IO_era5-wind_MASKING.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "905fd90a-c86b-4350-8b90-af8eb541ea2e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import boto3\n",
    "import botocore\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import os.path\n",
    "import os\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02dd1205-ebd3-4ccd-aa0c-632dc29fc7f4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "era5_bucket = 'era5-pds'\n",
    "\n",
    "# AWS access / secret keys required\n",
    "# s3 = boto3.resource('s3')\n",
    "# bucket = s3.Bucket(era5_bucket)\n",
    "\n",
    "# No AWS keys required\n",
    "client = boto3.client('s3', config=botocore.client.Config(signature_version=botocore.UNSIGNED))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "449fcd19-171d-4c9e-8c0b-4672d09b1032",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "var1='northward_wind_at_10_metres'\n",
    "var2='eastward_wind_at_10_metres'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c30f871f-930c-4869-8e85-ba305ffb0fd6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "years = list(range(2000, 2021))\n",
    "months = [\"{:02d}\".format(n) for n in range(1, 13)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f08d6fc-c7ee-443c-a9e2-44d4ce563d16",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Downloading 2020/09/data/eastward_wind_at_10_metres.nc from S3...\n",
      "Downloading 2020/09/data/northward_wind_at_10_metres.nc from S3...\n",
      "Downloading 2020/10/data/eastward_wind_at_10_metres.nc from S3...\n",
      "Downloading 2020/10/data/northward_wind_at_10_metres.nc from S3...\n",
      "Downloading 2020/11/data/eastward_wind_at_10_metres.nc from S3...\n",
      "Downloading 2020/11/data/northward_wind_at_10_metres.nc from S3...\n",
      "Downloading 2020/12/data/eastward_wind_at_10_metres.nc from S3...\n",
      "Downloading 2020/12/data/northward_wind_at_10_metres.nc from S3...\n"
     ]
    }
   ],
   "source": [
    "s3_data_ptrn = '{year}/{month}/data/{var}.nc'\n",
    "data_file_ptrn = 'era5/temp/{year}{month}_{var}.nc'\n",
    "\n",
    "\n",
    "data_east = xr.Dataset()\n",
    "data_north = xr.Dataset()\n",
    "\n",
    "for year in years:\n",
    "    for month in months:\n",
    "        s3_data_key_east = s3_data_ptrn.format(year=year, month=month, var=var2)\n",
    "        data_file_east = data_file_ptrn.format(year=year, month=month, var=var2)\n",
    "        if not os.path.isfile(data_file_east): # check if file already exists\n",
    "            print(\"Downloading %s from S3...\" % s3_data_key_east)\n",
    "            client.download_file(era5_bucket, s3_data_key_east, data_file_east)\n",
    "        data_east = xr.combine_by_coords([data_east, xr.open_dataset(data_file_east).sel(lat=slice(25, 5), lon=slice(60, 80))])\n",
    "        os.remove(data_file_east)\n",
    "\n",
    "        s3_data_key_north = s3_data_ptrn.format(year=year, month=month, var=var1)\n",
    "        data_file_north = data_file_ptrn.format(year=year, month=month, var=var1)\n",
    "        if not os.path.isfile(data_file_north): # check if file already exists\n",
    "            print(\"Downloading %s from S3...\" % s3_data_key_north)\n",
    "            client.download_file(era5_bucket, s3_data_key_north, data_file_north)\n",
    "        data_north = xr.combine_by_coords([data_north, xr.open_dataset(data_file_north).sel(lat=slice(25, 5), lon=slice(60, 80))])\n",
    "        os.remove(data_file_north)\n",
    " "
   ]
  },
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   "id": "93020345-9d71-47bc-aeb4-246420a6145a",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 19 objects available for January, 2021\n",
      "--\n",
      "2021/01/data/air_pressure_at_mean_sea_level.nc\n",
      "2021/01/data/air_temperature_at_2_metres.nc\n",
      "2021/01/data/air_temperature_at_2_metres_1hour_Maximum.nc\n",
      "2021/01/data/air_temperature_at_2_metres_1hour_Minimum.nc\n",
      "2021/01/data/dew_point_temperature_at_2_metres.nc\n",
      "2021/01/data/eastward_wind_at_100_metres.nc\n",
      "2021/01/data/eastward_wind_at_10_metres.nc\n",
      "2021/01/data/integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation.nc\n",
      "2021/01/data/lwe_thickness_of_surface_snow_amount.nc\n",
      "2021/01/data/northward_wind_at_100_metres.nc\n",
      "2021/01/data/northward_wind_at_10_metres.nc\n",
      "2021/01/data/precipitation_amount_1hour_Accumulation.nc\n",
      "2021/01/data/sea_surface_temperature.nc\n",
      "2021/01/data/sea_surface_wave_from_direction.nc\n",
      "2021/01/data/sea_surface_wave_mean_period.nc\n",
      "2021/01/data/significant_height_of_wind_and_swell_waves.nc\n",
      "2021/01/data/snow_density.nc\n",
      "2021/01/data/surface_air_pressure.nc\n",
      "2021/01/main.nc\n"
     ]
    }
   ],
   "source": [
    "keys = []\n",
    "date = datetime.date(2021,1,1) # update to desired date\n",
    "prefix = date.strftime('%Y/%m/')\n",
    "\n",
    "response = client.list_objects_v2(Bucket=era5_bucket, Prefix=prefix)\n",
    "response_meta = response.get('ResponseMetadata')\n",
    "\n",
    "data = xr.Dataset()\n",
    "if response_meta.get('HTTPStatusCode') == 200:\n",
    "    contents = response.get('Contents')\n",
    "    if contents == None:\n",
    "        print(\"No objects are available for %s\" % date.strftime('%B, %Y'))\n",
    "    else:\n",
    "        for obj in contents:\n",
    "            keys.append(obj.get('Key'))\n",
    "        print(\"There are %s objects available for %s\\n--\" % (len(keys), date.strftime('%B, %Y')))\n",
    "        for k in keys:\n",
    "            print(k)\n",
    "else:\n",
    "    print(\"There was an error with your request.\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 47,
   "id": "2422fb19-cf88-413e-937d-2fc350a73a7c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
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    "tags": []
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   "outputs": [],
   "source": [
    "f200701 = xr.open_dataset('era5/200701_eastward_wind_at_10_metres.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "845982af-c37c-4a8c-9b17-3e9a507f80b8",
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    "jupyter": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                      (lon: 81, lat: 81, time0: 744)\n",
       "Coordinates:\n",
       "  * lon                          (lon) float32 60.0 60.25 60.5 ... 79.75 80.0\n",
       "  * lat                          (lat) float32 25.0 24.75 24.5 ... 5.5 5.25 5.0\n",
       "  * time0                        (time0) datetime64[ns] 2000-01-01 ... 2000-0...\n",
       "Data variables:\n",
       "    northward_wind_at_10_metres  (time0, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fb390126-dc1a-4b97-9552-1b9394041733' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb390126-dc1a-4b97-9552-1b9394041733' 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'>lon</span>: 81</li><li><span class='xr-has-index'>lat</span>: 81</li><li><span class='xr-has-index'>time0</span>: 744</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a9c8329c-b496-4564-9d6d-c5adf1e87d56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9c8329c-b496-4564-9d6d-c5adf1e87d56' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.25 60.5 ... 79.5 79.75 80.0</div><input id='attrs-8b21e9f3-4e2a-4da5-ae2a-c1bcb8ef2500' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b21e9f3-4e2a-4da5-ae2a-c1bcb8ef2500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eea98922-884c-4168-996f-d02d5a6ef186' class='xr-var-data-in' type='checkbox'><label for='data-eea98922-884c-4168-996f-d02d5a6ef186' 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></dl></div><div class='xr-var-data'><pre>array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,\n",
       "       62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,\n",
       "       65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,\n",
       "       67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,\n",
       "       70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,\n",
       "       72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,\n",
       "       75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,\n",
       "       77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,\n",
       "       80.  ], 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'>25.0 24.75 24.5 ... 5.5 5.25 5.0</div><input id='attrs-185e1494-c767-47ab-b8fe-aaf6ab72323e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-185e1494-c767-47ab-b8fe-aaf6ab72323e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c0b59da-2ffe-4130-a2f1-828e3baa93d7' class='xr-var-data-in' type='checkbox'><label for='data-2c0b59da-2ffe-4130-a2f1-828e3baa93d7' 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></dl></div><div class='xr-var-data'><pre>array([25.  , 24.75, 24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75,\n",
       "       22.5 , 22.25, 22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25,\n",
       "       20.  , 19.75, 19.5 , 19.25, 19.  , 18.75, 18.5 , 18.25, 18.  , 17.75,\n",
       "       17.5 , 17.25, 17.  , 16.75, 16.5 , 16.25, 16.  , 15.75, 15.5 , 15.25,\n",
       "       15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time0</span></div><div class='xr-var-dims'>(time0)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-31T23:00:00</div><input id='attrs-013e0029-d7b0-4a86-936d-5a4083c635ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-013e0029-d7b0-4a86-936d-5a4083c635ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fc4727b-adbe-4354-bb14-213a8833333b' class='xr-var-data-in' type='checkbox'><label for='data-4fc4727b-adbe-4354-bb14-213a8833333b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T02:00:00.000000000&#x27;, ..., &#x27;2000-01-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-31T22:00:00.000000000&#x27;, &#x27;2000-01-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ece2952f-7b1a-49d8-b290-7c09b6b344d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ece2952f-7b1a-49d8-b290-7c09b6b344d8' 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>northward_wind_at_10_metres</span></div><div class='xr-var-dims'>(time0, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d07c0378-5a53-4590-8d35-53592a802e57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d07c0378-5a53-4590-8d35-53592a802e57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d3984ca-928a-4a2d-b26a-2cb808bc5560' class='xr-var-data-in' type='checkbox'><label for='data-7d3984ca-928a-4a2d-b26a-2cb808bc5560' 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>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre V wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10v</dd><dt><span>nameCDM :</span></dt><dd>10_metre_V_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>[4881384 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87a85861-27d1-46a2-bc0f-2dc2d022bb43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87a85861-27d1-46a2-bc0f-2dc2d022bb43' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>tilte :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:                      (lon: 81, lat: 81, time0: 744)\n",
       "Coordinates:\n",
       "  * lon                          (lon) float32 60.0 60.25 60.5 ... 79.75 80.0\n",
       "  * lat                          (lat) float32 25.0 24.75 24.5 ... 5.5 5.25 5.0\n",
       "  * time0                        (time0) datetime64[ns] 2000-01-01 ... 2000-0...\n",
       "Data variables:\n",
       "    northward_wind_at_10_metres  (time0, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts"
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                     (lon: 81, lat: 81, time0: 184104)\n",
       "Coordinates:\n",
       "  * lon                         (lon) float32 60.0 60.25 60.5 ... 79.75 80.0\n",
       "  * lat                         (lat) float32 25.0 24.75 24.5 ... 5.5 5.25 5.0\n",
       "  * time0                       (time0) datetime64[ns] 2000-01-01 ... 2020-12...\n",
       "Data variables:\n",
       "    eastward_wind_at_10_metres  (time0, lat, lon) float32 -1.728 ... -5.688\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9a6efb6c-1cc3-40f0-b0b8-2276c60d3bbe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a6efb6c-1cc3-40f0-b0b8-2276c60d3bbe' 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'>lon</span>: 81</li><li><span class='xr-has-index'>lat</span>: 81</li><li><span class='xr-has-index'>time0</span>: 184104</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5db0d18e-b3c9-4aa9-beb3-f567ffbd1ead' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5db0d18e-b3c9-4aa9-beb3-f567ffbd1ead' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.25 60.5 ... 79.5 79.75 80.0</div><input id='attrs-70dc3b7f-f830-42d8-95da-a7fc2ef5a011' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70dc3b7f-f830-42d8-95da-a7fc2ef5a011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da7a13b7-538e-4f9a-8704-d08c9a7b2ba7' class='xr-var-data-in' type='checkbox'><label for='data-da7a13b7-538e-4f9a-8704-d08c9a7b2ba7' 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></dl></div><div class='xr-var-data'><pre>array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,\n",
       "       62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,\n",
       "       65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,\n",
       "       67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,\n",
       "       70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,\n",
       "       72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,\n",
       "       75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,\n",
       "       77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,\n",
       "       80.  ], 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'>25.0 24.75 24.5 ... 5.5 5.25 5.0</div><input id='attrs-fca6c0c5-3c67-4a39-97c5-c45bda981650' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fca6c0c5-3c67-4a39-97c5-c45bda981650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4c771ef-160e-4783-b497-de29ecf76801' class='xr-var-data-in' type='checkbox'><label for='data-f4c771ef-160e-4783-b497-de29ecf76801' 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></dl></div><div class='xr-var-data'><pre>array([25.  , 24.75, 24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75,\n",
       "       22.5 , 22.25, 22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25,\n",
       "       20.  , 19.75, 19.5 , 19.25, 19.  , 18.75, 18.5 , 18.25, 18.  , 17.75,\n",
       "       17.5 , 17.25, 17.  , 16.75, 16.5 , 16.25, 16.  , 15.75, 15.5 , 15.25,\n",
       "       15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time0</span></div><div class='xr-var-dims'>(time0)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-178e0fdf-1a7e-4eb7-81fa-63d8bb6ccf5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-178e0fdf-1a7e-4eb7-81fa-63d8bb6ccf5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-354bb046-fb44-4147-8307-5e35c07fa05d' class='xr-var-data-in' type='checkbox'><label for='data-354bb046-fb44-4147-8307-5e35c07fa05d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b567baa-9500-48da-8c27-e91a53c4cef6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b567baa-9500-48da-8c27-e91a53c4cef6' 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>eastward_wind_at_10_metres</span></div><div class='xr-var-dims'>(time0, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.728 -2.138 ... -5.438 -5.688</div><input id='attrs-1c98ea53-202f-43fc-b134-240a0bf2cb06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c98ea53-202f-43fc-b134-240a0bf2cb06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9f27e4a-7c58-4e4c-8ee9-cf7792ddf149' class='xr-var-data-in' type='checkbox'><label for='data-b9f27e4a-7c58-4e4c-8ee9-cf7792ddf149' 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>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre U wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10u</dd><dt><span>nameCDM :</span></dt><dd>10_metre_U_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.7278748 , -2.138031  , -2.662445  , ..., -1.9954529 ,\n",
       "         -1.8772888 , -1.513031  ],\n",
       "        [-1.2767029 , -1.7151794 , -2.2747498 , ..., -1.9046326 ,\n",
       "         -1.7688904 , -1.8284607 ],\n",
       "        [-1.0140076 , -1.4622498 , -2.0003357 , ..., -1.9788513 ,\n",
       "         -2.0520935 , -2.2952576 ],\n",
       "        ...,\n",
       "        [-4.2327576 , -4.0765076 , -3.8382263 , ..., -3.6868591 ,\n",
       "         -5.4495544 , -6.3802185 ],\n",
       "        [-4.070648  , -3.9183044 , -3.7220154 , ..., -5.8577576 ,\n",
       "         -6.7669373 , -7.3509216 ],\n",
       "        [-3.894867  , -3.7054138 , -3.5296326 , ..., -6.575531  ,\n",
       "         -7.311859  , -7.316742  ]],\n",
       "\n",
       "       [[-2.406372  , -2.7423096 , -3.1397705 , ..., -1.7218018 ,\n",
       "         -1.3878174 , -0.9659424 ],\n",
       "        [-2.1358643 , -2.505005  , -2.9122314 , ..., -1.5587158 ,\n",
       "         -1.256958  , -1.4503174 ],\n",
       "        [-1.928833  , -2.3311768 , -2.6973877 , ..., -1.8018799 ,\n",
       "         -1.6505127 , -2.060669  ],\n",
       "...\n",
       "        [-1.625     , -1.625     , -1.6875    , ..., -1.75      ,\n",
       "         -3.5625    , -5.375     ],\n",
       "        [-1.5       , -1.5       , -1.5       , ..., -3.875     ,\n",
       "         -4.875     , -6.        ],\n",
       "        [-1.5       , -1.375     , -1.375     , ..., -4.75      ,\n",
       "         -5.4375    , -5.5       ]],\n",
       "\n",
       "       [[-1.125     , -1.375     , -2.1875    , ..., -1.5625    ,\n",
       "         -2.125     , -1.875     ],\n",
       "        [-1.625     , -2.0625    , -2.875     , ..., -1.6875    ,\n",
       "         -2.1875    , -1.9375    ],\n",
       "        [-2.1875    , -2.625     , -3.0625    , ..., -1.625     ,\n",
       "         -2.        , -1.6875    ],\n",
       "        ...,\n",
       "        [-1.125     , -1.125     , -1.1875    , ..., -1.5625    ,\n",
       "         -3.25      , -5.125     ],\n",
       "        [-1.        , -1.        , -0.9375    , ..., -3.6875    ,\n",
       "         -4.8125    , -6.125     ],\n",
       "        [-0.9375    , -0.875     , -0.8125    , ..., -4.6875    ,\n",
       "         -5.4375    , -5.6875    ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d252ab9-3bfc-4cd6-a5a4-5332951a8bd9' class='xr-section-summary-in' type='checkbox'  ><label for='section-2d252ab9-3bfc-4cd6-a5a4-5332951a8bd9' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6afa4d4-034c-4012-98c8-0850e77f2bee' class='xr-index-data-in' type='checkbox'/><label for='index-e6afa4d4-034c-4012-98c8-0850e77f2bee' 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([ 60.0, 60.25,  60.5, 60.75,  61.0, 61.25,  61.5, 61.75,  62.0, 62.25,\n",
       "        62.5, 62.75,  63.0, 63.25,  63.5, 63.75,  64.0, 64.25,  64.5, 64.75,\n",
       "        65.0, 65.25,  65.5, 65.75,  66.0, 66.25,  66.5, 66.75,  67.0, 67.25,\n",
       "        67.5, 67.75,  68.0, 68.25,  68.5, 68.75,  69.0, 69.25,  69.5, 69.75,\n",
       "        70.0, 70.25,  70.5, 70.75,  71.0, 71.25,  71.5, 71.75,  72.0, 72.25,\n",
       "        72.5, 72.75,  73.0, 73.25,  73.5, 73.75,  74.0, 74.25,  74.5, 74.75,\n",
       "        75.0, 75.25,  75.5, 75.75,  76.0, 76.25,  76.5, 76.75,  77.0, 77.25,\n",
       "        77.5, 77.75,  78.0, 78.25,  78.5, 78.75,  79.0, 79.25,  79.5, 79.75,\n",
       "        80.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</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-fb5d8fd1-104c-41a7-8eea-891059d002e5' class='xr-index-data-in' type='checkbox'/><label for='index-fb5d8fd1-104c-41a7-8eea-891059d002e5' 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([ 25.0, 24.75,  24.5, 24.25,  24.0, 23.75,  23.5, 23.25,  23.0, 22.75,\n",
       "        22.5, 22.25,  22.0, 21.75,  21.5, 21.25,  21.0, 20.75,  20.5, 20.25,\n",
       "        20.0, 19.75,  19.5, 19.25,  19.0, 18.75,  18.5, 18.25,  18.0, 17.75,\n",
       "        17.5, 17.25,  17.0, 16.75,  16.5, 16.25,  16.0, 15.75,  15.5, 15.25,\n",
       "        15.0, 14.75,  14.5, 14.25,  14.0, 13.75,  13.5, 13.25,  13.0, 12.75,\n",
       "        12.5, 12.25,  12.0, 11.75,  11.5, 11.25,  11.0, 10.75,  10.5, 10.25,\n",
       "        10.0,  9.75,   9.5,  9.25,   9.0,  8.75,   8.5,  8.25,   8.0,  7.75,\n",
       "         7.5,  7.25,   7.0,  6.75,   6.5,  6.25,   6.0,  5.75,   5.5,  5.25,\n",
       "         5.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f54d900a-676e-4aac-88f3-84152ff93162' class='xr-index-data-in' type='checkbox'/><label for='index-f54d900a-676e-4aac-88f3-84152ff93162' 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;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 01:00:00&#x27;,\n",
       "               &#x27;2000-01-01 02:00:00&#x27;, &#x27;2000-01-01 03:00:00&#x27;,\n",
       "               &#x27;2000-01-01 04:00:00&#x27;, &#x27;2000-01-01 05:00:00&#x27;,\n",
       "               &#x27;2000-01-01 06:00:00&#x27;, &#x27;2000-01-01 07:00:00&#x27;,\n",
       "               &#x27;2000-01-01 08:00:00&#x27;, &#x27;2000-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-12-31 14:00:00&#x27;, &#x27;2020-12-31 15:00:00&#x27;,\n",
       "               &#x27;2020-12-31 16:00:00&#x27;, &#x27;2020-12-31 17:00:00&#x27;,\n",
       "               &#x27;2020-12-31 18:00:00&#x27;, &#x27;2020-12-31 19:00:00&#x27;,\n",
       "               &#x27;2020-12-31 20:00:00&#x27;, &#x27;2020-12-31 21:00:00&#x27;,\n",
       "               &#x27;2020-12-31 22:00:00&#x27;, &#x27;2020-12-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time0&#x27;, length=184104, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb8cd30e-e159-44ee-895a-1d9cdf9ad74a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb8cd30e-e159-44ee-895a-1d9cdf9ad74a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>tilte :</span></dt><dd>ERA5 forecasts</dd><dt><span>title :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:                     (lon: 81, lat: 81, time0: 184104)\n",
       "Coordinates:\n",
       "  * lon                         (lon) float32 60.0 60.25 60.5 ... 79.75 80.0\n",
       "  * lat                         (lat) float32 25.0 24.75 24.5 ... 5.5 5.25 5.0\n",
       "  * time0                       (time0) datetime64[ns] 2000-01-01 ... 2020-12...\n",
       "Data variables:\n",
       "    eastward_wind_at_10_metres  (time0, lat, lon) float32 -1.728 ... -5.688\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_east"
   ]
  },
  {
   "cell_type": "code",
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   "id": "be8e217c-96a1-4afd-9d0f-569379e3f5f7",
   "metadata": {
    "tags": []
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                      (lon: 81, lat: 81, time0: 184104)\n",
       "Coordinates:\n",
       "  * lon                          (lon) float32 60.0 60.25 60.5 ... 79.75 80.0\n",
       "  * lat                          (lat) float32 25.0 24.75 24.5 ... 5.5 5.25 5.0\n",
       "  * time0                        (time0) datetime64[ns] 2000-01-01 ... 2020-1...\n",
       "Data variables:\n",
       "    northward_wind_at_10_metres  (time0, lat, lon) float32 -1.07 ... -3.188\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-36fef9e1-e9ca-4469-9877-a54f620a9322' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36fef9e1-e9ca-4469-9877-a54f620a9322' 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'>lon</span>: 81</li><li><span class='xr-has-index'>lat</span>: 81</li><li><span class='xr-has-index'>time0</span>: 184104</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8613a7e8-2ffc-45f1-b34a-0e5f305c6590' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8613a7e8-2ffc-45f1-b34a-0e5f305c6590' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.25 60.5 ... 79.5 79.75 80.0</div><input id='attrs-14240e48-8da1-412f-8b3c-a7d7e12976f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14240e48-8da1-412f-8b3c-a7d7e12976f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab71e899-43d7-402a-b570-3cfd68ee4ab3' class='xr-var-data-in' type='checkbox'><label for='data-ab71e899-43d7-402a-b570-3cfd68ee4ab3' 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></dl></div><div class='xr-var-data'><pre>array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,\n",
       "       62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,\n",
       "       65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,\n",
       "       67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,\n",
       "       70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,\n",
       "       72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,\n",
       "       75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,\n",
       "       77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,\n",
       "       80.  ], 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'>25.0 24.75 24.5 ... 5.5 5.25 5.0</div><input id='attrs-a3c543a0-7b52-4e1f-8c6b-e77b592cdb00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3c543a0-7b52-4e1f-8c6b-e77b592cdb00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26c8deec-b922-41a3-8804-d85c85d03dd3' class='xr-var-data-in' type='checkbox'><label for='data-26c8deec-b922-41a3-8804-d85c85d03dd3' 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></dl></div><div class='xr-var-data'><pre>array([25.  , 24.75, 24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75,\n",
       "       22.5 , 22.25, 22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25,\n",
       "       20.  , 19.75, 19.5 , 19.25, 19.  , 18.75, 18.5 , 18.25, 18.  , 17.75,\n",
       "       17.5 , 17.25, 17.  , 16.75, 16.5 , 16.25, 16.  , 15.75, 15.5 , 15.25,\n",
       "       15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time0</span></div><div class='xr-var-dims'>(time0)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-4e5adfc3-d572-4880-b7b9-13094c12c325' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e5adfc3-d572-4880-b7b9-13094c12c325' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04f0c572-0205-4ea0-a6aa-00ab7f90e59e' class='xr-var-data-in' type='checkbox'><label for='data-04f0c572-0205-4ea0-a6aa-00ab7f90e59e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c0b25d2-7dde-4941-a738-7d7695cc1251' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c0b25d2-7dde-4941-a738-7d7695cc1251' 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>northward_wind_at_10_metres</span></div><div class='xr-var-dims'>(time0, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.07 -1.319 ... -3.062 -3.188</div><input id='attrs-25cc1f36-f0d4-4caf-b45a-26a6ee16c963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25cc1f36-f0d4-4caf-b45a-26a6ee16c963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-513f74c3-024f-4386-bed7-11a2a446e6d0' class='xr-var-data-in' type='checkbox'><label for='data-513f74c3-024f-4386-bed7-11a2a446e6d0' 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>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre V wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10v</dd><dt><span>nameCDM :</span></dt><dd>10_metre_V_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.0697479 , -1.3187714 , -1.6449432 , ...,  1.1988068 ,\n",
       "          1.6988068 ,  1.7945099 ],\n",
       "        [-1.2494354 , -1.6390839 , -1.9496307 , ...,  1.1109161 ,\n",
       "          1.6031036 ,  1.5435333 ],\n",
       "        [-1.5619354 , -1.915451  , -2.2025604 , ...,  0.45466614,\n",
       "          0.63435364,  0.5044708 ],\n",
       "        ...,\n",
       "        [-5.8246307 , -5.9681854 , -6.129318  , ..., -2.0101776 ,\n",
       "         -3.2289276 , -4.290451  ],\n",
       "        [-5.2621307 , -5.48674   , -5.7064667 , ..., -3.158615  ,\n",
       "         -4.223068  , -5.2494354 ],\n",
       "        [-4.761154  , -5.064865  , -5.2894745 , ..., -4.000412  ,\n",
       "         -4.941818  , -5.7425995 ]],\n",
       "\n",
       "       [[-0.9208374 , -1.0829468 , -1.414978  , ...,  1.6133423 ,\n",
       "          2.0791626 ,  2.0264282 ],\n",
       "        [-1.2108765 , -1.4960327 , -1.7792358 , ...,  1.4629517 ,\n",
       "          1.9785767 ,  1.8369751 ],\n",
       "        [-1.6259155 , -1.8807983 , -2.0819702 , ...,  0.80181885,\n",
       "          1.1240845 ,  0.9805298 ],\n",
       "...\n",
       "        [-4.25      , -4.1875    , -4.1875    , ..., -1.625     ,\n",
       "         -1.5       , -1.8125    ],\n",
       "        [-4.1875    , -4.125     , -4.0625    , ..., -2.125     ,\n",
       "         -2.25      , -2.5625    ],\n",
       "        [-4.3125    , -4.1875    , -4.0625    , ..., -2.6875    ,\n",
       "         -2.6875    , -2.75      ]],\n",
       "\n",
       "       [[-1.0625    , -1.4375    , -1.625     , ...,  0.125     ,\n",
       "          0.0625    , -0.0625    ],\n",
       "        [-1.        , -1.0625    , -1.25      , ...,  0.1875    ,\n",
       "          0.125     ,  0.375     ],\n",
       "        [-1.125     , -1.1875    , -1.25      , ..., -0.0625    ,\n",
       "         -0.3125    , -0.1875    ],\n",
       "        ...,\n",
       "        [-4.375     , -4.3125    , -4.3125    , ..., -1.75      ,\n",
       "         -1.875     , -2.5       ],\n",
       "        [-4.375     , -4.3125    , -4.25      , ..., -2.5625    ,\n",
       "         -2.8125    , -3.3125    ],\n",
       "        [-4.4375    , -4.375     , -4.3125    , ..., -2.9375    ,\n",
       "         -3.0625    , -3.1875    ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9f7c6ac-2fa9-4bef-9256-eda826320b5b' class='xr-section-summary-in' type='checkbox'  ><label for='section-f9f7c6ac-2fa9-4bef-9256-eda826320b5b' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f8a2e05-ad33-44a2-b281-ec52c0a20e57' class='xr-index-data-in' type='checkbox'/><label for='index-6f8a2e05-ad33-44a2-b281-ec52c0a20e57' 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([ 60.0, 60.25,  60.5, 60.75,  61.0, 61.25,  61.5, 61.75,  62.0, 62.25,\n",
       "        62.5, 62.75,  63.0, 63.25,  63.5, 63.75,  64.0, 64.25,  64.5, 64.75,\n",
       "        65.0, 65.25,  65.5, 65.75,  66.0, 66.25,  66.5, 66.75,  67.0, 67.25,\n",
       "        67.5, 67.75,  68.0, 68.25,  68.5, 68.75,  69.0, 69.25,  69.5, 69.75,\n",
       "        70.0, 70.25,  70.5, 70.75,  71.0, 71.25,  71.5, 71.75,  72.0, 72.25,\n",
       "        72.5, 72.75,  73.0, 73.25,  73.5, 73.75,  74.0, 74.25,  74.5, 74.75,\n",
       "        75.0, 75.25,  75.5, 75.75,  76.0, 76.25,  76.5, 76.75,  77.0, 77.25,\n",
       "        77.5, 77.75,  78.0, 78.25,  78.5, 78.75,  79.0, 79.25,  79.5, 79.75,\n",
       "        80.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</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-186d450a-deef-4f9e-a1c5-8a5cb2505f2e' class='xr-index-data-in' type='checkbox'/><label for='index-186d450a-deef-4f9e-a1c5-8a5cb2505f2e' 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([ 25.0, 24.75,  24.5, 24.25,  24.0, 23.75,  23.5, 23.25,  23.0, 22.75,\n",
       "        22.5, 22.25,  22.0, 21.75,  21.5, 21.25,  21.0, 20.75,  20.5, 20.25,\n",
       "        20.0, 19.75,  19.5, 19.25,  19.0, 18.75,  18.5, 18.25,  18.0, 17.75,\n",
       "        17.5, 17.25,  17.0, 16.75,  16.5, 16.25,  16.0, 15.75,  15.5, 15.25,\n",
       "        15.0, 14.75,  14.5, 14.25,  14.0, 13.75,  13.5, 13.25,  13.0, 12.75,\n",
       "        12.5, 12.25,  12.0, 11.75,  11.5, 11.25,  11.0, 10.75,  10.5, 10.25,\n",
       "        10.0,  9.75,   9.5,  9.25,   9.0,  8.75,   8.5,  8.25,   8.0,  7.75,\n",
       "         7.5,  7.25,   7.0,  6.75,   6.5,  6.25,   6.0,  5.75,   5.5,  5.25,\n",
       "         5.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3b34e8c8-fa43-479c-ab1b-555e2325fd95' class='xr-index-data-in' type='checkbox'/><label for='index-3b34e8c8-fa43-479c-ab1b-555e2325fd95' 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;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 01:00:00&#x27;,\n",
       "               &#x27;2000-01-01 02:00:00&#x27;, &#x27;2000-01-01 03:00:00&#x27;,\n",
       "               &#x27;2000-01-01 04:00:00&#x27;, &#x27;2000-01-01 05:00:00&#x27;,\n",
       "               &#x27;2000-01-01 06:00:00&#x27;, &#x27;2000-01-01 07:00:00&#x27;,\n",
       "               &#x27;2000-01-01 08:00:00&#x27;, &#x27;2000-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-12-31 14:00:00&#x27;, &#x27;2020-12-31 15:00:00&#x27;,\n",
       "               &#x27;2020-12-31 16:00:00&#x27;, &#x27;2020-12-31 17:00:00&#x27;,\n",
       "               &#x27;2020-12-31 18:00:00&#x27;, &#x27;2020-12-31 19:00:00&#x27;,\n",
       "               &#x27;2020-12-31 20:00:00&#x27;, &#x27;2020-12-31 21:00:00&#x27;,\n",
       "               &#x27;2020-12-31 22:00:00&#x27;, &#x27;2020-12-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time0&#x27;, length=184104, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e84f505-9914-4005-9144-d176eed65a3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e84f505-9914-4005-9144-d176eed65a3f' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>tilte :</span></dt><dd>ERA5 forecasts</dd><dt><span>title :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                      (lon: 81, lat: 81, time0: 184104)\n",
       "Coordinates:\n",
       "  * lon                          (lon) float32 60.0 60.25 60.5 ... 79.75 80.0\n",
       "  * lat                          (lat) float32 25.0 24.75 24.5 ... 5.5 5.25 5.0\n",
       "  * time0                        (time0) datetime64[ns] 2000-01-01 ... 2020-1...\n",
       "Data variables:\n",
       "    northward_wind_at_10_metres  (time0, lat, lon) float32 -1.07 ... -3.188\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_north"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "08ced32d-7620-40b2-9fde-53be095516ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc6d03e2070>"
      ]
     },
     "execution_count": 21,
     "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": [
    "data_north['v_wind'].isel(time=0).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4aacc360-e2ec-4bfc-92c4-6e2c91d624e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_north = data_north.rename({'time0':'time', 'northward_wind_at_10_metres': 'v_wind'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3de04df0-69db-4c0e-975b-27ba5847aa79",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_east = data_east.rename({'time0':'time', 'eastward_wind_at_10_metres': 'u_wind'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec0ccf84-6515-4cfa-a6a9-274804d77dd5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m wind_reanalysis \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241m.\u001b[39mcombine_by_coords([data_north, data_east])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'xr' is not defined"
     ]
    }
   ],
   "source": [
    "wind_reanalysis = xr.combine_by_coords([data_north, data_east])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a0c8e67-bb49-4298-b93d-bf7c5b73d38a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "wind_reanalysis.to_netcdf('wind.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2113500d-4061-427f-ba79-596bd8779d1c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "wind_reanalysis = wind_reanalysis.rename_vars({'eastward_wind_at_10_metres': 'u_wind', 'northward_wind_at_10_metres':'v_wind'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10c2ac9d-e8af-4e62-b169-c2c480096e70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "w = wind_reanalysis.resample(time='12H').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3dcd05-795b-4321-867e-3220628203ca",
   "metadata": {},
   "source": [
    "# Open dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d8102ab-4d1f-48d8-8100-2700444f5043",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "wind = xr.open_dataset('era5/wind_12h_avg.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56167e46-6684-448c-8432-94bfc68ac02a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       ".xr-section-summary {\n",
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       ".xr-section-summary > span {\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
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       "\n",
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       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
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       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
       ".xr-var-list,\n",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
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       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       ".xr-var-data {\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  height: 1.5em !important;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lon: 81, lat: 81, time: 15342)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 60.0 60.25 60.5 60.75 61.0 ... 79.25 79.5 79.75 80.0\n",
       "  * lat      (lat) float32 25.0 24.75 24.5 24.25 24.0 ... 6.0 5.75 5.5 5.25 5.0\n",
       "  * time     (time) datetime64[ns] 2000-01-01 ... 2020-12-31T12:00:00\n",
       "Data variables:\n",
       "    u_wind   (time, lat, lon) float32 ...\n",
       "    v_wind   (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-835bc8fe-8f29-440c-ac5b-f0cef86860c8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-835bc8fe-8f29-440c-ac5b-f0cef86860c8' 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'>lon</span>: 81</li><li><span class='xr-has-index'>lat</span>: 81</li><li><span class='xr-has-index'>time</span>: 15342</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4b8063fb-1871-463d-8669-f5600ed2299a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4b8063fb-1871-463d-8669-f5600ed2299a' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.25 60.5 ... 79.5 79.75 80.0</div><input id='attrs-3ae989a5-4a83-429c-8a85-3230f6db5ba8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ae989a5-4a83-429c-8a85-3230f6db5ba8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c182766b-cc21-4c12-8a4b-87aa077034c8' class='xr-var-data-in' type='checkbox'><label for='data-c182766b-cc21-4c12-8a4b-87aa077034c8' 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></dl></div><div class='xr-var-data'><pre>array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,\n",
       "       62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,\n",
       "       65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,\n",
       "       67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,\n",
       "       70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,\n",
       "       72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,\n",
       "       75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,\n",
       "       77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,\n",
       "       80.  ], 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'>25.0 24.75 24.5 ... 5.5 5.25 5.0</div><input id='attrs-38b30aa2-d31a-4d01-9bbc-bc1c79f88555' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b30aa2-d31a-4d01-9bbc-bc1c79f88555' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea0e10c4-95e0-4885-8e46-43d0d660778c' class='xr-var-data-in' type='checkbox'><label for='data-ea0e10c4-95e0-4885-8e46-43d0d660778c' 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></dl></div><div class='xr-var-data'><pre>array([25.  , 24.75, 24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75,\n",
       "       22.5 , 22.25, 22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25,\n",
       "       20.  , 19.75, 19.5 , 19.25, 19.  , 18.75, 18.5 , 18.25, 18.  , 17.75,\n",
       "       17.5 , 17.25, 17.  , 16.75, 16.5 , 16.25, 16.  , 15.75, 15.5 , 15.25,\n",
       "       15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ], 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'>2000-01-01 ... 2020-12-31T12:00:00</div><input id='attrs-e5619c05-e4ce-4e75-9a29-4acabb510c40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5619c05-e4ce-4e75-9a29-4acabb510c40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5faca74-146f-4a12-9bf4-93d4a21be769' class='xr-var-data-in' type='checkbox'><label for='data-a5faca74-146f-4a12-9bf4-93d4a21be769' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-02T00:00:00.000000000&#x27;, ..., &#x27;2020-12-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2020-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-482bc0cd-500b-4cf2-97bb-6d2499468456' class='xr-section-summary-in' type='checkbox'  checked><label for='section-482bc0cd-500b-4cf2-97bb-6d2499468456' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_wind</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-1292a8c3-ecaf-4005-a2b3-615c887a9005' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1292a8c3-ecaf-4005-a2b3-615c887a9005' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bb4f440-9fdc-4218-a5ff-e76d632d0216' class='xr-var-data-in' type='checkbox'><label for='data-1bb4f440-9fdc-4218-a5ff-e76d632d0216' 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>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre U wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10u</dd><dt><span>nameCDM :</span></dt><dd>10_metre_U_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>[100658862 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_wind</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-1d719220-3b64-44e4-8aea-589eecf09dae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d719220-3b64-44e4-8aea-589eecf09dae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beb3abf5-cbd8-46ae-9948-83a13a9a73f0' class='xr-var-data-in' type='checkbox'><label for='data-beb3abf5-cbd8-46ae-9948-83a13a9a73f0' 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>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre V wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10v</dd><dt><span>nameCDM :</span></dt><dd>10_metre_V_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>[100658862 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfa7da09-818c-4f55-8d05-d44fd98b8d29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bfa7da09-818c-4f55-8d05-d44fd98b8d29' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>tilte :</span></dt><dd>ERA5 forecasts</dd><dt><span>title :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 81, lat: 81, time: 15342)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 60.0 60.25 60.5 60.75 61.0 ... 79.25 79.5 79.75 80.0\n",
       "  * lat      (lat) float32 25.0 24.75 24.5 24.25 24.0 ... 6.0 5.75 5.5 5.25 5.0\n",
       "  * time     (time) datetime64[ns] 2000-01-01 ... 2020-12-31T12:00:00\n",
       "Data variables:\n",
       "    u_wind   (time, lat, lon) float32 ...\n",
       "    v_wind   (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0496644-fb6f-4181-95d9-79aa6a372eae",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "wind =wind.assign(speed=np.sqrt(wind.u_wind**2+wind.v_wind**2))\n",
    "wind = wind.assign(direction=np.rad2deg(np.arctan2(wind.v_wind, wind.u_wind)))\n",
    "wind.to_netcdf('era5/wind_avg_12h_processed.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a5bb6fcc-5366-4d57-a9b7-7eea2d414240",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ind = pd.read_csv('era5/india_bounds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6636113-a70b-4d55-bc1d-943e1a335cb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = wind.isel(time=0)['speed'].plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d78eb844-86f3-425c-b10f-af57b02d98f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.image.AxesImage"
      ]
     },
     "execution_count": 17,
     "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": [
    "type(i.imshow())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "53f3c3ea-e91b-4935-b285-0d8ec027136b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff8d4050910>"
      ]
     },
     "execution_count": 32,
     "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": [
    "i.imshow()\n",
    "plt.scatter(ind['x'], ind['y'], c='red', s = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f51dcb30-e356-45c3-a4bb-c4812ef1bfc8",
   "metadata": {},
   "source": [
    "# Masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1a887c44-c9a4-4943-b25e-45f44b0c6763",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# only applies to dataset with lon,lat, and time coordinates\n",
    "\n",
    "def mask_array(dataset, lon_var_name='lon', lat_var_name='lat'):\n",
    "    mask = pd.read_csv('mask.csv')\n",
    "    flag = mask.assign(flag=1).set_index([\"lat\", \"lon\"]).flag.to_xarray().fillna(0).rename({'lat': lat_var_name, 'lon': lon_var_name})\n",
    "    flag_reindex = flag.reindex(lon=dataset[lon_var_name], lat=ex[lat_var_name], method='nearest', tolerance=1e-2, fill_value=0)\n",
    "    return dataset.where(flag_reindex)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c826539-6760-43bc-9b75-30140bce04e8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff60a5cafd0>"
      ]
     },
     "execution_count": 25,
     "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": [
    "flag.plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5436fd4d-0e1c-4b3b-bd50-1721510784d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ex = wind.u_wind.isel(time=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b3a61e1b-0c9a-45e1-8acc-5125a49be121",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;flag&#x27; (lat: 81, lon: 81)&gt;\n",
       "array([[1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 60.0 60.25 60.5 60.75 61.0 ... 79.25 79.5 79.75 80.0\n",
       "  * lat      (lat) float32 25.0 24.75 24.5 24.25 24.0 ... 6.0 5.75 5.5 5.25 5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'flag'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 81</li><li><span class='xr-has-index'>lon</span>: 81</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9c3434d4-e6d7-4b92-9169-560b2d16e723' class='xr-array-in' type='checkbox' checked><label for='section-9c3434d4-e6d7-4b92-9169-560b2d16e723' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class='xr-array-data'><pre>array([[1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])</pre></div></div></li><li class='xr-section-item'><input id='section-9cbaad12-6266-4a8e-9cd7-ad16ace0f07b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9cbaad12-6266-4a8e-9cd7-ad16ace0f07b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>60.0 60.25 60.5 ... 79.5 79.75 80.0</div><input id='attrs-1bc527d3-a885-428d-97cc-155bad301215' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bc527d3-a885-428d-97cc-155bad301215' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67fe2be0-cbce-47e6-9d4d-c5567ab9065e' class='xr-var-data-in' type='checkbox'><label for='data-67fe2be0-cbce-47e6-9d4d-c5567ab9065e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,\n",
       "       62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,\n",
       "       65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,\n",
       "       67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,\n",
       "       70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,\n",
       "       72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,\n",
       "       75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,\n",
       "       77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,\n",
       "       80.  ], 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'>25.0 24.75 24.5 ... 5.5 5.25 5.0</div><input id='attrs-e54df8cd-696a-401f-a583-3c90f49f9871' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e54df8cd-696a-401f-a583-3c90f49f9871' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d6a2e15-b00f-488e-9835-0b8714bcfc87' class='xr-var-data-in' type='checkbox'><label for='data-9d6a2e15-b00f-488e-9835-0b8714bcfc87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([25.  , 24.75, 24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75,\n",
       "       22.5 , 22.25, 22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25,\n",
       "       20.  , 19.75, 19.5 , 19.25, 19.  , 18.75, 18.5 , 18.25, 18.  , 17.75,\n",
       "       17.5 , 17.25, 17.  , 16.75, 16.5 , 16.25, 16.  , 15.75, 15.5 , 15.25,\n",
       "       15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18b3164a-19e1-4b77-af8c-958c3993beda' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-18b3164a-19e1-4b77-af8c-958c3993beda' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'flag' (lat: 81, lon: 81)>\n",
       "array([[1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 60.0 60.25 60.5 60.75 61.0 ... 79.25 79.5 79.75 80.0\n",
       "  * lat      (lat) float32 25.0 24.75 24.5 24.25 24.0 ... 6.0 5.75 5.5 5.25 5.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flag_reindex_1 = flag.reindex(lon=wind.lon, lat=wind.lat, method='nearest', tolerance=1e-2, fill_value=0)\n",
    "flag_reindex_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2815008d-4116-47ad-bf21-e6c03359d0ca",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff6090ea7f0>"
      ]
     },
     "execution_count": 44,
     "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": [
    "flag_reindex.plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b47c533b-a84d-4dce-bd0f-71475c867367",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff60a8108e0>"
      ]
     },
     "execution_count": 45,
     "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": [
    "flag_reindex_1.plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "397feedf-002e-459b-9a0e-5a479e413a2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "wind_masked = wind.where(flag_reindex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "019a46d4-248a-4330-a829-c2fc216fbaa0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff612a7a700>"
      ]
     },
     "execution_count": 37,
     "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": [
    "wind_masked.isel(time=2)['v_wind'].plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d44c39d0-5363-4c18-b273-6698fb740614",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "wind_masked= wind_masked.assign(speed=np.sqrt(wind_masked.u_wind**2+wind_masked.v_wind**2))\n",
    "wind_masked = wind_masked.assign(direction=np.rad2deg(np.arctan2(wind_masked.v_wind, wind_masked.u_wind)))\n",
    "wind_masked.to_netcdf('era5/wind_avg_12h_processed.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "868a91d0-5cae-4c51-a06b-8f96c715d60d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 15342, lat: 81, lon: 81)\n",
       "Coordinates:\n",
       "  * lon        (lon) float32 60.0 60.25 60.5 60.75 ... 79.25 79.5 79.75 80.0\n",
       "  * lat        (lat) float32 25.0 24.75 24.5 24.25 24.0 ... 5.75 5.5 5.25 5.0\n",
       "  * time       (time) datetime64[ns] 2000-01-01 ... 2020-12-31T12:00:00\n",
       "Data variables:\n",
       "    u_wind     (time, lat, lon) float32 -4.615 -4.889 -5.186 ... -6.365 -6.531\n",
       "    v_wind     (time, lat, lon) float32 -0.1133 -0.3022 ... -2.354 -2.552\n",
       "    speed      (time, lat, lon) float32 4.616 4.898 5.217 ... 6.416 6.786 7.012\n",
       "    direction  (time, lat, lon) float32 -178.6 -176.5 -173.8 ... -159.7 -158.7\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-22a5e885-9762-4e60-977b-318ac49b9d0c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22a5e885-9762-4e60-977b-318ac49b9d0c' 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>: 15342</li><li><span class='xr-has-index'>lat</span>: 81</li><li><span class='xr-has-index'>lon</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e39259b9-ba5f-4c90-87f4-78361ff46657' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e39259b9-ba5f-4c90-87f4-78361ff46657' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 60.25 60.5 ... 79.5 79.75 80.0</div><input id='attrs-3d214913-79ac-4372-9b28-9d066037709c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d214913-79ac-4372-9b28-9d066037709c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31008ba2-aef8-44ba-8e00-e69c646a06df' class='xr-var-data-in' type='checkbox'><label for='data-31008ba2-aef8-44ba-8e00-e69c646a06df' 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></dl></div><div class='xr-var-data'><pre>array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,\n",
       "       62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,\n",
       "       65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,\n",
       "       67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,\n",
       "       70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,\n",
       "       72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,\n",
       "       75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,\n",
       "       77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,\n",
       "       80.  ], 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'>25.0 24.75 24.5 ... 5.5 5.25 5.0</div><input id='attrs-59a642b1-693c-410d-ade2-5fbd249089d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59a642b1-693c-410d-ade2-5fbd249089d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-593ce848-661b-46c4-bde9-3b1bbd2fbcb4' class='xr-var-data-in' type='checkbox'><label for='data-593ce848-661b-46c4-bde9-3b1bbd2fbcb4' 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></dl></div><div class='xr-var-data'><pre>array([25.  , 24.75, 24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75,\n",
       "       22.5 , 22.25, 22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25,\n",
       "       20.  , 19.75, 19.5 , 19.25, 19.  , 18.75, 18.5 , 18.25, 18.  , 17.75,\n",
       "       17.5 , 17.25, 17.  , 16.75, 16.5 , 16.25, 16.  , 15.75, 15.5 , 15.25,\n",
       "       15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ], 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'>2000-01-01 ... 2020-12-31T12:00:00</div><input id='attrs-19d73522-f6d1-49e6-8394-57a8edb983ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19d73522-f6d1-49e6-8394-57a8edb983ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f88b2cb-d9d3-4f0d-88f2-858a3f03d8fb' class='xr-var-data-in' type='checkbox'><label for='data-1f88b2cb-d9d3-4f0d-88f2-858a3f03d8fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-02T00:00:00.000000000&#x27;, ..., &#x27;2020-12-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2020-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e20d366-102e-4817-96a0-bb120447508e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e20d366-102e-4817-96a0-bb120447508e' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_wind</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'>-4.615 -4.889 ... -6.365 -6.531</div><input id='attrs-9f27315f-2bf8-4934-9c78-bf266bb4ea7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f27315f-2bf8-4934-9c78-bf266bb4ea7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bbaff16-417a-4276-a5db-3a79b239b160' class='xr-var-data-in' type='checkbox'><label for='data-7bbaff16-417a-4276-a5db-3a79b239b160' 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>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre U wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10u</dd><dt><span>nameCDM :</span></dt><dd>10_metre_U_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>array([[[-4.614839  , -4.88909   , -5.185965  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-4.4955354 , -4.85841   , -5.17091   , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-4.1627717 , -4.5481887 , -4.883394  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [-4.5090446 , -4.3877883 , -4.2414665 , ..., -0.29892096,\n",
       "         -0.7768669 , -1.5647901 ],\n",
       "        [-4.379162  , -4.2806106 , -4.1252556 , ..., -1.545096  ,\n",
       "         -2.2694614 , -3.1251743 ],\n",
       "        [-4.2215285 , -4.1442986 , -4.000337  , ..., -2.934663  ,\n",
       "         -3.6397412 , -4.3622346 ]],\n",
       "\n",
       "       [[-3.6108093 , -3.5725605 , -3.4760437 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-3.7548523 , -3.7054546 , -3.6112976 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-3.8491719 , -3.7581074 , -3.667857  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [-2.6354167 , -2.6302083 , -2.640625  , ..., -7.1458335 ,\n",
       "         -7.46875   , -7.6145835 ],\n",
       "        [-2.5260417 , -2.5416667 , -2.5364583 , ..., -7.0729165 ,\n",
       "         -7.2916665 , -7.4114585 ],\n",
       "        [-2.421875  , -2.453125  , -2.4479167 , ..., -6.7135415 ,\n",
       "         -6.90625   , -6.9427085 ]],\n",
       "\n",
       "       [[-1.046875  , -1.1145834 , -1.390625  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-1.7135416 , -1.9010416 , -2.1458333 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-2.375     , -2.4427083 , -2.46875   , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [-2.9114583 , -2.828125  , -2.75      , ..., -4.4895835 ,\n",
       "         -5.2760415 , -6.140625  ],\n",
       "        [-2.8229167 , -2.7395833 , -2.671875  , ..., -5.7864585 ,\n",
       "         -6.21875   , -6.7760415 ],\n",
       "        [-2.7552083 , -2.6510417 , -2.5885417 , ..., -6.0520835 ,\n",
       "         -6.3645835 , -6.53125   ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_wind</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'>-0.1133 -0.3022 ... -2.354 -2.552</div><input id='attrs-cfb15075-93c8-479b-abcf-f3b2a1a4e2c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfb15075-93c8-479b-abcf-f3b2a1a4e2c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e485196-971d-46bf-bb8b-7eb04bdf7966' class='xr-var-data-in' type='checkbox'><label for='data-6e485196-971d-46bf-bb8b-7eb04bdf7966' 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>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre V wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10v</dd><dt><span>nameCDM :</span></dt><dd>10_metre_V_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.11325201, -0.30221686, -0.56377286, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-0.43999353, -0.6987012 , -0.9303093 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-0.7580274 , -0.9941928 , -1.1575228 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [-6.8303742 , -6.843477  , -6.856253  , ..., -3.1311557 ,\n",
       "         -3.2800815 , -3.3709831 ],\n",
       "        [-6.677298  , -6.7287307 , -6.775931  , ..., -3.6716015 ,\n",
       "         -4.0007033 , -4.2469597 ],\n",
       "        [-6.434378  , -6.5234084 , -6.556286  , ..., -4.2118034 ,\n",
       "         -4.566458  , -5.103975  ]],\n",
       "\n",
       "       [[ 2.013265  ,  2.0283203 ,  1.9934896 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 2.023763  ,  1.9551595 ,  1.922933  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 1.8644205 ,  1.8095703 ,  1.747233  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [-6.8229165 , -6.8385415 , -6.8697915 , ..., -1.1197916 ,\n",
       "         -1.40625   , -1.78125   ],\n",
       "        [-6.8020835 , -6.8229165 , -6.8177085 , ..., -1.4635416 ,\n",
       "         -1.6979166 , -1.9947916 ],\n",
       "        [-6.7916665 , -6.8072915 , -6.8125    , ..., -1.5520834 ,\n",
       "         -1.7760416 , -1.9947916 ]],\n",
       "\n",
       "       [[ 0.6354167 ,  0.41145834,  0.31770834, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 0.05729167, -0.16145833, -0.25      , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-0.5989583 , -0.8541667 , -0.9791667 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [-5.8229165 , -5.7864585 , -5.7604165 , ..., -2.2083333 ,\n",
       "         -2.3802083 , -2.6770833 ],\n",
       "        [-5.90625   , -5.8645835 , -5.8125    , ..., -2.1145833 ,\n",
       "         -2.3645833 , -2.65625   ],\n",
       "        [-5.9791665 , -5.9114585 , -5.84375   , ..., -2.1302083 ,\n",
       "         -2.3541667 , -2.5520833 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed</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'>4.616 4.898 5.217 ... 6.786 7.012</div><input id='attrs-4fa06258-02c3-496a-8a25-d924df2e8890' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4fa06258-02c3-496a-8a25-d924df2e8890' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-320c7c83-3cf1-4d26-b3f4-63bf2c932b90' class='xr-var-data-in' type='checkbox'><label for='data-320c7c83-3cf1-4d26-b3f4-63bf2c932b90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[4.6162286 , 4.898422  , 5.2165194 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [4.517016  , 4.908394  , 5.2539306 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [4.231226  , 4.6555815 , 5.0187044 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [8.184466  , 8.129321  , 8.062149  , ..., 3.145392  ,\n",
       "         3.3708243 , 3.7164629 ],\n",
       "        [7.985197  , 7.974926  , 7.9329042 , ..., 3.9834633 ,\n",
       "         4.599574  , 5.272891  ],\n",
       "        [7.695617  , 7.7285233 , 7.680337  , ..., 5.1333747 ,\n",
       "         5.8395424 , 6.714138  ]],\n",
       "\n",
       "       [[4.134148  , 4.108196  , 4.007104  , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [4.265505  , 4.189635  , 4.0913496 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [4.276937  , 4.1710806 , 4.0627575 , ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [7.314206  , 7.3269124 , 7.359819  , ..., 7.23304   ,\n",
       "         7.5999846 , 7.820149  ],\n",
       "        [7.2559786 , 7.280952  , 7.274254  , ..., 7.2227488 ,\n",
       "         7.486743  , 7.6752143 ],\n",
       "        [7.2105627 , 7.2358165 , 7.2389536 , ..., 6.890617  ,\n",
       "         7.1309614 , 7.2236    ]],\n",
       "\n",
       "       [[1.2246231 , 1.1881052 , 1.4264559 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [1.7144991 , 1.9078858 , 2.1603472 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [2.4493625 , 2.587745  , 2.6558416 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [6.5102186 , 6.440605  , 6.383173  , ..., 5.003309  ,\n",
       "         5.7880917 , 6.6988096 ],\n",
       "        [6.546193  , 6.4729176 , 6.3971925 , ..., 6.1607275 ,\n",
       "         6.6531277 , 7.2780766 ],\n",
       "        [6.583434  , 6.4786854 , 6.3913975 , ..., 6.416035  ,\n",
       "         6.786017  , 7.012158  ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>direction</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'>-178.6 -176.5 ... -159.7 -158.7</div><input id='attrs-be64e5f6-b255-46f1-97c5-713812700240' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be64e5f6-b255-46f1-97c5-713812700240' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbc53e0a-6664-4473-b7fd-e1c6983ce95f' class='xr-var-data-in' type='checkbox'><label for='data-dbc53e0a-6664-4473-b7fd-e1c6983ce95f' 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>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>nameECMWF :</span></dt><dd>10 metre V wind component</dd><dt><span>shortNameECMWF :</span></dt><dd>10v</dd><dt><span>nameCDM :</span></dt><dd>10_metre_V_wind_component_surface</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>array([[[-178.5942  , -176.46278 , -173.79565 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-174.41006 , -171.81624 , -169.8009  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-169.67969 , -167.6696  , -166.66513 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [-123.43063 , -122.666504, -121.742065, ...,  -95.4533  ,\n",
       "         -103.324615, -114.900444],\n",
       "        [-123.257996, -122.46325 , -121.333466, ..., -112.822495,\n",
       "         -119.56475 , -126.34792 ],\n",
       "        [-123.26846 , -122.427635, -121.38957 , ..., -124.86767 ,\n",
       "         -128.55695 , -130.51965 ]],\n",
       "\n",
       "       [[ 150.85744 ,  150.41426 ,  150.16599 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 151.67653 ,  152.1819  ,  151.96571 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 154.15587 ,  154.28867 ,  154.52856 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [-111.11952 , -111.037506, -111.025856, ..., -171.09386 ,\n",
       "         -169.33693 , -166.83377 ],\n",
       "        [-110.37312 , -110.43132 , -110.407196, ..., -168.30922 ,\n",
       "         -166.89185 , -164.93584 ],\n",
       "        [-109.62598 , -109.81749 , -109.76473 , ..., -166.98265 ,\n",
       "         -165.57806 , -163.96947 ]],\n",
       "\n",
       "       [[ 148.7437  ,  159.73793 ,  167.13081 , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 178.08504 , -175.14543 , -173.3547  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-165.84555 , -160.72627 , -158.3655  , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [-116.56504 , -116.04701 , -115.519615, ..., -153.80836 ,\n",
       "         -155.71819 , -156.44458 ],\n",
       "        [-115.54565 , -115.03916 , -114.68717 , ..., -159.92584 ,\n",
       "         -159.18146 , -158.59451 ],\n",
       "        [-114.74031 , -114.15423 , -113.89137 , ..., -160.60898 ,\n",
       "         -159.7013  , -158.65694 ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-933ed14f-a739-4dae-b397-890c04db105a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-933ed14f-a739-4dae-b397-890c04db105a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>tilte :</span></dt><dd>ERA5 forecasts</dd><dt><span>title :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 15342, lat: 81, lon: 81)\n",
       "Coordinates:\n",
       "  * lon        (lon) float32 60.0 60.25 60.5 60.75 ... 79.25 79.5 79.75 80.0\n",
       "  * lat        (lat) float32 25.0 24.75 24.5 24.25 24.0 ... 5.75 5.5 5.25 5.0\n",
       "  * time       (time) datetime64[ns] 2000-01-01 ... 2020-12-31T12:00:00\n",
       "Data variables:\n",
       "    u_wind     (time, lat, lon) float32 -4.615 -4.889 -5.186 ... -6.365 -6.531\n",
       "    v_wind     (time, lat, lon) float32 -0.1133 -0.3022 ... -2.354 -2.552\n",
       "    speed      (time, lat, lon) float32 4.616 4.898 5.217 ... 6.416 6.786 7.012\n",
       "    direction  (time, lat, lon) float32 -178.6 -176.5 -173.8 ... -159.7 -158.7\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    tilte:        ERA5 forecasts\n",
       "    title:        ERA5 forecasts"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind_masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38703411-4739-4a75-b6c8-6b048fb7eb0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "wind_masked = wind"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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   "name": "python3"
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    "name": "ipython",
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   "name": "python",
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