{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Excercises 2\n",
    "\n",
    "## Notebook #2: WA state hourly data\n",
    "\n",
    "UW Geospatial Data Analysis  \n",
    "CEE498/CEWA599  \n",
    "David Shean  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Hourly data for WA state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the WA temperature, precipitation and snow depth products\n",
    "* Note: these are extracted for 12:00 UTC\n",
    "* Really want daily averages for much of this, but I didn't have time to go back and requery ERA5\n",
    "* Let's use what we have to play around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fn = 'WA_era5-land-monthly_averaged_reanalysis_by_hour_of_day.grib'\n",
    "#wa_t = xr.open_dataset(fn, engine='cfgrib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn_list = ['era5_data/era5_WA_1979-2021_6hr/era5_WA_1979-2021_6hr_2m_temperature.nc', \\\n",
    "           'era5_data/era5_WA_1979-2021_6hr/era5_WA_1979-2021_6hr_total_precipitation.nc', \n",
    "           'era5_data/era5_WA_1979-2021_6hr/era5_WA_1979-2021_6hr_snow_depth.nc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use open_mfdataset to merge when opening\n",
    "* Could have used `open_dataset` on each nc, then combined\n",
    "* http://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html\n",
    "* See more details on merge/combine in xarray: http://xarray.pydata.org/en/stable/combining.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify chunks to split\n",
    "#chunks={'time':2048}\n",
    "chunks=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "wa_merge = xr.open_mfdataset(fn_list, combine='by_coords', chunks=chunks)\n",
    "wa_merge = wa_merge.sel(step=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 13, longitude: 30, time: 61592)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "  * latitude   (latitude) float64 48.75 48.5 48.25 48.0 ... 46.25 46.0 45.75\n",
       "  * longitude  (longitude) float64 -124.5 -124.2 -124.0 ... -117.8 -117.5 -117.2\n",
       "Data variables:\n",
       "    sd         (time, latitude, longitude) float32 dask.array&lt;chunksize=(61592, 13, 30), meta=np.ndarray&gt;\n",
       "    t2m        (time, latitude, longitude) float32 dask.array&lt;chunksize=(61592, 13, 30), meta=np.ndarray&gt;\n",
       "    tp         (time, latitude, longitude) float32 dask.array&lt;chunksize=(61592, 13, 30), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0....</pre><div class='xr-wrap' hidden><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-3d9c3e74-b945-4fb8-be63-79f51232d59f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d9c3e74-b945-4fb8-be63-79f51232d59f' 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>: 13</li><li><span class='xr-has-index'>longitude</span>: 30</li><li><span class='xr-has-index'>time</span>: 61592</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b011d853-da8f-474d-a591-5dc4bd0eee01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b011d853-da8f-474d-a591-5dc4bd0eee01' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-02-26T18:00:00</div><input id='attrs-c39655d2-a193-4f3f-ab78-bc801468b157' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c39655d2-a193-4f3f-ab78-bc801468b157' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08fe83c2-c194-4a9c-baf8-f5ac0b3948ac' class='xr-var-data-in' type='checkbox'><label for='data-08fe83c2-c194-4a9c-baf8-f5ac0b3948ac' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-02-26T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-26T12:00:00.000000000&#x27;, &#x27;2021-02-26T18:00:00.000000000&#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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>48.75 48.5 48.25 ... 46.0 45.75</div><input id='attrs-5ef419c3-2426-42f2-bec9-8a55d44b4a22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ef419c3-2426-42f2-bec9-8a55d44b4a22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7945732a-8575-4b7d-b16b-6ee9646d0be5' class='xr-var-data-in' type='checkbox'><label for='data-7945732a-8575-4b7d-b16b-6ee9646d0be5' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([48.75, 48.5 , 48.25, 48.  , 47.75, 47.5 , 47.25, 47.  , 46.75, 46.5 ,\n",
       "       46.25, 46.  , 45.75])</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'>-124.5 -124.2 ... -117.5 -117.2</div><input id='attrs-3f2c9844-e841-4725-9f61-87041f1d49bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f2c9844-e841-4725-9f61-87041f1d49bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3d2f244-9071-4046-b931-713690251104' class='xr-var-data-in' type='checkbox'><label for='data-f3d2f244-9071-4046-b931-713690251104' 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>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-124.5 , -124.25, -124.  , -123.75, -123.5 , -123.25, -123.  , -122.75,\n",
       "       -122.5 , -122.25, -122.  , -121.75, -121.5 , -121.25, -121.  , -120.75,\n",
       "       -120.5 , -120.25, -120.  , -119.75, -119.5 , -119.25, -119.  , -118.75,\n",
       "       -118.5 , -118.25, -118.  , -117.75, -117.5 , -117.25])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f1a4114-0237-448f-8d88-bc31210140f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1f1a4114-0237-448f-8d88-bc31210140f5' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sd</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=(61592, 13, 30), meta=np.ndarray&gt;</div><input id='attrs-eba8c53b-e558-4d25-9bc8-5517d7b79fd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eba8c53b-e558-4d25-9bc8-5517d7b79fd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31383395-28a4-4416-bf35-569b98d28874' class='xr-var-data-in' type='checkbox'><label for='data-31383395-28a4-4416-bf35-569b98d28874' 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>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 96.08 MB </td> <td> 96.08 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592, 13, 30) </td> <td> (61592, 13, 30) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >30</text>\n",
       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,126.000852,83.294544)\">13</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)\">61592</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</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=(61592, 13, 30), meta=np.ndarray&gt;</div><input id='attrs-80339421-d4ef-4472-9033-101e2c94d3e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80339421-d4ef-4472-9033-101e2c94d3e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cabf6e7-3e09-444b-b2cb-541dbc43148c' class='xr-var-data-in' type='checkbox'><label for='data-5cabf6e7-3e09-444b-b2cb-541dbc43148c' 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>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 96.08 MB </td> <td> 96.08 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592, 13, 30) </td> <td> (61592, 13, 30) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >30</text>\n",
       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,126.000852,83.294544)\">13</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)\">61592</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</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=(61592, 13, 30), meta=np.ndarray&gt;</div><input id='attrs-3d3031e4-7ef7-49d7-a60e-a38f6c8fca30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d3031e4-7ef7-49d7-a60e-a38f6c8fca30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb536634-b3fb-492f-9d9d-da9387efb195' class='xr-var-data-in' type='checkbox'><label for='data-bb536634-b3fb-492f-9d9d-da9387efb195' 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>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>accum</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 96.08 MB </td> <td> 96.08 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592, 13, 30) </td> <td> (61592, 13, 30) </td></tr>\n",
       "    <tr><th> Count </th><td> 17 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >30</text>\n",
       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,126.000852,83.294544)\">13</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)\">61592</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-4fde695a-1897-4a41-a9da-3c58a838c6e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4fde695a-1897-4a41-a9da-3c58a838c6e8' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0.9.8.5/ecCodes-2.20.0 with {&quot;source&quot;: &quot;/home/jovyan/gda_course_2021_solutions/modules/09_NDarrays_xarray_ERA5/era5_WA_1979-2021_6hr_2m_temperature.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 13, longitude: 30, time: 61592)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "  * latitude   (latitude) float64 48.75 48.5 48.25 48.0 ... 46.25 46.0 45.75\n",
       "  * longitude  (longitude) float64 -124.5 -124.2 -124.0 ... -117.8 -117.5 -117.2\n",
       "Data variables:\n",
       "    sd         (time, latitude, longitude) float32 dask.array<chunksize=(61592, 13, 30), meta=np.ndarray>\n",
       "    t2m        (time, latitude, longitude) float32 dask.array<chunksize=(61592, 13, 30), meta=np.ndarray>\n",
       "    tp         (time, latitude, longitude) float32 dask.array<chunksize=(61592, 13, 30), meta=np.ndarray>\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0...."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wa_merge['t2m'] -= 273.15\n",
    "wa_merge['t2m'].attrs['units'] = 'C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert meters to mm\n",
    "wa_merge['tp'] *= 1000\n",
    "wa_merge['tp'].attrs['units'] = 'mm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (time: 61592, latitude: 13, longitude: 30)&gt;\n",
       "dask.array&lt;sub, shape=(61592, 13, 30), dtype=float32, chunksize=(61592, 13, 30), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "  * latitude   (latitude) float64 48.75 48.5 48.25 48.0 ... 46.25 46.0 45.75\n",
       "  * longitude  (longitude) float64 -124.5 -124.2 -124.0 ... -117.8 -117.5 -117.2\n",
       "Attributes:\n",
       "    GRIB_paramId:                             167\n",
       "    GRIB_shortName:                           2t\n",
       "    GRIB_units:                               K\n",
       "    GRIB_name:                                2 metre temperature\n",
       "    GRIB_cfVarName:                           t2m\n",
       "    GRIB_dataType:                            an\n",
       "    GRIB_missingValue:                        9999\n",
       "    GRIB_numberOfPoints:                      390\n",
       "    GRIB_totalNumber:                         0\n",
       "    GRIB_typeOfLevel:                         surface\n",
       "    GRIB_NV:                                  0\n",
       "    GRIB_stepUnits:                           1\n",
       "    GRIB_stepType:                            instant\n",
       "    GRIB_gridType:                            regular_ll\n",
       "    GRIB_gridDefinitionDescription:           Latitude/Longitude Grid\n",
       "    GRIB_Nx:                                  30\n",
       "    GRIB_iDirectionIncrementInDegrees:        0.25\n",
       "    GRIB_iScansNegatively:                    0\n",
       "    GRIB_longitudeOfFirstGridPointInDegrees:  -124.5\n",
       "    GRIB_longitudeOfLastGridPointInDegrees:   -117.25\n",
       "    GRIB_Ny:                                  13\n",
       "    GRIB_jDirectionIncrementInDegrees:        0.25\n",
       "    GRIB_jPointsAreConsecutive:               0\n",
       "    GRIB_jScansPositively:                    0\n",
       "    GRIB_latitudeOfFirstGridPointInDegrees:   48.75\n",
       "    GRIB_latitudeOfLastGridPointInDegrees:    45.75\n",
       "    long_name:                                2 metre temperature\n",
       "    units:                                    C\n",
       "    coordinates:                              number time step surface latitu...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 61592</li><li><span class='xr-has-index'>latitude</span>: 13</li><li><span class='xr-has-index'>longitude</span>: 30</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fa99b25c-9521-44a9-9cc5-c56aa11f974f' class='xr-array-in' type='checkbox' checked><label for='section-fa99b25c-9521-44a9-9cc5-c56aa11f974f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(61592, 13, 30), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 96.08 MB </td> <td> 96.08 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592, 13, 30) </td> <td> (61592, 13, 30) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >30</text>\n",
       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,126.000852,83.294544)\">13</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)\">61592</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-caacc1aa-e4a6-4d6d-bf16-43ff77b66068' class='xr-section-summary-in' type='checkbox'  checked><label for='section-caacc1aa-e4a6-4d6d-bf16-43ff77b66068' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-02-26T18:00:00</div><input id='attrs-ed1b0bd9-7e81-4f7d-919a-bccf162d82b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed1b0bd9-7e81-4f7d-919a-bccf162d82b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0122919f-8567-4341-802e-2d856d274ffa' class='xr-var-data-in' type='checkbox'><label for='data-0122919f-8567-4341-802e-2d856d274ffa' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-02-26T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-26T12:00:00.000000000&#x27;, &#x27;2021-02-26T18:00:00.000000000&#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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>48.75 48.5 48.25 ... 46.0 45.75</div><input id='attrs-31f306b4-2075-43c7-adb7-512f76536039' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31f306b4-2075-43c7-adb7-512f76536039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fec9e0d-4c2a-48de-a2fe-d7cba7c9a31d' class='xr-var-data-in' type='checkbox'><label for='data-0fec9e0d-4c2a-48de-a2fe-d7cba7c9a31d' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([48.75, 48.5 , 48.25, 48.  , 47.75, 47.5 , 47.25, 47.  , 46.75, 46.5 ,\n",
       "       46.25, 46.  , 45.75])</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'>-124.5 -124.2 ... -117.5 -117.2</div><input id='attrs-ed2b94e9-7617-4291-aa47-e8db4b993c4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2b94e9-7617-4291-aa47-e8db4b993c4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05835967-99b6-4927-8f47-5c8d89a7c7d0' class='xr-var-data-in' type='checkbox'><label for='data-05835967-99b6-4927-8f47-5c8d89a7c7d0' 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>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-124.5 , -124.25, -124.  , -123.75, -123.5 , -123.25, -123.  , -122.75,\n",
       "       -122.5 , -122.25, -122.  , -121.75, -121.5 , -121.25, -121.  , -120.75,\n",
       "       -120.5 , -120.25, -120.  , -119.75, -119.5 , -119.25, -119.  , -118.75,\n",
       "       -118.5 , -118.25, -118.  , -117.75, -117.5 , -117.25])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02c1a49a-f336-4ab5-b1e8-d321c3ef5c3b' class='xr-section-summary-in' type='checkbox'  ><label for='section-02c1a49a-f336-4ab5-b1e8-d321c3ef5c3b' class='xr-section-summary' >Attributes: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>C</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 't2m' (time: 61592, latitude: 13, longitude: 30)>\n",
       "dask.array<sub, shape=(61592, 13, 30), dtype=float32, chunksize=(61592, 13, 30), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "  * latitude   (latitude) float64 48.75 48.5 48.25 48.0 ... 46.25 46.0 45.75\n",
       "  * longitude  (longitude) float64 -124.5 -124.2 -124.0 ... -117.8 -117.5 -117.2\n",
       "Attributes:\n",
       "    GRIB_paramId:                             167\n",
       "    GRIB_shortName:                           2t\n",
       "    GRIB_units:                               K\n",
       "    GRIB_name:                                2 metre temperature\n",
       "    GRIB_cfVarName:                           t2m\n",
       "    GRIB_dataType:                            an\n",
       "    GRIB_missingValue:                        9999\n",
       "    GRIB_numberOfPoints:                      390\n",
       "    GRIB_totalNumber:                         0\n",
       "    GRIB_typeOfLevel:                         surface\n",
       "    GRIB_NV:                                  0\n",
       "    GRIB_stepUnits:                           1\n",
       "    GRIB_stepType:                            instant\n",
       "    GRIB_gridType:                            regular_ll\n",
       "    GRIB_gridDefinitionDescription:           Latitude/Longitude Grid\n",
       "    GRIB_Nx:                                  30\n",
       "    GRIB_iDirectionIncrementInDegrees:        0.25\n",
       "    GRIB_iScansNegatively:                    0\n",
       "    GRIB_longitudeOfFirstGridPointInDegrees:  -124.5\n",
       "    GRIB_longitudeOfLastGridPointInDegrees:   -117.25\n",
       "    GRIB_Ny:                                  13\n",
       "    GRIB_jDirectionIncrementInDegrees:        0.25\n",
       "    GRIB_jPointsAreConsecutive:               0\n",
       "    GRIB_jScansPositively:                    0\n",
       "    GRIB_latitudeOfFirstGridPointInDegrees:   48.75\n",
       "    GRIB_latitudeOfLastGridPointInDegrees:    45.75\n",
       "    long_name:                                2 metre temperature\n",
       "    units:                                    C\n",
       "    coordinates:                              number time step surface latitu..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_merge['t2m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wa_merge = wa_merge.dropna(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unset units (avoid plotting in x and y label)\n",
    "wa_merge['latitude'].attrs['units'] = None\n",
    "wa_merge['longitude'].attrs['units'] = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clip to WA state geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wa_merge_clip = wa_merge.rio.write_crs(world.crs);\n",
    "#wa_merge_clip = wa_merge_clip.rio.clip(wa_geom, crs=world.crs, drop=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute seasonal mean values for each variable\n",
    "* This is a simple `groupby()` and `mean()`\n",
    "* Inspect the output values - do these make sense?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "wa_merge_seasonal_mean = wa_merge.groupby('time.season').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 13, longitude: 30, season: 4)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 48.75 48.5 48.25 48.0 ... 46.25 46.0 45.75\n",
       "  * longitude  (longitude) float64 -124.5 -124.2 -124.0 ... -117.8 -117.5 -117.2\n",
       "  * season     (season) object &#x27;DJF&#x27; &#x27;JJA&#x27; &#x27;MAM&#x27; &#x27;SON&#x27;\n",
       "Data variables:\n",
       "    sd         (season, latitude, longitude) float32 dask.array&lt;chunksize=(1, 13, 30), meta=np.ndarray&gt;\n",
       "    t2m        (season, latitude, longitude) float32 dask.array&lt;chunksize=(1, 13, 30), meta=np.ndarray&gt;\n",
       "    tp         (season, latitude, longitude) float32 dask.array&lt;chunksize=(1, 13, 30), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><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-8b1deb71-3dc4-405c-b765-0ae1fd31bd18' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b1deb71-3dc4-405c-b765-0ae1fd31bd18' 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>: 13</li><li><span class='xr-has-index'>longitude</span>: 30</li><li><span class='xr-has-index'>season</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-679dd9cc-6b0e-4ba7-abcc-5e451e2b3e54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-679dd9cc-6b0e-4ba7-abcc-5e451e2b3e54' 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'>48.75 48.5 48.25 ... 46.0 45.75</div><input id='attrs-6c0b2fca-cd7d-4969-ad03-63dc0bc7e4e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c0b2fca-cd7d-4969-ad03-63dc0bc7e4e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe38d50-0edf-4b59-b821-31aadb5cbd97' class='xr-var-data-in' type='checkbox'><label for='data-dbe38d50-0edf-4b59-b821-31aadb5cbd97' 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>units :</span></dt><dd>None</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([48.75, 48.5 , 48.25, 48.  , 47.75, 47.5 , 47.25, 47.  , 46.75, 46.5 ,\n",
       "       46.25, 46.  , 45.75])</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'>-124.5 -124.2 ... -117.5 -117.2</div><input id='attrs-a7b35b15-77ef-455e-8fbb-7011e3bd70f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7b35b15-77ef-455e-8fbb-7011e3bd70f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0462d3a0-f11b-4a01-8f37-1d7d74586177' class='xr-var-data-in' type='checkbox'><label for='data-0462d3a0-f11b-4a01-8f37-1d7d74586177' 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>units :</span></dt><dd>None</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-124.5 , -124.25, -124.  , -123.75, -123.5 , -123.25, -123.  , -122.75,\n",
       "       -122.5 , -122.25, -122.  , -121.75, -121.5 , -121.25, -121.  , -120.75,\n",
       "       -120.5 , -120.25, -120.  , -119.75, -119.5 , -119.25, -119.  , -118.75,\n",
       "       -118.5 , -118.25, -118.  , -117.75, -117.5 , -117.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>season</span></div><div class='xr-var-dims'>(season)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DJF&#x27; &#x27;JJA&#x27; &#x27;MAM&#x27; &#x27;SON&#x27;</div><input id='attrs-28580633-95b4-4e72-aac0-ec99a6ae9fee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28580633-95b4-4e72-aac0-ec99a6ae9fee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12a7edee-8224-4360-b6bf-b9ddcae3eebc' class='xr-var-data-in' type='checkbox'><label for='data-12a7edee-8224-4360-b6bf-b9ddcae3eebc' 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;DJF&#x27;, &#x27;JJA&#x27;, &#x27;MAM&#x27;, &#x27;SON&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3345d2ee-380f-4d32-ac34-0172c7e42f79' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3345d2ee-380f-4d32-ac34-0172c7e42f79' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(season, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 30), meta=np.ndarray&gt;</div><input id='attrs-c48c3cce-dd34-471b-a019-4124f026dc50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c48c3cce-dd34-471b-a019-4124f026dc50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d6143c-532e-40ff-bf32-8c2e6da2d762' class='xr-var-data-in' type='checkbox'><label for='data-c4d6143c-532e-40ff-bf32-8c2e6da2d762' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 6.24 kB </td> <td> 1.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (4, 13, 30) </td> <td> (1, 13, 30) </td></tr>\n",
       "    <tr><th> Count </th><td> 22 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"203\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"52\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"52\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"57\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"63\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"69\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 33.60242404034126,23.60242404034126 33.60242404034126,75.60242404034126 10.0,52.00000000000001\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 153.60242404034125,23.60242404034126 33.60242404034126,23.60242404034126\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"33\" y1=\"75\" x2=\"153\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"33.60242404034126,23.60242404034126 153.60242404034125,23.60242404034126 153.60242404034125,75.60242404034126 33.60242404034126,75.60242404034126\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.602424\" y=\"95.602424\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >30</text>\n",
       "  <text x=\"173.602424\" y=\"49.602424\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,173.602424,49.602424)\">13</text>\n",
       "  <text x=\"11.801212\" y=\"83.801212\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.801212,83.801212)\">4</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(season, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 30), meta=np.ndarray&gt;</div><input id='attrs-a6615be6-6f93-4148-862c-33c40c36fac2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6615be6-6f93-4148-862c-33c40c36fac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcfcc5bd-aefb-4d16-b2ba-0845632f3dc9' class='xr-var-data-in' type='checkbox'><label for='data-fcfcc5bd-aefb-4d16-b2ba-0845632f3dc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 6.24 kB </td> <td> 1.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (4, 13, 30) </td> <td> (1, 13, 30) </td></tr>\n",
       "    <tr><th> Count </th><td> 23 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"203\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"52\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"52\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"57\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"63\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"69\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 33.60242404034126,23.60242404034126 33.60242404034126,75.60242404034126 10.0,52.00000000000001\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 153.60242404034125,23.60242404034126 33.60242404034126,23.60242404034126\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"33\" y1=\"75\" x2=\"153\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"33.60242404034126,23.60242404034126 153.60242404034125,23.60242404034126 153.60242404034125,75.60242404034126 33.60242404034126,75.60242404034126\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.602424\" y=\"95.602424\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >30</text>\n",
       "  <text x=\"173.602424\" y=\"49.602424\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,173.602424,49.602424)\">13</text>\n",
       "  <text x=\"11.801212\" y=\"83.801212\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.801212,83.801212)\">4</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(season, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 30), meta=np.ndarray&gt;</div><input id='attrs-36893bf6-55da-4bd9-9dd8-51a01a030ca3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36893bf6-55da-4bd9-9dd8-51a01a030ca3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2515beb4-cfdd-4e73-a1c9-bd4f52055cb6' class='xr-var-data-in' type='checkbox'><label for='data-2515beb4-cfdd-4e73-a1c9-bd4f52055cb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 6.24 kB </td> <td> 1.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (4, 13, 30) </td> <td> (1, 13, 30) </td></tr>\n",
       "    <tr><th> Count </th><td> 38 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"203\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"52\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"52\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"57\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"63\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"69\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 33.60242404034126,23.60242404034126 33.60242404034126,75.60242404034126 10.0,52.00000000000001\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 153.60242404034125,23.60242404034126 33.60242404034126,23.60242404034126\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"33\" y1=\"75\" x2=\"153\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"33.60242404034126,23.60242404034126 153.60242404034125,23.60242404034126 153.60242404034125,75.60242404034126 33.60242404034126,75.60242404034126\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.602424\" y=\"95.602424\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >30</text>\n",
       "  <text x=\"173.602424\" y=\"49.602424\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,173.602424,49.602424)\">13</text>\n",
       "  <text x=\"11.801212\" y=\"83.801212\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.801212,83.801212)\">4</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d743e7cb-4612-4987-b7d2-50b1470f82b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d743e7cb-4612-4987-b7d2-50b1470f82b7' 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: 13, longitude: 30, season: 4)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 48.75 48.5 48.25 48.0 ... 46.25 46.0 45.75\n",
       "  * longitude  (longitude) float64 -124.5 -124.2 -124.0 ... -117.8 -117.5 -117.2\n",
       "  * season     (season) object 'DJF' 'JJA' 'MAM' 'SON'\n",
       "Data variables:\n",
       "    sd         (season, latitude, longitude) float32 dask.array<chunksize=(1, 13, 30), meta=np.ndarray>\n",
       "    t2m        (season, latitude, longitude) float32 dask.array<chunksize=(1, 13, 30), meta=np.ndarray>\n",
       "    tp         (season, latitude, longitude) float32 dask.array<chunksize=(1, 13, 30), meta=np.ndarray>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_merge_seasonal_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hack to reorder seasons, as default is alphabetical\n",
    "#order = np.array(['DJF', 'MAM', 'JJA', 'SON'], dtype=object)\n",
    "order = np.array([0,2,1,3])\n",
    "#wa_merge_seasonal_mean.season.values\n",
    "wa_merge_seasonal_mean = wa_merge_seasonal_mean.isel(season=order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wa_merge_seasonal_resample = wa_merge.resample(time='Q-NOV').mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot seasonal mean grids\n",
    "* Note that plot will assume `x='longitude', y='latitude'`, but can also explicitly specify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    sd       (season, latitude, longitude) float32 dask.array<chunksize=(1, 13, 30), meta=np.ndarray>\n",
       "    t2m      (season, latitude, longitude) float32 dask.array<chunksize=(1, 13, 30), meta=np.ndarray>\n",
       "    tp       (season, latitude, longitude) float32 dask.array<chunksize=(1, 13, 30), meta=np.ndarray>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_merge_seasonal_mean.data_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in wa_merge_seasonal_mean.data_vars:\n",
    "    wa_merge_seasonal_mean[i].plot(col='season')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute mean monthly values for WA state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (month: 12)\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sd       (month) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    t2m      (month) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    tp       (month) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><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-b80692b8-7f2b-424c-adf8-3856b4136266' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b80692b8-7f2b-424c-adf8-3856b4136266' 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'>month</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-645a8de9-3945-4fb4-87b1-2670eea96325' class='xr-section-summary-in' type='checkbox'  checked><label for='section-645a8de9-3945-4fb4-87b1-2670eea96325' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-492e2e31-1e6f-499b-b79f-4292fcc48de8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-492e2e31-1e6f-499b-b79f-4292fcc48de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3974b669-580d-427b-b9e8-acf079f92494' class='xr-var-data-in' type='checkbox'><label for='data-3974b669-580d-427b-b9e8-acf079f92494' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-624d8d9f-2f00-4c38-9f74-acd3aeb936a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-624d8d9f-2f00-4c38-9f74-acd3aeb936a3' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-56179ae4-65fa-4eb0-9904-13940cbc198f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56179ae4-65fa-4eb0-9904-13940cbc198f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64a00f21-07fb-4f72-b443-feff96556250' class='xr-var-data-in' type='checkbox'><label for='data-64a00f21-07fb-4f72-b443-feff96556250' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 48 B </td> <td> 4 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (12,) </td> <td> (1,) </td></tr>\n",
       "    <tr><th> Count </th><td> 86 Tasks </td><td> 12 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"37\" />\n",
       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"37\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"37\" />\n",
       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"37\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"37\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"37\" />\n",
       "  <line x1=\"70\" y1=\"0\" x2=\"70\" y2=\"37\" />\n",
       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"37\" />\n",
       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"37\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"37\" />\n",
       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"37\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,37.56358655585696 0.0,37.56358655585696\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text>\n",
       "  <text x=\"140.000000\" y=\"18.781793\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,18.781793)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-b8afd26f-a593-43f4-bcdc-7642ebeca793' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8afd26f-a593-43f4-bcdc-7642ebeca793' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-938e62ef-e778-4a4c-a18a-2bc2858eb3ae' class='xr-var-data-in' type='checkbox'><label for='data-938e62ef-e778-4a4c-a18a-2bc2858eb3ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 48 B </td> <td> 4 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (12,) </td> <td> (1,) </td></tr>\n",
       "    <tr><th> Count </th><td> 87 Tasks </td><td> 12 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"37\" />\n",
       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"37\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"37\" />\n",
       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"37\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"37\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"37\" />\n",
       "  <line x1=\"70\" y1=\"0\" x2=\"70\" y2=\"37\" />\n",
       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"37\" />\n",
       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"37\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"37\" />\n",
       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"37\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,37.56358655585696 0.0,37.56358655585696\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text>\n",
       "  <text x=\"140.000000\" y=\"18.781793\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,18.781793)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-960c0c3f-0ebd-4f9d-b6fd-6644954c5dc4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-960c0c3f-0ebd-4f9d-b6fd-6644954c5dc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e1c56df-7219-4f8d-8ef6-e80c1e9af3e5' class='xr-var-data-in' type='checkbox'><label for='data-1e1c56df-7219-4f8d-8ef6-e80c1e9af3e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 48 B </td> <td> 4 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (12,) </td> <td> (1,) </td></tr>\n",
       "    <tr><th> Count </th><td> 102 Tasks </td><td> 12 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"37\" />\n",
       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"37\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"37\" />\n",
       "  <line x1=\"40\" y1=\"0\" x2=\"40\" y2=\"37\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"37\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"37\" />\n",
       "  <line x1=\"70\" y1=\"0\" x2=\"70\" y2=\"37\" />\n",
       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"37\" />\n",
       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"37\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"37\" />\n",
       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"37\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,37.56358655585696 0.0,37.56358655585696\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text>\n",
       "  <text x=\"140.000000\" y=\"18.781793\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,18.781793)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c004a1db-73cb-4953-8a5e-338e5cda0bcb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c004a1db-73cb-4953-8a5e-338e5cda0bcb' 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:  (month: 12)\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sd       (month) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    t2m      (month) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    tp       (month) float32 dask.array<chunksize=(1,), meta=np.ndarray>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_monthly_mean = wa_merge.groupby('time.month').mean('time').mean(['latitude','longitude'])\n",
    "wa_monthly_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axa = plt.subplots(3,1, sharex=True)\n",
    "wa_monthly_mean['t2m'].plot(ax=axa[0])\n",
    "wa_monthly_mean['tp'].plot(ax=axa[1])\n",
    "wa_monthly_mean['sd'].plot(ax=axa[2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper function for plotting WA grids with state outline overlay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotwa(ds_in, v_list=['t2m','tp','sd'], op='mean'):\n",
    "    f,axa = plt.subplots(1,3, figsize=(12,2), sharex=True, sharey=True)\n",
    "    for i,v in enumerate(v_list):\n",
    "        ds_in[v].plot(ax=axa[i], robust=True)\n",
    "        wa_state.plot(ax=axa[i], facecolor='none', edgecolor='black')\n",
    "        axa[i].set_title('WA State ERA5 Hourly: %s %s' % (op, v))\n",
    "    f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the WA state outline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#states_url = 'http://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_5m.json'\n",
    "states_url = 'http://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_500k.json'\n",
    "states_gdf = gpd.read_file(states_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "wa_state = states_gdf.loc[states_gdf['NAME'] == 'Washington']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wa_state.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-125.0458614 45.230853599999996 8.442559800000012 4.084327800000004\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,94.54603499999999)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -123.090546,49.001976 L -123.035393,49.002154 L -123.021459,48.977299 L -123.028091,48.973943 L -123.040967,48.977305 L -123.060717,48.975388 L -123.083834,48.976139 L -123.084498,48.986535 L -123.090546,49.001976 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -123.025486,48.717966 L -123.019699,48.721312 L -123.009787,48.722291 L -123.007511,48.718863 L -123.005086,48.694342 L -123.014449,48.684978 L -123.021215,48.681416 L -123.042337,48.675663 L -123.041645,48.678633 L -123.035672,48.68535 L -123.03636,48.69008 L -123.047058,48.695772 L -123.070427,48.699971 L -123.040179,48.717296 L -123.025486,48.717966 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.695907,48.737273 L -122.668947,48.706644 L -122.663259,48.697077 L -122.644901,48.691389 L -122.618225,48.670721 L -122.609576,48.645018 L -122.616956,48.645563 L -122.635299,48.651846 L -122.673538,48.680809 L -122.691795,48.711498 L -122.702223,48.717004 L -122.718833,48.716818 L -122.721981,48.723375 L -122.722262,48.731624 L -122.715709,48.748672 L -122.70306,48.743602 L -122.695907,48.737273 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.699266,48.621115 L -122.69806,48.62308 L -122.674173,48.629944 L -122.657016,48.609891 L -122.666149,48.608088 L -122.676796,48.610055 L -122.686136,48.613267 L -122.699266,48.621115 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.714512,48.60878 L -122.694672,48.596602 L -122.691745,48.590612 L -122.670638,48.568812 L -122.68944,48.543903 L -122.717278,48.539739 L -122.722407,48.540606 L -122.724031,48.549906 L -122.73048,48.565602 L -122.736199,48.569005 L -122.73944,48.573893 L -122.739898,48.583949 L -122.72493,48.603263 L -122.714512,48.60878 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.649405,48.588457 L -122.642597,48.588339 L -122.629321,48.5722 L -122.610841,48.561146 L -122.592901,48.553635 L -122.583985,48.551534 L -122.578856,48.54813 L -122.572967,48.529028 L -122.583565,48.53234 L -122.590194,48.536259 L -122.599948,48.536904 L -122.619858,48.529246 L -122.635738,48.526021 L -122.640414,48.52586 L -122.649256,48.528769 L -122.652041,48.531329 L -122.654342,48.537956 L -122.653612,48.548975 L -122.650786,48.554019 L -122.652385,48.583432 L -122.649405,48.588457 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.321721,48.019977 L -122.303455,48.005603 L -122.306629,48.004397 L -122.326115,48.010295 L -122.334524,48.018916 L -122.328343,48.021335 L -122.321721,48.019977 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.519535,48.288314 L -122.522756,48.285504 L -122.530976,48.282445 L -122.551793,48.281512 L -122.558332,48.282061 L -122.574872,48.294903 L -122.584086,48.297987 L -122.599532,48.298303 L -122.618466,48.294159 L -122.626757,48.288991 L -122.620748,48.282961 L -122.623779,48.269431 L -122.652639,48.265081 L -122.65343,48.25934 L -122.66921,48.240614 L -122.704142,48.238051 L -122.732326,48.229458 L -122.720811,48.214335 L -122.692971,48.222241 L -122.668385,48.223967 L -122.63126,48.220686 L -122.628352,48.222467 L -122.606406,48.208262 L -122.588138,48.18594 L -122.585778,48.182352 L -122.582595,48.170424 L -122.574905,48.155593 L -122.567936,48.148624 L -122.558205,48.119579 L -122.559911,48.114186 L -122.571615,48.105113 L -122.571853,48.102143 L -122.554559,48.077392 L -122.54512,48.05255 L -122.538953,48.050232 L -122.536713,48.026166 L -122.541525,48.017745 L -122.522793,48.01912 L -122.51145,48.038711 L -122.516314,48.057181 L -122.513994,48.059077 L -122.511081,48.075301 L -122.516906,48.081085 L -122.525422,48.096537 L -122.513276,48.097538 L -122.491104,48.094242 L -122.461606,48.068501 L -122.448419,48.054323 L -122.431266,48.045001 L -122.400628,48.036563 L -122.387382,48.03403 L -122.376259,48.034457 L -122.373263,48.000791 L -122.369161,47.995295 L -122.353611,47.981433 L -122.350254,47.969355 L -122.349597,47.958796 L -122.350741,47.953235 L -122.358812,47.93742 L -122.367876,47.932415 L -122.376837,47.923703 L -122.37578,47.910252 L -122.3773,47.905941 L -122.380497,47.904023 L -122.39042,47.905696 L -122.397349,47.912401 L -122.419274,47.912125 L -122.431035,47.914732 L -122.445519,47.930226 L -122.445759,47.93619 L -122.44079,47.946594 L -122.44076,47.951845 L -122.446682,47.963155 L -122.47266,47.988449 L -122.487505,47.990729 L -122.501257,47.987089 L -122.514813,47.981152 L -122.51778,47.974916 L -122.521219,47.972997 L -122.546824,47.967215 L -122.552053,47.973644 L -122.551032,47.977346 L -122.543063,47.985983 L -122.541564,47.992998 L -122.542924,47.996404 L -122.560018,48.006502 L -122.58178,48.010386 L -122.607342,48.030992 L -122.596786,48.038834 L -122.593621,48.0472 L -122.594922,48.056318 L -122.614028,48.072788 L -122.613217,48.079485 L -122.607291,48.088034 L -122.598301,48.110616 L -122.602109,48.135249 L -122.609568,48.15186 L -122.617464,48.159055 L -122.633167,48.163281 L -122.65602,48.162513 L -122.671235,48.157312 L -122.677337,48.154587 L -122.679556,48.155113 L -122.686626,48.174653 L -122.693084,48.181509 L -122.711508,48.193573 L -122.73503,48.199964 L -122.744612,48.20965 L -122.763042,48.215342 L -122.770045,48.224395 L -122.769939,48.227548 L -122.752563,48.260061 L -122.732022,48.279425 L -122.72259,48.304268 L -122.707077,48.315286 L -122.673731,48.354683 L -122.664928,48.374823 L -122.664659,48.401508 L -122.644798,48.405488 L -122.634991,48.404244 L -122.632643,48.401068 L -122.634024,48.398858 L -122.637339,48.398029 L -122.637892,48.395681 L -122.63582,48.395128 L -122.627809,48.3972 L -122.617174,48.407145 L -122.609715,48.411565 L -122.60198,48.409907 L -122.596732,48.405626 L -122.595351,48.3972 L -122.585038,48.395166 L -122.588891,48.363005 L -122.585162,48.353304 L -122.565525,48.348217 L -122.551334,48.342138 L -122.515979,48.320419 L -122.506568,48.310041 L -122.504729,48.300373 L -122.505828,48.297677 L -122.519535,48.288314 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -116.915989,45.995413 L -116.940681,45.996274 L -116.985882,45.996974 L -117.051304,45.996849 L -117.070047,45.99751 L -117.212616,45.998321 L -117.214534,45.99832 L -117.216731,45.998356 L -117.337668,45.998662 L -117.353928,45.996349 L -117.390738,45.998598 L -117.439943,45.998633 L -117.475148,45.997893 L -117.47536,45.997855 L -117.480103,45.99787 L -117.504833,45.998317 L -117.603163,45.998887 L -117.717852,45.999866 L -117.996911,46.000787 L -118.126197,46.000282 L -118.131019,46.00028 L -118.146028,46.000701 L -118.228941,46.000421 L -118.236584,46.000418 L -118.25253,46.000459 L -118.256368,46.000439 L -118.283526,46.000787 L -118.314982,46.000453 L -118.36779,46.000622 L -118.37836,46.000574 L -118.470756,46.000632 L -118.497027,46.00062 L -118.537119,46.00084 L -118.569392,46.000773 L -118.57571,46.000718 L -118.579906,46.000818 L -118.637725,46.00097 L -118.639332,46.000994 L -118.658717,46.000955 L -118.67787,46.000935 L -118.941242,46.000574 L -118.987129,45.999855 L -119.008558,45.97927 L -119.027056,45.969134 L -119.061462,45.958527 L -119.093221,45.942745 L -119.12612,45.932859 L -119.169496,45.927603 L -119.19553,45.92787 L -119.225745,45.932725 L -119.25715,45.939926 L -119.322509,45.933183 L -119.364396,45.921605 L -119.450256,45.917354 L -119.487829,45.906307 L -119.524632,45.908605 L -119.571584,45.925456 L -119.600549,45.919581 L -119.623393,45.905639 L -119.669877,45.856867 L -119.772927,45.845578 L -119.802655,45.84753 L -119.868135,45.835962 L -119.876144,45.834718 L -119.907461,45.828135 L -119.965744,45.824365 L -120.001148,45.811902 L -120.07015,45.785152 L -120.141352,45.773152 L -120.170453,45.761951 L -120.210754,45.725951 L -120.282156,45.72125 L -120.288656,45.72015 L -120.329057,45.71105 L -120.40396,45.699249 L -120.482362,45.694449 L -120.505863,45.700048 L -120.521964,45.709848 L -120.559465,45.738348 L -120.591166,45.746547 L -120.634968,45.745847 L -120.668869,45.730147 L -120.68937,45.715847 L -120.724171,45.706446 L -120.788872,45.686246 L -120.855674,45.671545 L -120.870042,45.661242 L -120.895575,45.642945 L -120.913476,45.640045 L -120.915876,45.641345 L -120.943977,45.656445 L -120.953077,45.656745 L -120.977978,45.649345 L -120.983478,45.648344 L -121.007449,45.653217 L -121.033582,45.650998 L -121.06437,45.652549 L -121.084933,45.647893 L -121.120064,45.623134 L -121.117052,45.618117 L -121.1222,45.616067 L -121.131953,45.609762 L -121.139483,45.611962 L -121.145534,45.607886 L -121.167852,45.606098 L -121.183841,45.606441 L -121.196556,45.616689 L -121.195233,45.629513 L -121.200367,45.649829 L -121.215779,45.671238 L -121.251183,45.67839 L -121.287323,45.687019 L -121.312198,45.699925 L -121.33777,45.704949 L -121.372574,45.703111 L -121.401739,45.692887 L -121.423592,45.69399 L -121.462849,45.701367 L -121.499153,45.720846 L -121.522392,45.724677 L -121.533106,45.726541 L -121.631167,45.704657 L -121.668362,45.705082 L -121.707358,45.694809 L -121.735104,45.694039 L -121.811304,45.706761 L -121.867167,45.693277 L -121.901855,45.670716 L -121.900858,45.662009 L -121.908267,45.654399 L -121.935149,45.644169 L -121.951838,45.644951 L -121.955734,45.643559 L -121.963547,45.632784 L -121.979797,45.624839 L -121.983038,45.622812 L -122.00369,45.61593 L -122.022571,45.615151 L -122.044374,45.609516 L -122.101675,45.583516 L -122.112356,45.581409 L -122.126197,45.582617 L -122.126197,45.582573 L -122.12949,45.582967 L -122.129548,45.582945 L -122.14075,45.584508 L -122.183695,45.577696 L -122.2017,45.564141 L -122.248993,45.547745 L -122.262625,45.544321 L -122.266701,45.543841 L -122.294901,45.543541 L -122.331502,45.548241 L -122.352802,45.569441 L -122.380302,45.575941 L -122.391802,45.574541 L -122.410706,45.567633 L -122.438674,45.563585 L -122.453891,45.567313 L -122.474659,45.578305 L -122.479315,45.579761 L -122.492259,45.583281 L -122.523668,45.589632 L -122.548149,45.596768 L -122.581406,45.60394 L -122.602606,45.607639 L -122.643907,45.609739 L -122.675008,45.618039 L -122.691008,45.624739 L -122.713309,45.637438 L -122.738109,45.644138 L -122.76381,45.657138 L -122.774511,45.680437 L -122.772511,45.699637 L -122.760108,45.734413 L -122.761451,45.759163 L -122.769532,45.780583 L -122.795605,45.81 L -122.795963,45.825024 L -122.785696,45.844216 L -122.785515,45.850536 L -122.785026,45.867699 L -122.798091,45.884333 L -122.81151,45.912725 L -122.806193,45.932416 L -122.813998,45.960984 L -122.837638,45.98082 L -122.856158,46.014469 L -122.878092,46.031281 L -122.884478,46.06028 L -122.904119,46.083734 L -122.962681,46.104817 L -123.004233,46.133823 L -123.009436,46.136043 L -123.022147,46.13911 L -123.03382,46.144336 L -123.041297,46.146351 L -123.051064,46.153599 L -123.105021,46.177676 L -123.115904,46.185268 L -123.166414,46.188973 L -123.213054,46.172541 L -123.231196,46.16615 L -123.251233,46.156452 L -123.280166,46.144843 L -123.301034,46.144632 L -123.332335,46.146132 L -123.371433,46.146372 L -123.430847,46.181827 L -123.427629,46.229348 L -123.447592,46.249832 L -123.468743,46.264531 L -123.474844,46.267831 L -123.479644,46.269131 L -123.501245,46.271004 L -123.516188,46.266153 L -123.526391,46.263404 L -123.538092,46.26061 L -123.547659,46.259109 L -123.547636,46.265595 L -123.559923,46.265098 L -123.564405,46.262172 L -123.581642,46.260502 L -123.613544,46.259988 L -123.669501,46.266832 L -123.679125,46.272502 L -123.68008,46.277943 L -123.678069,46.286469 L -123.67876,46.290721 L -123.680574,46.296025 L -123.687763,46.299235 L -123.700764,46.305278 L -123.724273,46.301161 L -123.724038,46.295058 L -123.728585,46.288725 L -123.741478,46.290274 L -123.75956,46.275073 L -123.766682,46.273499 L -123.775054,46.274599 L -123.782654,46.280227 L -123.795556,46.284501 L -123.806139,46.283588 L -123.875525,46.239787 L -123.909306,46.245491 L -123.919581,46.251217 L -123.954353,46.277001 L -123.969427,46.291398 L -123.970912,46.293866 L -123.970355,46.299352 L -123.974509,46.303063 L -123.985204,46.309039 L -124.001264,46.31326 L -124.020551,46.315737 L -124.029924,46.308312 L -124.035599,46.296843 L -124.038797,46.283675 L -124.044018,46.275925 L -124.060961,46.278761 L -124.080671,46.267239 L -124.082187,46.269159 L -124.081729,46.274714 L -124.076262,46.296498 L -124.071384,46.305504 L -124.064624,46.326899 L -124.058351,46.386503 L -124.057425,46.409315 L -124.057024,46.493338 L -124.061953,46.556165 L -124.06842,46.601397 L -124.069583,46.630651 L -124.06905,46.647258 L -124.059426,46.655507 L -124.048444,46.645827 L -124.035874,46.630822 L -124.052708,46.622796 L -124.050842,46.617421 L -124.028799,46.59104 L -124.023566,46.582559 L -124.023148,46.564113 L -124.026019,46.531589 L -124.031737,46.496375 L -124.026032,46.462978 L -124.001271,46.459992 L -123.990615,46.463019 L -123.99087,46.465738 L -123.994181,46.468868 L -123.99268,46.488617 L -123.988386,46.497008 L -123.983688,46.498542 L -123.979053,46.497378 L -123.979213,46.489949 L -123.97083,46.47537 L -123.968044,46.473497 L -123.943667,46.477197 L -123.921192,46.507731 L -123.896703,46.522665 L -123.897242,46.52848 L -123.894254,46.537028 L -123.903321,46.55191 L -123.916902,46.562633 L -123.920247,46.567343 L -123.922332,46.577057 L -123.928861,46.588875 L -123.939139,46.596326 L -123.955556,46.60357 L -123.959175,46.613581 L -123.960642,46.636364 L -123.940616,46.640862 L -123.921913,46.650262 L -123.920916,46.653576 L -123.923269,46.672708 L -123.915596,46.678649 L -123.895601,46.683672 L -123.864902,46.698685 L -123.851356,46.70256 L -123.84621,46.716795 L -123.848725,46.719898 L -123.862149,46.727749 L -123.870782,46.728327 L -123.87668,46.730657 L -123.893054,46.750204 L -123.898641,46.750205 L -123.910716,46.746715 L -123.916371,46.741322 L -123.91584,46.737322 L -123.91285,46.730647 L -123.916874,46.726739 L -123.929073,46.725278 L -123.948683,46.725369 L -123.968564,46.736106 L -123.974994,46.733391 L -123.979655,46.724658 L -123.975157,46.713971 L -123.966886,46.705184 L -123.973663,46.703353 L -123.987521,46.707507 L -123.994242,46.707929 L -124.003458,46.702337 L -124.022413,46.708973 L -124.042478,46.72004 L -124.042111,46.722783 L -124.046399,46.725686 L -124.063117,46.733664 L -124.080983,46.735003 L -124.092176,46.741624 L -124.096515,46.746202 L -124.095041,46.756812 L -124.096655,46.784374 L -124.101232,46.810656 L -124.108078,46.836388 L -124.122979,46.879809 L -124.138225,46.905534 L -124.117712,46.91238 L -124.110641,46.91252 L -124.093392,46.901168 L -124.090422,46.8955 L -124.089286,46.867716 L -124.073113,46.861493 L -124.066349,46.863504 L -124.061051,46.865127 L -124.055085,46.870429 L -124.049279,46.891253 L -124.046344,46.893972 L -124.03624,46.898473 L -124.01366,46.90363 L -124.009519,46.910325 L -123.985082,46.921916 L -123.979378,46.923038 L -123.957493,46.921261 L -123.915256,46.932964 L -123.882884,46.939946 L -123.86018,46.948556 L -123.876136,46.961054 L -123.889402,46.968904 L -123.898245,46.971927 L -123.921617,46.971864 L -123.939214,46.969739 L -123.947996,46.971818 L -123.959185,46.981759 L -123.991612,46.980215 L -124.012218,46.985176 L -124.019727,46.991189 L -124.010068,46.997882 L -124.005248,47.003915 L -124.017035,47.011717 L -124.016999,47.014848 L -124.026345,47.030187 L -124.065856,47.04114 L -124.106378,47.04264 L -124.122057,47.04165 L -124.141517,47.035142 L -124.149043,47.029294 L -124.151288,47.021112 L -124.139733,46.98837 L -124.138035,46.970959 L -124.124386,46.94387 L -124.141267,46.940266 L -124.158624,46.929439 L -124.180111,46.926357 L -124.174503,46.941623 L -124.171161,46.958443 L -124.169113,46.994508 L -124.173501,47.06637 L -124.176745,47.092999 L -124.183833,47.124807 L -124.182802,47.134041 L -124.185806,47.136017 L -124.189725,47.146827 L -124.195893,47.174 L -124.209017,47.218151 L -124.236349,47.287287 L -124.242234,47.295101 L -124.25359,47.30248 L -124.257452,47.304059 L -124.271193,47.305025 L -124.286369,47.325162 L -124.293288,47.339309 L -124.299943,47.34836 L -124.307509,47.352268 L -124.319379,47.355559 L -124.324091,47.367602 L -124.32665,47.388759 L -124.336724,47.415996 L -124.345155,47.48903 L -124.355955,47.545698 L -124.359028,47.547616 L -124.366221,47.582439 L -124.371746,47.599575 L -124.374927,47.603891 L -124.382215,47.632302 L -124.395983,47.665534 L -124.412106,47.691199 L -124.420219,47.725294 L -124.425195,47.738434 L -124.430546,47.746249 L -124.453927,47.765334 L -124.471687,47.766907 L -124.47657,47.769671 L -124.482154,47.797454 L -124.489737,47.816988 L -124.497987,47.822605 L -124.50668,47.82391 L -124.51278,47.822518 L -124.539927,47.836967 L -124.558254,47.855979 L -124.559034,47.863085 L -124.562363,47.866216 L -124.588172,47.877878 L -124.609538,47.879996 L -124.625512,47.887963 L -124.630153,47.892467 L -124.629706,47.896968 L -124.645442,47.935338 L -124.651966,47.943177 L -124.662334,47.951451 L -124.672427,47.964414 L -124.67083,47.982366 L -124.679024,48.015697 L -124.682157,48.035987 L -124.685393,48.049238 L -124.688359,48.054927 L -124.693676,48.058697 L -124.696542,48.069274 L -124.695114,48.087096 L -124.688602,48.092466 L -124.687101,48.098657 L -124.695088,48.114878 L -124.721725,48.153185 L -124.731703,48.160402 L -124.733174,48.163393 L -124.731746,48.169997 L -124.704153,48.184422 L -124.696111,48.198599 L -124.6909,48.212617 L -124.690389,48.219745 L -124.705031,48.238774 L -124.70592,48.239894 L -124.699663,48.245812 L -124.684677,48.255228 L -124.680877,48.26535 L -124.676319,48.295143 L -124.669265,48.296353 L -124.665908,48.299324 L -124.662068,48.31045 L -124.65894,48.331057 L -124.670072,48.341341 L -124.696703,48.349748 L -124.713817,48.366309 L -124.727022,48.371101 L -124.730863,48.3762 L -124.731828,48.381157 L -124.725839,48.386012 L -124.716947,48.389776 L -124.694511,48.389004 L -124.653243,48.390691 L -124.639389,48.385524 L -124.631108,48.376522 L -124.611782,48.378182 L -124.599278,48.381035 L -124.597331,48.381882 L -124.590733,48.373604 L -124.572864,48.366228 L -124.564841,48.367921 L -124.546259,48.353594 L -124.538821,48.349893 L -124.525453,48.349022 L -124.510582,48.343236 L -124.414007,48.300887 L -124.395593,48.288772 L -124.380874,48.284699 L -124.361351,48.287582 L -124.342412,48.277695 L -124.299146,48.268239 L -124.295589,48.262983 L -124.296924,48.261796 L -124.297643,48.260676 L -124.295693,48.259282 L -124.272017,48.25441 L -124.265824,48.254842 L -124.255109,48.258972 L -124.252267,48.261004 L -124.250882,48.264773 L -124.238582,48.262471 L -124.217873,48.253294 L -124.192692,48.246316 L -124.14129,48.227413 L -124.110974,48.220557 L -124.101773,48.216883 L -124.107215,48.200082 L -124.090717,48.196458 L -124.072124,48.189903 L -124.050734,48.177747 L -123.981032,48.164761 L -123.955347,48.165455 L -123.934921,48.16084 L -123.915589,48.159352 L -123.880068,48.160621 L -123.866677,48.154796 L -123.858821,48.154273 L -123.831571,48.157937 L -123.778122,48.155466 L -123.756395,48.161057 L -123.728736,48.1628 L -123.72829,48.160858 L -123.725352,48.159191 L -123.71835,48.158713 L -123.706226,48.1634 L -123.706432,48.165822 L -123.702743,48.166783 L -123.672445,48.162715 L -123.651408,48.156952 L -123.636967,48.150319 L -123.641108,48.146127 L -123.628819,48.139279 L -123.590839,48.134949 L -123.574214,48.140756 L -123.560591,48.150697 L -123.551131,48.151382 L -123.534879,48.14578 L -123.52232,48.135539 L -123.507235,48.131807 L -123.473379,48.134079 L -123.455458,48.140047 L -123.440128,48.142014 L -123.425586,48.142221 L -123.399808,48.139815 L -123.401526,48.137753 L -123.439127,48.141278 L -123.441972,48.124259 L -123.424668,48.118065 L -123.395048,48.114243 L -123.360923,48.115864 L -123.332699,48.11297 L -123.314578,48.113725 L -123.288265,48.121036 L -123.280178,48.117309 L -123.268917,48.116094 L -123.248615,48.115745 L -123.239129,48.118217 L -123.21719,48.127203 L -123.191521,48.143821 L -123.1644,48.165894 L -123.144783,48.175943 L -123.133445,48.177276 L -123.100656,48.186058 L -123.099969,48.183995 L -123.132417,48.174704 L -123.139258,48.16648 L -123.143229,48.156633 L -123.131422,48.152736 L -123.124816,48.153472 L -123.116479,48.150208 L -123.085154,48.127137 L -123.06621,48.120469 L -123.050446,48.102825 L -123.038727,48.081138 L -123.016651,48.08538 L -123.004128,48.090516 L -122.979413,48.09594 L -122.946119,48.098552 L -122.929095,48.096244 L -122.917942,48.091535 L -122.920911,48.088199 L -122.926644,48.0741 L -122.927975,48.06665 L -122.926851,48.064593 L -122.918602,48.058238 L -122.877641,48.047025 L -122.860994,48.025286 L -122.860822,48.007585 L -122.87457,47.998993 L -122.871992,47.993493 L -122.850854,47.995899 L -122.83745,48.006898 L -122.827482,48.046768 L -122.849273,48.053808 L -122.857727,48.065774 L -122.878255,48.076072 L -122.890313,48.076866 L -122.879314,48.092677 L -122.884814,48.103675 L -122.876282,48.110877 L -122.833173,48.134406 L -122.784076,48.142974 L -122.760447,48.14324 L -122.759046,48.140056 L -122.762454,48.131172 L -122.748911,48.117026 L -122.773177,48.106864 L -122.778466,48.106135 L -122.792902,48.09718 L -122.798464,48.092451 L -122.801399,48.087561 L -122.770559,48.053432 L -122.770496,48.047897 L -122.766648,48.04429 L -122.753859,48.035981 L -122.748532,48.030138 L -122.734268,48.03495 L -122.74229,48.049324 L -122.740007,48.054116 L -122.739271,48.069153 L -122.741184,48.070958 L -122.747389,48.070795 L -122.748345,48.072097 L -122.733257,48.091232 L -122.718558,48.097567 L -122.698465,48.103102 L -122.68724,48.101662 L -122.69164,48.096726 L -122.69222,48.087081 L -122.682264,48.042723 L -122.677153,48.036346 L -122.668942,48.032026 L -122.669868,48.017217 L -122.686898,48.008305 L -122.690066,48.00842 L -122.697185,48.014978 L -122.70184,48.016106 L -122.719488,48.023694 L -122.729112,48.019741 L -122.723374,48.008095 L -122.718082,47.987739 L -122.701294,47.972979 L -122.683223,47.972226 L -122.6788,47.96793 L -122.676215,47.958743 L -122.684688,47.944049 L -122.68445,47.939593 L -122.681924,47.936415 L -122.66238,47.9307 L -122.657722,47.931156 L -122.651063,47.920985 L -122.65399,47.91589 L -122.655085,47.905058 L -122.646494,47.894771 L -122.637425,47.889945 L -122.618873,47.890242 L -122.610341,47.887343 L -122.631857,47.874815 L -122.633879,47.868401 L -122.63636,47.866186 L -122.650083,47.86386 L -122.666417,47.867497 L -122.69376,47.868002 L -122.690974,47.860118 L -122.681602,47.850405 L -122.683742,47.838773 L -122.688596,47.831438 L -122.719609,47.813036 L -122.731956,47.809741 L -122.748061,47.800546 L -122.75054,47.773966 L -122.757885,47.757744 L -122.758498,47.746036 L -122.774808,47.735542 L -122.781682,47.70392 L -122.770684,47.692234 L -122.811929,47.679861 L -122.829899,47.689115 L -122.832139,47.695511 L -122.815027,47.725996 L -122.809673,47.747412 L -122.790619,47.792597 L -122.812616,47.840029 L -122.820178,47.835904 L -122.815027,47.807493 L -122.818803,47.788473 L -122.845612,47.777474 L -122.849529,47.771206 L -122.850424,47.738979 L -122.859047,47.73373 L -122.873919,47.729566 L -122.880462,47.720643 L -122.882247,47.71053 L -122.878083,47.699227 L -122.878608,47.69086 L -122.885221,47.683761 L -122.896524,47.674838 L -122.900093,47.66532 L -122.899498,47.652233 L -122.904042,47.646178 L -122.97244,47.6149 L -123.012998,47.567469 L -123.056305,47.500789 L -123.106486,47.45817 L -123.137444,47.401795 L -123.15598,47.355745 L -123.149342,47.350636 L -123.140169,47.347496 L -123.111298,47.362619 L -123.11127,47.369672 L -123.120234,47.39149 L -123.067845,47.462471 L -123.033938,47.496973 L -123.033276,47.509726 L -122.982482,47.563896 L -122.967284,47.585685 L -122.943459,47.590546 L -122.923947,47.600539 L -122.922044,47.614816 L -122.917103,47.620743 L -122.870647,47.637659 L -122.856611,47.649615 L -122.831148,47.655267 L -122.804498,47.653363 L -122.754186,47.671612 L -122.750061,47.715607 L -122.740159,47.736228 L -122.733012,47.737625 L -122.722686,47.748827 L -122.719712,47.760976 L -122.714801,47.768176 L -122.690562,47.778372 L -122.684085,47.798574 L -122.682015,47.800882 L -122.648108,47.825123 L -122.623192,47.836199 L -122.614585,47.850806 L -122.608105,47.856728 L -122.573672,47.857582 L -122.573098,47.874081 L -122.586339,47.902023 L -122.588235,47.912923 L -122.596228,47.92021 L -122.616701,47.925139 L -122.620316,47.931553 L -122.617022,47.938987 L -122.611956,47.940772 L -122.603861,47.940478 L -122.601507,47.931726 L -122.592184,47.922519 L -122.581846,47.920282 L -122.549072,47.919072 L -122.527593,47.905882 L -122.513986,47.880807 L -122.512778,47.863879 L -122.506122,47.831745 L -122.502224,47.826395 L -122.482529,47.815511 L -122.482437,47.809255 L -122.485214,47.804128 L -122.495346,47.79704 L -122.495458,47.786692 L -122.471402,47.765965 L -122.470333,47.757109 L -122.471844,47.749819 L -122.477344,47.746019 L -122.488491,47.743605 L -122.507638,47.74304 L -122.515193,47.743911 L -122.519325,47.74622 L -122.537318,47.74714 L -122.554454,47.745704 L -122.543161,47.710941 L -122.53094,47.704814 L -122.525851,47.705095 L -122.523962,47.708034 L -122.511196,47.708715 L -122.504604,47.699136 L -122.504452,47.685888 L -122.508709,47.670843 L -122.501935,47.662182 L -122.508164,47.643657 L -122.502116,47.639074 L -122.493205,47.635122 L -122.492809,47.629591 L -122.494518,47.623625 L -122.500357,47.617816 L -122.49824,47.598242 L -122.493933,47.588963 L -122.483805,47.586721 L -122.479089,47.583654 L -122.503672,47.575178 L -122.518367,47.57408 L -122.534664,47.566122 L -122.543118,47.556326 L -122.542355,47.53784 L -122.547207,47.528257 L -122.546611,47.52355 L -122.532909,47.522184 L -122.52305,47.524 L -122.500543,47.51528 L -122.494882,47.510265 L -122.530514,47.469041 L -122.531889,47.428827 L -122.551136,47.394456 L -122.537044,47.375896 L -122.551536,47.35954 L -122.55584,47.347519 L -122.57134,47.327219 L -122.575985,47.32642 L -122.573739,47.318419 L -122.571239,47.315619 L -122.550134,47.290496 L -122.547521,47.285344 L -122.563935,47.264797 L -122.578211,47.254804 L -122.585826,47.253852 L -122.59106,47.256231 L -122.600102,47.262418 L -122.605337,47.26908 L -122.614379,47.268129 L -122.629132,47.274315 L -122.674342,47.283833 L -122.684335,47.278122 L -122.692426,47.280026 L -122.697378,47.283969 L -122.671256,47.343774 L -122.634823,47.370583 L -122.632463,47.376394 L -122.639126,47.377822 L -122.671486,47.366876 L -122.725738,47.33047 L -122.74525,47.297158 L -122.749621,47.276408 L -122.718124,47.250045 L -122.719075,47.233388 L -122.714908,47.220928 L -122.737159,47.206263 L -122.742394,47.178661 L -122.75905,47.166288 L -122.771619,47.167109 L -122.832799,47.243412 L -122.816633,47.276457 L -122.799025,47.289306 L -122.795694,47.305962 L -122.796646,47.341654 L -122.804615,47.356835 L -122.813778,47.362593 L -122.821868,47.363069 L -122.825237,47.353398 L -122.819738,47.327964 L -122.822344,47.319763 L -122.84586,47.298405 L -122.863732,47.270221 L -122.856171,47.233788 L -122.838298,47.208353 L -122.857546,47.173983 L -122.85994,47.165574 L -122.852046,47.164359 L -122.814238,47.179482 L -122.786742,47.143049 L -122.775056,47.123114 L -122.721437,47.103179 L -122.67813,47.103866 L -122.650634,47.132738 L -122.631987,47.140589 L -122.619614,47.153914 L -122.614855,47.169143 L -122.590829,47.178107 L -122.580517,47.210416 L -122.561957,47.244099 L -122.527586,47.291531 L -122.547747,47.316403 L -122.547408,47.317734 L -122.540238,47.31852 L -122.533338,47.31662 L -122.471652,47.277321 L -122.4442,47.266723 L -122.429605,47.269707 L -122.418074,47.281765 L -122.409199,47.288556 L -122.413735,47.293921 L -122.424235,47.297521 L -122.432335,47.296021 L -122.444635,47.300421 L -122.443008,47.306333 L -122.423535,47.319121 L -122.364168,47.335953 L -122.336934,47.341421 L -122.324833,47.348521 L -122.325734,47.391521 L -122.328434,47.400621 L -122.335234,47.408421 L -122.348035,47.415921 L -122.355135,47.441921 L -122.367036,47.447621 L -122.383136,47.450521 L -122.368036,47.459221 L -122.361336,47.481421 L -122.365236,47.48842 L -122.386637,47.50222 L -122.396538,47.51522 L -122.393938,47.52482 L -122.398338,47.55012 L -122.409839,47.56892 L -122.421139,47.57602 L -122.401839,47.58392 L -122.387139,47.59572 L -122.375421,47.585181 L -122.370167,47.583087 L -122.358238,47.58482 L -122.342937,47.59122 L -122.339513,47.599113 L -122.344937,47.60912 L -122.367819,47.624213 L -122.386039,47.63172 L -122.393739,47.63102 L -122.40424,47.63392 L -122.414645,47.639766 L -122.429841,47.658919 L -122.407841,47.680119 L -122.403841,47.689419 L -122.393248,47.701602 L -122.38044,47.709119 L -122.37644,47.716519 L -122.37314,47.729219 L -122.382641,47.749119 L -122.380241,47.758519 L -122.394442,47.772219 L -122.397043,47.779719 L -122.394944,47.803318 L -122.392044,47.807718 L -122.353244,47.840618 L -122.346544,47.842418 L -122.339944,47.846718 L -122.33595,47.852306 L -122.329545,47.869418 L -122.330145,47.875318 L -122.333543,47.880246 L -122.328546,47.897917 L -122.321847,47.911817 L -122.310747,47.925117 L -122.309747,47.929117 L -122.311148,47.936717 L -122.307048,47.949117 L -122.278047,47.956517 L -122.249007,47.959507 L -122.230046,47.970917 L -122.226346,47.976417 L -122.232391,47.987713 L -122.23022,48.007154 L -122.228767,48.012468 L -122.224979,48.016626 L -122.231761,48.029876 L -122.281087,48.049793 L -122.305838,48.073415 L -122.321709,48.085507 L -122.326119,48.092877 L -122.343241,48.097631 L -122.363842,48.12393 L -122.365078,48.125822 L -122.363797,48.142759 L -122.364744,48.151304 L -122.370253,48.164809 L -122.363479,48.174438 L -122.362044,48.187568 L -122.372492,48.193022 L -122.382102,48.207106 L -122.385703,48.217811 L -122.396121,48.229233 L -122.425572,48.232887 L -122.430578,48.236237 L -122.433767,48.23655 L -122.449605,48.232598 L -122.45371,48.228859 L -122.453618,48.22683 L -122.449513,48.214736 L -122.444508,48.214522 L -122.441731,48.211776 L -122.442051,48.20935 L -122.45493,48.196639 L -122.461888,48.193137 L -122.464801,48.194767 L -122.47025,48.194007 L -122.478535,48.188087 L -122.479008,48.175703 L -122.475803,48.166792 L -122.442383,48.130841 L -122.411649,48.11321 L -122.379481,48.087384 L -122.360345,48.061527 L -122.358375,48.056133 L -122.363107,48.054546 L -122.377114,48.057568 L -122.38769,48.065189 L -122.390787,48.069477 L -122.393413,48.078472 L -122.400692,48.085255 L -122.423703,48.102941 L -122.44966,48.114041 L -122.4675,48.130353 L -122.477983,48.129048 L -122.486736,48.12095 L -122.489986,48.120617 L -122.512031,48.133931 L -122.522576,48.161712 L -122.53722,48.183745 L -122.538916,48.209683 L -122.534431,48.223005 L -122.535209,48.240213 L -122.530996,48.249821 L -122.503786,48.257045 L -122.499648,48.256611 L -122.497727,48.253389 L -122.493448,48.252043 L -122.480925,48.251706 L -122.474494,48.255227 L -122.466803,48.269604 L -122.463962,48.270541 L -122.406516,48.25183 L -122.395328,48.257187 L -122.392058,48.269628 L -122.371693,48.287839 L -122.376818,48.296099 L -122.38431,48.304123 L -122.408718,48.326413 L -122.424102,48.334346 L -122.442678,48.337934 L -122.475529,48.359912 L -122.482423,48.361737 L -122.497686,48.361837 L -122.507437,48.364666 L -122.533452,48.383409 L -122.539449,48.39719 L -122.547492,48.399889 L -122.554536,48.40604 L -122.558403,48.426758 L -122.551221,48.439465 L -122.557298,48.444438 L -122.568348,48.44499 L -122.575254,48.443333 L -122.581607,48.429244 L -122.61448,48.41488 L -122.649839,48.408526 L -122.665338,48.416453 L -122.674158,48.424726 L -122.678928,48.439466 L -122.677072,48.444059 L -122.674188,48.443327 L -122.674085,48.441979 L -122.667249,48.442503 L -122.654844,48.454087 L -122.657753,48.47294 L -122.664623,48.478128 L -122.689121,48.476849 L -122.695725,48.464785 L -122.695587,48.460558 L -122.700603,48.457632 L -122.710362,48.461584 L -122.712322,48.464143 L -122.712981,48.47879 L -122.701644,48.497622 L -122.684521,48.509123 L -122.679122,48.507797 L -122.676922,48.504484 L -122.671386,48.50398 L -122.615183,48.521427 L -122.606961,48.522152 L -122.599951,48.520946 L -122.598469,48.512169 L -122.568071,48.50821 L -122.556834,48.498812 L -122.537355,48.466749 L -122.532845,48.466057 L -122.526943,48.468004 L -122.515056,48.465554 L -122.511348,48.461825 L -122.500721,48.460887 L -122.471832,48.470724 L -122.469634,48.472187 L -122.46967,48.474975 L -122.473763,48.47975 L -122.478851,48.481736 L -122.483501,48.49243 L -122.484996,48.50962 L -122.483872,48.521891 L -122.485288,48.528106 L -122.498463,48.556206 L -122.504428,48.564775 L -122.52537,48.567344 L -122.531978,48.568644 L -122.534719,48.574246 L -122.547438,48.575074 L -122.55648,48.573171 L -122.55886,48.572219 L -122.560287,48.583165 L -122.554577,48.588399 L -122.552673,48.586972 L -122.547438,48.580785 L -122.534787,48.57596 L -122.512372,48.578067 L -122.495904,48.575927 L -122.488421,48.564665 L -122.482406,48.559653 L -122.478431,48.559303 L -122.44456,48.570115 L -122.433059,48.581609 L -122.425271,48.599522 L -122.448702,48.622624 L -122.46425,48.625717 L -122.486878,48.643122 L -122.49399,48.651596 L -122.500308,48.656163 L -122.506718,48.669692 L -122.519172,48.713095 L -122.515511,48.720992 L -122.505684,48.724524 L -122.495301,48.737328 L -122.490401,48.751128 L -122.510902,48.757728 L -122.528203,48.768428 L -122.535803,48.776128 L -122.567498,48.779185 L -122.596844,48.771492 L -122.598033,48.769489 L -122.606787,48.759143 L -122.627808,48.74466 L -122.637146,48.735708 L -122.638082,48.732486 L -122.626287,48.72093 L -122.612562,48.714932 L -122.605733,48.701066 L -122.606105,48.698556 L -122.615169,48.693839 L -122.620338,48.693651 L -122.630422,48.696625 L -122.646323,48.708001 L -122.673472,48.733082 L -122.666953,48.748445 L -122.661111,48.753962 L -122.647443,48.773998 L -122.645743,48.781538 L -122.646777,48.785011 L -122.656528,48.784969 L -122.659708,48.786523 L -122.680246,48.80275 L -122.693683,48.804475 L -122.697219,48.80281 L -122.698675,48.800522 L -122.699507,48.794906 L -122.699303,48.789063 L -122.703106,48.786321 L -122.709815,48.786205 L -122.7112,48.79146 L -122.709169,48.817829 L -122.711805,48.832408 L -122.717073,48.84719 L -122.722685,48.852855 L -122.785659,48.885066 L -122.793175,48.892927 L -122.792584,48.894732 L -122.783747,48.894639 L -122.751289,48.911239 L -122.747514,48.915582 L -122.745371,48.921227 L -122.746596,48.930731 L -122.755624,48.93866 L -122.766096,48.941955 L -122.770432,48.942528 L -122.787539,48.931702 L -122.818232,48.939062 L -122.821631,48.941369 L -122.822464,48.944911 L -122.817226,48.95597 L -122.796887,48.975026 L -122.774276,48.991038 L -122.766307,48.991672 L -122.756318,48.996881 L -122.756037,48.999512 L -122.75802,49.002357 L -122.407829,49.002193 L -122.405989,49.002239 L -122.251063,49.002494 L -122.098357,49.002146 L -121.751252,48.997399 L -121.395543,48.999851 L -121.345295,49.000843 L -121.256933,49.000088 L -121.251244,49.000167 L -121.248949,49.000925 L -121.2299,49.001158 L -121.12624,49.001412 L -121.059966,49.000621 L -120.978955,49.000367 L -120.716604,49.000188 L -120.376216,49.000705 L -120.001199,48.999418 L -119.876195,49.000448 L -119.702016,49.000269 L -119.701218,49.000258 L -119.4577,49.000261 L -119.428678,49.000253 L -119.137274,49.000297 L -119.132102,49.000262 L -118.002046,49.000437 L -118.001106,48.999911 L -117.884398,48.999912 L -117.8761,49.000546 L -117.607323,49.000843 L -117.268192,48.999928 L -117.268247,48.999818 L -117.126075,48.998888 L -117.032351,48.999188 L -117.032107,48.874926 L -117.033335,48.749921 L -117.033671,48.656902 L -117.034358,48.628523 L -117.034499,48.620769 L -117.035425,48.499914 L -117.035285,48.430113 L -117.035285,48.429816 L -117.035254,48.423144 L -117.035289,48.422732 L -117.035178,48.371221 L -117.035178,48.370878 L -117.038602,48.207939 L -117.039599,48.184387 L -117.039615,48.184015 L -117.039582,48.181124 L -117.039582,48.180853 L -117.039583,48.180313 L -117.039618,48.178142 L -117.039413,48.17725 L -117.039552,48.17396 L -117.041107,48.124904 L -117.041401,48.0855 L -117.041676,48.04556 L -117.04236,47.966343 L -117.04247,47.839009 L -117.041999,47.822399 L -117.042064,47.77863 L -117.042485,47.766525 L -117.042521,47.764896 L -117.042623,47.761223 L -117.042657,47.760857 L -117.042059,47.7451 L -117.042135,47.7441 L -117.041634,47.7353 L -117.041678,47.72271 L -117.041633,47.7064 L -117.041532,47.683194 L -117.041431,47.68 L -117.041431,47.678185 L -117.041431,47.67814 L -117.04085,47.574124 L -117.041174,47.55853 L -117.041276,47.55821 L -117.040745,47.532909 L -117.040545,47.527562 L -117.040514,47.522351 L -117.039945,47.477823 L -117.039971,47.463309 L -117.039948,47.434885 L -117.03995,47.412412 L -117.039882,47.399085 L -117.040176,47.3749 L -117.039843,47.347201 L -117.040019,47.259272 L -117.039899,47.225515 L -117.039888,47.203282 L -117.039871,47.181858 L -117.039836,47.154734 L -117.039657,46.825798 L -117.039828,46.815443 L -117.039398,46.700186 L -117.039771,46.471779 L -117.039763,46.46957 L -117.039741,46.462704 L -117.039813,46.425425 L -117.036562,46.422596 L -117.034696,46.418318 L -117.035545,46.410012 L -117.036455,46.407792 L -117.038282,46.40604 L -117.041737,46.395195 L -117.04195,46.39216 L -117.046915,46.379577 L -117.049474,46.37682 L -117.051775,46.375641 L -117.057516,46.371396 L -117.061045,46.367747 L -117.062785,46.365287 L -117.062748,46.353624 L -117.06263,46.352522 L -117.060703,46.349015 L -117.055983,46.345531 L -117.051735,46.343833 L -117.045469,46.34249 L -117.03445,46.34132 L -117.030672,46.340315 L -117.027744,46.338751 L -117.023844,46.335976 L -117.023149,46.334759 L -117.022706,46.32899 L -117.023424,46.326427 L -117.022939,46.320175 L -117.022293,46.31747 L -117.020663,46.314793 L -117.016413,46.311236 L -117.007486,46.305302 L -116.99726,46.303151 L -116.989794,46.299395 L -116.986688,46.296662 L -116.98463,46.292705 L -116.98491,46.289738 L -116.990894,46.280372 L -116.991422,46.278467 L -116.991134,46.276342 L -116.987391,46.272865 L -116.976054,46.26601 L -116.972591,46.263271 L -116.970298,46.261233 L -116.966742,46.256923 L -116.964379,46.253282 L -116.958801,46.24232 L -116.955264,46.23088 L -116.956031,46.225976 L -116.959428,46.219812 L -116.96613,46.209453 L -116.966569,46.207501 L -116.965841,46.203417 L -116.962966,46.19968 L -116.952416,46.193514 L -116.941724,46.185034 L -116.923958,46.17092 L -116.92187,46.167808 L -116.921258,46.164795 L -116.922648,46.160744 L -116.935473,46.142448 L -116.948336,46.125885 L -116.950276,46.123464 L -116.95098,46.118853 L -116.951265,46.111161 L -116.955263,46.102237 L -116.959548,46.099058 L -116.974058,46.097707 L -116.976957,46.09667 L -116.978823,46.095731 L -116.981747,46.092881 L -116.982498,46.091347 L -116.982479,46.089389 L -116.981962,46.084915 L -116.978938,46.080007 L -116.970009,46.076769 L -116.96319,46.075905 L -116.960416,46.076346 L -116.957372,46.075449 L -116.948564,46.067933 L -116.942656,46.061 L -116.931706,46.039651 L -116.923005,46.018293 L -116.91868,45.999875 L -116.91718,45.996575 L -116.915989,45.995413 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.948013,48.781059 L -122.967949,48.784153 L -122.971039,48.78553 L -122.971039,48.788624 L -122.971039,48.789654 L -122.9487,48.786217 L -122.942856,48.788624 L -122.939072,48.785873 L -122.948013,48.781059 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.896797,48.746002 L -122.923607,48.754597 L -122.92498,48.75597 L -122.918449,48.766968 L -122.912605,48.770405 L -122.897141,48.770061 L -122.875145,48.764217 L -122.883049,48.763531 L -122.891296,48.761127 L -122.89164,48.759064 L -122.877892,48.75116 L -122.883392,48.748066 L -122.896797,48.746002 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.830803,48.743252 L -122.846619,48.746002 L -122.847305,48.747723 L -122.842834,48.75116 L -122.818436,48.744629 L -122.830803,48.743252 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.756889,48.694714 L -122.776825,48.6954 L -122.775452,48.705025 L -122.76445,48.708462 L -122.756889,48.694714 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -123.130585,48.65572 L -123.135742,48.657093 L -123.159111,48.665344 L -123.167015,48.663624 L -123.178017,48.662251 L -123.182823,48.657436 L -123.200356,48.662594 L -123.218918,48.669811 L -123.228539,48.675308 L -123.237823,48.683903 L -123.237473,48.689056 L -123.228539,48.69009 L -123.21685,48.690777 L -123.20723,48.689404 L -123.187981,48.685276 L -123.172516,48.680809 L -123.149139,48.669125 L -123.127487,48.65675 L -123.130585,48.65572 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.580017,48.64476 L -122.583824,48.645237 L -122.581924,48.648094 L -122.585251,48.649521 L -122.592865,48.649998 L -122.593346,48.651424 L -122.587158,48.655231 L -122.586678,48.660465 L -122.583824,48.659988 L -122.577637,48.647617 L -122.580017,48.64476 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -123.105148,48.633377 L -123.118553,48.633377 L -123.138145,48.638535 L -123.146736,48.642658 L -123.163925,48.647812 L -123.163582,48.649189 L -123.156708,48.649189 L -123.135048,48.644375 L -123.127831,48.643688 L -123.116493,48.639565 L -123.105148,48.633377 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -123.045097,48.609428 L -123.052231,48.612759 L -123.053665,48.618469 L -123.048424,48.621799 L -123.041763,48.619419 L -123.037956,48.615612 L -123.037956,48.610855 L -123.045097,48.609428 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.60276,48.605293 L -122.610847,48.60672 L -122.614655,48.611004 L -122.613228,48.614334 L -122.609901,48.615765 L -122.600861,48.614334 L -122.598,48.611481 L -122.59848,48.607674 L -122.60276,48.605293 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.885674,48.588013 L -122.897568,48.589439 L -122.904228,48.59182 L -122.912323,48.590393 L -122.932312,48.59182 L -122.946587,48.59515 L -122.953728,48.601337 L -122.9561,48.618469 L -122.954201,48.62513 L -122.960861,48.628941 L -122.967522,48.628464 L -122.981804,48.638931 L -122.986084,48.637028 L -122.990372,48.632748 L -122.98513,48.62989 L -122.984657,48.627037 L -122.984657,48.622753 L -122.977516,48.618469 L -122.970856,48.609428 L -122.971329,48.602287 L -122.977043,48.599434 L -122.985611,48.600384 L -122.989418,48.599911 L -122.988464,48.59753 L -122.988464,48.5942 L -122.994652,48.59182 L -123.004646,48.59182 L -123.009407,48.596104 L -123.007973,48.598957 L -122.997032,48.601814 L -122.992744,48.604668 L -122.992744,48.605621 L -123.000359,48.615139 L -123.004166,48.62085 L -123.0056,48.62085 L -123.007973,48.615139 L -123.020828,48.607998 L -123.027489,48.608952 L -123.029869,48.618469 L -123.030342,48.62989 L -123.019394,48.638931 L -123.010834,48.65321 L -122.984657,48.675575 L -122.962769,48.68462 L -122.960388,48.690331 L -122.94754,48.699848 L -122.944206,48.702702 L -122.95182,48.710316 L -122.95182,48.712219 L -122.907562,48.716503 L -122.877579,48.712696 L -122.848549,48.703178 L -122.816666,48.69128 L -122.794777,48.678909 L -122.741951,48.662251 L -122.741478,48.660347 L -122.753372,48.654636 L -122.754799,48.649403 L -122.76384,48.642738 L -122.775742,48.639408 L -122.785255,48.635601 L -122.79525,48.629414 L -122.799057,48.623703 L -122.807152,48.618946 L -122.807152,48.616089 L -122.801918,48.610378 L -122.79763,48.602764 L -122.799057,48.601337 L -122.809532,48.603718 L -122.815712,48.605621 L -122.819519,48.600861 L -122.827141,48.599911 L -122.831421,48.601814 L -122.829514,48.610855 L -122.830467,48.616089 L -122.836182,48.617516 L -122.847603,48.620373 L -122.863304,48.638931 L -122.868538,48.644169 L -122.871391,48.642738 L -122.872345,48.641788 L -122.879486,48.647976 L -122.883293,48.660347 L -122.883766,48.68224 L -122.890434,48.691757 L -122.894714,48.694611 L -122.898521,48.690804 L -122.898994,48.689377 L -122.907562,48.69128 L -122.916603,48.689377 L -122.91803,48.686047 L -122.91613,48.683189 L -122.908989,48.672722 L -122.908035,48.670818 L -122.910896,48.663677 L -122.910416,48.659397 L -122.905182,48.651783 L -122.881386,48.637981 L -122.874252,48.622753 L -122.868065,48.616089 L -122.864731,48.616089 L -122.861877,48.612282 L -122.86235,48.604191 L -122.871391,48.594673 L -122.885674,48.588013 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.872704,48.418011 L -122.895309,48.420982 L -122.893524,48.423363 L -122.879845,48.428719 L -122.881035,48.4305 L -122.889366,48.435856 L -122.888771,48.437046 L -122.876869,48.438831 L -122.873299,48.435261 L -122.870926,48.435261 L -122.866165,48.438831 L -122.866165,48.441208 L -122.878654,48.444778 L -122.890556,48.445374 L -122.897095,48.445969 L -122.904236,48.438831 L -122.926247,48.437046 L -122.930412,48.441803 L -122.922081,48.445374 L -122.91851,48.450726 L -122.91851,48.454891 L -122.922081,48.457272 L -122.928627,48.457867 L -122.93457,48.454891 L -122.941711,48.457867 L -122.945282,48.465599 L -122.945877,48.481659 L -122.939926,48.503075 L -122.930412,48.519138 L -122.920296,48.522705 L -122.919106,48.523895 L -122.919106,48.529842 L -122.922676,48.538769 L -122.920296,48.550663 L -122.907806,48.550663 L -122.898285,48.555424 L -122.889961,48.572079 L -122.885796,48.573269 L -122.881035,48.570889 L -122.881035,48.556614 L -122.879845,48.555424 L -122.874489,48.555424 L -122.873299,48.563751 L -122.869141,48.564346 L -122.864975,48.561371 L -122.86438,48.552448 L -122.871513,48.549473 L -122.873299,48.545906 L -122.869141,48.542931 L -122.859024,48.544121 L -122.843559,48.543526 L -122.841774,48.539955 L -122.842964,48.532818 L -122.84594,48.532223 L -122.851883,48.534603 L -122.857239,48.533413 L -122.861404,48.525085 L -122.86557,48.510807 L -122.871513,48.50486 L -122.870926,48.50248 L -122.866165,48.497128 L -122.8507,48.481659 L -122.851883,48.475117 L -122.856644,48.463814 L -122.857239,48.460838 L -122.854858,48.459648 L -122.844154,48.462029 L -122.842964,48.460243 L -122.84951,48.450726 L -122.848915,48.448349 L -122.844154,48.447754 L -122.82869,48.460243 L -122.834038,48.469761 L -122.835823,48.485825 L -122.840584,48.503075 L -122.837013,48.516758 L -122.82869,48.5233 L -122.787643,48.525681 L -122.774551,48.517948 L -122.769203,48.509617 L -122.769798,48.507835 L -122.784073,48.509026 L -122.785858,48.50843 L -122.792397,48.500099 L -122.803108,48.491177 L -122.816193,48.482254 L -122.816788,48.478092 L -122.815002,48.475712 L -122.813217,48.473927 L -122.813217,48.46917 L -122.819168,48.460243 L -122.815598,48.454891 L -122.804893,48.452511 L -122.800133,48.447159 L -122.800728,48.444183 L -122.808464,48.436451 L -122.808464,48.434666 L -122.803108,48.431095 L -122.801918,48.425148 L -122.806084,48.425148 L -122.812622,48.420982 L -122.825714,48.423363 L -122.832253,48.426933 L -122.843559,48.426338 L -122.853668,48.426933 L -122.854858,48.425743 L -122.853668,48.419792 L -122.872704,48.418011 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.950996,48.117981 L -122.9291,48.133686 L -122.914825,48.128925 L -122.908638,48.127975 L -122.908638,48.125595 L -122.918159,48.126545 L -122.942429,48.119884 L -122.950996,48.117981 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.485474,47.528774 L -122.496773,47.53175 L -122.506294,47.544243 L -122.491425,47.545433 L -122.479523,47.541862 L -122.485474,47.528774 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.493126,47.330254 L -122.504913,47.330681 L -122.518852,47.333321 L -122.523376,47.337627 L -122.52813,47.345543 L -122.526031,47.358906 L -122.517799,47.368679 L -122.526733,47.398582 L -122.514702,47.414047 L -122.513329,47.449104 L -122.497864,47.475914 L -122.482742,47.483131 L -122.474686,47.511066 L -122.468559,47.510078 L -122.4524,47.503471 L -122.460442,47.493763 L -122.46003,47.486858 L -122.433388,47.466431 L -122.439415,47.458633 L -122.439934,47.417007 L -122.437653,47.407425 L -122.42733,47.40213 L -122.39505,47.399277 L -122.373627,47.388718 L -122.378479,47.38533 L -122.401764,47.381325 L -122.433334,47.367558 L -122.437813,47.365604 L -122.448395,47.354988 L -122.453995,47.343338 L -122.457497,47.342567 L -122.469704,47.344624 L -122.491234,47.335171 L -122.491066,47.332428 L -122.493126,47.330254 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.838051,47.25528 L -122.841858,47.25766 L -122.841377,47.26432 L -122.840431,47.268604 L -122.832336,47.272888 L -122.82663,47.271935 L -122.826149,47.269558 L -122.830437,47.261944 L -122.83329,47.255756 L -122.838051,47.25528 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.60849,47.216911 L -122.611465,47.218102 L -122.618607,47.225239 L -122.629906,47.237137 L -122.657272,47.262714 L -122.668571,47.270447 L -122.668571,47.275208 L -122.666786,47.276993 L -122.663811,47.277588 L -122.649536,47.275208 L -122.634666,47.268665 L -122.62812,47.267475 L -122.621574,47.258553 L -122.6073,47.24963 L -122.58886,47.237137 L -122.588264,47.234756 L -122.589455,47.227619 L -122.602539,47.217506 L -122.60849,47.216911 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.679276,47.190144 L -122.692963,47.19252 L -122.701881,47.197876 L -122.716164,47.197876 L -122.726868,47.204418 L -122.726868,47.207394 L -122.719727,47.21096 L -122.713783,47.218102 L -122.706047,47.228809 L -122.702477,47.231186 L -122.691772,47.231186 L -122.685226,47.232376 L -122.679871,47.231186 L -122.673927,47.226429 L -122.667381,47.224049 L -122.659645,47.219292 L -122.648941,47.214531 L -122.64418,47.209179 L -122.6418,47.205013 L -122.642395,47.200848 L -122.647751,47.197281 L -122.679276,47.190144 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.639122,47.146301 L -122.639603,47.147251 L -122.638649,47.155342 L -122.634369,47.16391 L -122.631989,47.16724 L -122.628654,47.165337 L -122.628181,47.161053 L -122.629608,47.154388 L -122.635315,47.148205 L -122.639122,47.146301 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.701286,47.124706 L -122.711998,47.127682 L -122.731033,47.140766 L -122.742928,47.151474 L -122.741737,47.155045 L -122.737579,47.15683 L -122.732819,47.159805 L -122.729843,47.166943 L -122.723892,47.17646 L -122.710808,47.185383 L -122.705452,47.187168 L -122.694153,47.184788 L -122.681656,47.181217 L -122.673927,47.174675 L -122.672737,47.166348 L -122.672737,47.149693 L -122.675713,47.141361 L -122.685822,47.13958 L -122.691772,47.141956 L -122.695343,47.140175 L -122.695938,47.135414 L -122.692963,47.130653 L -122.694748,47.125896 L -122.701286,47.124706 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.821754,48.587814 L -122.821754,48.590908 L -122.814537,48.601219 L -122.804573,48.597782 L -122.803886,48.596405 L -122.80423,48.594345 L -122.821754,48.587814 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.94944,48.545658 L -122.965622,48.548988 L -122.977997,48.551846 L -122.987038,48.557079 L -122.987991,48.561836 L -122.982758,48.56279 L -122.984657,48.56612 L -122.998932,48.578495 L -123.01416,48.578972 L -123.016541,48.581348 L -123.013687,48.586109 L -123.013214,48.587536 L -122.999886,48.587059 L -122.987511,48.590866 L -122.979897,48.593246 L -122.966576,48.594673 L -122.960861,48.589916 L -122.940872,48.586109 L -122.939445,48.581825 L -122.937546,48.576591 L -122.933739,48.575638 L -122.931358,48.577068 L -122.933258,48.580875 L -122.928505,48.585155 L -122.918983,48.587059 L -122.902802,48.580399 L -122.902802,48.576591 L -122.919456,48.562313 L -122.923264,48.556602 L -122.926125,48.554699 L -122.927551,48.554699 L -122.929451,48.561363 L -122.934212,48.562313 L -122.937065,48.55946 L -122.939926,48.551846 L -122.94944,48.545658 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.807686,48.531322 L -122.816605,48.537865 L -122.827316,48.55452 L -122.830292,48.574745 L -122.824341,48.583668 L -122.810066,48.587833 L -122.791031,48.580101 L -122.774971,48.567608 L -122.76902,48.55928 L -122.77021,48.552143 L -122.807686,48.531322 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.547516,48.519272 L -122.552795,48.523026 L -122.552917,48.527485 L -122.548744,48.528912 L -122.54351,48.526058 L -122.542709,48.522087 L -122.547516,48.519272 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.16885119600000023\" opacity=\"0.6\" d=\"M -122.964912,48.449539 L -123.021423,48.456081 L -123.036888,48.457867 L -123.065445,48.472736 L -123.06723,48.476902 L -123.06842,48.479874 L -123.074959,48.480469 L -123.092209,48.479874 L -123.118385,48.490582 L -123.142776,48.505455 L -123.163597,48.529251 L -123.171921,48.551853 L -123.176086,48.568512 L -123.174301,48.575054 L -123.174301,48.57803 L -123.182632,48.581596 L -123.201668,48.585167 L -123.204048,48.594685 L -123.195717,48.607773 L -123.179657,48.622047 L -123.145752,48.625023 L -123.105301,48.622643 L -123.10054,48.619076 L -123.09697,48.611935 L -123.098755,48.601227 L -123.098755,48.59885 L -123.09697,48.598255 L -123.076149,48.591709 L -123.051758,48.575649 L -123.035698,48.563751 L -123.022018,48.562561 L -123.011307,48.559586 L -123.004768,48.551258 L -123.005363,48.547096 L -123.001198,48.53936 L -122.992271,48.536388 L -122.982162,48.536388 L -122.969673,48.534603 L -122.968483,48.532818 L -122.968483,48.527466 L -122.972046,48.520325 L -122.982162,48.513783 L -123.000603,48.515568 L -123.004768,48.518543 L -123.015472,48.515568 L -123.017258,48.513187 L -123.017258,48.50605 L -123.017258,48.495342 L -123.008934,48.484039 L -123.007149,48.470951 L -123.006554,48.46917 L -122.986923,48.462624 L -122.979187,48.462624 L -122.971451,48.463814 L -122.970856,48.467979 L -122.969078,48.468575 L -122.960152,48.463814 L -122.958961,48.462029 L -122.962532,48.45013 L -122.964912,48.449539 z\" /></g></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.multipolygon.MultiPolygon at 0x7f3646b7e040>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_geom = wa_state.iloc[0].geometry\n",
    "wa_geom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create some plots of descriptive stats (mean, max, min, std) for 1979-present\n",
    "* Pass the relevant dataset (e.g. `wa_merge.mean('time')`) to the `plotwa` helper function\n",
    "* What do these metrics tell you?\n",
    "    * What is causing most of the temperature variability captured by the std?\n",
    "    * What is the highest temperature value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAACQCAYAAABgQXZPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABoeElEQVR4nO2dd3wc1bX4v2ebepdcZLn3gm2wwXQIgQQCIRXSA3lJSO+NvNSXnveSl/JSSfmlEtITEiCV3jHNgG3ci3rv2jrn98espJ2ZtbSSVtX3+/nMR3vvzJ05s5o5e8+955wrqorBYDAYDAaDwWAwZBvfdAtgMBgMBoPBYDAY5ibG2DAYDAaDwWAwGAyTgjE2DAaDwWAwGAwGw6RgjA2DwWAwGAwGg8EwKRhjw2AwGAwGg8FgMEwKxtgwGAwGg8FgMBgMk4IxNgxDiMgREbl4uuUwGAxzH6NvDAZDNjE6xUZEfiIin5tuOVIZ0dgQkY+KyK2uuv0nqHtlSvnTIqIicsYo5y8VkR+LSKOI9IjIPhH5SMp+FZFVmd6MiNwpIm/K9PgTtA+LSG/K9pfkvgtFxErW9YjIsyLyhjTnuDYp99Wu+mXJ+tRzf2Is95KUoXa893cyk+67E5FrRORREekWkVoR+W8RCUyXjCc7Rt8YfTNXMN/dzMDoFKNTDDOD0WY27gbOERE/gIgsAILAaa66VcljEREBXge0A9eMcv6vAYXAeqAEuBI4OK47yR7vVNXClO2FKfvqVbUQKAbeB/xARNa62l/DyPdemnLuz2Zf/LFzEnew84H3ApXADuC5wAenU6CTHKNvjL4xGLKJ0SlGpxhmAKMZG49gv5hbk+XzgTuAZ111B1W1Plk+D6gG3gO8UkRCI5z/dOBGVe1QVUtV96rq7wBE5O7kMU8mLehXiEiZiPxVRFpEpCP5uSZ5/OeT1/5W8vhvJevXicg/RaQ9aclfnU6QsaA2t2K/kJsH60VkKXABcB3wfBGZP9FrjYSIVIvIzcl7OyAib07Z55hGc48oiD3d+BER2QX0pb6sIrJARPpFpCKlblvyew+OItO1InKfiHxNRDpF5JCInJ2sPy4izSJyTcrxOSLyFRE5JiJNIvI9EclL7jvh/zu5/04R+Wzyej0i8g8RqUwjUwFwG1CdMiJTrarfVdV7VDWqqnXAL4FzXN/Rh0Rkl4j0iciPRGS+iNyWvN6/RKQsg3+VITOMvkmD0TcjyjRr9I3Yo+W/E5FfJ9s/JiJbxvAvMIwdo1PSYHTKiDLNOJ2SPLYy2b4z+X3dIyK+5L5Tk/qkR0R+DeRm9h+YOkY0NlQ1CjyE/TKS/HsPcK+r7u6UZtcAfwF+nSxfMcIlHgQ+LyJvEJHVrmsPnn9L0oL+dVLe/wcsBZYAA8C3ksd/LCnboFX/zqTS/ydwIzAPeBXwHRHZONJ9j4aI+ETkSuwR8QMpu14P7FTV3wN7gNekaX5UbJed/3eih2oM/AqoxVaMLwe+ICLPHUP7VwGXY49UxAcrVbURuBNIVWqvBW5S1VjyYT93hPPuAHYBFdjf/U3YSnlV8jzfEpHC5LFfBtZgK/5VwCLgk8l9J/x/p/Bq4A3Y/98QaWYmVLUPuIzkqE5yq3cfh/0sP+OqexlwSVLGF2J3Iv4T+3/vA949wvdgGANG36TH6Js5pW9eBPwWKE/K+qfROj+G8WN0SnqMTpldOiXJB7C/qypgPnY/RJPG8J+An2Prld9i91tmFqo64gZ8Gvhj8vOTwGrgUlfdNcnP+UA38OJk+fvAn0c4dx72F/YoEMN+6C9L2a/AqhHabwU6Usp3Am9KKb8CuMfV5vvAp05wvjuBfqAzZftsct+FgJWsiwAJ4L2u9vsH64CPAk+m7CsEtgMB7Afld8DfR7i3dLL0ArXJ/YuTMhSltPki8JPk558An0vZd+Fg22T5CPAfrmseAS5O+e7uS372A43AGRk8L9cC+1PKpyT/j/NT6tqS/zsB+oCVKfvOAg6P4f/98ZTy24G/naCt4/7T7H8D9otc6fo+XpNS/j3w3ZTyu4A/jfadmC3zDaNvjL6Zo/oG+9l+MKXsAxqA86b7vZvLG0anGJ0yN3TKZ4A/u58nbGO5HpCUuvtTv7uZsGWSjepu4Fyx3UWqVHV/8kbOTtZtYnhU4CVAHBgMvvolcJmIVKU7saoOqOoXVHUbtgX5G+C3IlKe7ngRyReR74vIURHpTl63VJK+l2lYCuxIWrGdItKJbakvGOF+362qpSlbagBUvaqWYvs7fhO4KEW2c4Dl2BYw2NbwKSKyNXmvvaq6U1XjqtoEvBN4nogUZyoLzhGWaqBdVXtS6o5iW9WZcnyEfX8GNojICuyR/S5VfTjD8zalfB4ASN5zal0htoWeDzya8v/5W7I+0/93Y8rn/uR5x4SIvBj4EvaPROso95LuPgzZw+ibYYy+yYzZpG+GvgNVtRge1TVMHkanDGN0SmbMRJ3yP9jG7D/Edu26PllfDdRp0spIcjTD+5wyMjE2HsAOfLoOuA9AVbuxLanrsB/ew8ljr8H+oo6JSCP2dE4Qe6prRJLn/AJQgP3Ap+MDwFpgh6oWMzwNKoOncR1/HLjL9eIVqurbRpNnFFkjwEewX8QXJ6uvScrxRPLeH0rWv/5Ep3HJPlbqgXIRKUqpWwLUJT/3Yb8Eg6RTTu7va3iHahhbcb4GO1ju5+OUcyRasV/ajSn/nxK1A9hg9P/3WEh7ryJyKfAD4IWq+tQ4zmvILkbfeGU1+iY7TLu+wR7NtU9q+1vXYH+3hsnD6BSvrEanZIcp0ymq2qOqH1DVFdgu3e9Pupw1AItEJPWcS8Z7Q5PFqMaGqg4AO4H3Y/sTDnJvsm4wg8Mi7Gw+V2BPFW0FtmD7s12T7twi8gkROV1EQiKSix2Q1YkdvAW2dbkipUkR9j+2Mzly8CnXKd3H/xVYIyKvE5FgcjtdRNaPdt+jobYv6FeBTyZlvxpbcW1N2d4FvEZEAiKyQ0TWJn0lK7BHFe5U1a5xXv849ujMF0UkV0Q2A2/EHokBeAJ4gYiUi51t473juMzPsKcUrwR+MR45RyI5svcD4GsiMg/s50hEnp88ZLT/91hoAipEpGSwQkQuwv6+XjaGEQ/DJGL0TXqMvpk4061vkmwTkZeKHcz6Xmx3lgcncB3DKBidkh6jUybOVOoUEblCRFYljYpubBe0BLYxHQfenfw/vRQYMWXzdJDpon53YQev3JtSd0+ybnD68XXAE6r6D1VtHNywH8jNIrIpzXkVO3imFdvKvQS4XFV7k/s/Dfw0OT11NfB1bB/JVmwF/TfX+b4BvFzsqP9vJqfnnge8Mnn+RmzFkTPCvQ5mghjcHh3h2B9jW5BXYT9QP3Pd+4+wfQUvxVYgfwN6gKexf2RGHS0ZhVcBy7Dv7Y/Yfpz/TO77ObYv6hHgHwwHu2WMqt6H7eP5mKoeGaxPfi/nTUTwFD6CPTX4YHKa8V/YIwEw+v87Y1R1L3Yw2qHk81QNfAJ7xOvWlP/3beO+E0O2MPomPUbfTJzp1Ddgu3a8AujAfoZfqqqx8V7HkDFGp6TH6JSJMyU6BTvW6F/YcS8PAN9R1TuTRuNLsY2qDmz98ocJXGdSEKebl8HgRERux07t98PplsVgMMxt5rK+EZFPYwd3vna6ZTEYThbmsk6ZTZiFUAwnREROB07DTtdoMBgMk4bRNwaDIZsYnTJzyNSNynCSISI/xZ6ye68rW4TBYDBkFaNvDAZDNjE6ZWZh3KgMBoPBYDAYDAbDpGBmNgwGg8FgMBgMBsOkMKtiNvwFBRosS1krxz0pkyZzseQmHOWcQNxzjKXOhuo6kSpowmLgaBv+ghA580vwi/Pi4UjQUQ70eoXxxZxtfHHLLYj3BmJOeTWR8B4zDrQ431FO5I6e9llcX524xNc0yxK566wTLV2Ugs91i/6w93vp7a5rVdW0Cy2lQ0RKN6wJdezeF12mqjNuwRvD9FFe7tOaxcMPZmciz7G/PZrvbkIs7Hzffa58Qu53A9K/C1YkQrylhUBZGb4S73Xyc6KO8uKcDkc5hPeFCqvz4kcjzvXFohGv2peo8/13v4OQ5p5cZX/M+56KW8e5ZtLV7x3v0sA40vaPNkHv854zEfQeNtBSOya98q/f1ejn/redux4Y8CfTYBoMAIR8uZrnS1lCwv0Miqsc9750ao3vkYowQJQwBRThc3XzNI2eiZY7X6CF+d2OcqXfmzAt7lIAvjQdsHR1o+E+7572+Y5yTlcaPRNzfnduvaJ+rxzi7m9l4OTjbmMFvforVuS8VqRubDoF4DtfnqeP7orwo192jXcNkxnHrDI2gmXl1LznfUNlf9j5f7DS3E1gnfOlWVPV4ij3x72/OLGE8wc8rvYDlYjEePCKr7Phsy9jwZZKxzHPHnIuAlt1j1eYwjrnCxtq7XeUfQPeF1qbnQtaJ9o7PMeMh9hZ2x3l9vUhR1nSdDZyO50vWrDXqRTCZd4XL1oirrLrgDS6NKfTWS7fG/Ucc+c/rh+TwfDJD5R37Nod4dwduUcY/yJEhjlIzWI/t9w6/D7/uceZwv6m2u3uJtQ+6/wBLKh1PvuBPu913M9+pMJ+nwYOHKTx29+h5nPvwl/g7AicuuqYo/zNZc6Mhov8RbjZH+t1lN+y79WO8uHDTtkB8uqc+ionjZoJ9jrff7/rtSyo876nwfYBR1nizhGLRLqOT5lTF6m7j5amUyBxZ6W7UxDP9xplPTXeuie/9f6M9YqIyAVn5VFc7OOmGxYkMHrFkEKer4izSl8yVJYcV7bakLPvoe2dnnMkel2KZAz27H59ihbqOUMuRlIMm+h5Xn12/FXOvsd/bndmgP+P4ibctFlO2fLF2+fJk5Ey9KbHfd4dv3mfo7zsFq+eCTU6QzKswlxHOVbs1CkA/gGnLvLFRh4YAfCFnW0Gqr2Lfdc9x/k9HPzwB8bUVxGRvM0bQpSV+BGRTar69Fjaz1RmlbEx1TTfvoc9X7iV+VdsYcU7nos/x1YOu97zS1qfvx4rmmDN288jt+pEq8sbZgIiUrplY4gHblnMRS+rQ0SWmtkNw3RR97ufEGlrpvCcHZRccD6B4mIA6q//Knmb1+IrzKfsqkuRgFHPM5l//GaR9bPfdPOlj1fy4mvqeeV14jOzG4bp4t/6B0qpYCUbKZVKAgTpp4dH9U6CGqJSFlJ9woXNDTOFr3+2qj8csThrex7f+nHnU8yRQQwTs3ECoh395NWUs+DyU2j665M8eMXXCTd0Du1vf6IWBB5592+nT0hDprz3bdeWkpPj4/p3lwH853QLZDg5ibQ2UXH+84m2t9D+pz9T9z9fJdHbC34/Vk8fGosz8NhuOm66dbpFNYzCF77ezsfeW87C+QHOPj0P4OXTLZPh5ENV6dQ2TuM8OmjhCe7jkO7GIoGfAJ20UCSl7NGdtFA/3eIaRkBE8n78qy7edm0p5+7Io7PL4gSLSc46zNBZEk1Y1P31KQqWVpCzbD4PXPVdx/6SU5fw5Ludq93nzi8iUOCdnjPMOC646oX27NMVlxQAnDGt0hhOGmJ93XQdeIKipevp7+ji+C++Q878akjGXgXmzaPlpl8Plf2lxfgK8shZs2wapTaMhoj4TjslhzUrbf1/1ZWFfPOHnRcCv5lWwQwnBd3aTjedzKeGY+zjMHspwJ4hjROjh04iREhgu/3kY7tbllJ5wnMaZgRnX3h2PoUF9jzAm15bzD/v6n8Z9ortsxozs5HEiibY+/U7ePR9v+Ppj/+Rks01SMiPL9e2x3Kqiol3DrD0zRcAkBiIIT4f0a7wdIptyJC4L05EY0Rt5WtcHQxTQrS7jfq7/8SzP/8i4YZaACItjUP7NRkgXnTuOQAMPL0Pf1U5Vm9/2vMZZg7iVyIaI6IxEuki6tO1EblURJ4VkQMicn2a/S8SkV0i8oSI7BSRc7MuuGHW00Yze3mMu7iZPOyBtAjD8VGdtGIRJ4BtDLdqPUFyiOGNdzDMKHyhXCWmcWIaJ5SjMEf66bNrZiPHwrd8OHgoMuAK7nZHEgJleRFHOepKhxTwWQzEghDys+2G1/PkB39D9zP1lGxfhkYTQwkKmm7bhS83QLSlm2BlIblFQeZtqWLXv/dS+6uHKLjs+Y7ztm8s9sgyUOGcBSmsd379ZY+3edqMJyA8sNLplzmwyjua0b7O+d3FXGEnIWdcPQBxV8Yqy5XxIZ7v/f7jeSOX3Vl8AGIFznLH6jSzR//wVo1EROOEdE68s4YsE8LvCLQ+JbfWsf8XiR2eNu7kFO73ZTCYOid/GbEzr+LYg7+j9c6/EcgrJj7QTW7ZAsIdjQzs2UNuxUJi+44QXFiN+HyUbj2bpl/+nJyc+fQvcwY6pgsId9NmOdtsKGt0lPui3vepJeCMXvdFvMfktjvLec3OlzfY7dS14A0I1xyn3rHSZJ7yBHu7AjXdWf0A/BFnZz+RM3raO79X3DGhKBG17y+m3iyHbkTED3wbuASoBR4RkZtVdXfKYf8GblZVFZHN2DMl6yYmqWE60ESCREfXUFlc2ajE73xGrViaZ+gEIUDLWEsvXTRxnHqOAPaMhp8ACeLEiFJFNb10k0sBPnwsZTVP6r2cfmgtuUGnHim7o8xR/ry+wFHet+kxjwzvr7zPUc7zjR4M3pxwJq945d5Xe45puH+Ro7xwl/M78Pd7vyfNdeorK+T8bjVNNjp3NjBP5jzA1zeykgh1ezsw5U+7AsRHPIOXBFaKXslO9tGZwEnf+xqIDf/4tT9yGE0oFeevof9As+fYUHkBDX96HCsco/LURTz04VspWVXB7hseRNOlrTXMGCKaIJzcDIappPPoExQvXEUgJxcrYY8shjuGDQAJhAg312H19ZK7ZCkNP/g+ofkL6Lr7rukS2ZABljKkU6KZxYWfARxQ1UOqGgVuAl6UeoCq9urwSrsFZJSQ03CyoShNHKeGlXQynLFy0G0KSBohSoAAffRwjAOA0tgx6z1ypoTRDI3JIoESIUGEBLE55IRx0hsbqSx8wWYSfRFWfeAyKi/0DiaF6+1RikRvhMN/sF/YxnuPsPFtZ3lGLQwzi37Voc1gmEqq1pxFIhZhw0s+TG6Rd5Yx3pfUK93ddN9vjxaGDx+i5PwLplROw9iwGNYr4cxsgkXA8ZRybbLOgYi8RET2ArcA/5ENWQ1zC5/4WMQKAgTZwcX40ziphLBnOHvpoos2ooTpp5cFpRunWlzDGIirElaLsFpEjbExNwkW5VJx1kpqb3yAyufYxkag1On3M/95G4fiOMq3LASfsPLqzVMuq2FsRNRHOLkZDFNJyaL1RHo76G06RMni9Z79/vxC8hcOuz7mb9xI7vIV5K9eM5ViGsaIBUM6JWrrlQ3JOIvB7TpXk3QjUh4rRVX/qKrrgBcDn8223Ia5QTVLaeAoPvxpjY1KFgzFc/jwU8ECVrCB3JDXxdswc4gp9FtCvyVErLkziG16Xi5Wv+diuncd56n33AhAvHOAnHm2f2PNVdto+sczWOE4F/7oKtqfbABL6T7YPtIpDTOAsPqHNoNhKvEHQqy64HUcuecmGp74JwC+wLD7ZuXmc+lvOExw/nzKX3A5/c88Q/joEdTMws1oFBnSKRHb2NitqttTthtcTWqBxSnlGjhxLlJVvRtYKSImhZDBQ4lUsJQ1PMDfiWInqimkdGh/gCAD9LKKTZRSQRuN9NB1grMZZgoJfEN6JTqHBkfnzp1kiZzKQjZ/87Ws++SLCFUV4csLEmm2V6f05QSpPHc1AA9eb+fBP/0zz6NkVcW0yWvIjH4rRF9yMximmqL5K9nyqk9Tve0FBPKKsOLDgYXBQjs4O9bcTPuttxCsqGTRW9/uWPXXMPNIqAzplLAVHL0BPAKsFpHlIhICXgncnHqAiKyS5D9eRE4DQoA3c4jBACyR1ZzHFSxgCTnk0Uvn0L4aVgJQz1HaaWYhS1mD8cKY6cTVR1gDhDVAjLkzODqrslHF27qIt3UTqJj8acCK89ZQcd4arGicul/eDz5h4WWnoPEErffuJ9zSh/h9lG2cj/iNzTbTCWsAn5nVMKShsSVBNKqEQpPbuRefn4VbnsvCLc8l0t1G44F7CBaWUlC9kgXnXknjvXa/05efR87iJZMqi2Hi2DMbtpERYfRsVKoaF5F3An8H/MCPVfUZEXlrcv/3gJcBrxeRGDAAvELNFNesJE4UVZ30QYMcyWVTcumodm2mieNUsIB8KaRYy+mmHR9+QuSQS/6kymKYOHF8Q3olqrOqiz4is+pONBLl2Pu+wfL/OJfqK7fSmevMkdrbm+dp0xsefSTb73MG4eQHU3JR58HWt20fKjbdf4SiFRWEcgWf38e/XnUj296xncUvPN1hdOxb5L1uOOGUL7/ZpYTGkVvfX1joqYvPcxpjrad4R916l7oCj4qcKdzCXd42vtjISlPF+5vornL399MNCMaLnI36PSGUY2dAc7wXNxiA+haLqvNa2fy+85h3+mLaws4f5MVFnZ42jRWljvJAr/N9j5R43xVHmufySqrWvASABBCNdlF82g66H3sIEnD0c59h4VWvo3G9c9bUnWLVL96Bjm0u1VNUdaejnO8/x9Pm5j7nIrWJPK/+Sriqgr1OWXy9adYc6htwFCXHeZJQvzfvv+VKj+uef9eg9z1Wn/MgnyuNZajLm6LSF5lY8KWF0K92us9IBqlvAVT1VuBWV933Uj5/GfjyhAQzzAgG6OMJvYe1upV8KcSdCFET2c+MWC7zKGeefX5VLBIsYjnN1NHIcTpp45RnouSJs98wr32eo5zIWeoo31G+2nOtL81/cszyBV36Khz3dgBKDjp//3NbnFmhAi093hNbznfZ51oxIOgbx4CwNbp+CDR2euoq2nq9B46BmPrpU1tPhnV6MmJNBrNqSD5vUSlbvnI1Lffs5/H33MTA4aYJ+zW7DY2RSETiPPPt+1j16lNZetEyBtoHWPOSNRy89QD7fvDAhOQwTC5hKzi0GQypFK+sYP0bz+DxL9/JY1+4nWjn1C6oF25uoOupnZSctoPSM84l1tFG2VkX0PC7X9Dz6FiztBumCgsZ0ikRa1aN2xmmgEJKKKOSR7ido7qPRIYGabao4zA+fFSzjFKqsEhQRhUPx/9JRAdGP4FhWoirb0ivxObQAOms0pDi81G0dgFbvnI1dX9+nAMf/H/k1FRSfMZqirevgkWrJvX6dbcfIFSWR9P9R6i//QAAXUe6KVxYSP2/9rL2rd4RQ8PMIKxBZA69uIbsIcCi565i3llLeOob9/LQ1d+l4syVFK1byLxLNsCSyXWDaH/wDkq37qDhNz8j3t0Jfj/h+loChcV03b+Hom0rJ/X6hvFh6bAbVXR2/ZQapgDBxzJZxzyt4Rke5iBPs1CXUkQpC1mKXybvmUlogqM8y3LW8wh3IPgIEiJGhARxOrSZBbJ09BMZppw4fsLJmY2ozp3B0dmlIZO/+eITal5yGvkXn0n7v56k4/ZdtN2yk9DKxZS95Dnkrpkcf+envnY3iQF7Ov6CL17EEz94jIaH6gBYeMnaSbmmITv0WyEsM/poGIFgfojTPnoRi958McdvfIiWO/dS+9tHaLtkJWtfdyr587wuixMl1tNF19M7h8pL3vJ+jn3/f+ndbbsoLHyzGcCYqVj46LNsN6oBa+64OxiyS74UcjoX0a+9HORpjnGAQ+xmia5mCWvwpXGFnCgt1DFAH7vZSZAQa9nK0zxMHYcBqJTqrF/TkB0SyZkNgOgcGiCdVW5UbiTgp+LS01j139ey/qfvIbigkvpPfZ/+J54dtz9krLOfgz+6j2iXd5qxeKXtP122aQH3f/Yewu1hKjfaWQk7nqjDik7tNKkhcyIaJGLZm8EwEoHCXJZfdwGn3XAN2378BjoPtHHnW/9E16H2cbttDtQfpfXh2z3txT9sAOfWLKHpr78jUFqGLzcXfD4673pmQvdimDwsZEinxMxAhmEU8qWQU+RMzpbncyrncZi9PMWDRDRNrFOG1OsRWtSbPTmH4fhQPwEaOEYZVUPlJj027msaJpe4+glrkLAGiZkA8ZmHLxig4vUvIHfdUtpv+gfRr/6SYGUpgeI8JOAnUttCorufsvPWUbCmmlBlIfGufvw5fuZdtgURIdrWy2Ov/jYA1ZdthJI8EtE46rPoPtjGksvXU7S8nGN/2Q1AcWUerc+0cs6nzuf+z99LpHOAvOSaHIaZRUQDMIdeXMPUECor4LyvXc7+X+/i3g/cQrQnQsH8IiJBO11t5NBxUKXi3OeiVoKCVevo3fs0xau3kF9jL9TXd2Q/x371XQAqTn8OAFYsigb8hJtqqb7yNbTd/2/Ctc4OwMJXXEPbXX+ewrs1jAVVIZzUKdE5lKLSMPkUSSln6/M5wNM8wN8RFXLII0CQODF66SKHPJayhghh5lFNPUdYwQZyxDYkDuluDrGbUiqponooINzCIkqETezgaR4iTD9h7Di0XPKp9q2gS9tZhHHPnInE1E9/MkV/ZA71WWbVnajawTODuLNPaWsO+Su2kf+BbVjRKPG2dqjvRnx+5Lwg/pw82m7+M53H64j1dhNpbQCg9R+NJPp7iba3Un7WcwiH23jsE7dT+cbXU/fxz1K6fh799d1Eu8IsfeEGzvjvy3n4w7cMJSu477/uBmCguY+cKjsT1KIqVzoEoK4u1ymvO8tKkTO7FkBg5XJnRdw5exJb7F3jo3uF83sZmO8djQ1UOWduyoqcQbHhUu8MQCw+8g9qPO6dKFN1+rtrzHmM+NJksHKdRhIT95nvT4RIJMyshsFLwGdRkdM3VD615Lhjf02oHT4QQt+/hb7OOG31Ef59pAbx+QgWbyfaGebJHz5BoDiP5r8/RqShk44H76bo7A0kuvuJHGtmwVsvp+0vD1N/8E8UnrmJ2uu/S3DxQuLNbWgkSukrX0hJV5Dm2/5AqHIe0dZmGn79UyQYoK8bfMksTu+oO98h239U3e25n1xx6ohOy6lXdrYuxk38uPOY4ibv95TX6pwt9ve4RmQH0ozQxlxZoNwZXmJeneJzn8fvOibo/dlyZ6hSV5t0GawkMbHkIhZCf8J2owob3WIYBV/ImYktz5/HKZyPqkVEB+hbX0UiEUN8foKBPDo7D1Pb+TiB3CKO1t0FatHgq6WsYhWxWD+xaB8bqq5gf92/aV0aoLu/nkMNdxMilyhhAgRZzza6aKOeI/jwEaafQ9bTVEo1aiWG0vImGpsdspUcdaaAPFJX5rmfti19jnKheDPYdbjcC9919MWOctM+73qVSxudOiPY4rwOfRkk8HDrmUxmpN0pisebfGiCqY4T6hvywIhZc2cQY8qMDRHxAzuBOlW9QkS2At8DcoE48HZVfThb1/OFQoQWLiBY4PRNXPay64Y+JyIDdDcdQOMx8PkJlVWQu7CG/nkJGv/7G9R9/LMAdO5pZt6ZS2h+8BhH/7KbUKndme+t7Xac+8lP3sJZP34NoRJvCl7D9BK1AmBcHQwTQEQoLAtSWBZk/nxnXNi6dacMfY40ddKxpxWNxZGcIHkrqwlWlZC3/RQOv+lL9Nz1BACx4w3krFtJZO9BOm/6CxXnXwJAtHX4h19jcRq/8XsWfuiVZpG/GYalMpSFai75VhumFhEfuVJAoMQZsF1YuICiCy4cKve1HMN3pAXLShAI5lJatoL8biUnWMzjB27Elww4jxNFEOLE2MOjVGEbDhYWRZTSQyetWs9x3c8SWTNl92nIjDipMRtzp88ylXfyHmAPMLgIxH8D/6Wqt4nIC5LlC6dQHvw5eRSldBIGERHmvfM6xOej98GH6fj9zTQ/OOziMNA0nOc5WBBCAkIiZlG0uoqd7/8DWz79AnwL53nOa5g+BszMhmGKyJlfSnGZ9/33Fxew7LsfxF9aSMN/30j/4/uI7B1ObWtFhkcBA0UldkKMXB/x1i4av/ob5r/rpVMhviFDLISB5OIjEaNbDJNMQdUSisILXbVhKktWc94p78HnC3LPU1/HsuL48KHYI/P+FBe/BAlyyCOfQg5Zu0mQYLlv/RTehWE07JkNu2s+l2Y2piRAXERqgMuBH6ZUK8OGRwngjXKaRvwF+fjycil+zvmUb7Ff8FPeZ7swxPqiFC0tBaDqtGp2fOIiEv0xWh88Qt6CYpruPjBdYhtOQMQKDG0Gw3QRqChB/H4WXv9aAEKrlhJaao88+vKHFxMsO+sCitZvRuNxEGFg3zGix9L4NhmmDU3ObESswJzKh2+YXYgIeTll5AQLOWPdGwFYy6lD+wspGfq8hs2EyKWPbiqp5qD19JTLaxiZmOVnIBFiIBGyPTLmCFOVjerrwIeBVEe69wL/IyLHga8AH03XUESuE5GdIrIz1jW1i20Nsvba0znvhpez9MqN5C0ooqC6mDM+fiEAnftbqb//KGWn1rDoik30HmolkGdGuWYaUWNsGFJI1SsDHePPBjMRyt9wFVVvfx0V170KgFB5FfMus2cv2u+/g5wFNSR6B8hbvwSrdwDJ9fpEG6YPOxuVrVOic2gE0jB+HP0Vpj4dcl6olPVsYyFL2cgZAFSygHnUAPAMj7DEt5oAIVQsApi+ykxjcGZjLIMYInKpiDwrIgdE5Po0+9eJyAMiEhGRD46lbbaY9J6XiFwBNKvqoyJyYcqutwHvU9Xfi8jVwI+Ai93tVfUG4AaA3AUlE4voGydV24cDKte/5Uwe+69/cui3u1h66WoWX7ySh//rDqI9ETp31bHhgxez8JJ106BmDCMRTgTG5EYlIj8GBp/dTcm6cuDXwDLgCHC1qnozARhmPKl6pXx12ZTrFRGh8NztAPhLisg/YwsNv/85AFXPu5JEfx/Nf/8T4vfTc+9TLPr468lZPA/oGeGshqlEVYYCw+fSCKRh/KTqlWIpn3K9EgzksUjspDILWcJefYwH+ScAp3IeB3maPdaj5FFAlDCn+5871SIaRmGsblTJeOhvA5cAtcAjInKzqu5OOawdeDfw4nG0zQpToSHPAa5MxmXkAsUi8gvghdhxHAC/xelilZZYex/9Tx+hbIttpS+b38rxtuEsCf6F/cQa8x1tCp2JZQDIa3NmKhiocE7wDKxwZnN58sBiLj3Fnm7MPzWfx5L1R/+2n/Wn5XHGq5dxfE8f4c4w5141H7D7n03hYsd5jofcuscZ8Nl2RgUVj7Y7Dwn4PeXoQud5u1bkOMo9rhWPA30Qrnbek9+VOcr9UPv9FlUFvY66wmCU0egIO4Pj+2Pe0dhoYuQXyLK8gbDR2MQe1aj60bGNPv4E+Bbws5S664F/q+qXkiMA1wMfmZBghmkn2tTFi+P/Yn61/YytCHjXyynzOfXKtcX/cpT3xlwZU4Aft5/rKN+8f5OjLPHh5zx/6yn0P2wv5Nfyj5tZcs27SPT2Eg13krN4McGijcSPw+3HN1OxtnWo3XtaXsmrl+10nPe0vCOOsh/lqXDNUPnlix7jO3udWa3ymp3vXKwISg469WRek3MGSPpcM0KBAERd2afcuLNTxWJgufSiL00gfDBloCAc8WSkkrirDM5jIjEs14yzjKKHRsNChmY05pJvtSE7ROgnoQn8Yj8b6nrOJeD6nX5kL/u+uNlRl7/EmYSmZ1cJTkIsuc2le5LpHFUtKqmmCTvedA+PsokdHGM/vXSxTNeSb+WjJECcsoVu3Unkiu1D5aV/gaMvdF6mx5XxqQfvDPHHap2Nnrx3taPsBxY86Mxyl9vovB+JufRxQT50dDmq0q6r5q5zZ5earIQb7ux5YyShw3ol7k5Zmp4zgAOqeghARG4CXgQMGQyq2gw0i8jlY22bLcbkRiUia0Tk3yLydLK8WUQ+PlIbVf2oqtao6jLglcDtqvpa7BiNC5KHXQTsH+36wbJ8nvrUX+g92ALgMDQAj6GRjtEMjXRcesrTPPvXg/Q09LLrl3uG6uetKabtSC8XvmM9JUuLKFk8bAS4DY1M8BgaaRjN0EiH29DIBLehkQluQyMdoxkaadtM0NAAiCX8RJNbJqjq3dijAam8CPhp8vNPcY0SGGYGY9VTwaDwsXe20tmefiFQt6GRCW5DIx1qWXTddjuaSND+i98P1YfKq0j09zH/0pdhDQwQqKoa2pdqaAAeQyMdqYYG4DE00uE2NDJiNEMjHW5DIx1B14xkmtS33jbOY9yGRjZQZUinxK1ZvT6uIQPGqlcSxDnAU1ia2bvkNjQywWNoAGHtp04PESM6ZGgECBIlTCElrOYUooTJp/CE5001NMBraGQLt6GRES5DIy2jGRozmIT6iFoBosNuVKcNuuYlt+tcTRYBqcPqtcm6TJhI2zEx1l7cD4APAd8HUNVdInIj8LlxXPvNwDdEJACEAfcX6CFUmseC521j57tu4uxfvhHw5n6eLO789AOO8mtuOIdgnp+fXnM3K8+Zx+F/H+NFP3r+lMljGBvhRIBAYuhxrxSR1F7aDcnp79GYr6oNAKraICIm5djMZEx6akF1gAWL/Lzrtc38/FZ3tpfJQyNROv/0N/qfeAarfwB/fgHL3vh+Oh97gM7H7meg7gj+wkIKTzt19JMZphxLhXBSp0TMzMbJwJj0Sh4FdNDCQZ5mNWM3JMZLG43s0Z3UUgrAPBaxiR3s5E7qOEQTx1nOOnJl7IMohsknrj7CyZnaWMIH8JiqfmqEJummaDK1ribSdkyMdTgmP81aGBkPm6vqnap6RfLzvaq6TVW3qOoOVX00k3MsfumplGys5sgvHhqD2BOj7pFGR/m8j57Bsh2VRPvjqAVHHmpl3qZKCqrMyztTiVn+oQ1oVdXtKVsmhoZh9jA2PSXw6f+tZP+eKPf8e+qSUHT97Q4AokfsgaUlr38n/vwCxO8n0txItK2F/E0bkQlOyxsmB0WGdErcGBsnA2PSK4KfNWyhnqP069g9BcbLcbWzYfbQSQ75nMKZxIkSJEgf3fTRMxQwbph5WEk3qmjmeqUWSF2ptYbMs7tOpO2YGKux0SoiK0laPiLycqAh61KNwrr3PZeW+w4SOTw1l979+31Dn3NLc1j/0tV01PZz41vup2h+Hst2VNHwePMIZzBMN/GEj1jCT2xiftpNIrIQIPnX/NNnJmPWU4Gg8LX/N4/PfrANKxPXnglihaP03T88uVbygueSUzmf7qcepfXuvxOqqCJ/2SrCBw9NuiyG8aEqQzrFuFGdFIxZr5RRRTXLOMDUpJjt7q2jl86h8imyAxFhD4/TRhO55FPOPDpomRJ5DGMnYfnGqlceAVaLyHIRCWGHK9yc4eUm0nZMjFVDvgN7CnGdiNRhp699W7aFGo286lLKTl3MwL400d9ZRuMJah8c1idnvvc0RITS6nxKqvNYsq2Cxj2dVK4tn3RZDOMnOsaYjRNwM3BN8vM1wJ8nLJhhMhiXnjrrwjxCOcLBQ2OPcRor0ePNJLqHM0sVnn8mAPnLVgFQsnk7vXufIrRoUtxnDVlASYnZSBhj4yRgzHpFRKhmGV20oVMQN9DW5Vzjq1TseK/5yZmMeSyikzYKGXtMqWFqsDRlxjSDAHFVjQPvBP6OvXD2b1T1GRF5q4i8FUBEFohILfB+4OMiUisixSdqOxn3NaaYjWTE+sUiUgD4VHXa8jBWnbuKA7/aRenzz5jU6/Q9+ATR3uHAx3kbKwHw+YXz37aOZ26r474f7uM5nxs9INQwfcQTPnQMHQIR+RX2ivaVyZf0U8CXgN+IyBuBY8BVkyCqYYJMRE9d+Px8/vyXAT74vsnLP6+WRcdf7x8q+4oK8BcXQq8dHF68aRt9h/bRf/QAFW94zaTJYZgYqjJkZCTMzMacZ7x6JZ9CAgTp0tahzv9kEI+HOVJ3z1B5ESuGPi+QxezTJznCs+SSR2EynsMw80hYPqLxZDaqDPssqnorcKur7nspnxshve9curaTQUbGhoi8/wT1AKjq/2ZRphMSEKU8ZwCA4rOqeOazx1lc3IwvZN/G0TShLr1dzowLvTXef1603Jm54IINttvU3t/u5uiPHgTg3T/ZwrLNxeTk9wA95EqM3gURHq7v5sp31PD7D97Ff3xvG2vPs5VJXX+p5zqScArocw2gatA76q4uf+1EjrMcz/PedKzUmf0it3zAc0xxnjNNXU2xM8PD6kKvh1BZ0Jn5oj/hzIRVGyr1tOmOOTNUuWcWetOkxk24rPm+6MQXM4sl/FjxzGc1VPVVJ9hlEpPPUMarp8IaZHekGoDqs/K492eHeM97x5aNaV2wwFFOaPq0iqpK/Zd+Sf/j+8mprmHey19JaN4CpNOHJNVQIL8QKxYlf+kqjv/XZ1jyiU8SKLZHIjt7nXFhz/bP91wjx+fMCvX3lo2Ocn+HN7as3JV3LafLO7vjG3Blmwq7VhNyp7UF1JWhSt3HpBntdceoeL7JdCPEiZH/XxLw6n0JjiMTjkuM2Bg7BSJyKfAN7KyfP1TVL7n2v4bhdNq9wNtU9ckJCWqYEBPt/4gI5TqP9kQDJVbpCY/LafU+Q4k2Z6rb0kPOZ9/3mJ0dM6ZR7oz+DoBlrGMRy8mTAkjJhBUih2LKaKaOR7mL03nO8IlcGbMsV1peCXjflSciCxzlXQNLPMc8fGSZo1xy0Lk/t9W7Ipm0udLaDrj6L+5UuIDGXXXpUuGOhoxjwCBdmu7xXDuFwZkN8PaFZjOZzmwUJf+uBU5n2KfrhcDd2RYqEwJ5QfIXl9FzoIWSDePPIOM2NAYZaBvg4a/Yhsan/7GDqiXetK4P39pK05EwO/9mp6MM906++4VhfFiWD8zo41xnwnpq1dZCfvD+GKo61JnIJn2PPkv/4/vJ27ic6le+3dOxTkTCtD98F6GqBURb7MQUGh19fRvD1KPI0IxGJjMbGS6gdRi4QFU7ROQy7AXidmRbdsOYmLBeKaWCRibP7Xtv3M6vs8q/mWXWGs/+Hu2kly7aaMQiQSLzvD6GKcZSIZEcvEi35thsJSNjQ1X/C0BE/gGcNjh9KCKfxl6Qb1ooXFFJz8HWCRkbJ+LRb9pJJ17y+5dTtaTVsz8etbj/j/bo/7odxSzbMY8tl01d2kzD2LASPjB+1XOabOipwrIgBQXCsWMJli7N/pqnDV++EYBFn3g9HPLOtA3UHgbACofJrV5C6eWXEqyszLochiygSb0CaGadgkwW37o/5fgHOYHrg2HqyIpeoYQenpoU+aIaocE6zHzfYlYENmGlGZxoTK63kUcBgnAGF02KLIaJY6W6Z86hPstY72QJkPokR4FlWZNmjBQsKWegvjPr5+1v6efQ3w6y5MKlFNWkD6Ty+YWadfZsR/3BMLvvbCYWmdj0mWHySMR9Q9tUIiLFIlI+uE3pxU9eJqSn1qwOcPhI9t/l3kefBaD6P193wnS2OVULwecj3tNJIL+Ivl3Gg2amopqiV+xOwYYsL771RuC27EptmADj1it5FBIhnPECf2Nhd9xeBmBj4MwTHlOB7W5ZyyFyyKd5crKbGrKAqj1jmrB8WCdwx52NjHXo7ufAwyLyR+xkHC8BfpZ1qTIkp7KA/uMdWT/vX1/3JwB2fPjsEx7j8wvxpLthW32ElkP9dDdHqFhs1tqYiWhC0MTUvbgi8hbgM8AAw4vkKKRE7Rkmiwnpqfnz/TS3ZNfYGOiM0PClX4PPR/6WlSc8zp+XPxyT4PPR89BDVF11dVZlMWSLFJ1i/92tqm8fsYGXtCmKROQ52MaGyTwycxi3XvGJj6CGiBIhF69L9nhpsxpotmpZ4l9DQE6c1CJBAsGHYlFAEYfZwwLH8gqGmYJaMjQoas2hmY2xZqP6vIjcBpyXrHqDqj6efbEyI1iSR7Qju4twxRqbCHeEOfXt28irOLFSUFU6myOsPaOY5mNhKpbks/uOZs57/bKsymPIDmr50KmN2fggsFFVvT54hkllonqqvNxHa0t2RyB332y7Ry3+4nWI78TPYbynC0QoXLkeK2oncYg2NBBaaFw0ZxzKkE7RzEYgM1pAS0Q2Az8ELlPVtokLasgGE9UrIXKIEs6qsfFMzHb3Xu3fOuJx/fSiWJQznwBBBugjoQn8YhajnGmoxdAgxiRMhE0bYzI2RGQJ0Ar8MbVOVY9lW7B0+H0WJaHhzATVawrZe6CZ0pD9o5w7z5tBqau0e9TzVuUNZ1na8+NHqAd2vGIJuSG7vsLfy7OP9rBkbT6xiMUPP3mU7S9eRLjPYv055Tz78BGCBcoDt7RT+GJbDx1sq/BcJ7fV+YOkfuegVqw8jRJyPWzhcqdyiDmT4ACQKHCOyq6v9PZ3lxY608+szXeukr446P2NC4rzvP2WMxtVScBr+PUmch3lsOUcfemMee/ZnckrG2klNS5ofEqnJA8CU7cctWGIseqp9lgBv2nYPlTOXdLDQw+2kde11S6LN5iyJuR8P9rjzqx3j7Y5Rw3v+dqvAQjULCaedMQIRCz6D+8nf8UaIvW1NPz7VgpWrQeFglXraPrbHwjVLKLn6ccpW2ZnfvGLU2dYabKV/OyI052i8YAz5qNkn7dNvmsmxz/gvWfpd2aO0XDYVfZmlnFno/L8eqbLAONeVNHvPEfat9gdzO/KTiNx70+dJCa+7sGgTslw1nRoAS2gDnsBrVc7ZLKf3T8Ar1PVfd5TGKaLifZ/Ciimh06KKQNA487nevHnHvRe0+1umfL+JDROmD4WsQKJg5UM+o5qhAH6KJFyjutB+ugmTD8BguSRTx2HyaOAbl8H5b5kNjvXe+i2nRf91U/jWc5jvnnEmZixocvrch7c4/x9L2hyvpfBWq9nivY6s156Mti5M08BOtpCrJn02mXs+kDSDTKcwEU2U1RlaEbDmkNJbcbqRnULw9O+ecBy4Flg4wlbTCL58woJFoY4/Jc9LH/h+nGdI9XQAIj1RCmoyiOQ63xgPvXKZzn78nLuv8XupC85tRyfH/70tSMs31JEf6ho9AfeMG2I5U09PMl8FLhfRB4ChnphqvruqRTiJGVCemrpaWXc8uU9tB7po3JZGmt+HORWlxJY7fSgi3W0Ufez71P1/Ctp+bud4CZYUgZq0fS3P1C4bjPh9gYkOHlrfhgmgMqQTslEt6hqXEQGF9DyAz8eXHwruf97wCeBCuA7yWxocVXdfqJzGqaUCemVCuZzmD1U67KsZLqLYXfCy8WZ/rqJWp7lcdboFvbxJPNZTDftxIlRx2GqZTlNehzfGGY13IaGYRJRgcGEEydbNqpBVPWU1LKInAa8JasSjQHx+9j2kQu470O3Ubq6krzVE3c12Pyec7jio07D5fg+ezZl0NBYt72AaDiBlYDyRTkcfrKHda9dzrpXb57w9Q2TRFzsber4PnA78BSe+SnDZDJRPVW1vJBzXr+cn7/rUd5+09nkFo7eZjRO/9mbqG0qc9T1H7QHrgcNjcoLX0DbPf8AIGdhDb17d1F17WvJ37Jp4gIYso8yrFMyHMjIYPGtNwFvylQEEXkpdlyHAveq6h9HaWIYJxPVK9WyjDo9zG52spHTJyxPruRxsc8bz9WG7aWwDzu5xFJW05TMSxAil3o9zHb/RZSKyXI3I7GGZ0xnSs9BRP7CCeLLAFT1ytHOMaHcjqr6mIhM/K2ZAFWnVrPu9ady8I/PsOnDk+PX/LefNQGwbEMeR3YPsHdnH3t32jMigeRiUatftpHcsuz5YhqyiyRkqmc24qqadjEow9QyHj110dtXcezJDvbe2cyOK+ZNilwt/7oFgGB5BbH2NlrvHO6DRhrqCBSXUrBt66Rc25AFdHhGQ6ahUyAi3wFWAb9KVr1FRC5W1XdMvTQnH+PRK6dyLvdyKxENkyO5ozcYIzGN0koDQULEiALCw9w+tD9KmKWylrJJXMncMEFSZkxn0MzGV5J/XwosAH6RLL8KOJLJCcYas5HaefIBpwEtYznHZFB93jLu/eDkrbY+f5kdm/DCNy/g/953mKLyAD3tcVaeVswHfrGF6IDFg9EsDH8aJg2xprxDcEcy9eVfcLpRtZ+4iSEbZENP+XzCugvnceDBtkkzNnw5IRKRMIVrN9HxwF1D9QuvfBXFm05F1Zln0zDzGNIp0zMCeQGwSdVOXyYiP4VJWszBkBW9EpAgZVpFB80swLvi9kQJJLt0uRSQQx69DK/GfR6X4ydIwBfK+nUNWcRieKZ0agdIT4iq3gUgIp9V1fNTdv1FRDJa2HKsjnhFKVsOtg/ji8Z4jqxTtKyMWF+McGvf6AePg8qFtrHxf++zM8r0tMcpLA9w+TuW4PMJuQUmo8NMR+IytE0RryYZtwE8mtx2TtXFT3KyoqeWnVbGsSeyn1p7kPwldgzHoKEhObmAULRxK+IP4Atkf1FBQ/YQTdEr09MpeBYcPdbFwK7pEOQkISt6pZQKOpmsJGP2c9hDx5ChkU8hJZSTI3kEJJCVeBHD5CHJmQ1JyLTMmI5ClYgMBR8mP2c0TTbWX7PdqupYMVNErmIaVxFPykDpqgq6D7WSW5mdgM5UQrk+qlfkUn8omfWqwEcg5GPtGaVZv5ZhcpCEvU0Vqrp86q5mcJEVPTVvZREdtQPEIgmCOdkfUAhVOgM7NRqlePM2fAETED5bGNQp09QpqAD2iMjDyfLpwAMicjNk5kdtGBNZ0SuFlNJCQ1YFS6WSBbQynF2yn15OHcrWa5jx6LA+GUeCrMnmvcCdInIIO4ZjOeBevDQtYzU2Por3xUpX50FE/Ngju3WqeoWI/BpYm9xdCnSq6tYRz4ES8g2nPavK7Rn+vKKAUH0dywqKHG26c5x+kZ1RZ1xF3PIR8iVcdc6ORfDMrbz8pxv5ySv+Sazfvv6Z//dy7ugeTvX2dNMCR5vYAaccAJVHnE9O0WFnZlT1e0ccOtY6Fwnsd4WlDMz3/soVVDjPe0qpd7XQ5TnO2d8VIWfa4FCannmuOFPQJfy9jnKRfwA3fa70uO5UnY3xEk+bPFeqy6cTE4/FEWvqRh9FpBioUtWDrvrNqmpGHiefMempWMJPXc/wc/jngS1DnwNVD/P9u9aQu8xpGCRciy25V3q10jxrVpfTfaHopZcQ3Lqapu/9EFCCNdWUvuHlDASH3+nQfOe7fOGyA47yrrZqz3Uam53vVH6dU9aCRq/OyGtyprENpFu/aGDkVLeeNLd403u6U2yKL10vfRyGnerYyqTXt2O7JmPKRjUJ5AGXpZQF+DLw2ekQ5iRg3P2fVAoooo80KfnTpGfV+NisWAG2yrkc1X0cYjcAS1hDhS/lNzRNumnfaufYWP0Fzud5+7b9njbHe0od5cBD3j7PwgecesST6rbH2YcAe9DFQcLZF9HEJI0ajmNRi3Rrd8kELQSxGPLAmCa9MhLFwCZsI+NK4GzsdNCjkpGxISKXAS8AFonIN10X9iY9Ts97gD3JNqjqK1LO/1VIcS4cByWLi+iq66W3uZ/8ilx8/sw8xNyGxonIKQryxj8+n6MPNVGxsoSOUm9OacPMReIwFesXicjVwNeBZhEJAteq6iPJ3T/B9vM1TAJZ0lMOcqrLiBxvIVBaQKA0+3FZuSuWsehjHyZy9Bi561YhQeM6NWtQW6/A1M6aphAY9KUeRETy3HWGiZFtvZJDHnHihNVe+2Kklb/Hy1JZQ7nOI0qYcjELgs4qNEWfzDw3qk+o6m9FpAi4BPgq8F1gx2gNM/1lq8eelbgS2/d8kB7gfaM1FpEa4HLg88D7XfsEuBq4KENZ0hLMD/DUr/dx8F/H8AV8vOxHl1C0MLsuVYEcPyvPt0cSOyYnPMQwSUxhgPh/AttUtUFEzgB+LiL/qap/4ATrkRmyxoT0VDpirT3UffX3+ApyyVtVzZJPvRbxZfffGCgpJrB5E2nW5zPMcIZ0yhS6O4jI24C3AytEJHWmtAi4b+okOWnIql4REVQt7uVW/ARYqmtYIRuyJOowRVI6eMGsn9swiaSsCSYzJxvVIINm0OXA91T1zyLy6UwaZmRsqOqTwJMi8ktVHc8I4deBD2MrQzfnAU2q6p2nA5IZfa4DKFyQn+4QgKFF+K7++aXc9uF7+P0b/8k57z2VklOXmJS0BiQOvqmJ4/eragOAqj4sIs8B/po0uGeeB+YcYix6KlWvBKtGmKVMzpCu/dmH2POyz1L3tT9QcsFm8k5ZhQRMYoiTGVEY9OrNcII8W9wI3AZ8Ebg+pb7HZLvLPuPVK7mcuL8CdmzFElbzGPfg1wAlVFAqFVmT2zA7kZSZjRkYIF4nIt8HLga+LCI5ZJhoKqODROQ3yY+Pi8gu9zZK2yuAZlV99ASHvIrhPOEeVPUGVd2uqtvzyk6cl3owlqJ5TzvP+fgOlp5Tzd8/eh+/u+LXaBp/XcPJxWCA+BS4O/SIyMrBQtLwuBA7a0lGK80axsdY9FSqXgmUnHgG1J9juzhYAxEWf+xVWP0Rjn/uRpp+NHmptg2zBJ1SvTJ8WdUuVT2iqq9S1aMpmzE0JoHx6pUgOWnONkwnbZRSxVq2Us8RdnIHTVo7WbdhmC1oiifGzDM2rgb+Dlyqqp1AOfChTBpm6kb1nuTfK8YsGpwDXCkiLwBygWIR+YWqvlZEAtiLhGwbx3kdBPPtW3nqN/uoXFPG7j8fZMHmSla+ZL1J9WaYyg7B2wCfiHxZVT8CoKo9Sb/fvVMiwcnLRPRUWkLzS+CZWtpve4T+Pcfp23WIoh3rKHnOqdm6hGEWM4N9qw3ZI6t6ZXDwM06Meo5wjP3EiVHDCkoxMxsnO3aAePLz9MSCnRBV7Qf+kFJugMxSq2XqRjV4srcPdqAGEZEvAx/xthpq+1HsjA2IyIXAB1X1tcndFwN7VTMz532iFPqHM6BYKRMzp165mOaHa3n6r8fpbejhHbc+j+IF+RzqrySa8kPQG/MuaOPOJONzZRNIzYA1yP6OSkd5oME5OlrU4DVw8huc2VsCx11B/LnekRD/CqcL2EC181etqLoHN2sqnZmmgmmeWHf2qa05zuwYwTSTXjHXL2qf5Sznivd7irl8l9zfbTBNm674yNPP48GXGHZ5mEySU+6IyCWkvBeqGhURE+kziYxXTyViPjqahj08Q0XD2VBKr7uatts/Rcsv7yB/xxaWfO8zSDCAP5QglpI0xed3vgvx/tGDPq08Z5vied7MLKctcKrGy8ufdJS7Yl4X0aYWl1uY61VONzXv73XqJrq8ekV7XY+vJ+NTBj1u1zFps7l4Klw1VprMUhGn/OLSTaRZs0QSE8waM31uVIYpZCL9n3SICGfrpdzP39jLY6xlK4tlVbbE9V7PFWMmG1d7jql/TqmjvGnrIUf5ndX/9rT5S5dzwOXmIu/ip56sdq7sU9aAN4Nluvd7NHQcbbJB1rLppZ5Th/XJDHSjGjdjDUm8JE3dZWnqMuWVjOBCNVZWnmunn33Jf59B8QjxHam4DQ3D3GRwWnKyX14ReZuIPAWsdU23HwaeHK29IStkTU+Jz0fBuacRmFdB+euvNNmiDMPo1OkVw4wga3olXwoRhBpWUsPK0RsYTh6sFPfMDPWKiFwqIs+KyAERuT7NfhGRbyb37xKR01L2HRGRp0TkCRGZtIWHM019m5XsF6p6J3BnSvnaTNtmwvx1JZQtLmDB+tJsntYwB5hCNyoTvDlNTFaWnpzli5FAAH9h9hcMNcxuZnAgpyFLTJZeqWQhZVQaN2+Dg7EGiCfXsPs2tjFcCzwiIjer6u6Uwy4DVie3HXjT1T5HVTNaL2O8ZDpMNys6UPllIXqaB4j0xQh3x9h3RwPW8jhVWxaM3tgwp/HFwTcFqUVVtQt7zZhXTf7VDC4mRU/5igqINbWiiQTxlnZ673mU0svPJFCaLrme4WQh1Y1qpvlWG7LKpOiVEDn00UNC4/TQRTtNLMfEmJ70jD1m4wzggKoeAhCRm7AT0qQaGy8CfqZ2wNCDIlIqIgtTXAQnnUxjNhwdKBGZhx3sXSgihap6bPJEzJzcohArzp7P9678J32tgz68u3jBr66ieFnpdIpmmGamOmOMYeqZLD2Vs6KGrj/1cuw/PjZU13fvTpZ853rTMTiZSR2BNLplzjJZeqWShexm59BK3wARBlg/8Xw5hlmMKAyGgiRDXE9zuTfdoKo3pJQXAcdTyrV4F9lLd8wi7OBuBf4h9tLn33edO2uMyQFZRF4I/C9QDTQDS7FXBZ8RKT19fuEl/3MGT/zxCI27O2nY3UncFyS33KyzcbLjiys+n0mBfDKQbT0VqCpn/vVvpvuf95Po6iG8ax/529YaQ+NkR229AuCbYLC5YeaTbb1SJdWcpudTy0ESJGilgRIqR29omNOI5XGjekxVPzVSkzR1boU00jHnqGp90oj+p4jsVdW7xyR0BozVseRzwJnAPlVdDjyXGbZi6c3/uZN//c9TVCwrpKuuj4tveBGh4pHzXRvmPmKZ2Y2TiKzrqdp3fx6NREm0dpCzaglVb3pxFsQ0zHaGdIqJ2TgZyKpeiWqYh/gXZVTRTjOrOIVqWZotWQ2zFR2zXqkFFqeUa7BXvc/oGFUd/NsM/BHbLSvrjDW1SkxV20TEJyI+Vb0jmfptSvCh5KTkL+1NOI2I2kgZzbURElGLA4/3UnPBMrqtAkhJUdkd9i4M6HeNePvynWkfWyPewNBY3JXezGW2Rcu88vcsdabdDTU6zxtd4PUB75/nNEhXrnU+QyuL2jxtqnM7HeV0qW8tl6EbdqWkrPCPHgxb4brnUsubxq5fnSkpn4mWOMoHIt54mmd6qh3lUBbySvoSUxOzYZgRjE1PqSCx4YdjfpkzDXTZggGOAtGdjxPri3LaJ59HrMj5rOcFY45yZ8A7mxrpcL7fVsD5XEeiXnV8rLfUUb7Nf4qjHFfvQ+0LOPWZJ+FemkVOJeyU351KFkCjMU+d8yTpXrBR3t2M0uW67ica9RwieS69XuDMRqgFaRaEjU/MQkhNUWlS354UZLX/k0i+G83UEyVMFQu9B6V9p1LI4P3xFRY6T9nR7Tkm6NIzTx9z/gYnFnsHxh9uXeIoB/o9h0CDMw2/NRB2lDU2Bfnox4k7ZfAJDvLWZaLTRjrl2FcQfwRYLSLLgTrsLK+vdh1zM/DOZDzHDqBLVRtEpADwJdcCKwCeB3xmQjdwAsba/eoUkULgbuCXIvINYEY9LdVn2C/JqivXcPT2I4RbvLnrU3EbGoa5iS+uQ5thzpN1PZU3v4hIez9brr+IfT95ZNryuhtmEDqsVzJdsyODFJXrROQBEYmIyAezLrNhImRVr4RSVhivZjlH2TdxCQ2zHrFS+isZ6BVVjQPvxF7Zew/wG1V9RkTeKiJvTR52K3AIOAD8ADu7GsB84F4ReRJ4GLhFVf+W3TuyGevMxouAMPA+4DVACZNkBY2XQZep3PI8SpaX0vrwUWounxEhJYZpROLetcEMc5as6yl/rq0qyzct5IlD/yTW0kVofukExTTManRsWWMyTFHZDrwbeHF2hTVkgazqFb8EQMEiQQU1HOAp1rAlS6IaZi3W2Bf1U9VbsQ2K1LrvpXxW4B1p2h2CqXnoxmRsqGrqErI/zbIsWSGQZ6/ce+u1N1O8pJgFF07eypyG2YMkxhYgLiJHgB5sP5C4qm6fJNEMWWYy9JSVdLn591U/ZdHz1hKcVzJKC8NcRxgODE+7kLCXUVNUJv2mm0Xk8iyLa5ggk9X/aaWBVhrYgPmJMbjcqOaQe2ami/r14I1uB1vfqqoWZ1WqCTA4eF2wsJALvngRsQITHG6wOwU+GbPry6QvdGPIHpOppwpqSuiv66LqjCVsfPd5NKVN7mE4qUjJRpV0o9qQhRSVhhnGVPR/VrCBhZjgcEPSjSo5iCFzyF0303U2Zs3qVQqsfvFazvroOQA0h0c+3nByIHGQtL8XhrnCZOqpQF6Q0z79fKovWm1XeGMsDScZooo4jY3dqvr2kZqkqTNKaYYzqXqFIGdzKSExg6IGG7HAl8zFcdLNbMwUfGJR5B+2HnJ9zuwohf4IDVY3WhBjSZ69sGd5qM9xTH2w1HPecML5NbhHwNv6vZmZelucdUWHnNmp8pu9vyH5Ta5YsqCzTaTM+++IO5OqeLJpFZR5s8ZszKt1lFcGWzzHLHZlwimWsa9FYrnysgXTZGa4b2Ceo/zD2vMc5X1PLcaNhpzn9RePkgUnA3xxxTf8u145yggkTNFCN4YZgAX+3uFnt67RmUquTsro7AyQ6J5HezJLi7Q6OwedZc4MSeL3vv+a43yuS6udFsvFNd4A0VeUPeQo39G73lHe313laZOIOd9Dv0tF+GNp+rd+ZxsJBj2HqOtE7uxUGh/7eyoB73VwZ4FJOHWVlHpd2OI1zvUJepc59ZknIxcQ6k3j+/REWjHTk7rORmaJJzJJUWk4ibBI4Md/4gPGkdlI/M7z6YpFjnLdhaWeNn3bndn1zl950Lnf8hpDx445dc/Cw15ZNexSPq53Od39zZTkG+l0kxS4+knpZI27+nneLtrI6HDCiUxjNmYDs8rYyIT+jigNu7s4/kQbi7dWDNUnYgkiPTFwPStuQ8MwN5G4ps5stGYQgzElC90YZgeJ7j4GnnyWYM18gvPKh+qtgYFkTmXjVnXSoQzNbJBZNqpMUlQaThISGsfCook65mk1ARnu3EY0TJAQvtFS3xrmHKluVL4ZYnhlgznX037890fo74hy41vu59qfnY+/qoQHv/cMB26vpbd5gIt/9FLyFxTS19BDTkkugQXlo5/UMOuRhIWMYZggdaEbERlc6MYYGychGk8QPVRL9FAtA7v2U/2l9xCvbaH77nvpffgRfPl5LPrfD2P1h4m3dBBcvIBAcZp1HQxzDEUStk7JxLdaVeMiMpii0g/8eDBFZXL/90RkAbATKAYsEXkvsEFVjePeHKML2/tiDztpo4ZNegZNHKeZOpqpYwFLWK+nEWGAMAOUUmmMj5MAsXSsgxizgjlnbGy8rIbCylyK5ufy63c9SCAvSMXKYjZcuZxoX4y733sL0R57XiunNJczv/YiSlZVjnJWw2zHF7fwZTglPZUL3RhmPppIkLtpFSUvOJe+B3bR+IUfEW/qoOCUTVRc9XL6nniC2nd/AY3G8BUX4i/MZ+Fn3o4vNzT6yQ2zFlHwxWyd4stwgcAMUlQ2YrtXGeY4fgJUsoCVbOJpHuZx7qGfXipZwHq2cZR93MmfUZQ8CiijymSsOhlQjBvVbOD5128e+lxYmUtHu7Lm+YuR5CILa9510dD+Z360k+O37aXkXecO1fU39RAotMgtNSOTcwlJWAgZv7nzgT8mn5kAcONkLXRjmPn4ckIsuP4/AMjduJLee58gVLCI3GV29pjic87CSonZaPz89wnvOUT+qesAUFXiDa34ApX4cowBMmdQxjSzYTCkUiLlbMXue2zTC2jkOPOoJlfsQM1FLB86NqFx7uTPrNVT8Ys/WZcgTD95FJgZjzmEWIovGVcnc2gR4jlnbKSy4qx59CZOnOUhpyyP+gf309/YjSaUPTc8SOujtVixBBuv2cL6127G5zcv8VxA4haS4czGVC50Y5hdiN9P0QXbPAHiqfiLCxl4aj85K2uI1bfQ/otbSHT1onGLitdcRf4pG6ZQYsOkoYoMzmgk5tAQpGHKCUkOSzjxmmB+CeDXAK00UKZVtFDPQZ4BIJc8NurpFMiMWYHAMAHESnXPnDt6ZVYZGz6UnJQMVF1Rbwal7riz7kifMyajKzo8Y5F/yRnkHernrjf/Dk0oC1+0le3vv5zqRB33/Nc91N5xmOf+73NZXurNFtEbdo5QDnQ5X3Qr5A0YDZc7sxsMnO/MepPITZPBpsqZuzd8qMJR/uOhCooWO915e6qdszKfWfgPz3krfIWeOods6k2hEHN11vvVmVlif8x7zv87cpGjfGzvAke58JjXmAtXOuviniPGjsQSiDWH8sgZsoeABoffvbxC57Mf8HsVfne/S3WGh3VE+StfSusPb6TuQ19HcnMoe/Gl5J++leiuo7TceCMDjz1D5VUvpyLfmSnvlPzjuNkWcuqZ37resdqOUk+bwqechlBBo1OvREr8FB9yZp+x8py6yRf3vsvuNzXR1uE5ZlRcI7DizjwFSMD53Uq+U6fHlnkzcPUvcOq8aIFXrxQ2OjVJsHuCWe7U1ivAsNFhMEwSp3Iez/AIUcLkU8RWzqaIMmo5yCPcwSo9hRpZ4cnm5O93ZsorPuZ9VhM5znfs3vqNzjIbqXzC2WZZi/P9yX1oj1dol/6yF7JOIe2M4Ci/0+kGDbMws+PL9Q4g+cqdfbTebU4Px8JdTZ422j2x8CqxUrLczaEuy5QZGyLixw58q1PVK5J17wLeid2fvEVVPzyRa7gNjVFl8vtY+faLWPl2Z4e4sLiQS79zKU/++En+eu1f2frec6i5cMVERJs03IaG4QTErXGlETQYxoq/qJD577vOU5+3aiU1H/kwzT/9GfXf/D/mffpCCpdPfbyY29A4GXAbGllB1dYrMPzXYJgkSqScs3m+p34xq6jQBTzG3XRoC+vZ7shslQ3chsbJgNvQmDJ0eGaDOTSzMZU+Qu8BhkxfEXkO8CJgs6puBL4yhbKMiviErW/ayoVfuJBHvngX8fDE13owTB8SjyMxezMYpgtfTg7z3/RGCrZsZv937ppucQwTQFSHdIq41w8wGKaQfCnkTJ6HRYJaDky3OIYJIJZle2LEEsNGxxxgSmY2RKQGuBz4PPD+ZPXbgC+p2v46qto8FbKMlXmb51Gyopyjf9vPyhcbX2tV5Y67I7S1W0SSVvfg1GjjoJ9h0jNCBJqb9mD7Gwgo9DU0ONrEWwURX3KZAkFEiDX57eN9ApZiFYNaCXuZPZ8MBfuPibg1t+YkDbMW8fko2nEmDf/zb3oPt07L7MZMI6FxWqx6FAtiyZ+lpIeFDI3S2u99otm7SGk0GkLRoTbxguG1T0SEcJeC+BARBEFRggNgaTx5ZhlWXJmiQDypU8zMhmGaCUiARbqcffokS1iDT0ZYLPAkIaz9dNIKtnY4IQJI3DkbJP2pXVIFhEhti320gIiPvr7uFN0h9nHRsK3HSOqVsWINx4KJSX07Zr4OfBgoSqlbA5wnIp8HwsAHVfURd0MRuQ64DqCsenoyRG1+2w4e+sztLHneaoL52Z2enE309Vt89Rs9/O5P/Zy+LYSFOgyL7pQpv0Fjor3n4FBZROjvyh0yLABifQBquyQoqFokAhagaMJCfD4siYHf7iio6gn8PEchHk8uvmYwOPWKv2zqp8v9BfnUvPRUjv7yYTZ+/AVTfv2ZxID2sSf+CFGNkC+FEPMP/0QLEHF2mqz2YT9pTY5jxPt8SWPCrrOCgzrI1i3dAwm7A6A6pIs0GsPnCwydaOTuSDp0eLVgM7NhwKlXcsmf8utXykKO6D4aOMoiZqbr91TRqa08ow9RQAn+ZFfX3fVPfeMl4XPuGcgBNGkw2IZErLHRVjDYfRV/78CQntHksVY8ig9f8tzj6KtYOqxP5pAb1aQbGyJyBdCsqo+KyIWua5cBZwKnA78RkRXqiiBS1RuAGwCWbCqeFjOv4pT5FNaU0HDfUZZccuKMEbOZvfuj7D0Q5fKLC3jimQjHmgb4911hdj8bY/XKIGUlPn7/lz62bApx6x+qqFkUyChA/BMHr3CUPQHiR9MFiDvL8VKv69PRt4wxvCcWs2dKDAaceiVnyeJp0SuLrzqN+6/+IdHOfkKlU98xmQratRkfPooopZsOIoRp1UYi9FNMOVbMotE6Ro1/FSsCm/CLH8lxBmp6AsRXV3uu4w4Qj5Q49Uq6mI10AeL/vvtjGd8bqrZegWGjw3BSk6pXiqV8WvTKStnIs/o4i2RuGhuWWrRQTzFl+PDRRzd99NKuTVhYFFNOH9100cYqOYWFsiyj8/pceiddzMZ4AsT/3vaDjK4/hDU3s9xNxczGOcCVIvICIBcoFpFfALXAH5LGxcNiL+9cCXjnyKcZESGvMp9otzdD01zg6b0RnvuyOpbWBHjFdQ2sXh5i8WIfZ2zL4QOX5LHn2Rh9/cq3v1bO2TtOnPJzRhNPGDcqw4wikBfCnxck1h2ek8ZGnR7mcDJML0qYIkoJaR4VsoBc8ummnRwJsSP0PPKlaJSzzUBUh92ozMyGYYYQIpcY0aEZvLnGMzxCPz3004sgFFFKjuazQOx1j3q1kwqZzwa2E5BZuK6RlapXjLGRMar6UeCjAMmZjQ+q6mtF5K3ARcCdIrIGCAGtI53Lh0WRbzgVbCTgdGmqj5R42rSHnT/inf3ejFWl+c7sLMUBZ7rZmtxOYhcU88SfnmXNNfaw+9L8Nscx9xcud5TrGr1WsRV1ugMUVvQ7r1Pa6ZUt5JSlrs+ZYvfU8jpPm3OL9znKC/zOGYdoVLnnkQH2HojS2Bznpj/28NnrK7judSX09Vvk5wkt1nBazh3n249JU8LP0ZQBvL/3OpeiuKt1tUeWY/vnO8rBHueIY7plUHyuQcJQy8QfU03EUcu4URlOgA7/KBfnOQcVFhd1eg5/1lUOR5y6KNrhdfn0RZ0//AePzyO4cT3P3tpEyeXr+F1gm6fNmbm/d5TbYwWOcqTP+2Oa50o2Fep2doQl6u0Y+1zpMen3ZqzSsPN70bhzZiCuMTpoIcIAA/RRzxG2yXMooBiLBH4JONJLzmMZkuf9nsQ1opiocOqv/oXeNlbQ+d2G+pw/0hLz/mjH8ybm066qaHJGQ42xYZgMRkvpmibDYr4W4MNHj7ZRTDnxfYcc+4t7+z1tclucv9PqWl8sZ1+D99JdzhF8zSD5iltnZELY6qWDVuJE6aGTHjrYwSVJ9ya1Y1NSvqf5sthzjnQptt3frW/+PKesRd6+Ym6rS0+mmdF0p+4eM1YiZcZ07uiV6Vxn48fAj0XkaSAKXON2oZoK3IbGibAsJZg7ewOu4nHlx7/q5rs/6eTwsTirlwc5ZUOIhfMCfOpDFbz8CvsHvSB/bnbINRpL7U8aDDMCtSwkNHvjwGIa5Qh7aaaOKBFKKCeXAvLIZwtnUyj2AJB/di3plBmqaNTufGjCZCs0zAwGYxt9s/id69NuDurTdNGGRYIyqgiSQyElLGPd0Crqc5LUmY05tDbYlD6NqnoncGfycxR47VRefyJULC2k4ZlO4tEEgdDsedBb6yN88MYWbvlXHwvnBfj6Z6vYsjGH0pLZcw/ZQGNxVBxhpwbDtBNcWEXk2SNwydnTLcqY6Ig3clyfpo1GqqhmC2eTR+Hc7gS4UR0azdU51CkwzH7yKaSTVgqZPauKqyoNeoRmq5ZO2lgqa1nNFnI1d066g50Qy0pJPGHcqE46qjeVkVsUpPVgDwvWl2btvNGGduI9/eStXjT2tu293PrFe+ht7GPra9ax5tJlQ/u622LUHQzznY8c5toXFfD/vjGfHaedZC/tMI+2xI9fuEAW025nWE6z1KnBMPUUnnsa9bdkd70NVYuB5uP4c/LJKfWutj0aHQN1PNvyL3z4WVd6LsXB4XP0J7rpSrSwd+BBlrOO5ay3M0idfCgKA7EuciWfVj0O9qK1BsO0U80ymqmjJosZqRIapzPSSFGwgpB/bAsoA9TG93M8/iz5Usx63zaCyXgKVYteumi0jtOq9SyT9azndEIy6HI5dzrcGWFZQ+6ZcykWzBgbGaKq9LZFyC8fX4B0oqePjt/fSay+idDSRcSb22hqbSLe3Y8vJ0jOokp6l+TTX9fJwkvWs+Ci1fgC6UcJI83dNP7lCRpv28WWl63glKtW889PPEDjrlasuMVt++tpOByhZnUuz3vNPL703rErhjnGV46y7wPztWYwYPWz0y2QwQCQ6OrFVzz+znr0aD1df70LjcYI1swneriW+sMt+II5xMN9lK7agq+jn0QiRs3qCykqW5L2PKpKd7iRIx07ae09yPqSC0holEdb/0J1/loiiX46I/UkNE6+v4RT8i+krP+kNDIAUFXdImdzhGdZqRtpoRHg59Mtl8EAECFMiPEvFdDc/BQN9Y8QChUSCObT13yYnlgr+YESwok+FhWsp6e/iVxfPitCW8jzFaQ9j6UJWq16jsb3ECHMBv82GqyjPJa4izKpYoBeOrSVICHypYhT/eeToyd5f8WyYDD+JcOYDRG5FPgG4Ad+qKpfcu2X5P4XAP3Atar6WCZts4UxNjJEE0psII41jkVW4u1dNH/zRoLz51F08TnEjtWTt2U9JRvOJHfFQjRh0XXHE4QibZRtreHobx/n2W/fzRnfvopgXpymnXVowiIRSVD7ZDudjx2l8sJ1bP7GazjrFDvY66U/vJj9fz9KTlGQ57zMz8rNBYRyBuMvurL4Tcw+VLVxqaxhL4+TQy6qune6ZTIYAKy+ASTgx4pERz/YRcvuVho/fxOlL78Ef3ERsbomip57JuWtNeSWzSPW20nHs4+SJ3bg475Hf0UgVMCOVW+gu6+e3oEmLCuBT/wcb3wQy4pTU7qFDfMvIdhv/8jlB0rojDRSkFPKssB6Cn1lQ7OjiZNcr+ziAV8x5dY+nmQxK9mtO03ghmFGECcKDK4pk3k7VYv6hp0cPno7q9e8kFisn1i0l5XFZ1AUrCTHn09XtInmgUNUB1fQnWjjgb6bWRRcxWrfVpqt48Q0QpwYPvwcjO+iQIpZHFjLfN8SJJGg1FdFvR4mSoRiylnjO5U8KUiRYe4sZDcuEhYatVWJJkYPuhcRP/Bt4BLsLK+PiMjNqro75bDLgNXJbQfwXWBHhm2zwklvbHT252UUJO4L+LjwXRv44dV3cNYbVrNgSxULNpWPGL9h9YfpvfcxOv90O3mbVlP++pciwQBstVciz0tmoxK/j7Lnbx/KRrXgwtXs+cadPPvte+h84jjV5y4DgUjHAJpbwOm/eiu+0OC/zj5H6eIiTn/TJgDWu7JRGeAY+xcGCDbEia2fblkMhkFyVi8lWD2P+o9+jYq3bKdoZQXFqypHdHfsPt7NvpsP8MxNeyh9yfMoft45jv25D9uzr8HCUuZtey5Fx+0frIXLz+Kx27/K04f+QFvXAeaVrcey4vSFW6ku3sDy8jNTrmtno6vIWUxFjp3dRfu8WWxOZgZnN/bzFA0cnYU5Ng1zlWqWs4sHeJS7WKJrKaLU0aFPR0fnYWrrHqC7p5b1G66mvHx4TbHUbFQlofmUhOajXd0sDK5gWWgjd/X9jgFfDz3aSamvEksT9Ggnm4PnUuFfONRWSSAic3YNkCwQq08cImrZfdIe7QAYbXrjDOCAqh4CEJGbgBcBqQbDi4CfJZMwPSgipSKyEFiWQdusILPJihSRFuBohodXMkoq3SnEyJKeiciyVFXH7pBuMLgweiUrzBRZJiqH0SuGrDBL9cpMkQPmjiwZ65Sku9NWIDUt6FnAtSnlG5KLRw62eTlwqaq+KVl+HbBDVd+ZcsxfgS+p6r3J8r+Bj2AbGyO2zRazamZjLD8CIrJTVbdPpjyZYmRJz0ySxXDyYvTKxJkpsswUOQyG2ahXZooccHLKkpx5eNxV/SjwrRGapZsGd88inOiYTNpmhVllbBgMBoPBYDAYDAbAjrVIXcmwBqjP8JhQBm2zwtxcwc1gMBgMBoPBYJjbPAKsFpHlIhICXgnc7DrmZuD1YnMm0KWqDRm2zQpzeWbjhtEPmTKMLOmZSbIYDJkwk55ZI4uXmSKHwTAWZspzO1PkACNLRqhqXETeCfwdO33tj1X1GRF5a3L/94BbsdPeHsDOKvSGkdpOhpyzKkDcYDAYDAaDwWAwzB6MG5XBYDAYDAaDwWCYFIyxYTAYDAaDwWAwGCaFWW9siMhVIvKMiFgisj2l/hIReVREnkr+vShN25tF5OnpkkVE8kXkFhHZm2yXtWXix/O9iMi2ZP0BEfmmjLSyWHZkqRCRO0SkV0S+5WrzqqQsu0TkbyJSmQ1ZDIZMMHolO7Ik9xm9YjBg9Eq2ZEnuM3plNqGqs3oD1gNrgTuB7Sn1pwLVyc+bgDpXu5cCNwJPT5csQD7wnOTnEHAPcNl0fS/Aw9gLyAhw2xTIUgCcC7wV+FZKfQBoBiqT5f8GPj3dz5rZTp7N6JXsfS9Gr5jNbPZm9Er2vhejV2bXNuuzUanqHgC3UauqqQujPAPkikiOqkZEpBB4P3Ad8JtplKUfuCN5TFREHsPOczzlsgDlQLGqPpBs9zPgxdgv8WTJ0gfcKyKrXE0kuRWISBtQjJ1FwWCYEoxeyY4sGL1iMAxh9Ep2ZMHolVnHrHejypCXAY+raiRZ/izwVewUYNMtCwAiUgq8EPj3NMmyCHvhl0Fqk3VTjqrGgLcBT2EvMLMB+NF0yGIwjIDRK6PLYvSKwTA2jF4ZXRajV2YZs2JmQ0T+BSxIs+tjqvrnUdpuBL4MPC9Z3gqsUtX3iciy6ZQlpT4A/Ar4pqoemiZZJrRs/URkSXOuIPbLeypwCPg/4KPA58ZyHoNhJIxemRJZjF4xnFQYvTIlshi9MsuYFcaGql48nnYiUgP8EXi9qh5MVp8FbBORI9j3P09E7lTVC6dBlkFuAPar6tfHcs4sy1KLc0p0TMvWj1eWE7A1ec6DACLyG+D6LJ7fYDB6ZWpkMXrFcFJh9MqUyGL0yixjzrpRJaf5bgE+qqr3Ddar6ndVtVpVl2EH++zL9MXNtizJfZ8DSoD3TqYMo8mi9tL1PSJyZjKrw+uBMVn4WaQO2CAiVcnyJcCeaZLFYBjC6JWxyWL0isEwOkavjE0Wo1dmIToDotQnsgEvwbZyI0AT8Pdk/ceBPuCJlG2eq+0yspvdYUyyYFvjiv1gDta/abq+F2A78DRwEPgWyRXmJ0uW5L4jQDvQmzxmQ7L+rcnvZRfwF6Biup81s508m9Er2ftejF4xm9nszeiV7H0vRq/Mrk2SX5TBYDAYDAaDwWAwZJU560ZlMBgMBoPBYDAYphdjbBgMBoPBYDAYDIZJwRgbBoPBYDAYDAaDYVIwxobBYDAYDAaDwWCYFIyxYTAYDAaDwWAwGCYFY2xMEiLSOwnnvFJErk9+frGIbBjHOe4Uke3Zls1gMEw+Rq8YDIZsYnSKYSowxsYsQlVvVtUvJYsvBsb8AhsMBkMqRq8YDIZsYnSKwY0xNiYZsfkfEXlaRJ4SkVck6y9MWu6/E5G9IvLL5EqYiMgLknX3isg3ReSvyfprReRbInI2cCXwPyLyhIisTB0FEJFKETmS/JwnIjeJyC4R+TWQlyLb80TkARF5TER+KyKFU/vtGAyG8WD0isFgyCZGpxgmk8B0C3AS8FJgK7AFqAQeEZG7k/tOBTYC9cB9wDkishP4PnC+qh4WkV+5T6iq94vIzcBfVfV3AMl3Px1vA/pVdbOIbAYeSx5fib0658Wq2iciHwHeD3wmC/dsMBgmF6NXDAZDNjE6xTBpGGNj8jkX+JWqJoAmEbkLOB3oBh5W1VoAEXkCWAb0AodU9XCy/a+A6yZw/fOBbwKo6i4R2ZWsPxN7avO+5MsfAh6YwHUMBsPUYfSKwWDIJkanGCYNY2xMPic044FIyucE9v9jpONHIs6wW1yua5+eQK5/quqrxnk9g8EwfRi9YjAYsonRKYZJw8RsTD53A68QEb+IVGFb7w+PcPxeYIWILEuWX3GC43qAopTyEWBb8vPLXdd/DYCIbAI2J+sfxJ4KXZXcly8iazK5IYPBMO0YvWIwGLKJ0SmGScMYG5PPH4FdwJPA7cCHVbXxRAer6gDwduBvInIv0AR0pTn0JuBDIvK4iKwEvgK8TUTux/a3HOS7QGFySvLDJJWHqrYA1wK/Su57EFg3kRs1GAxThtErBoMhmxidYpg0RDXdrJVhOhGRQlXtTWZ8+DawX1W/Nt1yGQyG2YvRKwaDIZsYnWLIFDOzMTN5czII6xmgBDvjg8FgMEwEo1cMBkM2MTrFkBFmZsNgMBgMBoPBYDBMCmZmw2AwGAwGg8FgMEwKxtgwGAwGg8FgMBgMk4IxNgwGg8FgMBgMBsOkYIwNg8FgMBgMBoPBMCkYY8NgMBgMBoPBYDBMCv8fwZ08i1G1hJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x144 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotwa(wa_merge.mean('time'), op='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x144 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotwa(wa_merge.max('time'), op='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x144 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotwa(wa_merge.min('time'), op='min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x144 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotwa(wa_merge.std('time'), op='std')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a plot showing the conditions the day before and after the Mt. St. Helen's eruption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x144 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x144 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre = wa_merge.sel(time='1980-05-18T06:00')\n",
    "plotwa(pre)\n",
    "post = wa_merge.sel(time='1980-05-19T06:00')\n",
    "plotwa(post)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract the time series for Seattle and Mt. Rainier temperature and create line plot\n",
    "* You'll need to look up coordinates\n",
    "    * Check the longitude system in the DataArrays (-180 to 180, or 0 to 360)\n",
    "* Can use the `sel()` method to extract time series for the grid cell nearest the specified 'longitude' and 'latitude' coordinates\n",
    "    * http://xarray.pydata.org/en/stable/indexing.html\n",
    "    * Remember, these are large grid cells (~31x31 km), so one grid cell could cover the most of Mt. Rainier (a single temperature value that includes the cold summit and the warmer, lowland river valleys)\n",
    "* Sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_coord = (-122.25, 47.5)\n",
    "rainier_coord = (121.7603, 46.8523)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 61592)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "    latitude   float64 47.5\n",
       "    longitude  float64 -122.2\n",
       "Data variables:\n",
       "    sd         (time) float32 dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;\n",
       "    t2m        (time) float32 dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;\n",
       "    tp         (time) float32 dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0....</pre><div class='xr-wrap' hidden><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-8bd1216a-5253-4c9c-a657-393afad26d20' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8bd1216a-5253-4c9c-a657-393afad26d20' 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>: 61592</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5932da98-6db6-4a0f-92b5-7af78163923e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5932da98-6db6-4a0f-92b5-7af78163923e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-02-26T18:00:00</div><input id='attrs-d50e9820-e093-403e-9f44-5b1d8d926b02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d50e9820-e093-403e-9f44-5b1d8d926b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-740f73fc-6622-4746-ab90-4ae4887bd12b' class='xr-var-data-in' type='checkbox'><label for='data-740f73fc-6622-4746-ab90-4ae4887bd12b' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-02-26T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-26T12:00:00.000000000&#x27;, &#x27;2021-02-26T18:00:00.000000000&#x27;],\n",
       "      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'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.5</div><input id='attrs-94e7c712-6320-4ce3-b749-7e35da279070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94e7c712-6320-4ce3-b749-7e35da279070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4088e369-b19a-4635-b758-4f602e222d6e' class='xr-var-data-in' type='checkbox'><label for='data-4088e369-b19a-4635-b758-4f602e222d6e' 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>units :</span></dt><dd>None</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array(47.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-122.2</div><input id='attrs-be33e777-f1b3-445f-8d58-8c8128902311' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be33e777-f1b3-445f-8d58-8c8128902311' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1101250a-2939-4079-a3db-de9cb7d95b42' class='xr-var-data-in' type='checkbox'><label for='data-1101250a-2939-4079-a3db-de9cb7d95b42' 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>units :</span></dt><dd>None</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array(-122.25)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d831b22-269b-40d5-a3f5-d67536af28c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d831b22-269b-40d5-a3f5-d67536af28c2' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;</div><input id='attrs-338f5ce1-46ae-43c2-ac5f-6b1048a7ccdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-338f5ce1-46ae-43c2-ac5f-6b1048a7ccdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d512c6ea-55e7-4346-beca-f26c2179b85d' class='xr-var-data-in' type='checkbox'><label for='data-d512c6ea-55e7-4346-beca-f26c2179b85d' 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>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 246.37 kB </td> <td> 246.37 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592,) </td> <td> (61592,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >61592</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;</div><input id='attrs-03baad23-c9b6-46a6-815d-e8d12c5b67f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03baad23-c9b6-46a6-815d-e8d12c5b67f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d2a5350-e943-4106-975c-932ba43de63e' class='xr-var-data-in' type='checkbox'><label for='data-7d2a5350-e943-4106-975c-932ba43de63e' 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>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>C</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 246.37 kB </td> <td> 246.37 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592,) </td> <td> (61592,) </td></tr>\n",
       "    <tr><th> Count </th><td> 4 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >61592</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;</div><input id='attrs-c266a479-f995-4f14-81f0-a219416054db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c266a479-f995-4f14-81f0-a219416054db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68f705a1-fafa-4fb2-b7da-955f3d625019' class='xr-var-data-in' type='checkbox'><label for='data-68f705a1-fafa-4fb2-b7da-955f3d625019' 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>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>accum</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 246.37 kB </td> <td> 246.37 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592,) </td> <td> (61592,) </td></tr>\n",
       "    <tr><th> Count </th><td> 19 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >61592</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bed33a3d-e2f6-44d2-86fc-c1803194a66d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bed33a3d-e2f6-44d2-86fc-c1803194a66d' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0.9.8.5/ecCodes-2.20.0 with {&quot;source&quot;: &quot;/home/jovyan/gda_course_2021_solutions/modules/09_NDarrays_xarray_ERA5/era5_WA_1979-2021_6hr_2m_temperature.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 61592)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "    latitude   float64 47.5\n",
       "    longitude  float64 -122.2\n",
       "Data variables:\n",
       "    sd         (time) float32 dask.array<chunksize=(61592,), meta=np.ndarray>\n",
       "    t2m        (time) float32 dask.array<chunksize=(61592,), meta=np.ndarray>\n",
       "    tp         (time) float32 dask.array<chunksize=(61592,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0...."
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ts = wa_merge.sel(longitude=sea_coord[0], latitude=sea_coord[1], method='nearest')\n",
    "sea_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 61592)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "    latitude   float64 46.75\n",
       "    longitude  float64 -117.2\n",
       "Data variables:\n",
       "    sd         (time) float32 dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;\n",
       "    t2m        (time) float32 dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;\n",
       "    tp         (time) float32 dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0....</pre><div class='xr-wrap' hidden><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-0b4d3736-d737-42b5-84ab-9d8889f9680e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b4d3736-d737-42b5-84ab-9d8889f9680e' 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>: 61592</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0f31baa5-f949-48a6-be8d-1381affcdd1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f31baa5-f949-48a6-be8d-1381affcdd1a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2021-02-26T18:00:00</div><input id='attrs-ac522942-e5ff-4693-88d1-1a9214ef3df7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac522942-e5ff-4693-88d1-1a9214ef3df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6778face-bc69-4ea1-b6a4-1b70ffcd0c2e' class='xr-var-data-in' type='checkbox'><label for='data-6778face-bc69-4ea1-b6a4-1b70ffcd0c2e' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-02-26T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-26T12:00:00.000000000&#x27;, &#x27;2021-02-26T18:00:00.000000000&#x27;],\n",
       "      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'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.75</div><input id='attrs-558432c3-8e27-444b-bb0d-1a93f32963db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-558432c3-8e27-444b-bb0d-1a93f32963db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22860193-9d3c-426c-92cb-6ce8431c37c2' class='xr-var-data-in' type='checkbox'><label for='data-22860193-9d3c-426c-92cb-6ce8431c37c2' 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>units :</span></dt><dd>None</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array(46.75)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-117.2</div><input id='attrs-b4dc277d-a81b-4fbf-8b58-a145f35bd882' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4dc277d-a81b-4fbf-8b58-a145f35bd882' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e73bf0c0-6060-4c63-842f-ba468b109b4d' class='xr-var-data-in' type='checkbox'><label for='data-e73bf0c0-6060-4c63-842f-ba468b109b4d' 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>units :</span></dt><dd>None</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array(-117.25)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5bdac046-3afc-4b63-bc2e-f5e97b9092fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5bdac046-3afc-4b63-bc2e-f5e97b9092fe' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;</div><input id='attrs-13bcd01b-7b21-400f-959d-cc9a0dcd403a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13bcd01b-7b21-400f-959d-cc9a0dcd403a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d09f7e8c-fcf9-44cf-ab20-a3b4b294216c' class='xr-var-data-in' type='checkbox'><label for='data-d09f7e8c-fcf9-44cf-ab20-a3b4b294216c' 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>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 246.37 kB </td> <td> 246.37 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592,) </td> <td> (61592,) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >61592</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;</div><input id='attrs-ee90661c-fde7-4099-bcd8-5a4526c6f8b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee90661c-fde7-4099-bcd8-5a4526c6f8b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-614d2294-d1de-4169-a576-5c41444eec01' class='xr-var-data-in' type='checkbox'><label for='data-614d2294-d1de-4169-a576-5c41444eec01' 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>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>units :</span></dt><dd>C</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 246.37 kB </td> <td> 246.37 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592,) </td> <td> (61592,) </td></tr>\n",
       "    <tr><th> Count </th><td> 4 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >61592</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(61592,), meta=np.ndarray&gt;</div><input id='attrs-6a6eb586-2e58-42c7-8595-a7210420279c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a6eb586-2e58-42c7-8595-a7210420279c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e44d846-bbb9-4716-8d2d-7a4cb8c7c7b7' class='xr-var-data-in' type='checkbox'><label for='data-7e44d846-bbb9-4716-8d2d-7a4cb8c7c7b7' 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>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>390</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>accum</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_Nx :</span></dt><dd>30</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>-124.5</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>-117.25</dd><dt><span>GRIB_Ny :</span></dt><dd>13</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>48.75</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>45.75</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>coordinates :</span></dt><dd>number time step surface latitude longitude valid_time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 246.37 kB </td> <td> 246.37 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (61592,) </td> <td> (61592,) </td></tr>\n",
       "    <tr><th> Count </th><td> 19 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >61592</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f52a31e4-c64d-4cd4-aea6-493bf2724777' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f52a31e4-c64d-4cd4-aea6-493bf2724777' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0.9.8.5/ecCodes-2.20.0 with {&quot;source&quot;: &quot;/home/jovyan/gda_course_2021_solutions/modules/09_NDarrays_xarray_ERA5/era5_WA_1979-2021_6hr_2m_temperature.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 61592)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00\n",
       "    latitude   float64 46.75\n",
       "    longitude  float64 -117.2\n",
       "Data variables:\n",
       "    sd         (time) float32 dask.array<chunksize=(61592,), meta=np.ndarray>\n",
       "    t2m        (time) float32 dask.array<chunksize=(61592,), meta=np.ndarray>\n",
       "    tp         (time) float32 dask.array<chunksize=(61592,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2021-03-04T16:33:29 GRIB to CDM+CF via cfgrib-0...."
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainier_ts = wa_merge.sel(longitude=rainier_coord[0], latitude=rainier_coord[1], method='nearest')\n",
    "rainier_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('1979-01-01T00:00:00.000000000')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainier_ts.time.values.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,1.5))\n",
    "v = 't2m'\n",
    "rainier_ts[v].plot(ax=ax, label='Rainer %s' % v)\n",
    "sea_ts[v].plot(ax=ax, label='Seattle %s' % v)\n",
    "ax.axhline(0,color='k',lw=0.5)\n",
    "ax.legend(fontsize=8)\n",
    "ax.set_xlim(rainier_ts.time.values.min(), rainier_ts.time.values.max())\n",
    "ax.set_title(\"ERA5 Hourly T: Seattle and Mt. Rainier\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,1.5))\n",
    "v = 't2m'\n",
    "cy = 2021\n",
    "rainier_ts[v].loc[f'{cy}-01-01':].plot(ax=ax, label='Rainer %s' % v)\n",
    "sea_ts[v].loc[f'{cy}-01-01':].plot(ax=ax, label='Seattle %s' % v)\n",
    "ax.axhline(0,color='k',lw=0.5)\n",
    "ax.legend(fontsize=8)\n",
    "ax.set_title(\"ERA5 Hourly T: Seattle and Mt. Rainier, WA\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper function to plot time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotv(ds_in, v_list=['t2m','tp','sd']):\n",
    "    f,axa = plt.subplots(3,1, figsize=(10,6), sharex=True)\n",
    "    for i,v in enumerate(v_list):\n",
    "        ds_in[v].dropna(dim='time').plot(ax=axa[i], lw=0.5)\n",
    "        axa[i].axhline(0,color='k',lw=0.5)\n",
    "    f.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotv(wa_merge.sel(longitude=sea_coord[0], latitude=sea_coord[1], method='nearest'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotv(wa_merge.sel(longitude=rainier_coord[0], latitude=rainier_coord[1], method='nearest'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute stats for monthly, annual time periods\n",
    "* Can use `groupby('time.year').max(dim='time')`\n",
    "* Plot snow depth for April and September"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a map that shows the year containing the maximum T value at each grid cell\n",
    "* These should mostly be in the most recent decade\n",
    "* Note the `.compute()` is needed to converty dask array to ndarray for use in vectorized indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_t_idx = wa_merge['t2m'].argmax(dim='time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;year&#x27; (time: 61592)&gt;\n",
       "array([1979, 1979, 1979, ..., 2021, 2021, 2021])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'year'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 61592</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4210d042-2820-4bde-8514-2f3511937022' class='xr-array-in' type='checkbox' checked><label for='section-4210d042-2820-4bde-8514-2f3511937022' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1979 1979 1979 1979 1979 1979 1979 ... 2021 2021 2021 2021 2021 2021</span></div><div class='xr-array-data'><pre>array([1979, 1979, 1979, ..., 2021, 2021, 2021])</pre></div></div></li><li class='xr-section-item'><input id='section-4624406e-2ad8-4215-8613-d01880a99243' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4624406e-2ad8-4215-8613-d01880a99243' class='xr-section-summary' >Coordinates: <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 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'>1979-01-01 ... 2021-02-26T18:00:00</div><input id='attrs-8579d359-6e87-434e-a540-cd11c8130ed7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8579d359-6e87-434e-a540-cd11c8130ed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2142f034-3a23-4b9c-8ed0-f4f606bc0bc9' class='xr-var-data-in' type='checkbox'><label for='data-2142f034-3a23-4b9c-8ed0-f4f606bc0bc9' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-02-26T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-26T12:00:00.000000000&#x27;, &#x27;2021-02-26T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-145d3ffe-3da5-424b-9422-38a5845313d3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-145d3ffe-3da5-424b-9422-38a5845313d3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'year' (time: 61592)>\n",
       "array([1979, 1979, 1979, ..., 2021, 2021, 2021])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 ... 2021-02-26T18:00:00"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_merge['t2m']['time.year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "wa_merge['t2m']['time.year'][max_t_idx].plot(ax=ax)\n",
    "wa_state.plot(ax=ax, facecolor='none', edgecolor='black');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;year&#x27; ()&gt;\n",
       "array(2006.)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'year'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-94d7512b-ced8-479a-b530-56645b930168' class='xr-array-in' type='checkbox' checked><label for='section-94d7512b-ced8-479a-b530-56645b930168' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.006e+03</span></div><div class='xr-array-data'><pre>array(2006.)</pre></div></div></li><li class='xr-section-item'><input id='section-c0af335b-0f7d-4541-8221-61acddc58cf0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c0af335b-0f7d-4541-8221-61acddc58cf0' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-6eff8217-384f-4629-a86c-86ea192672ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6eff8217-384f-4629-a86c-86ea192672ab' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'year' ()>\n",
       "array(2006.)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wa_merge['t2m']['time.year'][max_t_idx].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a new xarray Dataset with the SNOTEL data from Lab08"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export one of the derived WA grids to rasterio\n",
    "* https://github.com/robintw/XArrayAndRasterio/blob/master/rasterio_to_xarray.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load SRTM DEM for WA\n",
    "* Resample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot contours from SRTM DEM over temperature grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute correlation between temperature and elevation\n",
    "* https://github.com/pydata/xarray/issues/1115"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute atmospheric temperature lapse rate using SRTM elevations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What if my data are too big?\n",
    "* http://xarray.pydata.org/en/stable/dask.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
