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    "tags": []
   },
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   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xesmf as xe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7df9a03d-3d1d-444f-9efb-4e4a48e9765f",
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   "outputs": [],
   "source": [
    "### dr4292\n",
    "import iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ebfe053-a78c-4933-865a-f354aaaa6c04",
   "metadata": {},
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    "\"targetgrid\" is the N48 grid"
   ]
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  {
   "cell_type": "markdown",
   "id": "14c3fea8-9b45-4fb8-92a6-1ef69f6d41a1",
   "metadata": {},
   "source": [
    "\"era_clima_hour\" is the climatology calculated from ERA-I files on 241x480 grid, used here to downscale to N48 grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66aadec7-0fec-4c0b-99f5-a344f9897390",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "targetgrid = xr.open_dataset('/g/data/w40/pf4000/UM/um_experiments/n48/era_n48_tuv.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21518aaa-9c2f-405d-8f9b-61a685977aec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "era_clima_hour = xr.open_dataset('/g/data/w40/pf4000/UM/um_experiments/clima_erai/ta_ERAI_climate.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7792ed4-de0d-42a9-9b9e-d9d655240b2d",
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    "tags": []
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   "outputs": [],
   "source": [
    "hhrT = era_clima_hour.ta.sel(lev=60, method='nearest') #same as above for highres data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b8e6a57-daef-4073-89ef-26323cc35274",
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     "text": [
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/iris/fileformats/cf.py:584: UserWarning: Missing CF-netCDF formula term variable 'hyam', referenced by netCDF variable 'lev'\n",
      "  warnings.warn(\n",
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/iris/fileformats/cf.py:584: UserWarning: Missing CF-netCDF formula term variable 'hybm', referenced by netCDF variable 'lev'\n",
      "  warnings.warn(\n",
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/iris/fileformats/cf.py:584: UserWarning: Missing CF-netCDF formula term variable 'aps', referenced by netCDF variable 'lev'\n",
      "  warnings.warn(\n",
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/iris/fileformats/_nc_load_rules/helpers.py:660: UserWarning: Ignoring netCDF variable 'lev' invalid units 'level'\n",
      "  warnings.warn(msg)\n",
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/iris/analysis/cartography.py:412: UserWarning: Using DEFAULT_SPHERICAL_EARTH_RADIUS.\n",
      "  warnings.warn(\"Using DEFAULT_SPHERICAL_EARTH_RADIUS.\")\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (lat: 241, lon: 480)&gt;\n",
       "array([[2.23107665e-08, 2.23107665e-08, 2.23107665e-08, ...,\n",
       "        2.23107665e-08, 2.23107665e-08, 2.23107665e-08],\n",
       "       [1.78480398e-07, 1.78480398e-07, 1.78480398e-07, ...,\n",
       "        1.78480398e-07, 1.78480398e-07, 1.78480398e-07],\n",
       "       [3.56930214e-07, 3.56930214e-07, 3.56930214e-07, ...,\n",
       "        3.56930214e-07, 3.56930214e-07, 3.56930214e-07],\n",
       "       ...,\n",
       "       [3.56930214e-07, 3.56930214e-07, 3.56930214e-07, ...,\n",
       "        3.56930214e-07, 3.56930214e-07, 3.56930214e-07],\n",
       "       [1.78480398e-07, 1.78480398e-07, 1.78480398e-07, ...,\n",
       "        1.78480398e-07, 1.78480398e-07, 1.78480398e-07],\n",
       "       [2.23107665e-08, 2.23107665e-08, 2.23107665e-08, ...,\n",
       "        2.23107665e-08, 2.23107665e-08, 2.23107665e-08]])\n",
       "Dimensions without coordinates: lat, lon</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'></div><ul class='xr-dim-list'><li><span>lat</span>: 241</li><li><span>lon</span>: 480</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7e6d90e0-b672-4672-8588-d3493d78906d' class='xr-array-in' type='checkbox' checked><label for='section-7e6d90e0-b672-4672-8588-d3493d78906d' 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>2.231e-08 2.231e-08 2.231e-08 ... 2.231e-08 2.231e-08 2.231e-08</span></div><div class='xr-array-data'><pre>array([[2.23107665e-08, 2.23107665e-08, 2.23107665e-08, ...,\n",
       "        2.23107665e-08, 2.23107665e-08, 2.23107665e-08],\n",
       "       [1.78480398e-07, 1.78480398e-07, 1.78480398e-07, ...,\n",
       "        1.78480398e-07, 1.78480398e-07, 1.78480398e-07],\n",
       "       [3.56930214e-07, 3.56930214e-07, 3.56930214e-07, ...,\n",
       "        3.56930214e-07, 3.56930214e-07, 3.56930214e-07],\n",
       "       ...,\n",
       "       [3.56930214e-07, 3.56930214e-07, 3.56930214e-07, ...,\n",
       "        3.56930214e-07, 3.56930214e-07, 3.56930214e-07],\n",
       "       [1.78480398e-07, 1.78480398e-07, 1.78480398e-07, ...,\n",
       "        1.78480398e-07, 1.78480398e-07, 1.78480398e-07],\n",
       "       [2.23107665e-08, 2.23107665e-08, 2.23107665e-08, ...,\n",
       "        2.23107665e-08, 2.23107665e-08, 2.23107665e-08]])</pre></div></div></li><li class='xr-section-item'><input id='section-f4aee04a-c162-4ddb-b87e-9bc490f3d2f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f4aee04a-c162-4ddb-b87e-9bc490f3d2f1' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-506caf4f-0c9f-4d1a-a9d0-d4afd2c4df11' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-506caf4f-0c9f-4d1a-a9d0-d4afd2c4df11' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-75c2d444-e48f-4511-a0ce-99f3002f2fe4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75c2d444-e48f-4511-a0ce-99f3002f2fe4' 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 (lat: 241, lon: 480)>\n",
       "array([[2.23107665e-08, 2.23107665e-08, 2.23107665e-08, ...,\n",
       "        2.23107665e-08, 2.23107665e-08, 2.23107665e-08],\n",
       "       [1.78480398e-07, 1.78480398e-07, 1.78480398e-07, ...,\n",
       "        1.78480398e-07, 1.78480398e-07, 1.78480398e-07],\n",
       "       [3.56930214e-07, 3.56930214e-07, 3.56930214e-07, ...,\n",
       "        3.56930214e-07, 3.56930214e-07, 3.56930214e-07],\n",
       "       ...,\n",
       "       [3.56930214e-07, 3.56930214e-07, 3.56930214e-07, ...,\n",
       "        3.56930214e-07, 3.56930214e-07, 3.56930214e-07],\n",
       "       [1.78480398e-07, 1.78480398e-07, 1.78480398e-07, ...,\n",
       "        1.78480398e-07, 1.78480398e-07, 1.78480398e-07],\n",
       "       [2.23107665e-08, 2.23107665e-08, 2.23107665e-08, ...,\n",
       "        2.23107665e-08, 2.23107665e-08, 2.23107665e-08]])\n",
       "Dimensions without coordinates: lat, lon"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### dr4292 - calculate area weights for hi-res grid\n",
    "hc=iris.load_cube('/g/data/w40/pf4000/UM/um_experiments/clima_erai/ta_ERAI_climate.nc',\"ta\")\n",
    "hr_lat=hc.dim_coords[2]\n",
    "hr_lat.guess_bounds()\n",
    "hr_lon=hc.dim_coords[3]\n",
    "hr_lon.guess_bounds()\n",
    "hr_weights=iris.analysis.cartography.area_weights(hc[0][0],normalize=True)\n",
    "hr_weights=xr.DataArray(hr_weights).rename(dim_0=\"lat\",dim_1=\"lon\")\n",
    "hr_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fc9f32a-9d9f-44f8-a781-3a4e8b16c86a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### dr4292 - add weights here\n",
    "# hhrT_timeseries = hhrT.mean(dim=[\"lat\",\"lon\"]) #calculate mean to plot for comparison\n",
    "hhrT_timeseries = hhrT.weighted(hr_weights).mean(dim=[\"lat\",\"lon\"]) #calculate mean to plot for comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc11612e-3c00-4e07-9740-f85db3041f18",
   "metadata": {},
   "source": [
    "The data pre regridding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7868e51-dd15-4a0b-9760-4b871088b3ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150eebd44f40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hhrT_timeseries.plot(color='tab:blue',label='highres ERA-I',alpha=0.5)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd60f0b-ab1f-4f80-beac-efd37be5a581",
   "metadata": {},
   "source": [
    "From here the high resolution grid as the input and the low resolution grid as the output are prepared to calculate the regridder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0241c861-23de-4ebd-bcf2-226a6f4c3bd4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (longitude: 96, latitude: 73, hybrid: 1, t: 1,\n",
       "                  longitude_1: 96, latitude_1: 72)\n",
       "Coordinates:\n",
       "  * longitude    (longitude) float32 0.0 3.75 7.5 11.25 ... 348.8 352.5 356.2\n",
       "  * latitude     (latitude) float32 -90.0 -87.5 -85.0 -82.5 ... 85.0 87.5 90.0\n",
       "  * hybrid       (hybrid) float32 60.0\n",
       "  * t            (t) datetime64[ns] 1988-11-01\n",
       "  * longitude_1  (longitude_1) float32 1.875 5.625 9.375 ... 350.6 354.4 358.1\n",
       "  * latitude_1   (latitude_1) float32 -88.75 -86.25 -83.75 ... 83.75 86.25 88.75\n",
       "Data variables:\n",
       "    T            (t, hybrid, latitude, longitude) float32 ...\n",
       "    U            (t, hybrid, latitude, longitude_1) float32 ...\n",
       "    V            (t, hybrid, latitude_1, longitude) float32 ...\n",
       "Attributes:\n",
       "    history:  Fri May 22 15:33:09 BST 2020 - XCONV V1.94 03-May-2018</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-acee4bcf-90b3-4c32-a649-9c1eaf689e54' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-acee4bcf-90b3-4c32-a649-9c1eaf689e54' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>longitude</span>: 96</li><li><span class='xr-has-index'>latitude</span>: 73</li><li><span class='xr-has-index'>hybrid</span>: 1</li><li><span class='xr-has-index'>t</span>: 1</li><li><span class='xr-has-index'>longitude_1</span>: 96</li><li><span class='xr-has-index'>latitude_1</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-26ac0e85-9e72-4847-9537-7b2a6d2119d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26ac0e85-9e72-4847-9537-7b2a6d2119d7' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-4a12fb4f-117c-471f-b53b-36a5428de1c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a12fb4f-117c-471f-b53b-36a5428de1c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c51eb91-5774-4f7e-8031-2a0f372334d8' class='xr-var-data-in' type='checkbox'><label for='data-3c51eb91-5774-4f7e-8031-2a0f372334d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>modulo :</span></dt><dd> </dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -87.5 -85.0 ... 87.5 90.0</div><input id='attrs-36f1b5c3-e79e-433c-a22d-1809f39a0085' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36f1b5c3-e79e-433c-a22d-1809f39a0085' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8939791-f7e5-428a-bca7-36716c6abf47' class='xr-var-data-in' type='checkbox'><label for='data-c8939791-f7e5-428a-bca7-36716c6abf47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd></dl></div><div class='xr-var-data'><pre>array([-90. , -87.5, -85. , -82.5, -80. , -77.5, -75. , -72.5, -70. , -67.5,\n",
       "       -65. , -62.5, -60. , -57.5, -55. , -52.5, -50. , -47.5, -45. , -42.5,\n",
       "       -40. , -37.5, -35. , -32.5, -30. , -27.5, -25. , -22.5, -20. , -17.5,\n",
       "       -15. , -12.5, -10. ,  -7.5,  -5. ,  -2.5,   0. ,   2.5,   5. ,   7.5,\n",
       "        10. ,  12.5,  15. ,  17.5,  20. ,  22.5,  25. ,  27.5,  30. ,  32.5,\n",
       "        35. ,  37.5,  40. ,  42.5,  45. ,  47.5,  50. ,  52.5,  55. ,  57.5,\n",
       "        60. ,  62.5,  65. ,  67.5,  70. ,  72.5,  75. ,  77.5,  80. ,  82.5,\n",
       "        85. ,  87.5,  90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>hybrid</span></div><div class='xr-var-dims'>(hybrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0</div><input id='attrs-d759800f-249a-4fef-914e-bcc1c1f5386c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d759800f-249a-4fef-914e-bcc1c1f5386c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36c3b8a-d710-4b23-83a6-a3f208b3c33b' class='xr-var-data-in' type='checkbox'><label for='data-b36c3b8a-d710-4b23-83a6-a3f208b3c33b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid</dd><dt><span>units :</span></dt><dd>level</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([60.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1988-11-01</div><input id='attrs-5e2f5726-978d-4737-914d-870eafad7289' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e2f5726-978d-4737-914d-870eafad7289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b972fc15-0e9a-48af-8284-4eecf5da4b05' class='xr-var-data-in' type='checkbox'><label for='data-b972fc15-0e9a-48af-8284-4eecf5da4b05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>t</dd><dt><span>time_origin :</span></dt><dd>01-NOV-1988:00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1988-11-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude_1</span></div><div class='xr-var-dims'>(longitude_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.875 5.625 9.375 ... 354.4 358.1</div><input id='attrs-96ee1d8a-33d7-4565-9775-0cdd3db7dff2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96ee1d8a-33d7-4565-9775-0cdd3db7dff2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0305b4c2-53ed-48eb-ad10-6a44e4428c57' class='xr-var-data-in' type='checkbox'><label for='data-0305b4c2-53ed-48eb-ad10-6a44e4428c57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>modulo :</span></dt><dd> </dd></dl></div><div class='xr-var-data'><pre>array([  1.875,   5.625,   9.375,  13.125,  16.875,  20.625,  24.375,  28.125,\n",
       "        31.875,  35.625,  39.375,  43.125,  46.875,  50.625,  54.375,  58.125,\n",
       "        61.875,  65.625,  69.375,  73.125,  76.875,  80.625,  84.375,  88.125,\n",
       "        91.875,  95.625,  99.375, 103.125, 106.875, 110.625, 114.375, 118.125,\n",
       "       121.875, 125.625, 129.375, 133.125, 136.875, 140.625, 144.375, 148.125,\n",
       "       151.875, 155.625, 159.375, 163.125, 166.875, 170.625, 174.375, 178.125,\n",
       "       181.875, 185.625, 189.375, 193.125, 196.875, 200.625, 204.375, 208.125,\n",
       "       211.875, 215.625, 219.375, 223.125, 226.875, 230.625, 234.375, 238.125,\n",
       "       241.875, 245.625, 249.375, 253.125, 256.875, 260.625, 264.375, 268.125,\n",
       "       271.875, 275.625, 279.375, 283.125, 286.875, 290.625, 294.375, 298.125,\n",
       "       301.875, 305.625, 309.375, 313.125, 316.875, 320.625, 324.375, 328.125,\n",
       "       331.875, 335.625, 339.375, 343.125, 346.875, 350.625, 354.375, 358.125],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude_1</span></div><div class='xr-var-dims'>(latitude_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-88.75 -86.25 ... 86.25 88.75</div><input id='attrs-f092f403-9350-4b92-af36-d0d76f091fe6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f092f403-9350-4b92-af36-d0d76f091fe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9339502f-0879-43c4-8fc7-fbe9afe6c1ea' class='xr-var-data-in' type='checkbox'><label for='data-9339502f-0879-43c4-8fc7-fbe9afe6c1ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd></dl></div><div class='xr-var-data'><pre>array([-88.75, -86.25, -83.75, -81.25, -78.75, -76.25, -73.75, -71.25, -68.75,\n",
       "       -66.25, -63.75, -61.25, -58.75, -56.25, -53.75, -51.25, -48.75, -46.25,\n",
       "       -43.75, -41.25, -38.75, -36.25, -33.75, -31.25, -28.75, -26.25, -23.75,\n",
       "       -21.25, -18.75, -16.25, -13.75, -11.25,  -8.75,  -6.25,  -3.75,  -1.25,\n",
       "         1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,  21.25,\n",
       "        23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,  43.75,\n",
       "        46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,\n",
       "        68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cbc02922-faf9-4712-a766-bd49bb98e961' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cbc02922-faf9-4712-a766-bd49bb98e961' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(t, hybrid, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0d8a00b-4c6f-475a-9d31-56bc385b75c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0d8a00b-4c6f-475a-9d31-56bc385b75c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91cf0833-834a-4022-9bbe-1d0337e67fa6' class='xr-var-data-in' type='checkbox'><label for='data-91cf0833-834a-4022-9bbe-1d0337e67fa6' 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>source :</span></dt><dd>GRIB data</dd><dt><span>name :</span></dt><dd>T</dd><dt><span>title :</span></dt><dd>Temperature</dd><dt><span>date :</span></dt><dd>01/11/88</dd><dt><span>time :</span></dt><dd>00:00</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>valid_min :</span></dt><dd>225.01167</dd><dt><span>valid_max :</span></dt><dd>308.95673</dd></dl></div><div class='xr-var-data'><pre>[7008 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(t, hybrid, latitude, longitude_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9219d31-9431-4ae7-8cb9-1b492de85b32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9219d31-9431-4ae7-8cb9-1b492de85b32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f89dece2-eb27-44bc-807d-b0a6dd58e2b8' class='xr-var-data-in' type='checkbox'><label for='data-f89dece2-eb27-44bc-807d-b0a6dd58e2b8' 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>source :</span></dt><dd>GRIB data</dd><dt><span>name :</span></dt><dd>U</dd><dt><span>title :</span></dt><dd>U velocity</dd><dt><span>date :</span></dt><dd>01/11/88</dd><dt><span>time :</span></dt><dd>00:00</dd><dt><span>long_name :</span></dt><dd>U velocity</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>valid_min :</span></dt><dd>-22.024864</dd><dt><span>valid_max :</span></dt><dd>17.052599</dd></dl></div><div class='xr-var-data'><pre>[7008 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V</span></div><div class='xr-var-dims'>(t, hybrid, latitude_1, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f14a53d-6ef7-4111-aaf7-5758948fd357' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f14a53d-6ef7-4111-aaf7-5758948fd357' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce534024-72f5-4a88-a0aa-4fccce5665cb' class='xr-var-data-in' type='checkbox'><label for='data-ce534024-72f5-4a88-a0aa-4fccce5665cb' 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>source :</span></dt><dd>GRIB data</dd><dt><span>name :</span></dt><dd>V</dd><dt><span>title :</span></dt><dd>V velocity</dd><dt><span>date :</span></dt><dd>01/11/88</dd><dt><span>time :</span></dt><dd>00:00</dd><dt><span>long_name :</span></dt><dd>V velocity</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>valid_min :</span></dt><dd>-16.232796</dd><dt><span>valid_max :</span></dt><dd>16.679754</dd></dl></div><div class='xr-var-data'><pre>[6912 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b37bc48f-a6bd-4648-aaa5-3b7f222ec6a8' class='xr-section-summary-in' type='checkbox'  ><label for='section-b37bc48f-a6bd-4648-aaa5-3b7f222ec6a8' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-75852e5e-7741-4d0b-a466-a609a72f7d08' class='xr-index-data-in' type='checkbox'/><label for='index-75852e5e-7741-4d0b-a466-a609a72f7d08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([   0.0,   3.75,    7.5,  11.25,   15.0,  18.75,   22.5,  26.25,\n",
       "                30.0,  33.75,   37.5,  41.25,   45.0,  48.75,   52.5,  56.25,\n",
       "                60.0,  63.75,   67.5,  71.25,   75.0,  78.75,   82.5,  86.25,\n",
       "                90.0,  93.75,   97.5, 101.25,  105.0, 108.75,  112.5, 116.25,\n",
       "               120.0, 123.75,  127.5, 131.25,  135.0, 138.75,  142.5, 146.25,\n",
       "               150.0, 153.75,  157.5, 161.25,  165.0, 168.75,  172.5, 176.25,\n",
       "               180.0, 183.75,  187.5, 191.25,  195.0, 198.75,  202.5, 206.25,\n",
       "               210.0, 213.75,  217.5, 221.25,  225.0, 228.75,  232.5, 236.25,\n",
       "               240.0, 243.75,  247.5, 251.25,  255.0, 258.75,  262.5, 266.25,\n",
       "               270.0, 273.75,  277.5, 281.25,  285.0, 288.75,  292.5, 296.25,\n",
       "               300.0, 303.75,  307.5, 311.25,  315.0, 318.75,  322.5, 326.25,\n",
       "               330.0, 333.75,  337.5, 341.25,  345.0, 348.75,  352.5, 356.25],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d8a5c7e-8032-43eb-bee7-d5103e67fd47' class='xr-index-data-in' type='checkbox'/><label for='index-3d8a5c7e-8032-43eb-bee7-d5103e67fd47' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-90.0, -87.5, -85.0, -82.5, -80.0, -77.5, -75.0, -72.5, -70.0,\n",
       "              -67.5, -65.0, -62.5, -60.0, -57.5, -55.0, -52.5, -50.0, -47.5,\n",
       "              -45.0, -42.5, -40.0, -37.5, -35.0, -32.5, -30.0, -27.5, -25.0,\n",
       "              -22.5, -20.0, -17.5, -15.0, -12.5, -10.0,  -7.5,  -5.0,  -2.5,\n",
       "                0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,\n",
       "               22.5,  25.0,  27.5,  30.0,  32.5,  35.0,  37.5,  40.0,  42.5,\n",
       "               45.0,  47.5,  50.0,  52.5,  55.0,  57.5,  60.0,  62.5,  65.0,\n",
       "               67.5,  70.0,  72.5,  75.0,  77.5,  80.0,  82.5,  85.0,  87.5,\n",
       "               90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>hybrid</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8851599d-01d3-42d7-ac01-aa7f1389b40d' class='xr-index-data-in' type='checkbox'/><label for='index-8851599d-01d3-42d7-ac01-aa7f1389b40d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([60.0], dtype=&#x27;float64&#x27;, name=&#x27;hybrid&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-04a7f2fa-d110-4024-b2a4-3dee2e77d149' class='xr-index-data-in' type='checkbox'/><label for='index-04a7f2fa-d110-4024-b2a4-3dee2e77d149' 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;1988-11-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;t&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c29c4a8-cf3e-44c4-901f-060f97b85ac0' class='xr-index-data-in' type='checkbox'/><label for='index-2c29c4a8-cf3e-44c4-901f-060f97b85ac0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([  1.875,   5.625,   9.375,  13.125,  16.875,  20.625,  24.375,\n",
       "               28.125,  31.875,  35.625,  39.375,  43.125,  46.875,  50.625,\n",
       "               54.375,  58.125,  61.875,  65.625,  69.375,  73.125,  76.875,\n",
       "               80.625,  84.375,  88.125,  91.875,  95.625,  99.375, 103.125,\n",
       "              106.875, 110.625, 114.375, 118.125, 121.875, 125.625, 129.375,\n",
       "              133.125, 136.875, 140.625, 144.375, 148.125, 151.875, 155.625,\n",
       "              159.375, 163.125, 166.875, 170.625, 174.375, 178.125, 181.875,\n",
       "              185.625, 189.375, 193.125, 196.875, 200.625, 204.375, 208.125,\n",
       "              211.875, 215.625, 219.375, 223.125, 226.875, 230.625, 234.375,\n",
       "              238.125, 241.875, 245.625, 249.375, 253.125, 256.875, 260.625,\n",
       "              264.375, 268.125, 271.875, 275.625, 279.375, 283.125, 286.875,\n",
       "              290.625, 294.375, 298.125, 301.875, 305.625, 309.375, 313.125,\n",
       "              316.875, 320.625, 324.375, 328.125, 331.875, 335.625, 339.375,\n",
       "              343.125, 346.875, 350.625, 354.375, 358.125],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude_1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-804cbae1-8047-4c9d-aa92-30394be97880' class='xr-index-data-in' type='checkbox'/><label for='index-804cbae1-8047-4c9d-aa92-30394be97880' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-88.75, -86.25, -83.75, -81.25, -78.75, -76.25, -73.75, -71.25,\n",
       "              -68.75, -66.25, -63.75, -61.25, -58.75, -56.25, -53.75, -51.25,\n",
       "              -48.75, -46.25, -43.75, -41.25, -38.75, -36.25, -33.75, -31.25,\n",
       "              -28.75, -26.25, -23.75, -21.25, -18.75, -16.25, -13.75, -11.25,\n",
       "               -8.75,  -6.25,  -3.75,  -1.25,   1.25,   3.75,   6.25,   8.75,\n",
       "               11.25,  13.75,  16.25,  18.75,  21.25,  23.75,  26.25,  28.75,\n",
       "               31.25,  33.75,  36.25,  38.75,  41.25,  43.75,  46.25,  48.75,\n",
       "               51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,  68.75,\n",
       "               71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude_1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dda0d021-e286-4a03-8609-3179ae3ddf42' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dda0d021-e286-4a03-8609-3179ae3ddf42' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>Fri May 22 15:33:09 BST 2020 - XCONV V1.94 03-May-2018</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (longitude: 96, latitude: 73, hybrid: 1, t: 1,\n",
       "                  longitude_1: 96, latitude_1: 72)\n",
       "Coordinates:\n",
       "  * longitude    (longitude) float32 0.0 3.75 7.5 11.25 ... 348.8 352.5 356.2\n",
       "  * latitude     (latitude) float32 -90.0 -87.5 -85.0 -82.5 ... 85.0 87.5 90.0\n",
       "  * hybrid       (hybrid) float32 60.0\n",
       "  * t            (t) datetime64[ns] 1988-11-01\n",
       "  * longitude_1  (longitude_1) float32 1.875 5.625 9.375 ... 350.6 354.4 358.1\n",
       "  * latitude_1   (latitude_1) float32 -88.75 -86.25 -83.75 ... 83.75 86.25 88.75\n",
       "Data variables:\n",
       "    T            (t, hybrid, latitude, longitude) float32 ...\n",
       "    U            (t, hybrid, latitude, longitude_1) float32 ...\n",
       "    V            (t, hybrid, latitude_1, longitude) float32 ...\n",
       "Attributes:\n",
       "    history:  Fri May 22 15:33:09 BST 2020 - XCONV V1.94 03-May-2018"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targetgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2517acde-261d-4812-a955-88d052e85578",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lrgrid = targetgrid.T[0,0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b6ef4e4-65fd-4de8-998f-8aab0b281358",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/iris/analysis/cartography.py:412: UserWarning: Using DEFAULT_SPHERICAL_EARTH_RADIUS.\n",
      "  warnings.warn(\"Using DEFAULT_SPHERICAL_EARTH_RADIUS.\")\n"
     ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (latitude: 73, longitude: 96)&gt;\n",
       "array([[1.23943683e-06, 1.23943683e-06, 1.23943683e-06, ...,\n",
       "        1.23943683e-06, 1.23943683e-06, 1.23943683e-06],\n",
       "       [9.91198529e-06, 9.91198529e-06, 9.91198529e-06, ...,\n",
       "        9.91198529e-06, 9.91198529e-06, 9.91198529e-06],\n",
       "       [1.98051801e-05, 1.98051801e-05, 1.98051801e-05, ...,\n",
       "        1.98051801e-05, 1.98051801e-05, 1.98051801e-05],\n",
       "       ...,\n",
       "       [1.98051801e-05, 1.98051801e-05, 1.98051801e-05, ...,\n",
       "        1.98051801e-05, 1.98051801e-05, 1.98051801e-05],\n",
       "       [9.91198529e-06, 9.91198529e-06, 9.91198529e-06, ...,\n",
       "        9.91198529e-06, 9.91198529e-06, 9.91198529e-06],\n",
       "       [1.23943683e-06, 1.23943683e-06, 1.23943683e-06, ...,\n",
       "        1.23943683e-06, 1.23943683e-06, 1.23943683e-06]])\n",
       "Dimensions without coordinates: latitude, longitude</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'></div><ul class='xr-dim-list'><li><span>latitude</span>: 73</li><li><span>longitude</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-62728aa1-b851-4224-a4de-1644c3c3b01f' class='xr-array-in' type='checkbox' checked><label for='section-62728aa1-b851-4224-a4de-1644c3c3b01f' 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>1.239e-06 1.239e-06 1.239e-06 ... 1.239e-06 1.239e-06 1.239e-06</span></div><div class='xr-array-data'><pre>array([[1.23943683e-06, 1.23943683e-06, 1.23943683e-06, ...,\n",
       "        1.23943683e-06, 1.23943683e-06, 1.23943683e-06],\n",
       "       [9.91198529e-06, 9.91198529e-06, 9.91198529e-06, ...,\n",
       "        9.91198529e-06, 9.91198529e-06, 9.91198529e-06],\n",
       "       [1.98051801e-05, 1.98051801e-05, 1.98051801e-05, ...,\n",
       "        1.98051801e-05, 1.98051801e-05, 1.98051801e-05],\n",
       "       ...,\n",
       "       [1.98051801e-05, 1.98051801e-05, 1.98051801e-05, ...,\n",
       "        1.98051801e-05, 1.98051801e-05, 1.98051801e-05],\n",
       "       [9.91198529e-06, 9.91198529e-06, 9.91198529e-06, ...,\n",
       "        9.91198529e-06, 9.91198529e-06, 9.91198529e-06],\n",
       "       [1.23943683e-06, 1.23943683e-06, 1.23943683e-06, ...,\n",
       "        1.23943683e-06, 1.23943683e-06, 1.23943683e-06]])</pre></div></div></li><li class='xr-section-item'><input id='section-d00fa1c2-7cf9-42c6-877d-dbb1527df968' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d00fa1c2-7cf9-42c6-877d-dbb1527df968' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-41ca1612-a316-4d60-a500-86733dd9bba1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41ca1612-a316-4d60-a500-86733dd9bba1' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-22062a92-6f40-4f5e-836a-60475c6c3802' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22062a92-6f40-4f5e-836a-60475c6c3802' 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>"
      ],
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       "<xarray.DataArray (latitude: 73, longitude: 96)>\n",
       "array([[1.23943683e-06, 1.23943683e-06, 1.23943683e-06, ...,\n",
       "        1.23943683e-06, 1.23943683e-06, 1.23943683e-06],\n",
       "       [9.91198529e-06, 9.91198529e-06, 9.91198529e-06, ...,\n",
       "        9.91198529e-06, 9.91198529e-06, 9.91198529e-06],\n",
       "       [1.98051801e-05, 1.98051801e-05, 1.98051801e-05, ...,\n",
       "        1.98051801e-05, 1.98051801e-05, 1.98051801e-05],\n",
       "       ...,\n",
       "       [1.98051801e-05, 1.98051801e-05, 1.98051801e-05, ...,\n",
       "        1.98051801e-05, 1.98051801e-05, 1.98051801e-05],\n",
       "       [9.91198529e-06, 9.91198529e-06, 9.91198529e-06, ...,\n",
       "        9.91198529e-06, 9.91198529e-06, 9.91198529e-06],\n",
       "       [1.23943683e-06, 1.23943683e-06, 1.23943683e-06, ...,\n",
       "        1.23943683e-06, 1.23943683e-06, 1.23943683e-06]])\n",
       "Dimensions without coordinates: latitude, longitude"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### dr4292 - calculate weights for low-res grid\n",
    "tc=iris.load_cube('/g/data/k10/pf4000/UM/um_experiments/climatology/climatology_hourofyear.nc',\"T\")\n",
    "lat=tc.dim_coords[2]\n",
    "lat.guess_bounds()\n",
    "lon=tc.dim_coords[3]\n",
    "lon.guess_bounds()\n",
    "lr_weights=iris.analysis.cartography.area_weights(tc[0][0],normalize=True)\n",
    "lr_weights=xr.DataArray(lr_weights).rename(dim_0=\"latitude\",dim_1=\"longitude\")\n",
    "lr_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8384e2f6-e496-4cb7-bf29-08661b17e774",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hrgrid = hhrT[0] #reduce to grid only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8436dfd-f5ae-417b-9360-13ced6262ea3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hrgrid = hrgrid.drop_vars(['time','lev']) #drop extra info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32b8e62a-8000-4de4-a751-e7ecd70cbe62",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ta&#x27; (lat: 241, lon: 480)&gt;\n",
       "array([[247.12662, 247.12662, 247.12662, ..., 247.12662, 247.12662, 247.12662],\n",
       "       [246.78316, 246.77962, 246.77637, ..., 246.79301, 246.78947, 246.78632],\n",
       "       [246.63676, 246.63649, 246.63593, ..., 246.63731, 246.63751, 246.63722],\n",
       "       ...,\n",
       "       [248.33058, 248.3356 , 248.34175, ..., 248.31961, 248.32268, 248.32622],\n",
       "       [249.1894 , 249.19536, 249.20233, ..., 249.17192, 249.1776 , 249.18317],\n",
       "       [250.28491, 250.28491, 250.28491, ..., 250.28491, 250.28491, 250.28491]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -180.0 -179.2 -178.5 -177.8 ... 177.8 178.5 179.2\n",
       "  * lat      (lat) float64 90.0 89.25 88.5 87.75 ... -87.75 -88.5 -89.25 -90.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Temperature \n",
       "    units:          K\n",
       "    code:           130\n",
       "    table:          128\n",
       "    MD5:            d41d8cd98f00b204e9800998ecf8427e</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'>'ta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 241</li><li><span class='xr-has-index'>lon</span>: 480</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cc3fc646-e01d-4d51-a537-e9f05ea55379' class='xr-array-in' type='checkbox' checked><label for='section-cc3fc646-e01d-4d51-a537-e9f05ea55379' 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>247.1 247.1 247.1 247.1 247.1 247.1 ... 250.3 250.3 250.3 250.3 250.3</span></div><div class='xr-array-data'><pre>array([[247.12662, 247.12662, 247.12662, ..., 247.12662, 247.12662, 247.12662],\n",
       "       [246.78316, 246.77962, 246.77637, ..., 246.79301, 246.78947, 246.78632],\n",
       "       [246.63676, 246.63649, 246.63593, ..., 246.63731, 246.63751, 246.63722],\n",
       "       ...,\n",
       "       [248.33058, 248.3356 , 248.34175, ..., 248.31961, 248.32268, 248.32622],\n",
       "       [249.1894 , 249.19536, 249.20233, ..., 249.17192, 249.1776 , 249.18317],\n",
       "       [250.28491, 250.28491, 250.28491, ..., 250.28491, 250.28491, 250.28491]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e7a53819-2afb-42d0-85a5-f41cd2fc9136' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7a53819-2afb-42d0-85a5-f41cd2fc9136' 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'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.2 ... 178.5 179.2</div><input id='attrs-e751dcf1-c474-46b2-8edf-ad158eedb715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e751dcf1-c474-46b2-8edf-ad158eedb715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89bd866e-63a5-4b95-9159-c36e8c94c9fc' class='xr-var-data-in' type='checkbox'><label for='data-89bd866e-63a5-4b95-9159-c36e8c94c9fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-180.  , -179.25, -178.5 , ...,  177.75,  178.5 ,  179.25])</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'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-d324d0b7-2932-41df-ab6f-afb11c685f46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d324d0b7-2932-41df-ab6f-afb11c685f46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5009e01c-f386-4fbd-9e9d-7297d92b3194' class='xr-var-data-in' type='checkbox'><label for='data-5009e01c-f386-4fbd-9e9d-7297d92b3194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df62376d-f37b-4cbf-8e6f-4b2fdb911148' class='xr-section-summary-in' type='checkbox'  ><label for='section-df62376d-f37b-4cbf-8e6f-4b2fdb911148' class='xr-section-summary' >Indexes: <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-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-12bbb3af-09fe-49d3-8a1c-99d114503398' class='xr-index-data-in' type='checkbox'/><label for='index-12bbb3af-09fe-49d3-8a1c-99d114503398' 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([ -180.0, -179.25,  -178.5, -177.75,  -177.0, -176.25,  -175.5, -174.75,\n",
       "        -174.0, -173.25,\n",
       "       ...\n",
       "         172.5,  173.25,   174.0,  174.75,   175.5,  176.25,   177.0,  177.75,\n",
       "         178.5,  179.25],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=480))</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-22d8c1e5-3db9-4c97-a0be-315f4b0a2e12' class='xr-index-data-in' type='checkbox'/><label for='index-22d8c1e5-3db9-4c97-a0be-315f4b0a2e12' 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([  90.0,  89.25,   88.5,  87.75,   87.0,  86.25,   85.5,  84.75,   84.0,\n",
       "        83.25,\n",
       "       ...\n",
       "       -83.25,  -84.0, -84.75,  -85.5, -86.25,  -87.0, -87.75,  -88.5, -89.25,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=241))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-69c1d55f-bb03-4d0f-89e3-d9d66f484a9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69c1d55f-bb03-4d0f-89e3-d9d66f484a9a' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Temperature </dd><dt><span>units :</span></dt><dd>K</dd><dt><span>code :</span></dt><dd>130</dd><dt><span>table :</span></dt><dd>128</dd><dt><span>MD5 :</span></dt><dd>d41d8cd98f00b204e9800998ecf8427e</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'ta' (lat: 241, lon: 480)>\n",
       "array([[247.12662, 247.12662, 247.12662, ..., 247.12662, 247.12662, 247.12662],\n",
       "       [246.78316, 246.77962, 246.77637, ..., 246.79301, 246.78947, 246.78632],\n",
       "       [246.63676, 246.63649, 246.63593, ..., 246.63731, 246.63751, 246.63722],\n",
       "       ...,\n",
       "       [248.33058, 248.3356 , 248.34175, ..., 248.31961, 248.32268, 248.32622],\n",
       "       [249.1894 , 249.19536, 249.20233, ..., 249.17192, 249.1776 , 249.18317],\n",
       "       [250.28491, 250.28491, 250.28491, ..., 250.28491, 250.28491, 250.28491]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -180.0 -179.2 -178.5 -177.8 ... 177.8 178.5 179.2\n",
       "  * lat      (lat) float64 90.0 89.25 88.5 87.75 ... -87.75 -88.5 -89.25 -90.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Temperature \n",
       "    units:          K\n",
       "    code:           130\n",
       "    table:          128\n",
       "    MD5:            d41d8cd98f00b204e9800998ecf8427e"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hrgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "265b37a2-7f68-4753-9012-7a233f21eaf5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/xesmf/backend.py:56: UserWarning: Latitude is outside of [-90, 90]\n",
      "  warnings.warn('Latitude is outside of [-90, 90]')\n",
      "/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/xesmf/backend.py:56: UserWarning: Latitude is outside of [-90, 90]\n",
      "  warnings.warn('Latitude is outside of [-90, 90]')\n"
     ]
    }
   ],
   "source": [
    "regridder_con = xe.Regridder(hrgrid, lrgrid, 'conservative', periodic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d60630d-93b9-4afb-a84c-0a6248eb0656",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "regridder_bil = xe.Regridder(hrgrid, lrgrid, 'bilinear', periodic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "53abb01d-ee3e-4c29-9b9a-f3d96cd60886",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xESMF Regridder \n",
       "Regridding algorithm:       conservative \n",
       "Weight filename:            conservative_241x480_73x96.nc \n",
       "Reuse pre-computed weights? False \n",
       "Input grid shape:           (241, 480) \n",
       "Output grid shape:          (73, 96) \n",
       "Periodic in longitude?      False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regridder_con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9f8eeffe-fee6-4eaf-939f-b3ae8a667b25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xESMF Regridder \n",
       "Regridding algorithm:       bilinear \n",
       "Weight filename:            bilinear_241x480_73x96_peri.nc \n",
       "Reuse pre-computed weights? False \n",
       "Input grid shape:           (241, 480) \n",
       "Output grid shape:          (73, 96) \n",
       "Periodic in longitude?      True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regridder_bil"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ad933b-f8b6-418f-ac34-13b542a1517d",
   "metadata": {},
   "source": [
    "Apply both regridders and calculate mean to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cbc3a1c0-cda7-4e6f-87fd-bc1f9d293a42",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hr2lr_con = regridder_con(hhrT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b1b25d0-8c2d-4c4e-a3bf-f8de687da48e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### dr4292 - add weights here\n",
    "# hr2lr_con_mean = hr2lr_con.mean(dim=[\"latitude\",\"longitude\"])\n",
    "hr2lr_con_mean = hr2lr_con.weighted(lr_weights).mean(dim=[\"latitude\",\"longitude\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bd6801b6-2b4c-4b0c-96d7-1cabef261ce4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hr2lr_bil = regridder_bil(hhrT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18f02614-3caf-4a4b-91e8-a2a444099e0f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### dr4292 - add weights here\n",
    "# hr2lr_bil_mean = hr2lr_bil.mean(dim=[\"latitude\",\"longitude\"])\n",
    "hr2lr_bil_mean = hr2lr_bil.weighted(lr_weights).mean(dim=[\"latitude\",\"longitude\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a4ac97aa-14f0-4ff0-8969-321e1a5eddcf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150ee20c8a00>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hhrT_timeseries.plot(color='tab:blue',label='orig highres ERA-I', alpha=0.5)\n",
    "hr2lr_con_mean.plot(color='tab:blue',label='regridded ERA-I')\n",
    "plt.title('Regridding method conservative')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f9c739b3-36d5-403e-9f42-2e274e2602d0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150ee21a8820>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hhrT_timeseries.plot(color='tab:blue',label='orig highres ERA-I', alpha=0.5)\n",
    "hr2lr_bil_mean.plot(color='tab:blue',label='regridded ERA-I')\n",
    "plt.title('Regridding method bilinear')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f54591ea-463c-40f4-b635-c178c1aa5beb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3]",
   "language": "python",
   "name": "conda-env-analysis3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}