ax=ax, ylabel='z score [-]', \n",
    "                      title=f\"Soil Moisture Z-Score drought classification (Lon: {extracted_ts['lon']}°, Lat: {extracted_ts['lat']}°)\")\n",
    "    plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c57a085-1fc6-41cf-9b45-f28046710307",
   "metadata": {},
   "source": [
    "## SMAPI & relative anomalies\n",
    "The **Soil Moisture Percetage Index (SMAPI)** ([Liu et al., 2019](https://doi.org/10.1016/j.rse.2018.10.026)) and relative anomalies use the difference between the observation climatology of the time series to express the anomaly relative to these average conditions in percent.\n",
    "\n",
    "$\\Large SMAPI_{k,i}=\\frac{SM_{k,i} - \\overline{SM_i}}{\\overline{SM_i}} \\cdot 100\\% $\n",
    "\n",
    "The thresholds below are taken from literature.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79c5f687-6275-4fed-8a47-117f76873e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcc0cd73100>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = anomaly_ts.copy()\n",
    "df.loc[:, 'smapi'] = (df['soil_moisture'] - df['climatology']) / df['climatology'] * 100\n",
    "bins = [-100, -50, -30, -15, -5, df.smapi.max()]\n",
    "df.loc[:,'smapi_mapped'] = pd.cut(df.smapi, bins, labels=['extreme', 'severe', 'moderate', 'mild', 'no_drought'])\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(15, 3))\n",
    "ax.axhline(0, color='k', linestyle='--')\n",
    "ax.axhspan(bins[0], bins[1], color='#b30000', alpha=0.5, label='extreme')\n",
    "ax.axhspan(bins[1], bins[2], color='#e34a33', alpha=0.5, label='severe')\n",
    "ax.axhspan(bins[2], bins[3], color='#fc8d59', alpha=0.5, label='moderate')\n",
    "ax.axhspan(bins[3], bins[4], color='#fdcc8a', alpha=0.5, label='mild')\n",
    "p = df['smapi'].plot(marker='o', color='black', ax=ax, ylabel='smapi [-]', \n",
    "                     title=f\"SMAPI drought classification at Lon: {extracted_ts['lon']}°, Lat: {extracted_ts['lat']}°\")\n",
    "plt.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9073ec27-acf5-4922-aa7b-e441b246b302",
   "metadata": {},
   "source": [
    "## Standardized Precipitation Index\n",
    "\n",
    "Here we compute SPI values for the previously extracted precipitation time series (see first cell in this chapter).\n",
    "SPI can be computed for different integration time lengths.\n",
    "\n",
    "SPI values can be aggregated to get an estimate for the duration, severity and intensity of a drought.\n",
    "\n",
    "1) **Duration**: Number of consecutive time stamps where SPI < 0\n",
    "2) **Severity**: $\\sum{SPI}$ over the duration of a drought\n",
    "3) **Intensity**: $ \\frac{Severity}{Duration}$\n",
    "\n",
    "**Try:**\n",
    "* **Changing the integration time length and see how it affects the SPI time series**\n",
    "* **Choosing a different location in the data extraction cell above, re-run this cell and see how SPI changes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "382f6cb7-8219-436b-b75c-d8f1fd79b77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c69082c0afee4870a71493405e46302d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Integration Time [Months]:', index=4, options=(0, 1, 2, 3, 12, 48)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import gamma, norm\n",
    "\n",
    "ts = extracted_ts['data'].copy().to_xarray()\n",
    "\n",
    "def calc_zscore(ts):\n",
    "    fit_alpha, fit_loc, fit_beta = gamma.fit(ts)\n",
    "    pvals = gamma(fit_alpha, loc=fit_loc, scale=fit_beta).cdf(ts)\n",
    "    zscore = norm.ppf(pvals)\n",
    "    return zscore\n",
    "\n",
    "def calc_spi_integrationtime(ts, integrationtime=None):\n",
    "    if (integrationtime is not None) and (integrationtime!=0):\n",
    "        ts = ts.rolling(time=integrationtime).mean().dropna(\"time\")\n",
    "    spi = ts.groupby('time.month').map(calc_zscore)\n",
    "    return spi\n",
    "\n",
    "def index_metrics(index):\n",
    "    duration = [0]\n",
    "    severity = [0]\n",
    "\n",
    "    for i in index:\n",
    "        if i < 0:\n",
    "            duration.append(duration[-1] + 1)\n",
    "            severity.append(severity[-1] + i)\n",
    "        else:\n",
    "            duration.append(0)\n",
    "            severity.append(0)\n",
    "    \n",
    "    duration = np.array(duration[1:])\n",
    "    severity = np.array(severity[1:])\n",
    "    \n",
    "    return duration, severity\n",
    "\n",
    "\n",
    "@widgets.interact(integration_time=widgets.Dropdown(options=[0, 1, 2, 3, 12, 48], value=12, description='Integration Time [Months]:', style={'description_width': 'initial'}))\n",
    "def plot_spi(integration_time):\n",
    "    spi = pd.DataFrame(data={'index': calc_spi_integrationtime(ts['precipitation'], integration_time).to_pandas()})\n",
    "    \n",
    "    duration, severity = index_metrics(spi['index'])\n",
    "    \n",
    "    spi.loc[:, 'duration'] = duration\n",
    "    spi.loc[:, 'severity'] = severity\n",
    "    spi.loc[:, 'intensity'] = spi['severity'] / spi['duration']\n",
    "    \n",
    "    fig, axs = plt.subplots(2, 2, figsize=(12, 5))\n",
    "    \n",
    "    axs[0, 0].fill_between(spi['index'].index, spi['index'].values, where=spi['index'].values>=0, color='blue')\n",
    "    axs[0, 0].fill_between(spi['index'].index, spi['index'].values, where=spi['index'].values<0, color='red')\n",
    "    axs[0, 0].set_ylabel('SPI[-]')\n",
    "    axs[0, 0].set_title(f\"SPI-{integration_time} at Lon: {extracted_ts['lon']}°, Lat: {extracted_ts['lat']}°\")\n",
    "    \n",
    "    spi['intensity'].fillna(0).plot(title=f'Intensity', ax=axs[1, 0], ylabel='Severity / Duration')\n",
    "    spi['duration'].plot(title=f'Duration', ax=axs[0, 1], ylabel='months')\n",
    "    spi['severity'].plot(title=f'Severity', ax=axs[1, 1], ylabel=\"$\\sum{SPI}$\")\n",
    "    plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14549872-7e05-45b3-8065-91c867b8582f",
   "metadata": {},
   "source": [
    "# Other indices\n",
    "\n",
    "## SMASI\n",
    "Another index that was shown in the lecture is the Standardised Vegetation Optical Depth Index [(Moesinger et al., 2022)](https://doi.org/10.5194/bg-19-5107-2022).\n",
    "A similar method was applied to different active and passive soil moisture products to make them comparable and create a long-term index. The following time series contains harmonised indices from 4 satellites.\n",
    "\n",
    "The plot below shows the SMASI index for the point in NW-Italy (Lon: 8.625°, Lat: 44.625°).\n",
    "\n",
    "**Try:**\n",
    "\n",
    "* **Comparing this index to the other ones above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "02ff7bba-a6da-4990-b015-ad10ae6df7ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e37d9680ba5c4347a08f8427309965f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Resampling:', options=('daily', '10-daily', 'monthly', '3-monthly'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smasi = pd.read_csv('./LTC_DATA/smasi_8.625_44.625.csv', index_col=0, parse_dates=True).loc['1991-01-01':,:].rename(columns={'sm_index_4x': 'index'})\n",
    "\n",
    "@widgets.interact(resample=widgets.Dropdown(options=['daily', '10-daily', 'monthly', '3-monthly', 'yearly'], value='daily', description='Resampling:', \n",
    "                                            style={'description_width': 'initial'}))\n",
    "def plot_smasi(resample=None):\n",
    "    smasi_mean = smasi.copy()\n",
    "    \n",
    "    smasi_mean = smasi_mean[~smasi_mean.index.duplicated(keep='first')]\n",
    "    smasi_mean = smasi_mean.resample(sampling[resample]).mean()\n",
    "        \n",
    "    fig, ax = plt.subplots(1,1, figsize=(15, 3))\n",
    "    smasi_mean.plot(ax=ax, ylabel='SMASI[-]', title='SMASI at Lon: 8.625°, Lat: 44.635°')\n",
    "    plt.axhline(0, color='k', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def578cc-0d87-4e97-a5e6-f002901b5be5",
   "metadata": {},
   "source": [
    "# Soil Moisture / VOD Lag analysis\n",
    "\n",
    "For this final example use pre-processed, daily, average VOD and soil moisture time series for larger study areas (bounding boxes around France, Italy, Spain and Europe). We then compute cross-correlation values between the soil moisture and VOD time series with a number of lags applied to the soil moisture data. \n",
    "\n",
    "<img src=\"img/study_areas.png\" width=\"30%\"/>\n",
    "\n",
    "\n",
    "**Try:**\n",
    "* **Changing the study area and see if you find differences in the computed cross correlations**.\n",
    "* **Changing the range of lags that is applied to the soil moisture data (positive values refer to shifts forward in time)**\n",
    "* **What causes these patterns and when do they repeat?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08936894-1ece-4262-9417-a5a140615a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00ef440803414b6098369c0ede34e261",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Study area:', index=3, options=('Europe', 'France', 'Spain', 'Ital…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def crosscorr(vod, sm, lag=0):\n",
    "    return vod.corr(sm.shift(lag))\n",
    "\n",
    "slider = widgets.IntRangeSlider(min=-366, max=366, value=[0, 200], step=1, style={'description_width': 'initial'}, description='Lags [days] (min, max):', layout=widgets.Layout(width='30%'), continuous_update=False)\n",
    "dropdown = widgets.Dropdown(options=['Europe', 'France', 'Spain', 'Italy'], value='Italy', description='Study area:', style={'description_width': 'initial'})\n",
    "@widgets.interact(study_area=dropdown,lags=slider)\n",
    "def plot(study_area, lags, Anomalies: bool = False):\n",
    "    vod_var, sm_var = 'anomaly' if Anomalies else 'vod_K', 'sm_anomaly' if Anomalies else 'sm'\n",
    "    df_vod = pd.read_csv(f'./LTC_DATA/mean_ts/vod_{study_area.lower()}.csv', index_col=0, parse_dates=True)[vod_var]\n",
    "    df_sm = pd.read_csv(f'./LTC_DATA/mean_ts/c3s_{study_area.lower()}.csv', index_col=0, parse_dates=True)[sm_var]\n",
    "    df = pd.concat([df_vod.loc['2000-01-01':'2020-12-31'], df_sm['1991-01-01':'2020-12-31']], axis=1)\n",
    "    x = np.arange(lags[0], lags[1], 1)\n",
    "    plt.plot(x, np.array([crosscorr(df[vod_var], df[sm_var], lag=lag) for lag in x]))\n",
    "    plt.title(f\"SM / VOD {'anomaly' if Anomalies else ''} cross-correlation ({study_area})\")\n",
    "    plt.xlabel('Lag [days]')\n",
    "    plt.ylabel('R[-]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e266fe5-68db-4680-8a6c-08541e59781e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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