convert_um_files_to_nc.ipynb

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    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import xarray as xr\n",
    "import iris\n",
    "import cartopy.crs as ccrs\n",
    "from iris.analysis.cartography import unrotate_pole, rotate_pole\n",
    "import pandas as pd"
   ]
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   "source": [
    "# Test code for file processing \n",
    "Note - I've converted this into a python script (um2nc_RNS.py) to run in command line: \n",
    "See: /home/563/slf563/simple_iris_wrap_RNS.sh"
   ]
  },
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   "cell_type": "code",
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   "id": "af12b6fb-e963-498e-a5e3-300404c30ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "date = '20240113T0000Z'\n",
    "fdir = '/scratch/jk72/slf563/cylc-run/u-di850/share/cycle/{}/Regn1/resn_1/RAL3P2_glomap/um/'.format(date)\n",
    "hour = '030'\n",
    "clat = -64.8\n",
    "clon = 141\n",
    "target_lat = -64.8 #  -68.5762 # For Davis \n",
    "target_lon = 141 # 77.9696"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88b5fad9-7ce3-4a6b-b25b-2cdb3868d9ff",
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/iris/fileformats/rules.py:353: UserWarning: Unable to create instance of HybridHeightFactory. The source data contains no field(s) for 'orography'.\n",
      "  warnings.warn(msg.format(factory=factory_name))\n"
     ]
    }
   ],
   "source": [
    "fname = 'umnsaa_paerbasic{}'.format(hour)\n",
    "cubes = iris.load(fdir+fname)"
   ]
  },
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   "execution_count": 6,
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       "<td class=\"iris iris-inclusion-cell\">36</td>\n",
       "<td class=\"iris iris-inclusion-cell\">90</td>\n",
       "<td class=\"iris iris-inclusion-cell\">450</td>\n",
       "<td class=\"iris iris-inclusion-cell\">400</td>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tmodel_level_number</td>\n",
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       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tgrid_latitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tgrid_longitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Auxiliary coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
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       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_period</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
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       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
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       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Scalar coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_reference_time</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"4\">2024-01-13 00:00:00</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tSTASH</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"4\">m01s38i439</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"4\">&#x27;Data from Met Office Unified Model&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tum_version</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"4\">&#x27;13.0&#x27;</td>\n",
       "</tr>\n",
       "</table>\n",
       "        </p>\n",
       "</div>\n",
       "    \n",
       "\n",
       "<button class=\"accordion-22590371713232\">3: mole_concentration_of_dimethyl_sulfide_in_sea_water / (nanomole/l) (time: 36; grid_latitude: 450; grid_longitude: 400)</button>\n",
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       "<table class=\"iris\" id=\"22590351930800\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Mole Concentration Of Dimethyl Sulfide In Sea Water (nanomole/l)</th>\n",
       "<th class=\"iris iris-word-cell\">time</th>\n",
       "<th class=\"iris iris-word-cell\">grid_latitude</th>\n",
       "<th class=\"iris iris-word-cell\">grid_longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">36</td>\n",
       "<td class=\"iris iris-inclusion-cell\">450</td>\n",
       "<td class=\"iris iris-inclusion-cell\">400</td>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tgrid_latitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tgrid_longitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Auxiliary coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_period</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Scalar coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_reference_time</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">2024-01-13 00:00:00</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tSTASH</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">m01s00i132</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Data from Met Office Unified Model&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tum_version</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;13.0&#x27;</td>\n",
       "</tr>\n",
       "</table>\n",
       "        </p>\n",
       "</div>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "[<iris 'Cube' of m01s00i096 / (unknown) (time: 36; grid_latitude: 450; grid_longitude: 400)>,\n",
       "<iris 'Cube' of m01s38i437 / (unknown) (time: 36; model_level_number: 90; grid_latitude: 450; grid_longitude: 400)>,\n",
       "<iris 'Cube' of m01s38i439 / (unknown) (time: 36; model_level_number: 90; grid_latitude: 450; grid_longitude: 400)>,\n",
       "<iris 'Cube' of mole_concentration_of_dimethyl_sulfide_in_sea_water / (nanomole/l) (time: 36; grid_latitude: 450; grid_longitude: 400)>]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cubes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d522fed-1cd0-415f-bfec-e50dbc263e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_exact_lat_lon(data,target_lat,target_lon,central_lat,central_lon): \n",
    "    grid_lon, grid_lat = rotate_pole(np.array(target_lon), np.array(target_lat), central_lon, central_lat+90)\n",
    "    grid_lon = grid_lon % 360\n",
    "    point_data = data\n",
    "    if 'grid_latitude' in data.dims: \n",
    "        point_data = point_data.sel(grid_latitude = grid_lat, grid_longitude = grid_lon, method='nearest')\n",
    "    if 'grid_latitude1' in data.dims: \n",
    "        point_data = point_data.sel(grid_latitude1 = grid_lat, grid_longitude1 = grid_lon, method='nearest')\n",
    "    if 'grid_latitude2' in data.dims: \n",
    "        point_data = point_data.sel(grid_latitude2 = grid_lat, grid_longitude2 = grid_lon, method='nearest')\n",
    "\n",
    "    return point_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "284035f1-345d-4aa7-96ad-078936cb1eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataout = xr.Dataset()\n",
    "for i,cube in enumerate(cubes[:]): \n",
    "\n",
    "    rotated_lons, rotated_lats = cube.coord('grid_longitude').points, cube.coord('grid_latitude').points\n",
    "    x, y = np.meshgrid(rotated_lons, rotated_lats)\n",
    "    lons, lats = unrotate_pole(x, y, clon, clat+90)\n",
    "    \n",
    "    data = xr.DataArray.from_iris(cube)\n",
    "    data = data.assign_coords({'latitude':(('grid_latitude','grid_longitude'),lats)})\n",
    "    data = data.assign_coords({'longitude':(('grid_latitude','grid_longitude'),lons)})\n",
    "    \n",
    "    if i > 0 and data.grid_latitude[0].values != dataout.grid_latitude[0].values:\n",
    "        data = data.rename({'grid_latitude':'grid_latitude1'})\n",
    "        data = data.rename({'grid_longitude':'grid_longitude1'})\n",
    "        data = data.rename({'latitude':'latitude1'})\n",
    "        data = data.rename({'longitude':'longitude1'})\n",
    "\n",
    "    if 'grid_longitude' in data.dims and i > 0 and data.grid_longitude[0].values != dataout.grid_longitude[0].values:\n",
    "        data = data.rename({'grid_longitude':'grid_longitude2'})\n",
    "        data = data.rename({'grid_latitude':'grid_latitude2'})\n",
    "        data = data.rename({'latitude':'latitude2'})\n",
    "        data = data.rename({'longitude':'longitude2'})\n",
    "\n",
    "    if 'time' in list(data.coords) and 'time' in list(dataout.coords):    \n",
    "        if data.time[0].values != dataout.time[0].values:\n",
    "            data = data.rename({'time':'time1'})\n",
    "            data = data.rename({'forecast_period':'forecast_period1'})\n",
    "            \n",
    "    \n",
    "    if 'level_height' in list(data.coords) and 'level_height' in list(dataout.coords):    \n",
    "        if data.level_height[0].values != dataout.level_height[0].values:\n",
    "            data = data.rename({'level_height':'level_height1'})\n",
    "            data = data.rename({'model_level_number':'model_level_number1'})\n",
    "\n",
    "    if 'sigma' in list(data.coords) and 'sigma' in list(dataout.coords):\n",
    "        if data.sigma[0].values != dataout.sigma[0].values:\n",
    "            data = data.rename({'sigma':'sigma1'})\n",
    "\n",
    "    if (data.attrs['STASH'].item == 229) and (data.attrs['STASH'].section == 15): \n",
    "        data = data.rename('potential_vorticity')\n",
    "    if data.attrs['STASH'].item == 293 and data.attrs['STASH'].section == 30:     \n",
    "        data = data.rename('w_component_of_wind')\n",
    "    if data.attrs['STASH'].item == 294 and data.attrs['STASH'].section == 30: \n",
    "        data = data.rename('temperature')\n",
    "    if data.attrs['STASH'].item == 295 and data.attrs['STASH'].section == 30:     \n",
    "        data = data.rename('specific_humidity')\n",
    "    if data.attrs['STASH'].item == 296 and data.attrs['STASH'].section == 30: \n",
    "        data = data.rename('relative_humidity')\n",
    "    if data.attrs['STASH'].item == 297 and data.attrs['STASH'].section == 30: \n",
    "        data = data.rename('geopotential_height')\n",
    "    if data.attrs['STASH'].item == 304 and data.attrs['STASH'].section == 30: \n",
    "        data = data.rename('heavyside_function')  \n",
    "\n",
    "    if data.attrs['STASH'].item == 401 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('dry_particle_diameter_soluble_nucleation_mode_aerosol')  \n",
    "    if data.attrs['STASH'].item == 402 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('dry_particle_diameter_soluble_aitken_mode_aerosol')  \n",
    "    if data.attrs['STASH'].item == 403 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('dry_particle_diameter_soluble_accumulation_mode_aerosol')  \n",
    "    if data.attrs['STASH'].item == 404 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('dry_particle_diameter_soluble_course_mode_aerosol')  \n",
    "    if data.attrs['STASH'].item == 405 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('dry_particle_diameter_insoluble_aitken_mode_aerosol')  \n",
    "    if data.attrs['STASH'].item == 406 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('dry_particle_diameter_insoluble_aitken_mode_aerosol')  \n",
    "    if data.attrs['STASH'].item == 407 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('dry_particle_diameter_insoluble_aitken_mode_aerosol')  \n",
    "\n",
    "    if data.attrs['STASH'].item == 96 and data.attrs['STASH'].section == 0: \n",
    "        data = data.rename('ocean_near_surface_chlorophyll_km_per_m3')  \n",
    "    if data.attrs['STASH'].item == 437 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('condensation_nuclei_number_concentration_greater_than_3nm_dry_diameter_per_cm3')  \n",
    "    if data.attrs['STASH'].item == 439 and data.attrs['STASH'].section == 38: \n",
    "        data = data.rename('CCN_greater_than_50nm_dry_diameter_AIT+ACC+COA_per_cm3')  \n",
    "\n",
    "    if data.attrs['STASH'].item == 230 and data.attrs['STASH'].section == 3: \n",
    "        data = data.rename({'height':'height1'})  \n",
    "    if data.attrs['STASH'].item == 209 and data.attrs['STASH'].section == 3: \n",
    "        data = data.rename({'height':'height1'}) \n",
    "    if data.attrs['STASH'].item == 210 and data.attrs['STASH'].section == 3: \n",
    "        data = data.rename({'height':'height1'}) \n",
    "\n",
    "    comp = dict(zlib=True, complevel=5)\n",
    "    data.encoding.update(comp) \n",
    "\n",
    "    dataout = dataout.merge(data)\n",
    "    \n",
    "dataout = dataout.assign_attrs({'central lat of rotated grid':clat,\n",
    "                                'central lon of rotated grid':clon,\n",
    "                                'history':'Data generated and processed by S. Fiddes sonya.fiddes@utas.edu.au {}'.format(datetime.today().date())})\n",
    "\n",
    "point_data = filter_exact_lat_lon(dataout,target_lat,target_lon,clat,clon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "714aa033-263f-404a-a38f-ad07bf1b841b",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                                                         (\n",
       "                                                                                     time: 36,\n",
       "                                                                                     grid_latitude: 450,\n",
       "                                                                                     grid_longitude: 400,\n",
       "                                                                                     time1: 36,\n",
       "                                                                                     model_level_number: 90)\n",
       "Coordinates:\n",
       "  * time                                                                            (time) datetime64[ns] ...\n",
       "  * grid_latitude                                                                   (grid_latitude) float32 ...\n",
       "  * grid_longitude                                                                  (grid_longitude) float32 ...\n",
       "    forecast_reference_time                                                         datetime64[ns] ...\n",
       "    forecast_period                                                                 (time) timedelta64[ns] ...\n",
       "    latitude                                                                        (grid_latitude, grid_longitude) float64 ...\n",
       "    longitude                                                                       (grid_latitude, grid_longitude) float64 ...\n",
       "  * time1                                                                           (time1) datetime64[ns] ...\n",
       "  * model_level_number                                                              (model_level_number) int64 ...\n",
       "    forecast_period1                                                                (time1) timedelta64[ns] ...\n",
       "    level_height                                                                    (model_level_number) float64 ...\n",
       "    sigma                                                                           (model_level_number) float64 ...\n",
       "Data variables:\n",
       "    ocean_near_surface_chlorophyll_km_per_m3                                        (time, grid_latitude, grid_longitude) float32 dask.array&lt;chunksize=(1, 450, 400), meta=np.ndarray&gt;\n",
       "    condensation_nuclei_number_concentration_greater_than_3nm_dry_diameter_per_cm3  (time1, model_level_number, grid_latitude, grid_longitude) float64 dask.array&lt;chunksize=(1, 1, 450, 400), meta=np.ndarray&gt;\n",
       "    CCN_greater_than_50nm_dry_diameter_AIT+ACC+COA_per_cm3                          (time1, model_level_number, grid_latitude, grid_longitude) float64 dask.array&lt;chunksize=(1, 1, 450, 400), meta=np.ndarray&gt;\n",
       "    mole_concentration_of_dimethyl_sulfide_in_sea_water                             (time, grid_latitude, grid_longitude) float64 dask.array&lt;chunksize=(1, 450, 400), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    central lat of rotated grid:  -64.8\n",
       "    central lon of rotated grid:  141\n",
       "    history:                      Data generated and processed by S. Fiddes s...</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-f5496987-15f0-4d1c-8a2f-1d10aa02cca7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f5496987-15f0-4d1c-8a2f-1d10aa02cca7' 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>: 36</li><li><span class='xr-has-index'>grid_latitude</span>: 450</li><li><span class='xr-has-index'>grid_longitude</span>: 400</li><li><span class='xr-has-index'>time1</span>: 36</li><li><span class='xr-has-index'>model_level_number</span>: 90</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-51da8b3a-2468-428b-bf9a-6600ea5ee4b3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51da8b3a-2468-428b-bf9a-6600ea5ee4b3' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-14T06:01:15 ... 2024-01-...</div><input id='attrs-b94a4895-f6d1-4b40-92c3-3a76893d6966' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b94a4895-f6d1-4b40-92c3-3a76893d6966' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99063345-2763-4aa2-a85c-77b62cf9dd84' class='xr-var-data-in' type='checkbox'><label for='data-99063345-2763-4aa2-a85c-77b62cf9dd84' 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;2024-01-14T06:01:15.000000000&#x27;, &#x27;2024-01-14T06:11:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T06:21:15.000000000&#x27;, &#x27;2024-01-14T06:31:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T06:41:15.000000000&#x27;, &#x27;2024-01-14T06:51:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T07:01:15.000000000&#x27;, &#x27;2024-01-14T07:11:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T07:21:15.000000000&#x27;, &#x27;2024-01-14T07:31:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T07:41:15.000000000&#x27;, &#x27;2024-01-14T07:51:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T08:01:15.000000000&#x27;, &#x27;2024-01-14T08:11:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T08:21:15.000000000&#x27;, &#x27;2024-01-14T08:31:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T08:41:15.000000000&#x27;, &#x27;2024-01-14T08:51:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:01:15.000000000&#x27;, &#x27;2024-01-14T09:11:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:21:15.000000000&#x27;, &#x27;2024-01-14T09:31:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:41:15.000000000&#x27;, &#x27;2024-01-14T09:51:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:01:15.000000000&#x27;, &#x27;2024-01-14T10:11:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:21:15.000000000&#x27;, &#x27;2024-01-14T10:31:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:41:15.000000000&#x27;, &#x27;2024-01-14T10:51:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:01:15.000000000&#x27;, &#x27;2024-01-14T11:11:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:21:15.000000000&#x27;, &#x27;2024-01-14T11:31:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:41:15.000000000&#x27;, &#x27;2024-01-14T11:51:15.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_latitude</span></div><div class='xr-var-dims'>(grid_latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.037 -3.024 -3.01 ... 3.011 3.024</div><input id='attrs-b7d2504d-4496-46c2-8555-ce1e27cb7562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7d2504d-4496-46c2-8555-ce1e27cb7562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1d8011c-7113-4d0d-b86c-ae9d5dc7d7c2' class='xr-var-data-in' type='checkbox'><label for='data-e1d8011c-7113-4d0d-b86c-ae9d5dc7d7c2' 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>grid_latitude</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([-3.0375, -3.024 , -3.0105, ...,  2.997 ,  3.0105,  3.024 ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_longitude</span></div><div class='xr-var-dims'>(grid_longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>177.3 177.3 177.3 ... 182.7 182.7</div><input id='attrs-71f24252-5986-42d3-8090-bfbbd94ec8b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71f24252-5986-42d3-8090-bfbbd94ec8b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c17677df-7ee3-4838-9b79-7a3ca46b31e0' class='xr-var-data-in' type='checkbox'><label for='data-c17677df-7ee3-4838-9b79-7a3ca46b31e0' 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>grid_longitude</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([177.3    , 177.3135 , 177.327  , ..., 182.6595 , 182.673  , 182.68651],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-13</div><input id='attrs-3df8ba33-2db8-49d9-8da5-8eb02a7fc7d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3df8ba33-2db8-49d9-8da5-8eb02a7fc7d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f03d8031-f459-4463-b5eb-cf166d53a9cd' class='xr-var-data-in' type='checkbox'><label for='data-f03d8031-f459-4463-b5eb-cf166d53a9cd' 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>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-01-13T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>1 days 06:01:14.999999930 ... 1 ...</div><input id='attrs-f9bd5aba-0880-4757-9a8f-d8b0b5ba53dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9bd5aba-0880-4757-9a8f-d8b0b5ba53dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04215de4-1559-4015-9ec1-040f6b578ff6' class='xr-var-data-in' type='checkbox'><label for='data-04215de4-1559-4015-9ec1-040f6b578ff6' 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>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([108074999999930, 108675000000000, 109275000000069, 109874999999930,\n",
       "       110475000000000, 111075000000069, 111674999999930, 112275000000000,\n",
       "       112875000000069, 113474999999930, 114075000000000, 114675000000069,\n",
       "       115274999999930, 115875000000000, 116475000000069, 117074999999930,\n",
       "       117675000000000, 118275000000069, 118874999999930, 119475000000000,\n",
       "       120075000000069, 120674999999930, 121275000000000, 121875000000069,\n",
       "       122474999999930, 123075000000000, 123675000000069, 124274999999930,\n",
       "       124875000000000, 125475000000069, 126074999999930, 126675000000000,\n",
       "       127275000000069, 127874999999930, 128475000000000, 129075000000069],\n",
       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(grid_latitude, grid_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-67.69 -67.69 ... -61.66 -61.66</div><input id='attrs-a7cb0cfc-9282-415b-b73e-2ec9cb05c720' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7cb0cfc-9282-415b-b73e-2ec9cb05c720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da6b9fc1-d385-45e5-9dd3-70cb50a5f960' class='xr-var-data-in' type='checkbox'><label for='data-da6b9fc1-d385-45e5-9dd3-70cb50a5f960' 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([[-67.68564572, -67.68715579, -67.68865671, ..., -67.69015185,\n",
       "        -67.68865671, -67.68715409],\n",
       "       [-67.67223103, -67.67374026, -67.67524033, ..., -67.67673464,\n",
       "        -67.67524033, -67.67373856],\n",
       "       [-67.65881624, -67.66032463, -67.66182387, ..., -67.66331734,\n",
       "        -67.66182387, -67.66032293],\n",
       "       ...,\n",
       "       [-61.68160454, -61.68281324, -61.68401457, ..., -61.68521126,\n",
       "        -61.68401457, -61.68281187],\n",
       "       [-61.66815891, -61.66936706, -61.67056786, ..., -61.67176401,\n",
       "        -61.67056786, -61.6693657 ],\n",
       "       [-61.6547137 , -61.65592132, -61.65712158, ..., -61.6583172 ,\n",
       "        -61.65712158, -61.65591996]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(grid_latitude, grid_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>133.9 133.9 134.0 ... 146.6 146.7</div><input id='attrs-67b1c36e-3e81-4544-8f35-d13936077322' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67b1c36e-3e81-4544-8f35-d13936077322' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cc683be-d9bf-4c2a-b55b-80b9d958eb70' class='xr-var-data-in' type='checkbox'><label for='data-7cc683be-d9bf-4c2a-b55b-80b9d958eb70' 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([[133.88323779, 133.9185317 , 133.95379018, ..., 148.01090699,\n",
       "        148.04620982, 148.08150819],\n",
       "       [133.88722691, 133.92250139, 133.95774046, ..., 148.00697616,\n",
       "        148.04225954, 148.07753847],\n",
       "       [133.89121149, 133.92646656, 133.96168624, ..., 148.0030498 ,\n",
       "        148.03831376, 148.07357328],\n",
       "       ...,\n",
       "       [135.30879138, 135.33710582, 135.3653905 , ..., 146.60629058,\n",
       "        146.6346095 , 146.66292617],\n",
       "       [135.31134738, 135.33964925, 135.36792137, ..., 146.6037723 ,\n",
       "        146.63207863, 146.66038274],\n",
       "       [135.31390107, 135.34219037, 135.37044993, ..., 146.60125631,\n",
       "        146.62955007, 146.6578416 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time1</span></div><div class='xr-var-dims'>(time1)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-14T06:02:30 ... 2024-01-...</div><input id='attrs-b7456386-5bed-47d6-ace8-43896bfee461' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7456386-5bed-47d6-ace8-43896bfee461' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15fec8f2-b05d-4947-ac65-93c8688c71ef' class='xr-var-data-in' type='checkbox'><label for='data-15fec8f2-b05d-4947-ac65-93c8688c71ef' 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;2024-01-14T06:02:30.000000000&#x27;, &#x27;2024-01-14T06:12:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T06:22:30.000000000&#x27;, &#x27;2024-01-14T06:32:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T06:42:30.000000000&#x27;, &#x27;2024-01-14T06:52:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T07:02:30.000000000&#x27;, &#x27;2024-01-14T07:12:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T07:22:30.000000000&#x27;, &#x27;2024-01-14T07:32:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T07:42:30.000000000&#x27;, &#x27;2024-01-14T07:52:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T08:02:30.000000000&#x27;, &#x27;2024-01-14T08:12:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T08:22:30.000000000&#x27;, &#x27;2024-01-14T08:32:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T08:42:30.000000000&#x27;, &#x27;2024-01-14T08:52:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:02:30.000000000&#x27;, &#x27;2024-01-14T09:12:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:22:30.000000000&#x27;, &#x27;2024-01-14T09:32:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:42:30.000000000&#x27;, &#x27;2024-01-14T09:52:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:02:30.000000000&#x27;, &#x27;2024-01-14T10:12:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:22:30.000000000&#x27;, &#x27;2024-01-14T10:32:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:42:30.000000000&#x27;, &#x27;2024-01-14T10:52:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:02:30.000000000&#x27;, &#x27;2024-01-14T11:12:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:22:30.000000000&#x27;, &#x27;2024-01-14T11:32:30.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:42:30.000000000&#x27;, &#x27;2024-01-14T11:52:30.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model_level_number</span></div><div class='xr-var-dims'>(model_level_number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 85 86 87 88 89 90</div><input id='attrs-bb047ac1-a424-4e70-b8c1-f09fb03f50dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb047ac1-a424-4e70-b8c1-f09fb03f50dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af51c63e-a005-4ca8-b27a-2f34854c6ce2' class='xr-var-data-in' type='checkbox'><label for='data-af51c63e-a005-4ca8-b27a-2f34854c6ce2' 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>model_level_number</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period1</span></div><div class='xr-var-dims'>(time1)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>1 days 06:02:30.000000069 ... 1 ...</div><input id='attrs-1c371c44-8646-482a-8ec2-78f3e4f746b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c371c44-8646-482a-8ec2-78f3e4f746b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ea4f2d5-d840-4325-a930-6ea459756526' class='xr-var-data-in' type='checkbox'><label for='data-7ea4f2d5-d840-4325-a930-6ea459756526' 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>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([108150000000069, 108749999999930, 109350000000000, 109950000000069,\n",
       "       110549999999930, 111150000000000, 111750000000069, 112349999999930,\n",
       "       112950000000000, 113550000000069, 114149999999930, 114750000000000,\n",
       "       115350000000069, 115949999999930, 116550000000000, 117150000000069,\n",
       "       117749999999930, 118350000000000, 118950000000069, 119549999999930,\n",
       "       120150000000000, 120750000000069, 121349999999930, 121950000000000,\n",
       "       122550000000069, 123149999999930, 123750000000000, 124350000000069,\n",
       "       124949999999930, 125550000000000, 126150000000069, 126749999999930,\n",
       "       127350000000000, 127950000000069, 128549999999930, 129150000000000],\n",
       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>level_height</span></div><div class='xr-var-dims'>(model_level_number)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 17.14 36.43 ... 3.867e+04 4e+04</div><input id='attrs-a82cf73b-ea19-4468-8bc6-679f13ffcba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a82cf73b-ea19-4468-8bc6-679f13ffcba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed9f0818-e94e-4a94-8626-2d8170b5ae29' class='xr-var-data-in' type='checkbox'><label for='data-ed9f0818-e94e-4a94-8626-2d8170b5ae29' 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>level_height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([5.0000000e+00, 1.7142860e+01, 3.6428576e+01, 6.2857120e+01,\n",
       "       9.6428560e+01, 1.3714284e+02, 1.8500000e+02, 2.4000000e+02,\n",
       "       3.0214288e+02, 3.7142856e+02, 4.4785720e+02, 5.3142880e+02,\n",
       "       6.2214280e+02, 7.2000000e+02, 8.2500000e+02, 9.3714280e+02,\n",
       "       1.0564288e+03, 1.1828572e+03, 1.3164288e+03, 1.4571432e+03,\n",
       "       1.6050000e+03, 1.7600000e+03, 1.9221432e+03, 2.0914288e+03,\n",
       "       2.2678576e+03, 2.4514292e+03, 2.6421432e+03, 2.8400004e+03,\n",
       "       3.0450004e+03, 3.2571432e+03, 3.4764292e+03, 3.7028576e+03,\n",
       "       3.9364288e+03, 4.1771440e+03, 4.4250000e+03, 4.6800000e+03,\n",
       "       4.9421440e+03, 5.2114280e+03, 5.4878560e+03, 5.7714280e+03,\n",
       "       6.0627600e+03, 6.3627800e+03, 6.6718200e+03, 6.9902080e+03,\n",
       "       7.3182560e+03, 7.6563000e+03, 8.0046760e+03, 8.3637400e+03,\n",
       "       8.7338640e+03, 9.1154200e+03, 9.5088120e+03, 9.9144480e+03,\n",
       "       1.0332752e+04, 1.0764168e+04, 1.1209144e+04, 1.1668152e+04,\n",
       "       1.2141680e+04, 1.2630228e+04, 1.3134308e+04, 1.3654456e+04,\n",
       "       1.4191212e+04, 1.4745140e+04, 1.5316820e+04, 1.5906840e+04,\n",
       "       1.6515812e+04, 1.7144356e+04, 1.7793112e+04, 1.8462740e+04,\n",
       "       1.9153904e+04, 1.9867296e+04, 2.0603612e+04, 2.1363572e+04,\n",
       "       2.2147916e+04, 2.2957384e+04, 2.3792748e+04, 2.4654788e+04,\n",
       "       2.5544296e+04, 2.6462096e+04, 2.7409008e+04, 2.8385884e+04,\n",
       "       2.9393580e+04, 3.0432976e+04, 3.1504968e+04, 3.2610464e+04,\n",
       "       3.3750388e+04, 3.4925684e+04, 3.6137308e+04, 3.7386240e+04,\n",
       "       3.8673472e+04, 4.0000000e+04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(model_level_number)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9994 0.998 0.9958 ... 0.0 0.0 0.0</div><input id='attrs-82fbfd66-7202-4337-9cf5-aef92c4c121e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82fbfd66-7202-4337-9cf5-aef92c4c121e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6adf2a3-af79-488a-b4ff-a8190c3c591d' class='xr-var-data-in' type='checkbox'><label for='data-b6adf2a3-af79-488a-b4ff-a8190c3c591d' 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>sigma</dd></dl></div><div class='xr-var-data'><pre>array([9.99427631e-01, 9.98038273e-01, 9.95833632e-01, 9.92816420e-01,\n",
       "       9.88990341e-01, 9.84360116e-01, 9.78931459e-01, 9.72711095e-01,\n",
       "       9.65706744e-01, 9.57927143e-01, 9.49382005e-01, 9.40082063e-01,\n",
       "       9.30039116e-01, 9.19265817e-01, 9.07775953e-01, 8.95584269e-01,\n",
       "       8.82706474e-01, 8.69159405e-01, 8.54960733e-01, 8.40129260e-01,\n",
       "       8.24684790e-01, 8.08648004e-01, 7.92040673e-01, 7.74885650e-01,\n",
       "       7.57206628e-01, 7.39028427e-01, 7.20376866e-01, 7.01278651e-01,\n",
       "       6.81761610e-01, 6.61854533e-01, 6.41587177e-01, 6.20990412e-01,\n",
       "       6.00096002e-01, 5.78936645e-01, 5.57546428e-01, 5.35959880e-01,\n",
       "       5.14212822e-01, 4.92342396e-01, 4.70386093e-01, 4.48382741e-01,\n",
       "       4.26326122e-01, 4.04193136e-01, 3.82011540e-01, 3.59813616e-01,\n",
       "       3.37637127e-01, 3.15522774e-01, 2.93516153e-01, 2.71666793e-01,\n",
       "       2.50028852e-01, 2.28662453e-01, 2.07632283e-01, 1.87009691e-01,\n",
       "       1.66872500e-01, 1.47305389e-01, 1.28401202e-01, 1.10260632e-01,\n",
       "       9.29932790e-02, 7.67184754e-02, 6.15659554e-02, 4.76762599e-02,\n",
       "       3.52021183e-02, 2.43087380e-02, 1.51749639e-02, 7.99431510e-03,\n",
       "       2.97573030e-03, 3.44814227e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1393a2d6-a335-4f78-82f2-cd8dcd7869bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1393a2d6-a335-4f78-82f2-cd8dcd7869bd' 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>ocean_near_surface_chlorophyll_km_per_m3</span></div><div class='xr-var-dims'>(time, grid_latitude, grid_longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 450, 400), meta=np.ndarray&gt;</div><input id='attrs-5e28634e-372c-4e6e-9a80-b226955e8e7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e28634e-372c-4e6e-9a80-b226955e8e7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ccd0ee8-bdd0-47f0-8770-6e0350c918e5' class='xr-var-data-in' type='checkbox'><label for='data-0ccd0ee8-bdd0-47f0-8770-6e0350c918e5' 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>Data from Met Office Unified Model</dd><dt><span>um_version :</span></dt><dd>13.0</dd><dt><span>STASH :</span></dt><dd>m01s00i096</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.72 MiB </td>\n",
       "                        <td> 703.12 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (36, 450, 400) </td>\n",
       "                        <td> (1, 450, 400) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 36 chunks in 74 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condensation_nuclei_number_concentration_greater_than_3nm_dry_diameter_per_cm3</span></div><div class='xr-var-dims'>(time1, model_level_number, grid_latitude, grid_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 450, 400), meta=np.ndarray&gt;</div><input id='attrs-478b2e6a-47c3-421a-a792-d147b3959e44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-478b2e6a-47c3-421a-a792-d147b3959e44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e85829e2-b949-4cc2-9bde-14b8b28232c5' class='xr-var-data-in' type='checkbox'><label for='data-e85829e2-b949-4cc2-9bde-14b8b28232c5' 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>Data from Met Office Unified Model</dd><dt><span>um_version :</span></dt><dd>13.0</dd><dt><span>STASH :</span></dt><dd>m01s38i437</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.35 GiB </td>\n",
       "                        <td> 1.37 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (1, 1, 450, 400) </td>\n",
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       "                        <td colspan=\"2\"> 3240 chunks in 6518 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CCN_greater_than_50nm_dry_diameter_AIT+ACC+COA_per_cm3</span></div><div class='xr-var-dims'>(time1, model_level_number, grid_latitude, grid_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 450, 400), meta=np.ndarray&gt;</div><input id='attrs-6c5acd2a-bd59-4926-87c6-d5d083289bbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c5acd2a-bd59-4926-87c6-d5d083289bbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca2eeb7-c8da-4c6c-98e4-8136b464b539' class='xr-var-data-in' type='checkbox'><label for='data-eca2eeb7-c8da-4c6c-98e4-8136b464b539' 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>Data from Met Office Unified Model</dd><dt><span>um_version :</span></dt><dd>13.0</dd><dt><span>STASH :</span></dt><dd>m01s38i439</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 4.35 GiB </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mole_concentration_of_dimethyl_sulfide_in_sea_water</span></div><div class='xr-var-dims'>(time, grid_latitude, grid_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 450, 400), meta=np.ndarray&gt;</div><input id='attrs-34e42c8c-77c9-47c9-beee-8fad92125567' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34e42c8c-77c9-47c9-beee-8fad92125567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d22c5319-38ef-4780-832e-b40fb81ccd0d' class='xr-var-data-in' type='checkbox'><label for='data-d22c5319-38ef-4780-832e-b40fb81ccd0d' 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>mole_concentration_of_dimethyl_sulfide_in_sea_water</dd><dt><span>units :</span></dt><dd>nanomole/l</dd><dt><span>source :</span></dt><dd>Data from Met Office Unified Model</dd><dt><span>um_version :</span></dt><dd>13.0</dd><dt><span>STASH :</span></dt><dd>m01s00i132</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 49.44 MiB </td>\n",
       "                        <td> 1.37 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (36, 450, 400) </td>\n",
       "                        <td> (1, 450, 400) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 36 chunks in 74 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2707bbbd-8a97-48f1-a8e9-2af066086a87' class='xr-section-summary-in' type='checkbox'  ><label for='section-2707bbbd-8a97-48f1-a8e9-2af066086a87' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c66e1509-8722-46c5-b88f-79dc57238110' class='xr-index-data-in' type='checkbox'/><label for='index-c66e1509-8722-46c5-b88f-79dc57238110' 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;2024-01-14 06:01:15&#x27;, &#x27;2024-01-14 06:11:15&#x27;,\n",
       "               &#x27;2024-01-14 06:21:15&#x27;, &#x27;2024-01-14 06:31:15&#x27;,\n",
       "               &#x27;2024-01-14 06:41:15&#x27;, &#x27;2024-01-14 06:51:15&#x27;,\n",
       "               &#x27;2024-01-14 07:01:15&#x27;, &#x27;2024-01-14 07:11:15&#x27;,\n",
       "               &#x27;2024-01-14 07:21:15&#x27;, &#x27;2024-01-14 07:31:15&#x27;,\n",
       "               &#x27;2024-01-14 07:41:15&#x27;, &#x27;2024-01-14 07:51:15&#x27;,\n",
       "               &#x27;2024-01-14 08:01:15&#x27;, &#x27;2024-01-14 08:11:15&#x27;,\n",
       "               &#x27;2024-01-14 08:21:15&#x27;, &#x27;2024-01-14 08:31:15&#x27;,\n",
       "               &#x27;2024-01-14 08:41:15&#x27;, &#x27;2024-01-14 08:51:15&#x27;,\n",
       "               &#x27;2024-01-14 09:01:15&#x27;, &#x27;2024-01-14 09:11:15&#x27;,\n",
       "               &#x27;2024-01-14 09:21:15&#x27;, &#x27;2024-01-14 09:31:15&#x27;,\n",
       "               &#x27;2024-01-14 09:41:15&#x27;, &#x27;2024-01-14 09:51:15&#x27;,\n",
       "               &#x27;2024-01-14 10:01:15&#x27;, &#x27;2024-01-14 10:11:15&#x27;,\n",
       "               &#x27;2024-01-14 10:21:15&#x27;, &#x27;2024-01-14 10:31:15&#x27;,\n",
       "               &#x27;2024-01-14 10:41:15&#x27;, &#x27;2024-01-14 10:51:15&#x27;,\n",
       "               &#x27;2024-01-14 11:01:15&#x27;, &#x27;2024-01-14 11:11:15&#x27;,\n",
       "               &#x27;2024-01-14 11:21:15&#x27;, &#x27;2024-01-14 11:31:15&#x27;,\n",
       "               &#x27;2024-01-14 11:41:15&#x27;, &#x27;2024-01-14 11:51:15&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2ffe7cb3-7f19-4cde-997c-2e8cf02525aa' class='xr-index-data-in' type='checkbox'/><label for='index-2ffe7cb3-7f19-4cde-997c-2e8cf02525aa' 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([-3.0374999046325684, -3.0239999294281006,  -3.010499954223633,\n",
       "        -2.996999979019165, -2.9835000038146973, -2.9699997901916504,\n",
       "       -2.9564998149871826,  -2.942999839782715,  -2.929499864578247,\n",
       "       -2.9159998893737793,\n",
       "       ...\n",
       "        2.9025001525878906,  2.9160003662109375,   2.929500102996826,\n",
       "         2.943000316619873,  2.9565000534057617,  2.9700002670288086,\n",
       "        2.9835004806518555,   2.997000217437744,   3.010500431060791,\n",
       "        3.0240001678466797],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;grid_latitude&#x27;, length=450))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-487d4e0e-e6d1-45d5-b35f-9b74a41e3427' class='xr-index-data-in' type='checkbox'/><label for='index-487d4e0e-e6d1-45d5-b35f-9b74a41e3427' 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([ 177.3000030517578, 177.31350708007812, 177.32699584960938,\n",
       "        177.3404998779297,    177.35400390625,  177.3675079345703,\n",
       "       177.38099670410156, 177.39450073242188,  177.4080047607422,\n",
       "        177.4215087890625,\n",
       "       ...\n",
       "       182.56500244140625, 182.57850646972656, 182.59201049804688,\n",
       "       182.60549926757812, 182.61900329589844, 182.63250732421875,\n",
       "          182.64599609375,  182.6595001220703, 182.67300415039062,\n",
       "       182.68650817871094],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;grid_longitude&#x27;, length=400))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9d3c548d-4035-4c3d-987e-10f6415adfda' class='xr-index-data-in' type='checkbox'/><label for='index-9d3c548d-4035-4c3d-987e-10f6415adfda' 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;2024-01-14 06:02:30&#x27;, &#x27;2024-01-14 06:12:30&#x27;,\n",
       "               &#x27;2024-01-14 06:22:30&#x27;, &#x27;2024-01-14 06:32:30&#x27;,\n",
       "               &#x27;2024-01-14 06:42:30&#x27;, &#x27;2024-01-14 06:52:30&#x27;,\n",
       "               &#x27;2024-01-14 07:02:30&#x27;, &#x27;2024-01-14 07:12:30&#x27;,\n",
       "               &#x27;2024-01-14 07:22:30&#x27;, &#x27;2024-01-14 07:32:30&#x27;,\n",
       "               &#x27;2024-01-14 07:42:30&#x27;, &#x27;2024-01-14 07:52:30&#x27;,\n",
       "               &#x27;2024-01-14 08:02:30&#x27;, &#x27;2024-01-14 08:12:30&#x27;,\n",
       "               &#x27;2024-01-14 08:22:30&#x27;, &#x27;2024-01-14 08:32:30&#x27;,\n",
       "               &#x27;2024-01-14 08:42:30&#x27;, &#x27;2024-01-14 08:52:30&#x27;,\n",
       "               &#x27;2024-01-14 09:02:30&#x27;, &#x27;2024-01-14 09:12:30&#x27;,\n",
       "               &#x27;2024-01-14 09:22:30&#x27;, &#x27;2024-01-14 09:32:30&#x27;,\n",
       "               &#x27;2024-01-14 09:42:30&#x27;, &#x27;2024-01-14 09:52:30&#x27;,\n",
       "               &#x27;2024-01-14 10:02:30&#x27;, &#x27;2024-01-14 10:12:30&#x27;,\n",
       "               &#x27;2024-01-14 10:22:30&#x27;, &#x27;2024-01-14 10:32:30&#x27;,\n",
       "               &#x27;2024-01-14 10:42:30&#x27;, &#x27;2024-01-14 10:52:30&#x27;,\n",
       "               &#x27;2024-01-14 11:02:30&#x27;, &#x27;2024-01-14 11:12:30&#x27;,\n",
       "               &#x27;2024-01-14 11:22:30&#x27;, &#x27;2024-01-14 11:32:30&#x27;,\n",
       "               &#x27;2024-01-14 11:42:30&#x27;, &#x27;2024-01-14 11:52:30&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time1&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>model_level_number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-337b4ab1-3dba-4219-a280-26c0ecd8eba8' class='xr-index-data-in' type='checkbox'/><label for='index-337b4ab1-3dba-4219-a280-26c0ecd8eba8' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;model_level_number&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-10caaa93-23a1-45d6-b7d2-d3f490f21ab8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10caaa93-23a1-45d6-b7d2-d3f490f21ab8' 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>central lat of rotated grid :</span></dt><dd>-64.8</dd><dt><span>central lon of rotated grid :</span></dt><dd>141</dd><dt><span>history :</span></dt><dd>Data generated and processed by S. Fiddes sonya.fiddes@utas.edu.au 2024-12-05</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                                                         (\n",
       "                                                                                     time: 36,\n",
       "                                                                                     grid_latitude: 450,\n",
       "                                                                                     grid_longitude: 400,\n",
       "                                                                                     time1: 36,\n",
       "                                                                                     model_level_number: 90)\n",
       "Coordinates:\n",
       "  * time                                                                            (time) datetime64[ns] ...\n",
       "  * grid_latitude                                                                   (grid_latitude) float32 ...\n",
       "  * grid_longitude                                                                  (grid_longitude) float32 ...\n",
       "    forecast_reference_time                                                         datetime64[ns] ...\n",
       "    forecast_period                                                                 (time) timedelta64[ns] ...\n",
       "    latitude                                                                        (grid_latitude, grid_longitude) float64 ...\n",
       "    longitude                                                                       (grid_latitude, grid_longitude) float64 ...\n",
       "  * time1                                                                           (time1) datetime64[ns] ...\n",
       "  * model_level_number                                                              (model_level_number) int64 ...\n",
       "    forecast_period1                                                                (time1) timedelta64[ns] ...\n",
       "    level_height                                                                    (model_level_number) float64 ...\n",
       "    sigma                                                                           (model_level_number) float64 ...\n",
       "Data variables:\n",
       "    ocean_near_surface_chlorophyll_km_per_m3                                        (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(1, 450, 400), meta=np.ndarray>\n",
       "    condensation_nuclei_number_concentration_greater_than_3nm_dry_diameter_per_cm3  (time1, model_level_number, grid_latitude, grid_longitude) float64 dask.array<chunksize=(1, 1, 450, 400), meta=np.ndarray>\n",
       "    CCN_greater_than_50nm_dry_diameter_AIT+ACC+COA_per_cm3                          (time1, model_level_number, grid_latitude, grid_longitude) float64 dask.array<chunksize=(1, 1, 450, 400), meta=np.ndarray>\n",
       "    mole_concentration_of_dimethyl_sulfide_in_sea_water                             (time, grid_latitude, grid_longitude) float64 dask.array<chunksize=(1, 450, 400), meta=np.ndarray>\n",
       "Attributes:\n",
       "    central lat of rotated grid:  -64.8\n",
       "    central lon of rotated grid:  141\n",
       "    history:                      Data generated and processed by S. Fiddes s..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d556039-0a93-4583-afac-01d84db2adaf",
   "metadata": {},
   "source": [
    "# Test code for extracting along ship track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bfb57191-9369-4ff7-8874-a6836c9a8a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_ship='MISO'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e81e9e2f-06bf-4b81-a954-543cc382d23e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracting data along MISO shiptrack\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "voyage data found for this time range\n",
      "write out data here\n"
     ]
    }
   ],
   "source": [
    "if extract_ship!='None': \n",
    "    print('extracting data along '+extract_ship+' shiptrack') \n",
    "    if extract_ship == 'MISO':\n",
    "        # This is the 1 min underway data - hopefully all RVI underway data is similar in structure\n",
    "        shiptrack = pd.read_csv('/g/data/jk72/slf563/OBS/campaigns/MISO/IN2024_V01_uwy_data.csv',index_col=0)\n",
    "        shiptrack.index = pd.DatetimeIndex(shiptrack.index)\n",
    "        shiptrack = shiptrack.drop(shiptrack[shiptrack['Latitude (degree_north)']>-62.505].index)\n",
    "        shiptrack = shiptrack.drop(shiptrack[shiptrack['Longitude (degree_east)']<137.3].index)\n",
    "        shiptrack = shiptrack.drop(shiptrack[shiptrack['Longitude (degree_east)']>145.4].index)\n",
    "        \n",
    "    #elif extract_ship == 'new ship track': \n",
    "        \n",
    "    else: \n",
    "        'Ship track not recognised - add code above for desired ship track'\n",
    "    \n",
    "    time = pd.DatetimeIndex(dataout.time) # convert model time to datetimeindex to loop through and \n",
    "\n",
    "    ship_data = xr.Dataset()\n",
    "    for t in time: \n",
    "        if t > shiptrack.index[0] and t < shiptrack.index[-1]:\n",
    "            print('voyage data found for this time range')\n",
    "\n",
    "            lat = shiptrack.loc['{}-{:02}-{} {:02}{:02}'.format(\n",
    "                t.year,t.month,t.day,t.hour,t.minute)]['Latitude (degree_north)']  \n",
    "            lon = shiptrack.loc['{}-{:02}-{} {:02}{:02}'.format(\n",
    "                t.year,t.month,t.day,t.hour,t.minute)]['Longitude (degree_east)']\n",
    "            \n",
    "            grid_lon, grid_lat = rotate_pole(lon, lat, clon, clat+90)\n",
    "            grid_lon = grid_lon % 360\n",
    "            \n",
    "            point = dataout.sel(time=t)\n",
    "            if 'time1' in dataout.dims:\n",
    "                point = point.sel(time1=t,method='nearest')\n",
    "                point = point.drop_vars('time1')\n",
    "                point = point.drop_vars('forecast_period1')\n",
    "            if 'grid_latitude' in dataout.dims:\n",
    "                point = point.sel(grid_latitude = grid_lat, grid_longitude = grid_lon, method='nearest')\n",
    "            if 'grid_latitude1' in dataout.dims:\n",
    "                point = point.sel(grid_latitude1 = grid_lat, grid_longitude1 = grid_lon, method='nearest')\n",
    "            if 'grid_latitude2' in dataout.dims:\n",
    "                point = point.sel(grid_latitude2 = grid_lat, grid_longitude2 = grid_lon, method='nearest')\n",
    "\n",
    "            reformatted_point = xr.Dataset()\n",
    "            for key in list(point.keys()):\n",
    "                tmp = point[key]\n",
    "                if 'grid_latitude' in point[key].dims: \n",
    "                    tmp = tmp.isel(grid_latitude=0,grid_longitude=0)\n",
    "                if 'grid_latitude1' in point[key].dims: \n",
    "                    tmp = tmp.isel(grid_latitude1=0,grid_longitude1=0)\n",
    "                if 'grid_latitude2' in point[key].dims: \n",
    "                    tmp = tmp.isel(grid_latitude2=0,grid_longitude2=0)\n",
    "                        \n",
    "                tmp = tmp.expand_dims('time')\n",
    "                reformatted_point[key] = tmp\n",
    "                \n",
    "            point = reformatted_point\n",
    "    \n",
    "            # Drop the current grid lat/lons because they are a bit useless and convlute things, \n",
    "            # and tell the actual grid lat lons to have a time dimension. \n",
    "            if 'grid_latitude' in list(point.coords):\n",
    "                point = point.drop_vars(['grid_latitude','grid_longitude'])\n",
    "                point['latitude'] = point.latitude.expand_dims('time')\n",
    "                point['longitude'] = point.longitude.expand_dims('time')\n",
    "            if 'grid_latitude1' in list(point.coords):\n",
    "                point = point.drop_vars(['grid_latitude1','grid_longitude1'])\n",
    "                point['latitude1'] = point.latitude1.expand_dims('time')\n",
    "                point['longitude1'] = point.longitude1.expand_dims('time')\n",
    "            if 'grid_latitude2' in list(point.coords):\n",
    "                point = point.drop_vars(['grid_latitude2','grid_longitude2'])\n",
    "                point['latitude2'] = point.latitude2.expand_dims('time')\n",
    "                point['longitude2'] = point.longitude2.expand_dims('time')\n",
    "    \n",
    "            # but then we need to include the grid lat/lon as a dimension for the aerosol conversions to work, so \n",
    "            # add in as a dummy dim, with only 1 point which equals 0 for all.. (so it doesnt try to put it on an\n",
    "            # actaul grid - need all values to be same so 'centre' (0) is now the ship location)\n",
    "            point = point.expand_dims('grid_latitude')\n",
    "            point = point.expand_dims('grid_longitude')\n",
    "\n",
    "            # rearrange dims to be time, level, lat, lon\n",
    "            if ('model_level_number' in point.dims) and ('model_level_number1' in point.dims):\n",
    "                point = point.transpose('time','model_level_number','model_level_number1','grid_latitude','grid_longitude')\n",
    "            elif 'model_level_number' in point.dims: \n",
    "                point = point.transpose('time','model_level_number','grid_latitude','grid_longitude')\n",
    "            else: \n",
    "                point = point.transpose('time','grid_latitude','grid_longitude')\n",
    "\n",
    "            # Add ship location as coordinate...\n",
    "            point = point.assign_coords({'ship_latitude':lat})\n",
    "            point['ship_latitude'] = point['ship_latitude'].ship_latitude.expand_dims('time')\n",
    "            point = point.assign_coords({'ship_longitude':lon})\n",
    "            point['ship_longitude'] = point['ship_longitude'].ship_longitude.expand_dims('time')\n",
    "            point = point.drop_vars('forecast_period')\n",
    "            \n",
    "            ship_data = ship_data.merge(point)\n",
    "\n",
    "    if len(ship_data)>0:\n",
    "        print('write out data here')\n",
    "    #ship_data.to_netcdf('{}_{}{}'.format(fout[:-3],extract_ship,fout[-3:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45396aea-57cf-47d7-ad8b-d7abc529314c",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                                                         (\n",
       "                                                                                     time: 17,\n",
       "                                                                                     model_level_number: 90,\n",
       "                                                                                     grid_latitude: 1,\n",
       "                                                                                     grid_longitude: 1)\n",
       "Coordinates:\n",
       "  * time                                                                            (time) datetime64[ns] ...\n",
       "  * model_level_number                                                              (model_level_number) int64 ...\n",
       "    forecast_reference_time                                                         datetime64[ns] ...\n",
       "    latitude                                                                        (time) float64 ...\n",
       "    longitude                                                                       (time) float64 ...\n",
       "    level_height                                                                    (model_level_number) float64 ...\n",
       "    sigma                                                                           (model_level_number) float64 ...\n",
       "    ship_latitude                                                                   (time) float64 ...\n",
       "    ship_longitude                                                                  (time) float64 ...\n",
       "Dimensions without coordinates: grid_latitude, grid_longitude\n",
       "Data variables:\n",
       "    ocean_near_surface_chlorophyll_km_per_m3                                        (time, grid_latitude, grid_longitude) float32 dask.array&lt;chunksize=(17, 1, 1), meta=np.ndarray&gt;\n",
       "    condensation_nuclei_number_concentration_greater_than_3nm_dry_diameter_per_cm3  (time, model_level_number, grid_latitude, grid_longitude) float64 dask.array&lt;chunksize=(17, 1, 1, 1), meta=np.ndarray&gt;\n",
       "    CCN_greater_than_50nm_dry_diameter_AIT+ACC+COA_per_cm3                          (time, model_level_number, grid_latitude, grid_longitude) float64 dask.array&lt;chunksize=(17, 1, 1, 1), meta=np.ndarray&gt;\n",
       "    mole_concentration_of_dimethyl_sulfide_in_sea_water                             (time, grid_latitude, grid_longitude) float64 dask.array&lt;chunksize=(17, 1, 1), meta=np.ndarray&gt;</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-9b88e940-c8b1-42ae-bf58-03558b9613d8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9b88e940-c8b1-42ae-bf58-03558b9613d8' 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>: 17</li><li><span class='xr-has-index'>model_level_number</span>: 90</li><li><span>grid_latitude</span>: 1</li><li><span>grid_longitude</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a552bc1d-1862-4648-871b-e4d80a435ab9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a552bc1d-1862-4648-871b-e4d80a435ab9' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2024-01-14T09:11:15 ... 2024-01-...</div><input id='attrs-3e023952-77e9-484a-b1ac-367e6f931a36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e023952-77e9-484a-b1ac-367e6f931a36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc8c0f30-2e40-440b-b178-ade25264e56c' class='xr-var-data-in' type='checkbox'><label for='data-dc8c0f30-2e40-440b-b178-ade25264e56c' 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;2024-01-14T09:11:15.000000000&#x27;, &#x27;2024-01-14T09:21:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:31:15.000000000&#x27;, &#x27;2024-01-14T09:41:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T09:51:15.000000000&#x27;, &#x27;2024-01-14T10:01:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:11:15.000000000&#x27;, &#x27;2024-01-14T10:21:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:31:15.000000000&#x27;, &#x27;2024-01-14T10:41:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T10:51:15.000000000&#x27;, &#x27;2024-01-14T11:01:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:11:15.000000000&#x27;, &#x27;2024-01-14T11:21:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:31:15.000000000&#x27;, &#x27;2024-01-14T11:41:15.000000000&#x27;,\n",
       "       &#x27;2024-01-14T11:51:15.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model_level_number</span></div><div class='xr-var-dims'>(model_level_number)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 85 86 87 88 89 90</div><input id='attrs-7533769c-f3e2-482c-ba2f-867f1bd5439c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7533769c-f3e2-482c-ba2f-867f1bd5439c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-206ff9f3-3eb4-4188-9a4c-81ac477effe6' class='xr-var-data-in' type='checkbox'><label for='data-206ff9f3-3eb4-4188-9a4c-81ac477effe6' 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>model_level_number</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-13</div><input id='attrs-5b4ebcf4-d2fb-496c-b78f-b4ed85510c02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b4ebcf4-d2fb-496c-b78f-b4ed85510c02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f05565f7-fb0d-44cc-8bad-234d5d3c3a0d' class='xr-var-data-in' type='checkbox'><label for='data-f05565f7-fb0d-44cc-8bad-234d5d3c3a0d' 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>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2024-01-13T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-66.54 -66.52 ... -66.48 -66.48</div><input id='attrs-455dce41-fd26-4b81-9654-d1d927fd6b3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-455dce41-fd26-4b81-9654-d1d927fd6b3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e96c240-9be9-4958-8af0-1ec0c033b613' class='xr-var-data-in' type='checkbox'><label for='data-6e96c240-9be9-4958-8af0-1ec0c033b613' 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([-66.53624791, -66.52369932, -66.51023054, -66.5111437 ,\n",
       "       -66.4994782 , -66.50036832, -66.50125227, -66.50212904,\n",
       "       -66.4903874 , -66.47776904, -66.47861616, -66.47861616,\n",
       "       -66.47861616, -66.47861616, -66.47861616, -66.47861616,\n",
       "       -66.47861616])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>145.3 145.3 145.3 ... 145.0 145.0</div><input id='attrs-5727defe-dcaf-41eb-ae94-f5ea0a41b253' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5727defe-dcaf-41eb-ae94-f5ea0a41b253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bca697b-5705-4ac5-b289-d71cff720449' class='xr-var-data-in' type='checkbox'><label for='data-0bca697b-5705-4ac5-b289-d71cff720449' 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([145.34122401, 145.3051378 , 145.30283613, 145.26904934,\n",
       "       145.19922306, 145.16548509, 145.13170656, 145.09792566,\n",
       "       145.02824172, 144.99233874, 144.95858443, 144.95858443,\n",
       "       144.95858443, 144.95858443, 144.95858443, 144.95858443,\n",
       "       144.95858443])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>level_height</span></div><div class='xr-var-dims'>(model_level_number)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 17.14 36.43 ... 3.867e+04 4e+04</div><input id='attrs-3b6cc3b0-ec5b-49d8-9525-31f7bab35414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b6cc3b0-ec5b-49d8-9525-31f7bab35414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e58c08-5353-4389-b5f1-3194a4f5ea04' class='xr-var-data-in' type='checkbox'><label for='data-53e58c08-5353-4389-b5f1-3194a4f5ea04' 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>level_height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([5.0000000e+00, 1.7142860e+01, 3.6428576e+01, 6.2857120e+01,\n",
       "       9.6428560e+01, 1.3714284e+02, 1.8500000e+02, 2.4000000e+02,\n",
       "       3.0214288e+02, 3.7142856e+02, 4.4785720e+02, 5.3142880e+02,\n",
       "       6.2214280e+02, 7.2000000e+02, 8.2500000e+02, 9.3714280e+02,\n",
       "       1.0564288e+03, 1.1828572e+03, 1.3164288e+03, 1.4571432e+03,\n",
       "       1.6050000e+03, 1.7600000e+03, 1.9221432e+03, 2.0914288e+03,\n",
       "       2.2678576e+03, 2.4514292e+03, 2.6421432e+03, 2.8400004e+03,\n",
       "       3.0450004e+03, 3.2571432e+03, 3.4764292e+03, 3.7028576e+03,\n",
       "       3.9364288e+03, 4.1771440e+03, 4.4250000e+03, 4.6800000e+03,\n",
       "       4.9421440e+03, 5.2114280e+03, 5.4878560e+03, 5.7714280e+03,\n",
       "       6.0627600e+03, 6.3627800e+03, 6.6718200e+03, 6.9902080e+03,\n",
       "       7.3182560e+03, 7.6563000e+03, 8.0046760e+03, 8.3637400e+03,\n",
       "       8.7338640e+03, 9.1154200e+03, 9.5088120e+03, 9.9144480e+03,\n",
       "       1.0332752e+04, 1.0764168e+04, 1.1209144e+04, 1.1668152e+04,\n",
       "       1.2141680e+04, 1.2630228e+04, 1.3134308e+04, 1.3654456e+04,\n",
       "       1.4191212e+04, 1.4745140e+04, 1.5316820e+04, 1.5906840e+04,\n",
       "       1.6515812e+04, 1.7144356e+04, 1.7793112e+04, 1.8462740e+04,\n",
       "       1.9153904e+04, 1.9867296e+04, 2.0603612e+04, 2.1363572e+04,\n",
       "       2.2147916e+04, 2.2957384e+04, 2.3792748e+04, 2.4654788e+04,\n",
       "       2.5544296e+04, 2.6462096e+04, 2.7409008e+04, 2.8385884e+04,\n",
       "       2.9393580e+04, 3.0432976e+04, 3.1504968e+04, 3.2610464e+04,\n",
       "       3.3750388e+04, 3.4925684e+04, 3.6137308e+04, 3.7386240e+04,\n",
       "       3.8673472e+04, 4.0000000e+04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(model_level_number)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9994 0.998 0.9958 ... 0.0 0.0 0.0</div><input id='attrs-d4bd2892-30cc-42a0-acc7-ff68eea2a444' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4bd2892-30cc-42a0-acc7-ff68eea2a444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f79e3eaa-5e67-4164-bd6a-47f6dc891e9e' class='xr-var-data-in' type='checkbox'><label for='data-f79e3eaa-5e67-4164-bd6a-47f6dc891e9e' 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>sigma</dd></dl></div><div class='xr-var-data'><pre>array([9.99427631e-01, 9.98038273e-01, 9.95833632e-01, 9.92816420e-01,\n",
       "       9.88990341e-01, 9.84360116e-01, 9.78931459e-01, 9.72711095e-01,\n",
       "       9.65706744e-01, 9.57927143e-01, 9.49382005e-01, 9.40082063e-01,\n",
       "       9.30039116e-01, 9.19265817e-01, 9.07775953e-01, 8.95584269e-01,\n",
       "       8.82706474e-01, 8.69159405e-01, 8.54960733e-01, 8.40129260e-01,\n",
       "       8.24684790e-01, 8.08648004e-01, 7.92040673e-01, 7.74885650e-01,\n",
       "       7.57206628e-01, 7.39028427e-01, 7.20376866e-01, 7.01278651e-01,\n",
       "       6.81761610e-01, 6.61854533e-01, 6.41587177e-01, 6.20990412e-01,\n",
       "       6.00096002e-01, 5.78936645e-01, 5.57546428e-01, 5.35959880e-01,\n",
       "       5.14212822e-01, 4.92342396e-01, 4.70386093e-01, 4.48382741e-01,\n",
       "       4.26326122e-01, 4.04193136e-01, 3.82011540e-01, 3.59813616e-01,\n",
       "       3.37637127e-01, 3.15522774e-01, 2.93516153e-01, 2.71666793e-01,\n",
       "       2.50028852e-01, 2.28662453e-01, 2.07632283e-01, 1.87009691e-01,\n",
       "       1.66872500e-01, 1.47305389e-01, 1.28401202e-01, 1.10260632e-01,\n",
       "       9.29932790e-02, 7.67184754e-02, 6.15659554e-02, 4.76762599e-02,\n",
       "       3.52021183e-02, 2.43087380e-02, 1.51749639e-02, 7.99431510e-03,\n",
       "       2.97573030e-03, 3.44814227e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ship_latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-66.53 -66.52 ... -66.48 -66.48</div><input id='attrs-5b32cf8c-898d-4191-b2a0-2d35ab4de0fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b32cf8c-898d-4191-b2a0-2d35ab4de0fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9b5716f-344c-4203-bfd4-aab0cac4c974' class='xr-var-data-in' type='checkbox'><label for='data-f9b5716f-344c-4203-bfd4-aab0cac4c974' 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([-66.5293358, -66.5191087, -66.512247 , -66.5083037, -66.5059414,\n",
       "       -66.5053678, -66.504935 , -66.4999468, -66.4932863, -66.4822538,\n",
       "       -66.4760219, -66.4766022, -66.4765968, -66.4765059, -66.4765715,\n",
       "       -66.4764963, -66.4764132])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ship_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>145.4 145.3 145.3 ... 144.9 144.9</div><input id='attrs-4d9b595c-baac-4228-a125-8f30f08f2abe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d9b595c-baac-4228-a125-8f30f08f2abe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0bc871e-f191-469e-88a4-2dbfc0251e72' class='xr-var-data-in' type='checkbox'><label for='data-d0bc871e-f191-469e-88a4-2dbfc0251e72' 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([145.3527872, 145.3164435, 145.2901068, 145.2528546, 145.2029083,\n",
       "       145.1746083, 145.1383971, 145.0865536, 145.0356215, 144.9882288,\n",
       "       144.9491385, 144.9482794, 144.9482472, 144.9477966, 144.9481695,\n",
       "       144.9477048, 144.9470143])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c714f685-a751-490c-9a5d-60ecd27140ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c714f685-a751-490c-9a5d-60ecd27140ec' 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>ocean_near_surface_chlorophyll_km_per_m3</span></div><div class='xr-var-dims'>(time, grid_latitude, grid_longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(17, 1, 1), meta=np.ndarray&gt;</div><input id='attrs-2900837f-ffff-414b-b24e-fa995674098e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2900837f-ffff-414b-b24e-fa995674098e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7ddb95a-01c3-4202-bd68-bc9af212407f' class='xr-var-data-in' type='checkbox'><label for='data-e7ddb95a-01c3-4202-bd68-bc9af212407f' 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>Data from Met Office Unified Model</dd><dt><span>um_version :</span></dt><dd>13.0</dd><dt><span>STASH :</span></dt><dd>m01s00i096</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
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       "               &#x27;2024-01-14 09:31:15&#x27;, &#x27;2024-01-14 09:41:15&#x27;,\n",
       "               &#x27;2024-01-14 09:51:15&#x27;, &#x27;2024-01-14 10:01:15&#x27;,\n",
       "               &#x27;2024-01-14 10:11:15&#x27;, &#x27;2024-01-14 10:21:15&#x27;,\n",
       "               &#x27;2024-01-14 10:31:15&#x27;, &#x27;2024-01-14 10:41:15&#x27;,\n",
       "               &#x27;2024-01-14 10:51:15&#x27;, &#x27;2024-01-14 11:01:15&#x27;,\n",
       "               &#x27;2024-01-14 11:11:15&#x27;, &#x27;2024-01-14 11:21:15&#x27;,\n",
       "               &#x27;2024-01-14 11:31:15&#x27;, &#x27;2024-01-14 11:41:15&#x27;,\n",
       "               &#x27;2024-01-14 11:51:15&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;10T&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>model_level_number</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1662e8d-f83e-48d7-844c-a64e98b600b2' class='xr-index-data-in' type='checkbox'/><label for='index-b1662e8d-f83e-48d7-844c-a64e98b600b2' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;model_level_number&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3cf5155-cd05-4625-b462-a0f224831665' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3cf5155-cd05-4625-b462-a0f224831665' 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.Dataset>\n",
       "Dimensions:                                                                         (\n",
       "                                                                                     time: 17,\n",
       "                                                                                     model_level_number: 90,\n",
       "                                                                                     grid_latitude: 1,\n",
       "                                                                                     grid_longitude: 1)\n",
       "Coordinates:\n",
       "  * time                                                                            (time) datetime64[ns] ...\n",
       "  * model_level_number                                                              (model_level_number) int64 ...\n",
       "    forecast_reference_time                                                         datetime64[ns] ...\n",
       "    latitude                                                                        (time) float64 ...\n",
       "    longitude                                                                       (time) float64 ...\n",
       "    level_height                                                                    (model_level_number) float64 ...\n",
       "    sigma                                                                           (model_level_number) float64 ...\n",
       "    ship_latitude                                                                   (time) float64 ...\n",
       "    ship_longitude                                                                  (time) float64 ...\n",
       "Dimensions without coordinates: grid_latitude, grid_longitude\n",
       "Data variables:\n",
       "    ocean_near_surface_chlorophyll_km_per_m3                                        (time, grid_latitude, grid_longitude) float32 dask.array<chunksize=(17, 1, 1), meta=np.ndarray>\n",
       "    condensation_nuclei_number_concentration_greater_than_3nm_dry_diameter_per_cm3  (time, model_level_number, grid_latitude, grid_longitude) float64 dask.array<chunksize=(17, 1, 1, 1), meta=np.ndarray>\n",
       "    CCN_greater_than_50nm_dry_diameter_AIT+ACC+COA_per_cm3                          (time, model_level_number, grid_latitude, grid_longitude) float64 dask.array<chunksize=(17, 1, 1, 1), meta=np.ndarray>\n",
       "    mole_concentration_of_dimethyl_sulfide_in_sea_water                             (time, grid_latitude, grid_longitude) float64 dask.array<chunksize=(17, 1, 1), meta=np.ndarray>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ship_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa30f4b5-f720-44db-a888-23c8eea254a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73be07d7-491e-45ae-9215-b5eb60eec372",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b19a2815-0a89-4123-848b-d8188c28b791",
   "metadata": {},
   "source": [
    "### This code can pull out just 1 degree around a point - but I havent implemented it anywhere yet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9b6f342c-6add-42d8-b9a8-6eb770754c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_lon, grid_lat = rotate_pole(np.array(target_lon-1), np.array(target_lat+1), 141, clat+90)\n",
    "point_data = dataout\n",
    "grid_lon = grid_lon % 360\n",
    "if 'grid_latitude' in dataout.dims: \n",
    "    point_data = point_data.sel(grid_latitude = slice(grid_lat[0]-0.5,grid_lat[0]+0.5), \n",
    "                   grid_longitude = slice(grid_lon[0]-0.5,grid_lon[0]+0.5))\n",
    "if 'grid_latitude1' in dataout.dims: \n",
    "    point_data = point_data.sel(grid_latitude1 = slice(grid_lat[0]-0.5,grid_lat[0]+0.5), \n",
    "                   grid_longitude1 = slice(grid_lon[0]-0.5,grid_lon[0]+0.5))\n",
    "if 'grid_latitude2' in dataout.dims: \n",
    "    point_data = point_data.sel(grid_latitude2 = slice(grid_lat[0]-0.5,grid_lat[0]+0.5), \n",
    "                   grid_longitude2 = slice(grid_lon[0]-0.5,grid_lon[0]+0.5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0a32f41-10cb-4b2b-bd93-dc0ac075dcb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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