get_BSS_climpred.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be44dbc5-eb36-4747-b004-aa655c9a19ee",
   "metadata": {},
   "source": [
    "## verify S2S prediction skill\n",
    "- verity using Brier Skill score\n",
    "- utilize `climpred`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c7f99178-80b6-45ee-8321-6f28690616cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from climpred import HindcastEnsemble,PerfectModelEnsemble\n",
    "import climpred\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fdd8cb7-4dba-4a1a-8dec-9e86aee8f4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f8b6ca-73ea-46f8-9505-549feba714cb",
   "metadata": {},
   "source": [
    "## 1. Read in observations and S2S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6469ae29-cfd0-4877-b0fc-a1789d755650",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_mfdataset('/glade/work/huili7/S2S/forecast_files_byweek/forecast_obs_persist_count*.nc',\n",
    "                      combine='nested',concat_dim='forecast_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e50d2477-5917-4458-af07-6529c484563c",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_rng = pd.date_range('20020101','20191231',freq='1D')\n",
    "init_date = date_rng.where(date_rng.dayofweek==0).dropna()\n",
    "init_date1 = pd.Series(init_date.format()).array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "846b1c16-b8e8-4cc7-b652-0ea7eb2061fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2002-01-07', '2002-01-14', '2002-01-21', '2002-01-28',\n",
       "               '2002-02-04', '2002-02-11', '2002-02-18', '2002-02-25',\n",
       "               '2002-03-04', '2002-03-11',\n",
       "               ...\n",
       "               '2019-10-28', '2019-11-04', '2019-11-11', '2019-11-18',\n",
       "               '2019-11-25', '2019-12-02', '2019-12-09', '2019-12-16',\n",
       "               '2019-12-23', '2019-12-30'],\n",
       "              dtype='datetime64[ns]', length=939, freq=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5be178af-8657-40d6-85c5-db8354401e25",
   "metadata": {
    "tags": []
   },
   "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;forecast&#x27; (lead: 6, basin: 6, init: 939)&gt;\n",
       "dask.array&lt;concatenate, shape=(6, 6, 939), dtype=float64, chunksize=(6, 6, 53), chunktype=numpy.ndarray&gt;\n",
       "Dimensions without coordinates: lead, basin, init</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'forecast'</div><ul class='xr-dim-list'><li><span>lead</span>: 6</li><li><span>basin</span>: 6</li><li><span>init</span>: 939</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8360dd27-b288-4955-bc5b-a3f0e2beeb3d' class='xr-array-in' type='checkbox' checked><label for='section-8360dd27-b288-4955-bc5b-a3f0e2beeb3d' 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=(6, 6, 52), 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> 264.09 kiB </td> <td> 14.91 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (6, 6, 939) </td> <td> (6, 6, 53) </td></tr>\n",
       "    <tr><th> Count </th><td> 54 Tasks </td><td> 18 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" 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=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,40.36121446433689 10.0,25.412616514582485\" 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=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"16\" y1=\"0\" x2=\"31\" y2=\"14\" />\n",
       "  <line x1=\"23\" y1=\"0\" x2=\"38\" y2=\"14\" />\n",
       "  <line x1=\"29\" y1=\"0\" x2=\"44\" y2=\"14\" />\n",
       "  <line x1=\"36\" y1=\"0\" x2=\"51\" y2=\"14\" />\n",
       "  <line x1=\"43\" y1=\"0\" x2=\"58\" y2=\"14\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"64\" y2=\"14\" />\n",
       "  <line x1=\"56\" y1=\"0\" x2=\"71\" y2=\"14\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"78\" y2=\"14\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
       "  <line x1=\"76\" y1=\"0\" x2=\"91\" y2=\"14\" />\n",
       "  <line x1=\"83\" y1=\"0\" x2=\"98\" y2=\"14\" />\n",
       "  <line x1=\"90\" y1=\"0\" x2=\"104\" y2=\"14\" />\n",
       "  <line x1=\"96\" y1=\"0\" x2=\"111\" y2=\"14\" />\n",
       "  <line x1=\"103\" y1=\"0\" x2=\"118\" y2=\"14\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"124\" y2=\"14\" />\n",
       "  <line x1=\"116\" y1=\"0\" x2=\"131\" y2=\"14\" />\n",
       "  <line x1=\"123\" y1=\"0\" x2=\"138\" y2=\"14\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"31\" y1=\"14\" x2=\"31\" y2=\"40\" />\n",
       "  <line x1=\"38\" y1=\"14\" x2=\"38\" y2=\"40\" />\n",
       "  <line x1=\"44\" y1=\"14\" x2=\"44\" y2=\"40\" />\n",
       "  <line x1=\"51\" y1=\"14\" x2=\"51\" y2=\"40\" />\n",
       "  <line x1=\"58\" y1=\"14\" x2=\"58\" y2=\"40\" />\n",
       "  <line x1=\"64\" y1=\"14\" x2=\"64\" y2=\"40\" />\n",
       "  <line x1=\"71\" y1=\"14\" x2=\"71\" y2=\"40\" />\n",
       "  <line x1=\"78\" y1=\"14\" x2=\"78\" y2=\"40\" />\n",
       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"40\" />\n",
       "  <line x1=\"91\" y1=\"14\" x2=\"91\" y2=\"40\" />\n",
       "  <line x1=\"98\" y1=\"14\" x2=\"98\" y2=\"40\" />\n",
       "  <line x1=\"104\" y1=\"14\" x2=\"104\" y2=\"40\" />\n",
       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"40\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"40\" />\n",
       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"40\" />\n",
       "  <line x1=\"131\" y1=\"14\" x2=\"131\" y2=\"40\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"40\" />\n",
       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >939</text>\n",
       "  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">6</text>\n",
       "  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">6</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-daabf9b7-372e-48f9-ba5e-8a6b8c36fe19' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-daabf9b7-372e-48f9-ba5e-8a6b8c36fe19' 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-47dc2e71-9993-4c10-b282-97060c601d1e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47dc2e71-9993-4c10-b282-97060c601d1e' 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 'forecast' (lead: 6, basin: 6, init: 939)>\n",
       "dask.array<concatenate, shape=(6, 6, 939), dtype=float64, chunksize=(6, 6, 53), chunktype=numpy.ndarray>\n",
       "Dimensions without coordinates: lead, basin, init"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = ds.forecast\n",
    "\n",
    "obs =obs.rename({'week':'lead'})\n",
    "\n",
    "obs = obs.rename({'forecast_n':'init'})\n",
    "obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7035c08-2385-40dd-8f5c-ff1821804b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = ds.forecast\n",
    "obs = obs.rename({'forecast_n':'init'}) \n",
    "obs = obs.rename({'week':'lead'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43c5765b-a4c9-4e7b-a4c2-84d77856b5f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs['init'] = init_date\n",
    "obs = obs.assign_coords(lead=np.arange(1,7))\n",
    "obs['lead'].attrs['units']='weeks'\n",
    "obs= obs.assign_coords(basin = ['at','ep','ni','nwp','sh','global'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c52f25-d139-4ca7-97f8-37afab26899d",
   "metadata": {},
   "source": [
    "read in hindcast data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b9b6619-3162-45b4-80af-ceac853741fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_mfdataset('/glade/work/huili7/S2S/forecast_files_byweek/forecast_skill*raw.nc',\n",
    "                      combine='nested',concat_dim='forecast_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb6ac38a-2aea-4706-8641-b7992dc527f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = ds.forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0e0f884-82ac-4c79-91bd-abb4245bcf3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = forecast.rename({'forecast_n':'init'})\n",
    "\n",
    "forecast['init'] = init_date\n",
    "forecast = forecast.rename({'week':'lead'})\n",
    "forecast['lead'].attrs['units']='weeks'\n",
    "forecast = forecast.assign_coords(basin = ['at','ep','ni','nwp','sh','global'])\n",
    "forecast['member'] = np.arange(1,12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46f93fe-d4cb-4242-95ad-b220bad42ec7",
   "metadata": {},
   "source": [
    "## 2. Make the forecast binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97765487-4d43-44f9-a84c-da870b9f6fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = obs.where(obs==0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcb5bbf1-b8a6-4ff2-94ce-f4c423a03046",
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = forecast.where(forecast==0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13162c6-c0bb-4a59-a172-190b4340dd61",
   "metadata": {},
   "source": [
    "## 3.Filter data to only retain TC season in each basin\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a79991-9b2b-4948-9235-80b1cd38db7d",
   "metadata": {},
   "source": [
    "### 3.1 trim S2S by TC season in each basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "061623d6-5094-4cf8-b1a0-4d8b75b92655",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define TC season in each basin\n",
    "month_at= [5,6,7,8,9,10,11]\n",
    "month_ep = [5,6,7,8,9,10]\n",
    "month_ni = [4,5,6,9,10,11]\n",
    "month_nwp = [4,5,6,7,8,9,10,11]\n",
    "month_sh = [1,2,3,4,10,11,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f5af6261-1694-43fa-83b1-620f945f9053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5, 6, 7, 8, 9, 10, 11],\n",
       " [5, 6, 7, 8, 9, 10],\n",
       " [4, 5, 6, 9, 10, 11],\n",
       " [4, 5, 6, 7, 8, 9, 10, 11],\n",
       " [1, 2, 3, 4, 10, 11, 12]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_basin = [month_at,month_ep,month_ni,month_nwp,month_sh]\n",
    "month_basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a860825-a203-412b-8498-ce87e9aa7914",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only consider TC season, make all other months zero\n",
    "# atlantic basin\n",
    "for ib in np.arange(0,5):  #basin\n",
    "    for i in np.arange(0,6): #lead\n",
    "        f_date = forecast.init + np.timedelta64(i,'W')\n",
    "        forecast[i,ib,:,:] = forecast.isel(basin=ib,lead=i).where(f_date.dt.month.isin(month_basin[ib]))\n",
    "        obs[i,ib,:] = obs.isel(basin=ib,lead=i).where(f_date.dt.month.isin(month_basin[ib]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11dcaa2e-d8d7-45d6-930e-0b60753a63b4",
   "metadata": {},
   "source": [
    "### 3.2  trim observations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5d419152-1685-4ae2-bd4b-de87c357a7f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obs_at = obs.isel(basin=0)\n",
    "# obs_at = obs_at.where(obs_at.time.dt.month.isin(month_at),0)\n",
    "\n",
    "# obs_ep = obs.isel(basin=1)\n",
    "# obs_ep = obs_ep.where(obs_ep.time.dt.month.isin(month_ep),0)\n",
    "\n",
    "# obs_ni = obs.isel(basin=2)\n",
    "# obs_ni = obs_ni.where(obs_ni.time.dt.month.isin(month_ni),0)\n",
    "\n",
    "# obs_nwp = obs.isel(basin=3)\n",
    "# obs_nwp = obs_nwp.where(obs_nwp.time.dt.month.isin(month_nwp),0)\n",
    "\n",
    "# obs_sh = obs.isel(basin=4)\n",
    "# obs_sh = obs_sh.where(obs_sh.time.dt.month.isin(month_sh),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8bb311-4db0-4752-8fd2-5ae5072c9341",
   "metadata": {},
   "source": [
    "## 4. Create forecast ensemble for each basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a69f6c79-6f25-443b-aef5-b73f78d007ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hindcast = HindcastEnsemble(forecast)\n",
    "\n",
    "# hindcast_at = hindcast.mean('member').isel(basin=0)\n",
    "# hindcast_at = hindcast_at.add_observations(obs_at)\n",
    "\n",
    "# hindcast_ep = hindcast.mean('member').isel(basin=1)\n",
    "# hindcast_ep = hindcast_ep.add_observations(obs_ep)\n",
    "\n",
    "# hindcast_ni = hindcast.mean('member').isel(basin=2)\n",
    "# hindcast_ni = hindcast_ni.add_observations(obs_ni)\n",
    "\n",
    "# hindcast_nwp = hindcast.mean('member').isel(basin=3)\n",
    "# hindcast_nwp = hindcast_nwp.add_observations(obs_nwp)\n",
    "\n",
    "# hindcast_sh = hindcast.mean('member').isel(basin=4)\n",
    "# hindcast_sh = hindcast_sh.add_observations(obs_sh)\n",
    "\n",
    "# hindcast_nwp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e425a5ca-6508-45f1-a68e-dbab9643918e",
   "metadata": {},
   "source": [
    "## 5. Verity using Brier Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d15e71-c300-4d87-9547-e804c9b337b0",
   "metadata": {},
   "source": [
    "### 5.1 get BS of S2S"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80932ab7-b07d-4618-aad0-8d70d88e9a85",
   "metadata": {},
   "source": [
    "**note**: week1 has prexisting storms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e615ff20-5d26-4b01-8550-d7de365a2b86",
   "metadata": {},
   "source": [
    "since week1 need to be compared with a different observation, we don't use the climpred package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e1e4db8-02c8-4614-81d9-de3506bbe819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get ensemble mean\n",
    "forecast_m= forecast.mean('member')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7125be3c-64d4-4aca-b21a-78f2eb7d7cec",
   "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;forecast&#x27; (lead: 6, basin: 6, init: 939)&gt;\n",
       "dask.array&lt;mean_agg-aggregate, shape=(6, 6, 939), dtype=float64, chunksize=(6, 6, 53), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6\n",
       "  * init     (init) datetime64[ns] 2002-01-07 2002-01-14 ... 2019-12-30\n",
       "  * basin    (basin) &lt;U6 &#x27;at&#x27; &#x27;ep&#x27; &#x27;ni&#x27; &#x27;nwp&#x27; &#x27;sh&#x27; &#x27;global&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'forecast'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lead</span>: 6</li><li><span class='xr-has-index'>basin</span>: 6</li><li><span class='xr-has-index'>init</span>: 939</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8a5fc8e8-063f-48a7-8ebf-12f50305cb33' class='xr-array-in' type='checkbox' checked><label for='section-8a5fc8e8-063f-48a7-8ebf-12f50305cb33' 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=(6, 6, 52), 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> 264.09 kiB </td> <td> 14.91 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (6, 6, 939) </td> <td> (6, 6, 53) </td></tr>\n",
       "    <tr><th> Count </th><td> 1236 Tasks </td><td> 18 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" 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=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,40.36121446433689 10.0,25.412616514582485\" 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=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"16\" y1=\"0\" x2=\"31\" y2=\"14\" />\n",
       "  <line x1=\"23\" y1=\"0\" x2=\"38\" y2=\"14\" />\n",
       "  <line x1=\"29\" y1=\"0\" x2=\"44\" y2=\"14\" />\n",
       "  <line x1=\"36\" y1=\"0\" x2=\"51\" y2=\"14\" />\n",
       "  <line x1=\"43\" y1=\"0\" x2=\"58\" y2=\"14\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"64\" y2=\"14\" />\n",
       "  <line x1=\"56\" y1=\"0\" x2=\"71\" y2=\"14\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"78\" y2=\"14\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
       "  <line x1=\"76\" y1=\"0\" x2=\"91\" y2=\"14\" />\n",
       "  <line x1=\"83\" y1=\"0\" x2=\"98\" y2=\"14\" />\n",
       "  <line x1=\"90\" y1=\"0\" x2=\"104\" y2=\"14\" />\n",
       "  <line x1=\"96\" y1=\"0\" x2=\"111\" y2=\"14\" />\n",
       "  <line x1=\"103\" y1=\"0\" x2=\"118\" y2=\"14\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"124\" y2=\"14\" />\n",
       "  <line x1=\"116\" y1=\"0\" x2=\"131\" y2=\"14\" />\n",
       "  <line x1=\"123\" y1=\"0\" x2=\"138\" y2=\"14\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"31\" y1=\"14\" x2=\"31\" y2=\"40\" />\n",
       "  <line x1=\"38\" y1=\"14\" x2=\"38\" y2=\"40\" />\n",
       "  <line x1=\"44\" y1=\"14\" x2=\"44\" y2=\"40\" />\n",
       "  <line x1=\"51\" y1=\"14\" x2=\"51\" y2=\"40\" />\n",
       "  <line x1=\"58\" y1=\"14\" x2=\"58\" y2=\"40\" />\n",
       "  <line x1=\"64\" y1=\"14\" x2=\"64\" y2=\"40\" />\n",
       "  <line x1=\"71\" y1=\"14\" x2=\"71\" y2=\"40\" />\n",
       "  <line x1=\"78\" y1=\"14\" x2=\"78\" y2=\"40\" />\n",
       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"40\" />\n",
       "  <line x1=\"91\" y1=\"14\" x2=\"91\" y2=\"40\" />\n",
       "  <line x1=\"98\" y1=\"14\" x2=\"98\" y2=\"40\" />\n",
       "  <line x1=\"104\" y1=\"14\" x2=\"104\" y2=\"40\" />\n",
       "  <line x1=\"111\" y1=\"14\" x2=\"111\" y2=\"40\" />\n",
       "  <line x1=\"118\" y1=\"14\" x2=\"118\" y2=\"40\" />\n",
       "  <line x1=\"124\" y1=\"14\" x2=\"124\" y2=\"40\" />\n",
       "  <line x1=\"131\" y1=\"14\" x2=\"131\" y2=\"40\" />\n",
       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"40\" />\n",
       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >939</text>\n",
       "  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">6</text>\n",
       "  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">6</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-1b86880c-c404-4414-bb1c-86bac1ec546c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b86880c-c404-4414-bb1c-86bac1ec546c' 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'>lead</span></div><div class='xr-var-dims'>(lead)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6</div><input id='attrs-a318f654-ef30-48fa-8669-8faf2d62c594' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a318f654-ef30-48fa-8669-8faf2d62c594' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-207d4067-71e5-476a-baa1-721f758a9f6f' class='xr-var-data-in' type='checkbox'><label for='data-207d4067-71e5-476a-baa1-721f758a9f6f' 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>weeks</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5, 6], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>init</span></div><div class='xr-var-dims'>(init)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-01-07 ... 2019-12-30</div><input id='attrs-812c93a8-4e2a-4e96-bc09-97486cfc18a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-812c93a8-4e2a-4e96-bc09-97486cfc18a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171b7ab6-378d-4cde-a30b-a6a1a3159db6' class='xr-var-data-in' type='checkbox'><label for='data-171b7ab6-378d-4cde-a30b-a6a1a3159db6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-01-07T00:00:00.000000000&#x27;, &#x27;2002-01-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-21T00:00:00.000000000&#x27;, ..., &#x27;2019-12-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-23T00:00:00.000000000&#x27;, &#x27;2019-12-30T00:00:00.000000000&#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'>basin</span></div><div class='xr-var-dims'>(basin)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;at&#x27; &#x27;ep&#x27; &#x27;ni&#x27; &#x27;nwp&#x27; &#x27;sh&#x27; &#x27;global&#x27;</div><input id='attrs-367b7897-f87d-40af-b6d5-a578de31dbfc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-367b7897-f87d-40af-b6d5-a578de31dbfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e95d3f9-5f4f-4cdc-adba-3aa0e16a8279' class='xr-var-data-in' type='checkbox'><label for='data-5e95d3f9-5f4f-4cdc-adba-3aa0e16a8279' 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;at&#x27;, &#x27;ep&#x27;, &#x27;ni&#x27;, &#x27;nwp&#x27;, &#x27;sh&#x27;, &#x27;global&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-863a812d-6191-469a-affc-0cecbadec0ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-863a812d-6191-469a-affc-0cecbadec0ed' 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 'forecast' (lead: 6, basin: 6, init: 939)>\n",
       "dask.array<mean_agg-aggregate, shape=(6, 6, 939), dtype=float64, chunksize=(6, 6, 53), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6\n",
       "  * init     (init) datetime64[ns] 2002-01-07 2002-01-14 ... 2019-12-30\n",
       "  * basin    (basin) <U6 'at' 'ep' 'ni' 'nwp' 'sh' 'global'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21af7f6b-6671-451b-8a53-6175d927aa6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = (forecast_m - obs) **2\n",
    "bs = bs.mean('init')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "367ef2d5-e012-4665-b80f-4a29c7d4640f",
   "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;forecast&#x27; (lead: 6)&gt;\n",
       "array([0.22907164, 0.23108883, 0.24090382, 0.27370576, 0.27329931,\n",
       "       0.27266706])\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6\n",
       "    basin    &lt;U6 &#x27;at&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'forecast'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lead</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-51e60ae5-86f2-4a85-86ee-1550511a3424' class='xr-array-in' type='checkbox' checked><label for='section-51e60ae5-86f2-4a85-86ee-1550511a3424' 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>0.2291 0.2311 0.2409 0.2737 0.2733 0.2727</span></div><div class='xr-array-data'><pre>array([0.22907164, 0.23108883, 0.24090382, 0.27370576, 0.27329931,\n",
       "       0.27266706])</pre></div></div></li><li class='xr-section-item'><input id='section-2d666a93-7c89-4705-8ab8-a91659bbf137' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d666a93-7c89-4705-8ab8-a91659bbf137' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lead</span></div><div class='xr-var-dims'>(lead)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6</div><input id='attrs-0e0a26ff-68fd-43e6-a1f5-3990ad3dda0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e0a26ff-68fd-43e6-a1f5-3990ad3dda0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f262fa6-0c24-4e1b-8986-585b1dd5f48b' class='xr-var-data-in' type='checkbox'><label for='data-5f262fa6-0c24-4e1b-8986-585b1dd5f48b' 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>weeks</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5, 6], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>basin</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;at&#x27;</div><input id='attrs-3dfab6b2-1cd9-4741-843a-59dd6f9f511f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3dfab6b2-1cd9-4741-843a-59dd6f9f511f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d577f468-bac4-4258-b67c-ac302a81025c' class='xr-var-data-in' type='checkbox'><label for='data-d577f468-bac4-4258-b67c-ac302a81025c' 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;at&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac3c18d7-5a90-47fe-9e3d-3d5d014cbd55' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac3c18d7-5a90-47fe-9e3d-3d5d014cbd55' 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 'forecast' (lead: 6)>\n",
       "array([0.22907164, 0.23108883, 0.24090382, 0.27370576, 0.27329931,\n",
       "       0.27266706])\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6\n",
       "    basin    <U6 'at'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs.isel(basin=0).compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987fa4e1-e4cc-4cc7-befd-dc187d83107f",
   "metadata": {},
   "source": [
    "### 5.2 get reference brier score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3091d5c8-1d11-415a-8b12-b429de06b1d1",
   "metadata": {},
   "source": [
    "- annual mean constant "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "83b02b34-d3e7-4c30-b4f7-f458ffe362bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_mfdataset('/glade/work/huili7/S2S/forecast_files_byweek/forecast_obs_count*.nc',\n",
    "                      combine='nested',concat_dim='forecast_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "88c0420e-740d-456d-9925-fac6c100f583",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_ref  = ds.forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d06444d2-8244-407d-80b2-fa1f996b331f",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_ref  =obs_ref.rename({'forecast_n':'init'})\n",
    "obs_ref = obs_ref.rename({'week':'lead'})\n",
    "obs_ref['init'] = init_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0248d55e-23c4-486f-99d4-c23a459bb813",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_ref = obs_ref.isel(lead=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "50176528-e3e3-4b60-8da0-a67e3035acbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_ref = obs_ref.where(obs_ref==0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e062613a-e643-40ac-ba89-12dd9dc14a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TC season in each basin\n",
    "obs_ref[0,:] = obs_ref.isel(basin=0).where(obs_ref.init.dt.month.isin(month_at))\n",
    "obs_ref[1,:] = obs_ref.isel(basin=1).where(obs_ref.init.dt.month.isin(month_ep))\n",
    "obs_ref[2,:] = obs_ref.isel(basin=2).where(obs_ref.init.dt.month.isin(month_ni))\n",
    "obs_ref[3,:] = obs_ref.isel(basin=3).where(obs_ref.init.dt.month.isin(month_nwp))\n",
    "obs_ref[4,:] = obs_ref.isel(basin=4).where(obs_ref.init.dt.month.isin(month_sh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "452d1922-7f9a-4f5c-89b5-24d90e7e145d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann_mean_forecast = obs_ref.mean('init').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d7e95d45-c67c-4a2d-85f8-44a396602979",
   "metadata": {},
   "outputs": [],
   "source": [
    "bs_ref = (ann_mean_forecast - obs) **2\n",
    "bs_ref = bs_ref.mean('init')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "58e3696a-df7e-4bef-bd9b-6b56f81d4736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b01636bb190>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "BSS = 1 - (bs / bs_ref)\n",
    "BSS.isel(basin=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859fc6bf-a5df-45df-9bd5-c73481d283b7",
   "metadata": {},
   "source": [
    "- monthly varying reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0302c1a1-7829-4f99-948e-80c5ebf08503",
   "metadata": {},
   "outputs": [],
   "source": [
    "mon_clim = obs_ref.groupby(obs_ref.init.dt.month).mean('init')\n",
    "mon_clim['basin']  = obs['basin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "727d8d04-e86b-4f38-9cd9-b6bf76ef31aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "mon_mean_forecast = forecast_m.copy(data=np.zeros(forecast_m.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b0fdd24b-a6a0-495a-b7d6-65ba7653df78",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(0,6): # lead\n",
    "    f_date = forecast.init + np.timedelta64(i,'W')\n",
    "    for ib in np.arange(0,6):\n",
    "        for im in np.arange(1,13):\n",
    "            mon_mean_forecast[i,ib,:] = xr.where(f_date.dt.month==im, mon_clim.sel(month=im, basin=mon_clim['basin'][ib]).values,mon_mean_forecast[i,ib,:])\n",
    "            \n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cf98651e-bd3d-4d8e-a6dc-c26f36ec65ca",
   "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;forecast&#x27; (lead: 6, basin: 6, init: 939)&gt;\n",
       "array([[[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.73417722, 0.73417722, 0.73417722, ..., 0.51851852,\n",
       "         0.51851852, 0.51851852],\n",
       "        [0.7721519 , 0.7721519 , 0.7721519 , ..., 0.77777778,\n",
       "         0.77777778, 0.77777778]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "...\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.71232877, 0.71232877, 0.71232877, ..., 0.73417722,\n",
       "         0.73417722, 0.73417722],\n",
       "        [0.73972603, 0.73972603, 0.73972603, ..., 0.7721519 ,\n",
       "         0.7721519 , 0.7721519 ]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.71232877, 0.71232877, 0.71232877, ..., 0.73417722,\n",
       "         0.73417722, 0.71232877],\n",
       "        [0.73972603, 0.73972603, 0.73972603, ..., 0.7721519 ,\n",
       "         0.7721519 , 0.73972603]]])\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6\n",
       "  * init     (init) datetime64[ns] 2002-01-07 2002-01-14 ... 2019-12-30\n",
       "  * basin    (basin) &lt;U6 &#x27;at&#x27; &#x27;ep&#x27; &#x27;ni&#x27; &#x27;nwp&#x27; &#x27;sh&#x27; &#x27;global&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'forecast'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lead</span>: 6</li><li><span class='xr-has-index'>basin</span>: 6</li><li><span class='xr-has-index'>init</span>: 939</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-244e6088-5508-443c-ab44-a3ecf8bd19d4' class='xr-array-in' type='checkbox' checked><label for='section-244e6088-5508-443c-ab44-a3ecf8bd19d4' 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>nan nan nan nan nan nan ... 0.7778 0.7722 0.7722 0.7722 0.7722 0.7397</span></div><div class='xr-array-data'><pre>array([[[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.73417722, 0.73417722, 0.73417722, ..., 0.51851852,\n",
       "         0.51851852, 0.51851852],\n",
       "        [0.7721519 , 0.7721519 , 0.7721519 , ..., 0.77777778,\n",
       "         0.77777778, 0.77777778]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "...\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.71232877, 0.71232877, 0.71232877, ..., 0.73417722,\n",
       "         0.73417722, 0.73417722],\n",
       "        [0.73972603, 0.73972603, 0.73972603, ..., 0.7721519 ,\n",
       "         0.7721519 , 0.7721519 ]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.71232877, 0.71232877, 0.71232877, ..., 0.73417722,\n",
       "         0.73417722, 0.71232877],\n",
       "        [0.73972603, 0.73972603, 0.73972603, ..., 0.7721519 ,\n",
       "         0.7721519 , 0.73972603]]])</pre></div></div></li><li class='xr-section-item'><input id='section-fd7a6ae7-aab4-460d-bb36-9ce0f87e6ebf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd7a6ae7-aab4-460d-bb36-9ce0f87e6ebf' 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'>lead</span></div><div class='xr-var-dims'>(lead)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6</div><input id='attrs-36602259-e37a-4dfc-8c6b-0cbb270d94c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36602259-e37a-4dfc-8c6b-0cbb270d94c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-138cf447-f8bf-4e35-a9fb-b832a1e2e4e4' class='xr-var-data-in' type='checkbox'><label for='data-138cf447-f8bf-4e35-a9fb-b832a1e2e4e4' 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>weeks</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5, 6], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>init</span></div><div class='xr-var-dims'>(init)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-01-07 ... 2019-12-30</div><input id='attrs-91b6e911-9a6a-409e-bba2-7f9fc0a2bd37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91b6e911-9a6a-409e-bba2-7f9fc0a2bd37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd3aed1-0a11-48af-a080-e086bb470268' class='xr-var-data-in' type='checkbox'><label for='data-6fd3aed1-0a11-48af-a080-e086bb470268' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-01-07T00:00:00.000000000&#x27;, &#x27;2002-01-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-21T00:00:00.000000000&#x27;, ..., &#x27;2019-12-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-23T00:00:00.000000000&#x27;, &#x27;2019-12-30T00:00:00.000000000&#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'>basin</span></div><div class='xr-var-dims'>(basin)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;at&#x27; &#x27;ep&#x27; &#x27;ni&#x27; &#x27;nwp&#x27; &#x27;sh&#x27; &#x27;global&#x27;</div><input id='attrs-59f065b4-626b-45de-a777-eaf6b3dbf89a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59f065b4-626b-45de-a777-eaf6b3dbf89a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba933be5-13a4-4e8e-a15f-7473d959589d' class='xr-var-data-in' type='checkbox'><label for='data-ba933be5-13a4-4e8e-a15f-7473d959589d' 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;at&#x27;, &#x27;ep&#x27;, &#x27;ni&#x27;, &#x27;nwp&#x27;, &#x27;sh&#x27;, &#x27;global&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ce91cb2-ebf7-4e59-b9fe-8121c920d776' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3ce91cb2-ebf7-4e59-b9fe-8121c920d776' 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 'forecast' (lead: 6, basin: 6, init: 939)>\n",
       "array([[[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.73417722, 0.73417722, 0.73417722, ..., 0.51851852,\n",
       "         0.51851852, 0.51851852],\n",
       "        [0.7721519 , 0.7721519 , 0.7721519 , ..., 0.77777778,\n",
       "         0.77777778, 0.77777778]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "...\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.71232877, 0.71232877, 0.71232877, ..., 0.73417722,\n",
       "         0.73417722, 0.73417722],\n",
       "        [0.73972603, 0.73972603, 0.73972603, ..., 0.7721519 ,\n",
       "         0.7721519 , 0.7721519 ]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [0.71232877, 0.71232877, 0.71232877, ..., 0.73417722,\n",
       "         0.73417722, 0.71232877],\n",
       "        [0.73972603, 0.73972603, 0.73972603, ..., 0.7721519 ,\n",
       "         0.7721519 , 0.73972603]]])\n",
       "Coordinates:\n",
       "  * lead     (lead) int32 1 2 3 4 5 6\n",
       "  * init     (init) datetime64[ns] 2002-01-07 2002-01-14 ... 2019-12-30\n",
       "  * basin    (basin) <U6 'at' 'ep' 'ni' 'nwp' 'sh' 'global'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon_mean_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce68d721-c3ef-43d0-ab17-45dd88829f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "bs_ref_m = (mon_mean_forecast - obs) **2\n",
    "bs_ref_m = bs_ref_m.mean('init')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1aff63a7-aed0-41be-a09d-0daff3068cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b0165c048d0>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "BSS_m = 1 - (bs / bs_ref_m)\n",
    "BSS_m.isel(basin=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1f6503af-efa6-427a-aa83-c37b15cef568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AT'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs['basin'].values[0].astype(str).upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "596537ba-b33b-412b-90d0-7fe4a532631a",
   "metadata": {},
   "source": [
    "## potential predicatability\n",
    "- chose each model as the observation and use the rest of the model as prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f7387982-c50a-4781-bf89-c3acb95d8e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open forecast file where lead=week2-6 are filled with values from week1\n",
    "# this is used as the \"perfect model\"\n",
    "dp = xr.open_mfdataset('/glade/work/huili7/S2S/forecast_files_byweek/forecast_skill_count_*potential.nc',\n",
    "                                           combine='nested',concat_dim='forecast_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "90f9135c-3be6-43d5-8b61-c895eab542a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = dp.forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc081880-23a4-43fb-bb6f-e21250662042",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = fp.rename({'forecast_n':'init'})\n",
    "\n",
    "fp['init'] = init_date\n",
    "fp = fp.rename({'week':'lead'})\n",
    "fp['lead'].attrs['units']='weeks'\n",
    "fp = fp.assign_coords(basin = ['at','ep','ni','nwp','sh','global'])\n",
    "fp['member'] = np.arange(1,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b76e36d2-0dbe-4c0e-9011-0eeecfdfc092",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = fp.where(fp==0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "76ae2049-1105-4425-8833-f675efb5ecc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only consider TC season, make all other months zero\n",
    "# atlantic basin\n",
    "for ib in np.arange(0,5):  #basin\n",
    "    for i in np.arange(0,6): #lead\n",
    "        f_date = forecast.init + np.timedelta64(i,'W')\n",
    "        fp[i,ib,:,:] = fp.isel(basin=ib,lead=i).where(f_date.dt.month.isin(month_basin[ib]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4f0c05b3-340f-4f92-9467-af76c0fecd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate Brier Score\n",
    "BSS_p = []\n",
    "for im in np.arange(1,12):\n",
    "    ref = fp.sel(member=im)\n",
    "    hindcast = forecast.drop_sel(member=im).mean('member')\n",
    "    \n",
    "    bsp = (hindcast - ref) **2\n",
    "    bsp = bsp.mean('init')\n",
    "    bsp =  1 - (bsp / bs_ref_m)\n",
    "    BSS_p.append(bsp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "24edb798-6351-4565-8c92-a2552faec3c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "BSS_p = xr.concat(BSS_p,dim='member')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8db8fac9-e08e-4dd7-be98-b63fc921549d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# average over each perfect model forecast\n",
    "BSS_p = BSS_p.mean('member')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd3fefc7-66ef-4436-9238-3ae204be4fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternative method using climpred\n",
    "# for each ensemble member, have a hindcast ensemble, calculate BS\n",
    "# then take the average\n",
    "# BSS_p = []\n",
    "# for im in np.arange(1,12):\n",
    "#     hindcast = HindcastEnsemble(forecast.drop_sel(member=im)).mean('member')\n",
    "#     hindcast = hindcast.add_observations(forecast.sel(member=im,lead=1).rename({'init':'time'}))\n",
    "#     hindcast = hindcast.verify(metric='brier_score',\n",
    "#         comparison='e2o', dim=['init'], alignment='same_verif')\n",
    "#     BS_p =  1 - (hindcast.forecast/ bs_ref_m)\n",
    "#     BSS_p.append(BS_p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327b81c0-3c47-4655-9416-eb9b8f68688d",
   "metadata": {},
   "source": [
    "## horray. Now let's plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4d30eed5-e953-47ea-b818-1d68e3172e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 2, figsize=(12, 10))\n",
    "fig.tight_layout(pad=4)\n",
    "\n",
    "axs = axs.flatten()\n",
    "\n",
    "for ib in np.arange(0,5):\n",
    "    BSS.isel(basin=ib).plot(ax = axs[ib],label ='const',color='steelblue',linewidth = 3)\n",
    "    BSS_m.isel(basin=ib).plot(ax = axs[ib],label ='monthly',color='salmon',linewidth=3)\n",
    "    BSS_p.isel(basin=ib).plot(ax= axs[ib],label='potential',color='darkgreen',linewidth=3)\n",
    "    axs[ib].plot(np.arange(0,8),np.zeros(8),color='black',linestyle='--')\n",
    "    axs[ib].set_ylim(-0.8, 0.8)\n",
    "    axs[ib].grid(color='0.95', linestyle='--')\n",
    "    axs[ib].set_xlim(1,6)\n",
    "    \n",
    "    axs[ib].set_ylabel(\"Brier Skill Score\",fontsize=12)\n",
    "    axs[ib].set_xlabel(\"Lead week\",fontsize=12)\n",
    "    axs[ib].set_title(obs['basin'].values[ib].astype(str).upper(),fontsize=20)\n",
    "    axs[ib].set_xticks(np.arange(1, 7))\n",
    "\n",
    "# plt.subplots_adjust(top=0.85)\n",
    "axs[0].legend()\n",
    "axs[-1].axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c9249c7-47cd-4649-b78c-d66320d6d598",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda3-geocat_env]",
   "language": "python",
   "name": "conda-env-miniconda3-geocat_env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}