1_GLDAS_Preparation.ipynb

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    "# GLDAS data preparation\n",
    "Monthly forcing (total precipitation and average air temperature at 2 m) and outputs of NOAH V2.1 (0.25◦ × 0.25◦) from http://earthdata.nasa.gov, from April 2002 to December 2016\n",
    "\n",
    "Processing steps:\n",
    "- summing soil moisture in all four soil layers (spanning from 0- to 200-cm depth), accumulative snow water, and total canopy water storage\n",
    "- to be consistent with the GRACE TWSA processing, the long-term mean from January 2004 to December 2009 was subtracted from NOAH TWS to obtain the simulated TWSA\n",
    "- clipping the data to 60–100◦ longitude, 7.75–47.75◦ latitude\n",
    "- resampling the grid from 0.25x0.25 to 0.3125x0.3125\n",
    "- normalising the forcing and output data\n",
    "\n",
    "Output: TWSA (kg m-2), air temperature (K) and rainfall (kg m-2 s-1) "
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    "#!pip install geopandas rasterio rioxarray xarray numpy\n",
    "\n",
    "import geopandas as gpd\n",
    "from rasterio.enums import Resampling\n",
    "import rioxarray\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd"
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   "source": [
    "#### 1. Read data and combine along the coordinates"
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   "source": [
    "# combine all .nc files along the time dimension\n",
    "ds = xr.open_mfdataset('./data/GLDAS/raw/*.nc4', combine = 'by_coords', parallel=True)"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 22GB\n",
       "Dimensions:                (time: 177, bnds: 2, lat: 600, lon: 1440)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 1kB 2002-04-01 ... 2016-12-01\n",
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       "    Lwnet_tavg             (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    Qle_tavg               (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    Qh_tavg                (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    Qg_tavg                (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    ...                     ...\n",
       "    Rainf_f_tavg           (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    Tair_f_inst            (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    Qair_f_inst            (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    Psurf_f_inst           (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    SWdown_f_tavg          (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    LWdown_f_tavg          (time, lat, lon) float32 612MB dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/19)\n",
       "    CDI:                    Climate Data Interface version 1.9.8 (https://mpi...\n",
       "    Conventions:            CF-1.6\n",
       "    history:                created on date: 2019-10-30T09:28:01.883\n",
       "    source:                 Noah_v3.6 forced with GDAS-AGRMET-GPCPv13rA1\n",
       "    institution:            NASA GSFC\n",
       "    missing_value:          -9999.0\n",
       "    ...                     ...\n",
       "    MAP_PROJECTION:         EQUIDISTANT CYLINDRICAL\n",
       "    SOUTH_WEST_CORNER_LAT:  -59.875\n",
       "    SOUTH_WEST_CORNER_LON:  -179.875\n",
       "    DX:                     0.25\n",
       "    DY:                     0.25\n",
       "    CDO:                    Climate Data Operators version 1.9.8 (https://mpi...</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-da758c2f-a522-4cda-ab1c-68646787d09a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-da758c2f-a522-4cda-ab1c-68646787d09a' 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>: 177</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 600</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-31d98173-b326-4cc2-94bc-f5e4f4f925b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31d98173-b326-4cc2-94bc-f5e4f4f925b8' 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'>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'>2002-04-01 ... 2016-12-01</div><input id='attrs-d58cd3a7-016e-4681-981e-ac57789416ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d58cd3a7-016e-4681-981e-ac57789416ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5c6ba89-dbc1-426c-be78-abf849c3b893' class='xr-var-data-in' type='checkbox'><label for='data-e5c6ba89-dbc1-426c-be78-abf849c3b893' 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>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-04-01T00:00:00.000000000&#x27;, &#x27;2002-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-06-01T00:00:00.000000000&#x27;, &#x27;2002-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-08-01T00:00:00.000000000&#x27;, &#x27;2002-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-10-01T00:00:00.000000000&#x27;, &#x27;2002-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-12-01T00:00:00.000000000&#x27;, &#x27;2003-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-02-01T00:00:00.000000000&#x27;, &#x27;2003-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-04-01T00:00:00.000000000&#x27;, &#x27;2003-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-06-01T00:00:00.000000000&#x27;, &#x27;2003-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-08-01T00:00:00.000000000&#x27;, &#x27;2003-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-10-01T00:00:00.000000000&#x27;, &#x27;2003-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-12-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-02-01T00:00:00.000000000&#x27;, &#x27;2004-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-04-01T00:00:00.000000000&#x27;, &#x27;2004-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-06-01T00:00:00.000000000&#x27;, &#x27;2004-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-08-01T00:00:00.000000000&#x27;, &#x27;2004-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-10-01T00:00:00.000000000&#x27;, &#x27;2004-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-12-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-02-01T00:00:00.000000000&#x27;, &#x27;2005-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-04-01T00:00:00.000000000&#x27;, &#x27;2005-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-06-01T00:00:00.000000000&#x27;, &#x27;2005-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-01T00:00:00.000000000&#x27;, &#x27;2005-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-10-01T00:00:00.000000000&#x27;, &#x27;2005-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-12-01T00:00:00.000000000&#x27;, &#x27;2006-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-02-01T00:00:00.000000000&#x27;, &#x27;2006-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-04-01T00:00:00.000000000&#x27;, &#x27;2006-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-06-01T00:00:00.000000000&#x27;, &#x27;2006-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-08-01T00:00:00.000000000&#x27;, &#x27;2006-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-10-01T00:00:00.000000000&#x27;, &#x27;2006-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-12-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-02-01T00:00:00.000000000&#x27;, &#x27;2007-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-04-01T00:00:00.000000000&#x27;, &#x27;2007-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-06-01T00:00:00.000000000&#x27;, &#x27;2007-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-08-01T00:00:00.000000000&#x27;, &#x27;2007-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-10-01T00:00:00.000000000&#x27;, &#x27;2007-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-12-01T00:00:00.000000000&#x27;, &#x27;2008-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-02-01T00:00:00.000000000&#x27;, &#x27;2008-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-04-01T00:00:00.000000000&#x27;, &#x27;2008-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-06-01T00:00:00.000000000&#x27;, &#x27;2008-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-08-01T00:00:00.000000000&#x27;, &#x27;2008-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-10-01T00:00:00.000000000&#x27;, &#x27;2008-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-12-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-02-01T00:00:00.000000000&#x27;, &#x27;2009-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-04-01T00:00:00.000000000&#x27;, &#x27;2009-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-06-01T00:00:00.000000000&#x27;, &#x27;2009-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-08-01T00:00:00.000000000&#x27;, &#x27;2009-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-10-01T00:00:00.000000000&#x27;, &#x27;2009-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-12-01T00:00:00.000000000&#x27;, &#x27;2010-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-01T00:00:00.000000000&#x27;, &#x27;2010-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-04-01T00:00:00.000000000&#x27;, &#x27;2010-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-06-01T00:00:00.000000000&#x27;, &#x27;2010-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-08-01T00:00:00.000000000&#x27;, &#x27;2010-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-10-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-04-01T00:00:00.000000000&#x27;, &#x27;2011-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-06-01T00:00:00.000000000&#x27;, &#x27;2011-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-08-01T00:00:00.000000000&#x27;, &#x27;2011-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-10-01T00:00:00.000000000&#x27;, &#x27;2011-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-12-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-02-01T00:00:00.000000000&#x27;, &#x27;2012-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-04-01T00:00:00.000000000&#x27;, &#x27;2012-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-01T00:00:00.000000000&#x27;, &#x27;2012-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-08-01T00:00:00.000000000&#x27;, &#x27;2012-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-10-01T00:00:00.000000000&#x27;, &#x27;2012-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-04-01T00:00:00.000000000&#x27;, &#x27;2013-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-06-01T00:00:00.000000000&#x27;, &#x27;2013-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-08-01T00:00:00.000000000&#x27;, &#x27;2013-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-10-01T00:00:00.000000000&#x27;, &#x27;2013-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-12-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-02-01T00:00:00.000000000&#x27;, &#x27;2014-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-04-01T00:00:00.000000000&#x27;, &#x27;2014-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-06-01T00:00:00.000000000&#x27;, &#x27;2014-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-08-01T00:00:00.000000000&#x27;, &#x27;2014-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-10-01T00:00:00.000000000&#x27;, &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-361a97ce-b862-4015-9268-2efc52a55e45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-361a97ce-b862-4015-9268-2efc52a55e45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65849fa5-71e7-4050-8ba1-30ebdf5d626b' class='xr-var-data-in' type='checkbox'><label for='data-65849fa5-71e7-4050-8ba1-30ebdf5d626b' 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>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
       "      dtype=float32)</pre></div></li><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'>-59.88 -59.62 ... 89.62 89.88</div><input id='attrs-ac5e16d6-6a4d-4744-a330-7b6af1d1dbbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac5e16d6-6a4d-4744-a330-7b6af1d1dbbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-005bc737-452f-4ced-9bc1-b779c7019990' class='xr-var-data-in' type='checkbox'><label for='data-005bc737-452f-4ced-9bc1-b779c7019990' 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>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-59.875, -59.625, -59.375, ...,  89.375,  89.625,  89.875],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9cd9556-0c1e-4c36-b046-5c03436d3a04' class='xr-section-summary-in' type='checkbox'  ><label for='section-c9cd9556-0c1e-4c36-b046-5c03436d3a04' class='xr-section-summary' >Data variables: <span>(37)</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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-54a66b00-2663-4e86-ac7e-8cc0f818367f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54a66b00-2663-4e86-ac7e-8cc0f818367f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7b2b72-5a6a-4a68-b4e3-f0222dccadb3' class='xr-var-data-in' type='checkbox'><label for='data-9b7b2b72-5a6a-4a68-b4e3-f0222dccadb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.77 kiB </td>\n",
       "                        <td> 16 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (177, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 177 chunks in 355 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"76\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"94\" x2=\"26\" y2=\"94\" />\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 583.37 MiB </td>\n",
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       "                        <th> Bytes </th>\n",
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       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 583.37 MiB </td>\n",
       "                        <td> 3.30 MiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Rainf_tavg</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=(1, 600, 1440), meta=np.ndarray&gt;</div><input id='attrs-48b88b90-7d02-4105-ab5a-0ef350a4902a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48b88b90-7d02-4105-ab5a-0ef350a4902a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56375139-9292-40fe-8363-ee18cec4a216' class='xr-var-data-in' type='checkbox'><label for='data-56375139-9292-40fe-8363-ee18cec4a216' 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>rainfall_flux</dd><dt><span>long_name :</span></dt><dd>Rain precipitation rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>vmin :</span></dt><dd>0.0</dd><dt><span>vmax :</span></dt><dd>0.00022311458</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 583.37 MiB </td>\n",
       "                        <td> 3.30 MiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Evap_tavg</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=(1, 600, 1440), meta=np.ndarray&gt;</div><input id='attrs-28387c3a-2868-4ab4-8d23-971e97a1c847' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28387c3a-2868-4ab4-8d23-971e97a1c847' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-557888ee-9239-4f46-9028-47912d870237' class='xr-var-data-in' type='checkbox'><label for='data-557888ee-9239-4f46-9028-47912d870237' 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>water_evaporation_flux</dd><dt><span>long_name :</span></dt><dd>Evapotranspiration</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>vmin :</span></dt><dd>-9.292825e-10</dd><dt><span>vmax :</span></dt><dd>7.180949e-05</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 583.37 MiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "                        <th> Bytes </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SoilTMP0_10cm_inst</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=(1, 600, 1440), meta=np.ndarray&gt;</div><input id='attrs-cef0cc61-f433-45bb-9aa5-4d288e655eaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cef0cc61-f433-45bb-9aa5-4d288e655eaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bef1e779-10c5-4c8c-872f-1704ce9f741b' class='xr-var-data-in' type='checkbox'><label for='data-bef1e779-10c5-4c8c-872f-1704ce9f741b' 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>soil_temperature</dd><dt><span>long_name :</span></dt><dd>Soil temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>vmin :</span></dt><dd>239.9783</dd><dt><span>vmax :</span></dt><dd>310.39734</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>ECanop_tavg</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=(1, 600, 1440), meta=np.ndarray&gt;</div><input id='attrs-34ac1fc8-3d24-49aa-aff3-e7ea2b948007' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34ac1fc8-3d24-49aa-aff3-e7ea2b948007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09ecf1ec-e6b8-4fbe-b2f5-c32741bc3998' class='xr-var-data-in' type='checkbox'><label for='data-09ecf1ec-e6b8-4fbe-b2f5-c32741bc3998' 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>evaporation_flux_from_canopy</dd><dt><span>long_name :</span></dt><dd>Canopy water evaporation</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>vmin :</span></dt><dd>0.0</dd><dt><span>vmax :</span></dt><dd>107.857124</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 583.37 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <td> 583.37 MiB </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWdown_f_tavg</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=(1, 600, 1440), meta=np.ndarray&gt;</div><input id='attrs-1a87a2b2-57f5-48b8-99a0-de2f15c74751' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a87a2b2-57f5-48b8-99a0-de2f15c74751' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58324c14-8bb9-413d-8d9b-34ec681dfe37' class='xr-var-data-in' type='checkbox'><label for='data-58324c14-8bb9-413d-8d9b-34ec681dfe37' 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>surface_downwelling_longwave_flux_in_air</dd><dt><span>long_name :</span></dt><dd>Downward long-wave radiation flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>vmin :</span></dt><dd>126.01254</dd><dt><span>vmax :</span></dt><dd>453.89032</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 583.37 MiB </td>\n",
       "                        <td> 3.30 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (177, 600, 1440) </td>\n",
       "                        <td> (1, 600, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 177 chunks in 355 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1744073d-5f4e-43cd-881f-4000f78181cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-1744073d-5f4e-43cd-881f-4000f78181cc' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e57d723-ecde-4f3d-a0b4-038e6d58c241' class='xr-index-data-in' type='checkbox'/><label for='index-1e57d723-ecde-4f3d-a0b4-038e6d58c241' 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;2002-04-01&#x27;, &#x27;2002-05-01&#x27;, &#x27;2002-06-01&#x27;, &#x27;2002-07-01&#x27;,\n",
       "               &#x27;2002-08-01&#x27;, &#x27;2002-09-01&#x27;, &#x27;2002-10-01&#x27;, &#x27;2002-11-01&#x27;,\n",
       "               &#x27;2002-12-01&#x27;, &#x27;2003-01-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;, &#x27;2016-06-01&#x27;,\n",
       "               &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;, &#x27;2016-10-01&#x27;,\n",
       "               &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=177, freq=None))</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-bb6d03e5-b2f5-4cf4-aa08-b1877077480f' class='xr-index-data-in' type='checkbox'/><label for='index-bb6d03e5-b2f5-4cf4-aa08-b1877077480f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,\n",
       "       -178.125, -177.875, -177.625,\n",
       "       ...\n",
       "        177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,\n",
       "        179.375,  179.625,  179.875],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><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-9b5478e1-9fd7-4b45-90ae-b785ceac3fb6' class='xr-index-data-in' type='checkbox'/><label for='index-9b5478e1-9fd7-4b45-90ae-b785ceac3fb6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-59.875, -59.625, -59.375, -59.125, -58.875, -58.625, -58.375, -58.125,\n",
       "       -57.875, -57.625,\n",
       "       ...\n",
       "        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,\n",
       "        89.625,  89.875],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dab1815b-6de5-4f18-a7d4-6c5151b6687b' class='xr-section-summary-in' type='checkbox'  ><label for='section-dab1815b-6de5-4f18-a7d4-6c5151b6687b' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>created on date: 2019-10-30T09:28:01.883</dd><dt><span>source :</span></dt><dd>Noah_v3.6 forced with GDAS-AGRMET-GPCPv13rA1</dd><dt><span>institution :</span></dt><dd>NASA GSFC</dd><dt><span>missing_value :</span></dt><dd>-9999.0</dd><dt><span>tavg definision: :</span></dt><dd>past 3-hour average</dd><dt><span>acc definision: :</span></dt><dd>past 3-hour accumulation</dd><dt><span>inst definision: :</span></dt><dd>instantaneous</dd><dt><span>title :</span></dt><dd>GLDAS2.1 LIS land surface model output monthly mean</dd><dt><span>references :</span></dt><dd>Rodell_etal_BAMS_2004, Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007</dd><dt><span>conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>website: https://ldas.gsfc.nasa.gov/gldas, https://lis.gsfc.nasa.gov/</dd><dt><span>MAP_PROJECTION :</span></dt><dd>EQUIDISTANT CYLINDRICAL</dd><dt><span>SOUTH_WEST_CORNER_LAT :</span></dt><dd>-59.875</dd><dt><span>SOUTH_WEST_CORNER_LON :</span></dt><dd>-179.875</dd><dt><span>DX :</span></dt><dd>0.25</dd><dt><span>DY :</span></dt><dd>0.25</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 22GB\n",
       "Dimensions:                (time: 177, bnds: 2, lat: 600, lon: 1440)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 1kB 2002-04-01 ... 2016-12-01\n",
       "  * lon                    (lon) float32 6kB -179.9 -179.6 ... 179.6 179.9\n",
       "  * lat                    (lat) float32 2kB -59.88 -59.62 ... 89.62 89.88\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables: (12/37)\n",
       "    time_bnds              (time, bnds) datetime64[ns] 3kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    Swnet_tavg             (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    Lwnet_tavg             (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    Qle_tavg               (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    Qh_tavg                (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    Qg_tavg                (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    ...                     ...\n",
       "    Rainf_f_tavg           (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    Tair_f_inst            (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    Qair_f_inst            (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    Psurf_f_inst           (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    SWdown_f_tavg          (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    LWdown_f_tavg          (time, lat, lon) float32 612MB dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "Attributes: (12/19)\n",
       "    CDI:                    Climate Data Interface version 1.9.8 (https://mpi...\n",
       "    Conventions:            CF-1.6\n",
       "    history:                created on date: 2019-10-30T09:28:01.883\n",
       "    source:                 Noah_v3.6 forced with GDAS-AGRMET-GPCPv13rA1\n",
       "    institution:            NASA GSFC\n",
       "    missing_value:          -9999.0\n",
       "    ...                     ...\n",
       "    MAP_PROJECTION:         EQUIDISTANT CYLINDRICAL\n",
       "    SOUTH_WEST_CORNER_LAT:  -59.875\n",
       "    SOUTH_WEST_CORNER_LON:  -179.875\n",
       "    DX:                     0.25\n",
       "    DY:                     0.25\n",
       "    CDO:                    Climate Data Operators version 1.9.8 (https://mpi..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7a2bb5-cd5c-453e-8523-c7b212bec9e6",
   "metadata": {},
   "source": [
    "#### 2. Extracting relevant variables from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd8116f2-f1d8-4ab1-aa90-350ed39b1ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tair_f = ds['Tair_f_inst']\n",
    "rainf_f = ds['Rainf_f_tavg']\n",
    "total_TWS = (ds['SWE_inst'] + ds['SoilMoi0_10cm_inst'] + ds['SoilMoi10_40cm_inst'] + \\\n",
    "    ds['SoilMoi40_100cm_inst'] + ds['SoilMoi100_200cm_inst'] + ds['CanopInt_inst'])\n",
    "\n",
    "tair_f.rio.write_nodata(np.nan, inplace=True)\n",
    "rainf_f.rio.write_nodata(np.nan, inplace=True)\n",
    "total_TWS.rio.write_nodata(np.nan, inplace=True)\n",
    "\n",
    "input_da = [tair_f, rainf_f, total_TWS]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc290059-89e8-4893-a582-f54ba96d7fd4",
   "metadata": {},
   "source": [
    "#### 3. Clipping the data to the shape around India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05d096d4-02c7-4c29-a597-197849b8fe9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = [\n",
    "    {\n",
    "        'type': 'Polygon',\n",
    "        'coordinates': [[\n",
    "            [ 60.0, 47.75 ],\n",
    "            [ 100.0, 47.75 ],\n",
    "            [ 100.0, 7.75 ],\n",
    "            [ 60.0, 7.75 ],\n",
    "            [ 60.0, 47.75 ]\n",
    "        ]]\n",
    "    }\n",
    "]\n",
    "clipped_da = []\n",
    "\n",
    "# clipping\n",
    "for inda in input_da:\n",
    "    inda.rio.set_spatial_dims(x_dim='lon', y_dim='lat', inplace=True)\n",
    "    inda.rio.write_crs('epsg:4326', inplace=True)\n",
    "    clipped_da.append(inda.rio.clip(shape, inda.rio.crs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020c4df3-ac2a-44e2-8b72-898cc54c3f67",
   "metadata": {},
   "source": [
    "#### 4. Convert TWS to TWSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33f77d93-ba89-4dbc-b14d-d42d9b645581",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdate = '2004-01-01'\n",
    "edate = '2009-12-01'\n",
    "clipped_da[2] -= clipped_da[2].sel(time = slice(sdate, edate)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008885e8-4d29-4755-b032-b1b4c9491185",
   "metadata": {},
   "source": [
    "#### 5. Resample data to 128x128 grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3daa792a-13ad-4133-8700-c60250fcbcfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sun et al. 2019 reprocesses the image to 128x128 grid\n",
    "upscale_factor = 128 / clipped_da[2].sizes['lon']\n",
    "new_width = int(clipped_da[2].rio.width * upscale_factor)\n",
    "new_height = int(clipped_da[2].rio.height * upscale_factor)\n",
    "\n",
    "resampled_da = []\n",
    "\n",
    "for ida in clipped_da:\n",
    "    resampled_da.append(ida.rio.reproject(\n",
    "        ida.rio.crs,\n",
    "        nodata=np.nan,\n",
    "        shape=(new_height, new_width),\n",
    "        resampling=Resampling.bilinear,\n",
    "        ).rename({'x': 'lon','y': 'lat'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9829c7-158c-4476-a533-33ae962378f7",
   "metadata": {},
   "source": [
    "#### 6. Use the 15th of each month as time\n",
    "For consistency with GRACE data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24620c72-44a1-4d10-8049-a656ba04d177",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_ds = xr.merge([\n",
    "    resampled_da[0].to_dataset(name=\"tair_f_inst\"),\n",
    "    resampled_da[1].to_dataset(name=\"rainf_f_tavg\"),\n",
    "    resampled_da[2].to_dataset(name=\"twsa_gldas\")]\n",
    ")\n",
    "merged_ds['time'] = ('time', [pd.Timestamp(date).replace(day=15) for date in merged_ds['time'].values])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aedab89c-38e7-405e-8121-8b5ad17c0b85",
   "metadata": {},
   "source": [
    "#### 7. Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d652c5e2-e1b0-4f0b-8345-1ac97ca77370",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_ds.rio.set_spatial_dims(x_dim='lon', y_dim='lat', inplace=True)\n",
    "merged_ds.rio.write_crs('epsg:4326', inplace=True)\n",
    "merged_ds.to_netcdf('./data/GLDAS/processed/TWSA_M_2002_2016_India_03125.nc4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e7f909d-a537-49df-b748-a79c67dc763c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f459aa4c710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual inspection: resampled output\n",
    "merged_ds['twsa_gldas'][2,:,:].plot(cmap='RdBu_r', vmin=-100, vmax=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39731906-1546-4ba6-b883-9630359611f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f45bbeb64d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual inspection: original resolution, clipped to the region and centred around 2004-2009 average\n",
    "clipped_da[2][2,:,:].plot(cmap='RdBu_r', vmin=-100, vmax=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}