REVIEW_CCPA_data_analysis.ipynb

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  {
   "cell_type": "markdown",
   "id": "1a57f935-5455-4177-a1f1-f5eadc9527a9",
   "metadata": {},
   "source": [
    "# CCPA vs. ERA5 data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd17f5d5-4cb3-4ba0-9c02-ebb347b9bec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import time\n",
    "import h5py\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "from glob import glob\n",
    "from scipy.stats import skew, kurtosis\n",
    "\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddbcf0eb-2a5b-4256-a160-e6b149a67a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.insert(0, '/glade/u/home/ksha/GAN_proj/')\n",
    "sys.path.insert(0, '/glade/u/home/ksha/GAN_proj/libs/')\n",
    "\n",
    "from namelist import *\n",
    "import data_utils as du"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ee96930-39ac-4d35-b4cb-476ba4153154",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec275455-f63e-48a3-a51b-679b1998771b",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e4b4b5-47f1-486c-8557-d1a0ddcc3d3b",
   "metadata": {},
   "source": [
    "**CONUS Land mask for CCPA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4db0b564-46ed-4598-b89a-eae88d62a297",
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   "outputs": [],
   "source": [
    "ds_static = xr.open_dataset(save_dir+'CCPA_domain.hdf')\n",
    "\n",
    "ds_static = ds_static.rename({\n",
    "    'phony_dim_0': 'latitude',\n",
    "    'phony_dim_1': 'longitude'\n",
    "})\n",
    "\n",
    "ds_static = ds_static.assign_coords({\n",
    "    'latitude': ds_static['lat_CCPA'].values[:, 0],\n",
    "    'longitude': ds_static['lon_CCPA'].values[0, :]\n",
    "})\n",
    "\n",
    "CCPA_y = ds_static['latitude'].values\n",
    "CCPA_x = ds_static['longitude'].values\n",
    "CCPA_x = (CCPA_x + 360) % 360\n",
    "ds_static = ds_static.assign_coords({'longitude': CCPA_x})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abaeff0-c5e9-4c8c-9de9-0b01ae20b668",
   "metadata": {},
   "source": [
    "**CONUS Land mask for ERA5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd1f28ae-e496-455c-a5db-7c41b2f6a7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ERA5_fn = '/glade/derecho/scratch/ksha/CREDIT_data/ERA5_plevel_base/accum/ERA5_plevel_6h_accum_{}.zarr'\n",
    "ds_ERA5_example = xr.open_zarr(ERA5_fn.format(1979))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfc41f76-be6e-429a-9f88-1bd1220dcb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "ERA5_y = ds_ERA5_example['latitude'].values\n",
    "ERA5_x = ds_ERA5_example['longitude'].values\n",
    "ERA5_lon, ERA5_lat = np.meshgrid(ERA5_x, ERA5_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6573908e-2fc9-4e30-8d5a-3c04e88fb337",
   "metadata": {},
   "source": [
    "**Prepare the CONUS mask using nearest neighbour (run once only)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a912bd5-541f-45db-bc38-f06211df41a7",
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   "outputs": [],
   "source": [
    "# import xesmf as xe\n",
    "\n",
    "# ds_out = xr.Dataset(\n",
    "#     {\n",
    "#         'latitude': (['latitude'], ERA5_y[120:280]),\n",
    "#         'longitude': (['longitude'], ERA5_x[720:])\n",
    "#     }\n",
    "# )\n",
    "\n",
    "# regridder = xe.Regridder(ds_static, ds_out, 'bilinear')\n",
    "# land_mask_ERA5 = regridder(ds_static['land_mask_CCPA'])\n",
    "# land_mask_ERA5 = xr.where(land_mask_ERA5 >= 0.5, 1, 0)\n",
    "\n",
    "# ds_land_mask = land_mask_ERA5.to_dataset(name='land_mask_ERA5')\n",
    "# ds_land_mask.to_netcdf(save_dir+'CCPA_ERA5_domain.nc', format='NETCDF4')\n",
    "\n",
    "ds_land_mask = xr.open_dataset(save_dir+'CCPA_ERA5_domain.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08556c04-1c71-400b-af49-dc39ccbf9415",
   "metadata": {},
   "source": [
    "## CCPA data analysis routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e664bc16-9d05-4957-a180-ef16f0fb78cd",
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   "source": [
    "def skew_func(data, axis):\n",
    "    return skew(data, axis=axis, nan_policy='omit')\n",
    "\n",
    "def kurtosis_func(data, axis):\n",
    "    return kurtosis(data, axis=axis, nan_policy='omit')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3ad9cc-4b3f-4010-a7b0-6ad81b05dba9",
   "metadata": {},
   "source": [
    "### Data gathering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25a9be91-ac82-4d5b-abfa-a554d66f6f40",
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   "source": [
    "fn_CCPA = camp_dir+'CCPA/CCPA_y{}.hdf'\n",
    "\n",
    "# define the range of years (20-yr)\n",
    "years = np.arange(2002, 2022)\n",
    "\n",
    "# list to hold datasets for each year\n",
    "ds_list = []\n",
    "\n",
    "for year in years:\n",
    "    # open HDF5\n",
    "    ds_CCPA = xr.open_dataset(fn_CCPA.format(year))\n",
    "    \n",
    "    # rename HDF5 coords to xarray coords\n",
    "    ds_CCPA = ds_CCPA.rename({\n",
    "        'phony_dim_0': 'day',\n",
    "        'phony_dim_1': 'hour',\n",
    "        'phony_dim_2': 'latitude',\n",
    "        'phony_dim_3': 'longitude'\n",
    "    })\n",
    "    \n",
    "    ds_CCPA = ds_CCPA.assign_coords({\n",
    "        'day': ds_CCPA['day'],\n",
    "        'hour': ds_CCPA['hour'],\n",
    "        'latitude': ds_static['latitude'].values,\n",
    "        'longitude': ds_static['longitude'].values\n",
    "    })\n",
    "    \n",
    "    # mask non-US grid as NaNs\n",
    "    ds_CCPA = ds_CCPA.where(ds_static['land_mask_CCPA'] != 0)\n",
    "    \n",
    "    # convert day / hour coords to time\n",
    "    ds_CCPA_stack = ds_CCPA.stack(time=('day', 'hour'))\n",
    "    ds_CCPA_stack = ds_CCPA_stack.reset_index('time')\n",
    "    start_date = pd.Timestamp(f'{year}-01-01')\n",
    "    actual_hours = ds_CCPA_stack['hour'] * 6\n",
    "    time_values = start_date + pd.to_timedelta(ds_CCPA_stack['day'], unit='D') + pd.to_timedelta(actual_hours, unit='H')\n",
    "    ds_CCPA_stack = ds_CCPA_stack.assign_coords(time=('time', time_values))\n",
    "    ds_CCPA_stack = ds_CCPA_stack.drop_vars(['day', 'hour'])\n",
    "    \n",
    "    # re-order dims\n",
    "    ds_CCPA_stack['CCPA'] = ds_CCPA_stack['CCPA'].transpose('time', 'latitude', 'longitude')\n",
    "    \n",
    "    ds_list.append(ds_CCPA_stack)\n",
    "\n",
    "ds_CCPA_FULL = xr.concat(ds_list, dim='time')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "f53c1d23-df35-44b8-b912-f95877627b2e",
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       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 29220, latitude: 224, longitude: 464)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 25.06 25.19 25.31 25.44 ... 52.69 52.81 52.94\n",
       "  * longitude  (longitude) float64 235.1 235.2 235.3 235.4 ... 292.7 292.8 292.9\n",
       "  * time       (time) datetime64[ns] 2002-01-01 ... 2021-12-31T18:00:00\n",
       "Data variables:\n",
       "    CCPA       (time, latitude, longitude) float64 nan nan nan ... nan nan nan</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-29aecf67-7b85-4013-a461-64dd2c74790f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-29aecf67-7b85-4013-a461-64dd2c74790f' 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>: 29220</li><li><span class='xr-has-index'>latitude</span>: 224</li><li><span class='xr-has-index'>longitude</span>: 464</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-55a88f68-ce62-457a-8a7c-5baa115ffba9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55a88f68-ce62-457a-8a7c-5baa115ffba9' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>25.06 25.19 25.31 ... 52.81 52.94</div><input id='attrs-03a67fbc-781d-44d7-88ff-b7ca1620ba62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03a67fbc-781d-44d7-88ff-b7ca1620ba62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4051cec8-fbbd-43cc-b970-50f69d507a9b' class='xr-var-data-in' type='checkbox'><label for='data-4051cec8-fbbd-43cc-b970-50f69d507a9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([25.063, 25.188, 25.313, ..., 52.688, 52.813, 52.938])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>235.1 235.2 235.3 ... 292.8 292.9</div><input id='attrs-98c6b69b-05d3-4b5e-8c44-e9b7745848df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98c6b69b-05d3-4b5e-8c44-e9b7745848df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d33929a0-15fd-4aab-9a86-fe65f55394b5' class='xr-var-data-in' type='checkbox'><label for='data-d33929a0-15fd-4aab-9a86-fe65f55394b5' 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([235.062, 235.187, 235.312, ..., 292.687, 292.812, 292.937])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-01-01 ... 2021-12-31T18:00:00</div><input id='attrs-ae86922f-32dd-4b4a-afeb-983377f0964f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae86922f-32dd-4b4a-afeb-983377f0964f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff06b1bb-60d8-4ec7-ad17-ad93ae68988d' class='xr-var-data-in' type='checkbox'><label for='data-ff06b1bb-60d8-4ec7-ad17-ad93ae68988d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T12:00:00.000000000&#x27;, &#x27;2021-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40f8d2a9-dadb-4331-b10c-edb9bc267a75' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40f8d2a9-dadb-4331-b10c-edb9bc267a75' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CCPA</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-90f05cd9-896f-41aa-810c-a631bcfa6c05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90f05cd9-896f-41aa-810c-a631bcfa6c05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2670a1f5-4332-4364-b398-31c3c49fb9ed' class='xr-var-data-in' type='checkbox'><label for='data-2670a1f5-4332-4364-b398-31c3c49fb9ed' 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([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1897881-213a-430b-a56c-18520df33543' class='xr-section-summary-in' type='checkbox'  ><label for='section-e1897881-213a-430b-a56c-18520df33543' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8379e85d-1501-4e4d-9e48-1ec0ffb6fbe1' class='xr-index-data-in' type='checkbox'/><label for='index-8379e85d-1501-4e4d-9e48-1ec0ffb6fbe1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([25.063, 25.188, 25.313, 25.438, 25.563, 25.688, 25.813, 25.938, 26.063,\n",
       "       26.188,\n",
       "       ...\n",
       "       51.813, 51.938, 52.063, 52.188, 52.313, 52.438, 52.563, 52.688, 52.813,\n",
       "       52.938],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=224))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-251ea1ae-6e36-4b42-aaef-27d35938e19d' class='xr-index-data-in' type='checkbox'/><label for='index-251ea1ae-6e36-4b42-aaef-27d35938e19d' 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([235.062, 235.187, 235.312, 235.437, 235.562, 235.687, 235.812, 235.937,\n",
       "       236.062, 236.187,\n",
       "       ...\n",
       "       291.812, 291.937, 292.062, 292.187, 292.312, 292.437, 292.562, 292.687,\n",
       "       292.812, 292.937],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=464))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e3b85bd5-ea70-4205-917b-55dd7b263ac6' class='xr-index-data-in' type='checkbox'/><label for='index-e3b85bd5-ea70-4205-917b-55dd7b263ac6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2002-01-01 00:00:00&#x27;, &#x27;2002-01-01 06:00:00&#x27;,\n",
       "               &#x27;2002-01-01 12:00:00&#x27;, &#x27;2002-01-01 18:00:00&#x27;,\n",
       "               &#x27;2002-01-02 00:00:00&#x27;, &#x27;2002-01-02 06:00:00&#x27;,\n",
       "               &#x27;2002-01-02 12:00:00&#x27;, &#x27;2002-01-02 18:00:00&#x27;,\n",
       "               &#x27;2002-01-03 00:00:00&#x27;, &#x27;2002-01-03 06:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-12-29 12:00:00&#x27;, &#x27;2021-12-29 18:00:00&#x27;,\n",
       "               &#x27;2021-12-30 00:00:00&#x27;, &#x27;2021-12-30 06:00:00&#x27;,\n",
       "               &#x27;2021-12-30 12:00:00&#x27;, &#x27;2021-12-30 18:00:00&#x27;,\n",
       "               &#x27;2021-12-31 00:00:00&#x27;, &#x27;2021-12-31 06:00:00&#x27;,\n",
       "               &#x27;2021-12-31 12:00:00&#x27;, &#x27;2021-12-31 18:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=29220, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c09712e7-8f69-4d3f-baa5-4cd955354acc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c09712e7-8f69-4d3f-baa5-4cd955354acc' 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:    (time: 29220, latitude: 224, longitude: 464)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 25.06 25.19 25.31 25.44 ... 52.69 52.81 52.94\n",
       "  * longitude  (longitude) float64 235.1 235.2 235.3 235.4 ... 292.7 292.8 292.9\n",
       "  * time       (time) datetime64[ns] 2002-01-01 ... 2021-12-31T18:00:00\n",
       "Data variables:\n",
       "    CCPA       (time, latitude, longitude) float64 nan nan nan ... nan nan nan"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_CCPA_FULL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a65ef6-f61e-4226-9105-8d7260b6613c",
   "metadata": {},
   "source": [
    "### Time series analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65d22f7b-cc62-4f61-8f31-50b6ca044d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "CCPA_mean_series = ds_CCPA_FULL.mean(dim=['latitude', 'longitude'], skipna=True)\n",
    "CCPA_std_series = ds_CCPA_FULL.std(dim=['latitude', 'longitude'], skipna=True)\n",
    "\n",
    "data_var = ds_CCPA_FULL['CCPA']\n",
    "\n",
    "q_vals = [0.90, 0.95, 0.99]\n",
    "\n",
    "quantiles = data_var.quantile(q=q_vals, dim=['latitude', 'longitude'], skipna=True)\n",
    "\n",
    "# Extract individual percentiles\n",
    "quantile_90 = quantiles.sel(quantile=0.90)\n",
    "quantile_95 = quantiles.sel(quantile=0.95)\n",
    "quantile_99 = quantiles.sel(quantile=0.99)\n",
    "\n",
    "ds_CCPA_series = xr.Dataset({\n",
    "    'mean': CCPA_mean_series['CCPA'],\n",
    "    'std': CCPA_std_series['CCPA'],\n",
    "    'q90': quantile_90.drop_vars('quantile'),\n",
    "    'q95': quantile_95.drop_vars('quantile'),\n",
    "    'q99': quantile_99.drop_vars('quantile'),\n",
    "})\n",
    "\n",
    "# ds_CCPA_series.to_netcdf(save_dir+'CCPA_2002_2022_series.nc', format='NETCDF4')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f59b2b-79d9-43c2-a541-0a0c362c45fa",
   "metadata": {},
   "source": [
    "### Spatial distribution analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4cd7d7e2-4735-4a94-8cf2-e92ed70c1ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "CCPA_mean = ds_CCPA_FULL.mean(dim='time', skipna=True)\n",
    "CCPA_std = ds_CCPA_FULL.std(dim='time', skipna=True)\n",
    "\n",
    "data_var = ds_CCPA_FULL['CCPA']\n",
    "\n",
    "CCPA_skew = xr.apply_ufunc(\n",
    "    skew_func,\n",
    "    data_var,\n",
    "    input_core_dims=[['time']],\n",
    "    kwargs={'axis': -1},\n",
    "    vectorize=True,\n",
    "    dask='parallelized',\n",
    "    output_dtypes=[float],\n",
    ")\n",
    "\n",
    "# Compute kurtosis over 'time' dimension\n",
    "CCPA_kurt = xr.apply_ufunc(\n",
    "    kurtosis_func,\n",
    "    data_var,\n",
    "    input_core_dims=[['time']],\n",
    "    kwargs={'axis': -1},\n",
    "    vectorize=True,\n",
    "    dask='parallelized',\n",
    "    output_dtypes=[float],\n",
    ")\n",
    "\n",
    "CCPA_kurt_np = CCPA_kurt.values\n",
    "\n",
    "q_vals = [0.90, 0.95, 0.99]\n",
    "\n",
    "quantiles = data_var.quantile(q=q_vals, dim='time', skipna=True)\n",
    "\n",
    "# Extract individual percentiles\n",
    "quantile_90 = quantiles.sel(quantile=0.90)\n",
    "quantile_95 = quantiles.sel(quantile=0.95)\n",
    "quantile_99 = quantiles.sel(quantile=0.99)\n",
    "\n",
    "ds_CCPA_analysis = xr.Dataset({\n",
    "    'mean': CCPA_mean['CCPA'],\n",
    "    'std': CCPA_std['CCPA'],\n",
    "    'skew': CCPA_skew,\n",
    "    'kurt': CCPA_kurt,\n",
    "    'q90': quantile_90.drop_vars('quantile'),\n",
    "    'q95': quantile_95.drop_vars('quantile'),\n",
    "    'q99': quantile_99.drop_vars('quantile'),\n",
    "})\n",
    "\n",
    "#ds_CCPA_analysis.to_netcdf(save_dir+'CCPA_2002_2022_analysis.nc', format='NETCDF4')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db970e70-e374-4697-bef2-9771bc7012dd",
   "metadata": {},
   "source": [
    "## ERA5 (CONUS domain) routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3819edc1-2a11-4f17-8683-e7f66ea15333",
   "metadata": {},
   "outputs": [],
   "source": [
    "years = np.arange(2002, 2022)\n",
    "\n",
    "# list to hold datasets for each year\n",
    "ds_list_ERA5 = []\n",
    "\n",
    "for year in years:\n",
    "    ds_ERA5 = xr.open_zarr(ERA5_fn.format(year))\n",
    "\n",
    "    da_precip = ds_ERA5['total_precipitation']\n",
    "    da_precip = da_precip.isel(latitude=slice(120, 280), longitude=slice(720, None))\n",
    "    da_precip = da_precip.where(ds_land_mask['land_mask_ERA5'] != 0)\n",
    "    ds_precip = da_precip.to_dataset(name='ERA5')\n",
    "    ds_list_ERA5.append(ds_precip)\n",
    "\n",
    "ds_ERA5_FULL = xr.concat(ds_list_ERA5, dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4fa47805-df0c-488f-a905-0447cc87b02e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rechunk time dim\n",
    "ds_ERA5_FULL = ds_ERA5_FULL.chunk(dict(time=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "407e3097-9063-4a77-ac48-cc0136121b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 160, longitude: 720, time: 29220)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 60.0 59.75 59.5 59.25 ... 20.75 20.5 20.25\n",
       "  * longitude  (longitude) float32 180.0 180.2 180.5 180.8 ... 359.2 359.5 359.8\n",
       "  * time       (time) datetime64[ns] 2002-01-01 ... 2021-12-31T18:00:00\n",
       "Data variables:\n",
       "    ERA5       (time, latitude, longitude) float32 dask.array&lt;chunksize=(29220, 160, 720), 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-8f3e2902-4ac3-4d41-95cb-803a080726d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f3e2902-4ac3-4d41-95cb-803a080726d6' 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'>latitude</span>: 160</li><li><span class='xr-has-index'>longitude</span>: 720</li><li><span class='xr-has-index'>time</span>: 29220</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2ca0174b-7230-49f9-9c23-bc7eb157ced7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ca0174b-7230-49f9-9c23-bc7eb157ced7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.0 59.75 59.5 ... 20.5 20.25</div><input id='attrs-3838ae68-9297-4e72-9cd3-ff54d01564e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3838ae68-9297-4e72-9cd3-ff54d01564e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e7aeda8-a198-4ce2-968f-c0644f901f00' class='xr-var-data-in' type='checkbox'><label for='data-2e7aeda8-a198-4ce2-968f-c0644f901f00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([60.  , 59.75, 59.5 , 59.25, 59.  , 58.75, 58.5 , 58.25, 58.  , 57.75,\n",
       "       57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75, 55.5 , 55.25,\n",
       "       55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25, 53.  , 52.75,\n",
       "       52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75, 50.5 , 50.25,\n",
       "       50.  , 49.75, 49.5 , 49.25, 49.  , 48.75, 48.5 , 48.25, 48.  , 47.75,\n",
       "       47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25, 46.  , 45.75, 45.5 , 45.25,\n",
       "       45.  , 44.75, 44.5 , 44.25, 44.  , 43.75, 43.5 , 43.25, 43.  , 42.75,\n",
       "       42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25, 41.  , 40.75, 40.5 , 40.25,\n",
       "       40.  , 39.75, 39.5 , 39.25, 39.  , 38.75, 38.5 , 38.25, 38.  , 37.75,\n",
       "       37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25, 36.  , 35.75, 35.5 , 35.25,\n",
       "       35.  , 34.75, 34.5 , 34.25, 34.  , 33.75, 33.5 , 33.25, 33.  , 32.75,\n",
       "       32.5 , 32.25, 32.  , 31.75, 31.5 , 31.25, 31.  , 30.75, 30.5 , 30.25,\n",
       "       30.  , 29.75, 29.5 , 29.25, 29.  , 28.75, 28.5 , 28.25, 28.  , 27.75,\n",
       "       27.5 , 27.25, 27.  , 26.75, 26.5 , 26.25, 26.  , 25.75, 25.5 , 25.25,\n",
       "       25.  , 24.75, 24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75,\n",
       "       22.5 , 22.25, 22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>180.0 180.2 180.5 ... 359.5 359.8</div><input id='attrs-7d50f38c-bc7d-40ff-89d7-55b92acd020a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d50f38c-bc7d-40ff-89d7-55b92acd020a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ceb5a1f-836d-4f07-bc3c-86abc87e1ec8' class='xr-var-data-in' type='checkbox'><label for='data-3ceb5a1f-836d-4f07-bc3c-86abc87e1ec8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([180.  , 180.25, 180.5 , ..., 359.25, 359.5 , 359.75], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-01-01 ... 2021-12-31T18:00:00</div><input id='attrs-e05e9dbc-1e6f-41af-b560-455196146584' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e05e9dbc-1e6f-41af-b560-455196146584' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94a31e58-8dde-4e62-abb9-93f559c9450c' class='xr-var-data-in' type='checkbox'><label for='data-94a31e58-8dde-4e62-abb9-93f559c9450c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T12:00:00.000000000&#x27;, &#x27;2021-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23f2cf1c-f1a8-4046-89de-7f4ad8aad1a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23f2cf1c-f1a8-4046-89de-7f4ad8aad1a3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ERA5</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(29220, 160, 720), meta=np.ndarray&gt;</div><input id='attrs-7a253d33-9f99-40a2-803b-72ef5b93f2a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a253d33-9f99-40a2-803b-72ef5b93f2a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-773526b3-302a-4508-9f5f-62b5bf50102f' class='xr-var-data-in' type='checkbox'><label for='data-773526b3-302a-4508-9f5f-62b5bf50102f' 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>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</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",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 12.54 GiB </td>\n",
       "                        <td> 12.54 GiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (29220, 160, 720) </td>\n",
       "                        <td> (29220, 160, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 83 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"160\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Colored Rectangle -->\n",
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       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"40\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
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       "\n",
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       "  <text x=\"95.701671\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
       "  <text x=\"130.815108\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,130.815108,83.294544)\">160</text>\n",
       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">29220</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f52fe4b1-78b0-477b-a6a1-647b6fb9ffeb' class='xr-section-summary-in' type='checkbox'  ><label for='section-f52fe4b1-78b0-477b-a6a1-647b6fb9ffeb' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-718ed9c3-08f7-493c-abe8-5160b0f0ce9e' class='xr-index-data-in' type='checkbox'/><label for='index-718ed9c3-08f7-493c-abe8-5160b0f0ce9e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5, 58.25,  58.0, 57.75,\n",
       "       ...\n",
       "        22.5, 22.25,  22.0, 21.75,  21.5, 21.25,  21.0, 20.75,  20.5, 20.25],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=160))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2135c0b8-4a10-4583-9134-04c350dfdc6a' class='xr-index-data-in' type='checkbox'/><label for='index-2135c0b8-4a10-4583-9134-04c350dfdc6a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 180.0, 180.25,  180.5, 180.75,  181.0, 181.25,  181.5, 181.75,  182.0,\n",
       "       182.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-646edfae-5ece-413b-8552-9e1376b9dfde' class='xr-index-data-in' type='checkbox'/><label for='index-646edfae-5ece-413b-8552-9e1376b9dfde' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2002-01-01 00:00:00&#x27;, &#x27;2002-01-01 06:00:00&#x27;,\n",
       "               &#x27;2002-01-01 12:00:00&#x27;, &#x27;2002-01-01 18:00:00&#x27;,\n",
       "               &#x27;2002-01-02 00:00:00&#x27;, &#x27;2002-01-02 06:00:00&#x27;,\n",
       "               &#x27;2002-01-02 12:00:00&#x27;, &#x27;2002-01-02 18:00:00&#x27;,\n",
       "               &#x27;2002-01-03 00:00:00&#x27;, &#x27;2002-01-03 06:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-12-29 12:00:00&#x27;, &#x27;2021-12-29 18:00:00&#x27;,\n",
       "               &#x27;2021-12-30 00:00:00&#x27;, &#x27;2021-12-30 06:00:00&#x27;,\n",
       "               &#x27;2021-12-30 12:00:00&#x27;, &#x27;2021-12-30 18:00:00&#x27;,\n",
       "               &#x27;2021-12-31 00:00:00&#x27;, &#x27;2021-12-31 06:00:00&#x27;,\n",
       "               &#x27;2021-12-31 12:00:00&#x27;, &#x27;2021-12-31 18:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=29220, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd0498af-5821-4eb2-9b7d-3b08240c55b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd0498af-5821-4eb2-9b7d-3b08240c55b4' 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:    (latitude: 160, longitude: 720, time: 29220)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 60.0 59.75 59.5 59.25 ... 20.75 20.5 20.25\n",
       "  * longitude  (longitude) float32 180.0 180.2 180.5 180.8 ... 359.2 359.5 359.8\n",
       "  * time       (time) datetime64[ns] 2002-01-01 ... 2021-12-31T18:00:00\n",
       "Data variables:\n",
       "    ERA5       (time, latitude, longitude) float32 dask.array<chunksize=(29220, 160, 720), meta=np.ndarray>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_ERA5_FULL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddca774c-b0e4-47b9-8864-016a919e51fb",
   "metadata": {},
   "source": [
    "### Time series analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75798ca0-7d3a-4383-bb18-1f9089c8a6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ERA5_mean_series = ds_ERA5_FULL.mean(dim=['latitude', 'longitude'], skipna=True)\n",
    "ERA5_std_series = ds_ERA5_FULL.std(dim=['latitude', 'longitude'], skipna=True)\n",
    "\n",
    "data_var = ds_ERA5_FULL['ERA5']\n",
    "\n",
    "q_vals = [0.90, 0.95, 0.99]\n",
    "\n",
    "quantiles = data_var.quantile(q=q_vals, dim=['latitude', 'longitude'], skipna=True)\n",
    "\n",
    "# Extract individual percentiles\n",
    "quantile_90 = quantiles.sel(quantile=0.90)\n",
    "quantile_95 = quantiles.sel(quantile=0.95)\n",
    "quantile_99 = quantiles.sel(quantile=0.99)\n",
    "\n",
    "ds_ERA5_series = xr.Dataset({\n",
    "    'mean': ERA5_mean_series['ERA5'],\n",
    "    'std': ERA5_std_series['ERA5'],\n",
    "    'q90': quantile_90.drop_vars('quantile'),\n",
    "    'q95': quantile_95.drop_vars('quantile'),\n",
    "    'q99': quantile_99.drop_vars('quantile'),\n",
    "})\n",
    "\n",
    "# ds_ERA5_series.to_netcdf(save_dir+'ERA5_2002_2022_series.nc', format='NETCDF4')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e16427a-8040-45af-8144-052687e66810",
   "metadata": {},
   "source": [
    "### Spatial distribution analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c12e85e5-dd98-48a0-95c4-7dade72658db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/derecho/scratch/ksha/tmp/ipykernel_64387/319367502.py:5: SmallSampleWarning: After omitting NaNs, one or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n",
      "  return kurtosis(data, axis=axis, nan_policy='omit')\n"
     ]
    }
   ],
   "source": [
    "ERA5_mean = ds_ERA5_FULL.mean(dim='time', skipna=True)\n",
    "ERA5_std = ds_ERA5_FULL.std(dim='time', skipna=True)\n",
    "\n",
    "data_var = ds_ERA5_FULL['ERA5']\n",
    "\n",
    "ERA5_skew = xr.apply_ufunc(\n",
    "    skew_func,\n",
    "    data_var,\n",
    "    input_core_dims=[['time']],\n",
    "    kwargs={'axis': -1},\n",
    "    vectorize=True,\n",
    "    dask='parallelized',\n",
    "    output_dtypes=[float],\n",
    ")\n",
    "\n",
    "ERA5_kurt = xr.apply_ufunc(\n",
    "    kurtosis_func,\n",
    "    data_var,\n",
    "    input_core_dims=[['time']],\n",
    "    kwargs={'axis': -1},\n",
    "    vectorize=True,\n",
    "    dask='parallelized',\n",
    "    output_dtypes=[float],\n",
    ")\n",
    "\n",
    "ERA5_kurt_np = ERA5_kurt.values\n",
    "\n",
    "q_vals = [0.90, 0.95, 0.99]\n",
    "\n",
    "quantiles_ERA5 = data_var.quantile(q=q_vals, dim='time', skipna=True)\n",
    "\n",
    "# Extract individual percentiles\n",
    "quantile_ERA5_90 = quantiles_ERA5.sel(quantile=0.90)\n",
    "quantile_ERA5_95 = quantiles_ERA5.sel(quantile=0.95)\n",
    "quantile_ERA5_99 = quantiles_ERA5.sel(quantile=0.99)\n",
    "\n",
    "ds_ERA5_analysis = xr.Dataset({\n",
    "    'mean': ERA5_mean['ERA5'],\n",
    "    'std': ERA5_std['ERA5'],\n",
    "    'skew': ERA5_skew,\n",
    "    'kurt': ERA5_kurt,\n",
    "    'q90': quantile_ERA5_90.drop_vars('quantile'),\n",
    "    'q95': quantile_ERA5_95.drop_vars('quantile'),\n",
    "    'q99': quantile_ERA5_99.drop_vars('quantile'),\n",
    "})\n",
    "\n",
    "# ds_ERA5_analysis.to_netcdf(save_dir+'ERA5_2002_2022_CONUS_analysis.nc', format='NETCDF4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21a43dc6-4f04-4dc9-86da-ce174a05c5a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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