aaronspring · August 4, 2021 16:53
diff --git a/join_bias_correction.ipynb b/join_bias_correction.ipynb
 {
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd11d9ea-1314-4a4e-939f-20f933824eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import climpred\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from climpred import HindcastEnsemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4078782e-5daa-4053-9fb4-98538d3740ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "init = climpred.tutorial.load_dataset('NMME_hindcast_Nino34_sst').isel(model=5).isel(M=slice(None,10)).rename({'M':'member'})\n",
    "obs = climpred.tutorial.load_dataset('NMME_OIv2_Nino34_sst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "07ff11ad-7fc5-4286-aad9-431a553e7966",
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = init.sel(L=0).rename({'S':'init'}).sel(init=slice('1985','2014'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1b40afd4-6c4d-4804-adba-0587e953c09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation = obs.rename({'time':'init'}).sel(init=forecast.init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97254822-8129-48a2-9fe7-93cd3ff2e8db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1dec14c5-d9ff-49af-81f9-5be84964922c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bias_correction import XBiasCorrection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "74d7219e-5566-4ab3-8ef5-b2181cd4feeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "from scipy.stats import gamma, norm\n",
    "from scipy.signal import detrend\n",
    "import xarray as xr\n",
    "\n",
    "\"\"\"\n",
    "module for bias corrections. \n",
    "Available methods include:\n",
    "- basic_quantile\n",
    "- modified quantile\n",
    "- gamma_mapping\n",
    "- normal_mapping \n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def quantile_correction(obs_data, mod_data, sce_data, modified=True):\n",
    "    cdf = ECDF(mod_data)\n",
    "    p = cdf(sce_data) * 100\n",
    "    cor = np.subtract(*[np.nanpercentile(x, p) for x in [obs_data, mod_data]])\n",
    "    if modified:\n",
    "        mid = np.subtract(*[np.nanpercentile(x, 50) for x in [obs_data, mod_data]])\n",
    "        g = np.true_divide(*[np.nanpercentile(x, 50) for x in [obs_data, mod_data]])\n",
    "\n",
    "        iqr_obs_data = np.subtract(*np.nanpercentile(obs_data, [75, 25]))\n",
    "        iqr_mod_data = np.subtract(*np.nanpercentile(mod_data, [75, 25]))\n",
    "\n",
    "        f = np.true_divide(iqr_obs_data, iqr_mod_data)\n",
    "        cor = g * mid + f * (cor - mid)\n",
    "        return sce_data + cor\n",
    "    else:\n",
    "        return sce_data + cor\n",
    "\n",
    "\n",
    "def gamma_correction(\n",
    "    obs_data, mod_data, sce_data, lower_limit=0.1, cdf_threshold=0.9999999\n",
    "):\n",
    "    obs_raindays, mod_raindays, sce_raindays = [\n",
    "        x[x >= lower_limit] for x in [obs_data, mod_data, sce_data]\n",
    "    ]\n",
    "    obs_gamma, mod_gamma, sce_gamma = [\n",
    "        gamma.fit(x) for x in [obs_raindays, mod_raindays, sce_raindays]\n",
    "    ]\n",
    "\n",
    "    obs_cdf = gamma.cdf(np.sort(obs_raindays), *obs_gamma)\n",
    "    mod_cdf = gamma.cdf(np.sort(mod_raindays), *mod_gamma)\n",
    "    sce_cdf = gamma.cdf(np.sort(sce_raindays), *sce_gamma)\n",
    "\n",
    "    obs_cdf[obs_cdf > cdf_threshold] = cdf_threshold\n",
    "    mod_cdf[mod_cdf > cdf_threshold] = cdf_threshold\n",
    "    sce_cdf[sce_cdf > cdf_threshold] = cdf_threshold\n",
    "\n",
    "    obs_cdf_intpol = np.interp(\n",
    "        np.linspace(1, len(obs_raindays), len(sce_raindays)),\n",
    "        np.linspace(1, len(obs_raindays), len(obs_raindays)),\n",
    "        obs_cdf,\n",
    "    )\n",
    "\n",
    "    mod_cdf_intpol = np.interp(\n",
    "        np.linspace(1, len(mod_raindays), len(sce_raindays)),\n",
    "        np.linspace(1, len(mod_raindays), len(mod_raindays)),\n",
    "        mod_cdf,\n",
    "    )\n",
    "\n",
    "    obs_inverse, mod_inverse, sce_inverse = [\n",
    "        1.0 / (1.0 - x) for x in [obs_cdf_intpol, mod_cdf_intpol, sce_cdf]\n",
    "    ]\n",
    "\n",
    "    adapted_cdf = 1 - 1.0 / (obs_inverse * sce_inverse / mod_inverse)\n",
    "    adapted_cdf[adapted_cdf < 0.0] = 0.0\n",
    "\n",
    "    initial = (\n",
    "        gamma.ppf(np.sort(adapted_cdf), *obs_gamma)\n",
    "        * gamma.ppf(sce_cdf, *sce_gamma)\n",
    "        / gamma.ppf(sce_cdf, *mod_gamma)\n",
    "    )\n",
    "\n",
    "    mod_frequency = 1.0 * mod_raindays.shape[0] / mod_data.shape[0]\n",
    "    sce_frequency = 1.0 * sce_raindays.shape[0] / sce_data.shape[0]\n",
    "\n",
    "    days_min = len(sce_raindays) * sce_frequency / mod_frequency\n",
    "\n",
    "    expected_sce_raindays = int(min(days_min, len(sce_data)))\n",
    "\n",
    "    sce_argsort = np.argsort(sce_data)\n",
    "    correction = np.zeros(len(sce_data))\n",
    "\n",
    "    if len(sce_raindays) > expected_sce_raindays:\n",
    "        initial = np.interp(\n",
    "            np.linspace(1, len(sce_raindays), expected_sce_raindays),\n",
    "            np.linspace(1, len(sce_raindays), len(sce_raindays)),\n",
    "            initial,\n",
    "        )\n",
    "    else:\n",
    "        initial = np.hstack(\n",
    "            (np.zeros(expected_sce_raindays - len(sce_raindays)), initial)\n",
    "        )\n",
    "\n",
    "    correction[sce_argsort[:expected_sce_raindays]] = initial\n",
    "    # correction = pd.Series(correction, index=sce_data.index)\n",
    "    return correction\n",
    "\n",
    "\n",
    "def normal_correction(obs_data, mod_data, sce_data, cdf_threshold=0.9999999):\n",
    "    obs_len, mod_len, sce_len = [len(x) for x in [obs_data, mod_data, sce_data]]\n",
    "    obs_mean, mod_mean, sce_mean = [x.mean() for x in [obs_data, mod_data, sce_data]]\n",
    "    obs_detrended, mod_detrended, sce_detrended = [\n",
    "        detrend(x) for x in [obs_data, mod_data, sce_data]\n",
    "    ]\n",
    "    obs_norm, mod_norm, sce_norm = [\n",
    "        norm.fit(x) for x in [obs_detrended, mod_detrended, sce_detrended]\n",
    "    ]\n",
    "\n",
    "    obs_cdf = norm.cdf(np.sort(obs_detrended), *obs_norm)\n",
    "    mod_cdf = norm.cdf(np.sort(mod_detrended), *mod_norm)\n",
    "    sce_cdf = norm.cdf(np.sort(sce_detrended), *sce_norm)\n",
    "\n",
    "    obs_cdf = np.maximum(np.minimum(obs_cdf, cdf_threshold), 1 - cdf_threshold)\n",
    "    mod_cdf = np.maximum(np.minimum(mod_cdf, cdf_threshold), 1 - cdf_threshold)\n",
    "    sce_cdf = np.maximum(np.minimum(sce_cdf, cdf_threshold), 1 - cdf_threshold)\n",
    "\n",
    "    sce_diff = sce_data - sce_detrended\n",
    "    sce_argsort = np.argsort(sce_detrended)\n",
    "\n",
    "    obs_cdf_intpol = np.interp(\n",
    "        np.linspace(1, obs_len, sce_len), np.linspace(1, obs_len, obs_len), obs_cdf\n",
    "    )\n",
    "    mod_cdf_intpol = np.interp(\n",
    "        np.linspace(1, mod_len, sce_len), np.linspace(1, mod_len, mod_len), mod_cdf\n",
    "    )\n",
    "    obs_cdf_shift, mod_cdf_shift, sce_cdf_shift = [\n",
    "        (x - 0.5) for x in [obs_cdf_intpol, mod_cdf_intpol, sce_cdf]\n",
    "    ]\n",
    "\n",
    "    obs_inverse, mod_inverse, sce_inverse = [\n",
    "        1.0 / (0.5 - np.abs(x)) for x in [obs_cdf_shift, mod_cdf_shift, sce_cdf_shift]\n",
    "    ]\n",
    "\n",
    "    adapted_cdf = np.sign(obs_cdf_shift) * (\n",
    "        1.0 - 1.0 / (obs_inverse * sce_inverse / mod_inverse)\n",
    "    )\n",
    "    adapted_cdf[adapted_cdf < 0] += 1.0\n",
    "    adapted_cdf = np.maximum(np.minimum(adapted_cdf, cdf_threshold), 1 - cdf_threshold)\n",
    "\n",
    "    xvals = norm.ppf(np.sort(adapted_cdf), *obs_norm) + obs_norm[-1] / mod_norm[-1] * (\n",
    "        norm.ppf(sce_cdf, *sce_norm) - norm.ppf(sce_cdf, *mod_norm)\n",
    "    )\n",
    "\n",
    "    xvals -= xvals.mean()\n",
    "    xvals += obs_mean + (sce_mean - mod_mean)\n",
    "\n",
    "    correction = np.zeros(sce_len)\n",
    "    correction[sce_argsort] = xvals\n",
    "    correction += sce_diff - sce_mean\n",
    "    # correction = pd.Series(correction, index=sce_data.index)\n",
    "    return correction\n",
    "\n",
    "\n",
    "class BiasCorrection(object):\n",
    "    def __init__(self, obs_data, mod_data, sce_data):\n",
    "        self.obs_data = obs_data\n",
    "        self.mod_data = mod_data\n",
    "        self.sce_data = sce_data\n",
    "\n",
    "    def correct(\n",
    "        self, method=\"modified_quantile\", lower_limit=0.1, cdf_threshold=0.9999999\n",
    "    ):\n",
    "        if method == \"gamma_mapping\":\n",
    "            corrected = gamma_correction(\n",
    "                self.obs_data,\n",
    "                self.mod_data,\n",
    "                self.sce_data,\n",
    "                lower_limit=lower_limit,\n",
    "                cdf_threshold=cdf_threshold,\n",
    "            )\n",
    "        elif method == \"normal_mapping\":\n",
    "            corrected = normal_correction(\n",
    "                self.obs_data, self.mod_data, self.sce_data, cdf_threshold=cdf_threshold\n",
    "            )\n",
    "        elif method == \"basic_quantile\":\n",
    "            corrected = quantile_correction(\n",
    "                self.obs_data, self.mod_data, self.sce_data, modified=False\n",
    "            )\n",
    "\n",
    "        elif method == \"modified_quantile\":\n",
    "            corrected = quantile_correction(\n",
    "                self.obs_data, self.mod_data, self.sce_data, modified=True\n",
    "            )\n",
    "\n",
    "        else:\n",
    "            raise Exception(\"Specify correct method for bias correction.\")\n",
    "\n",
    "        self.corrected = pd.Series(corrected, index=self.sce_data.index)\n",
    "        return self.corrected\n",
    "\n",
    "\n",
    "class XBiasCorrection(object):\n",
    "    def __init__(self, obs_data, mod_data, sce_data, dim=\"time\"):\n",
    "        self.obs_data = obs_data\n",
    "        self.mod_data = mod_data\n",
    "        self.sce_data = sce_data\n",
    "        self.dim = dim\n",
    "        # print(sce_data)\n",
    "\n",
    "    def correct(\n",
    "        self,\n",
    "        method=\"modified_quantile\",\n",
    "        lower_limit=0.1,\n",
    "        cdf_threshold=0.9999999,\n",
    "        vectorize=True,\n",
    "        dask=\"parallelized\",\n",
    "        join='exact'\n",
    "    ):\n",
    "        dtype = self._set_dtype()\n",
    "        dim = self.dim\n",
    "        if method == \"gamma_mapping\":\n",
    "            corrected = xr.apply_ufunc(\n",
    "                gamma_correction,\n",
    "                self.obs_data,\n",
    "                self.mod_data,\n",
    "                self.sce_data,\n",
    "                vectorize=vectorize,\n",
    "                dask=dask,\n",
    "                input_core_dims=[[dim], [dim], [dim]],\n",
    "                output_core_dims=[[dim]],\n",
    "                output_dtypes=[dtype],\n",
    "                join=join,\n",
    "                kwargs={\"lower_limit\": lower_limit, \"cdf_threshold\": cdf_threshold},\n",
    "            )\n",
    "        elif method == \"normal_mapping\":\n",
    "            corrected = xr.apply_ufunc(\n",
    "                normal_correction,\n",
    "                self.obs_data,\n",
    "                self.mod_data,\n",
    "                self.sce_data,\n",
    "                vectorize=vectorize,\n",
    "                dask=dask,\n",
    "                input_core_dims=[[dim], [dim], [dim]],\n",
    "                output_core_dims=[[dim]],\n",
    "                output_dtypes=[dtype],\n",
    "                join=join,\n",
    "                kwargs={\"cdf_threshold\": cdf_threshold},\n",
    "            )\n",
    "        elif method == \"basic_quantile\":\n",
    "            corrected = xr.apply_ufunc(\n",
    "                quantile_correction,\n",
    "                self.obs_data,\n",
    "                self.mod_data,\n",
    "                self.sce_data,\n",
    "                vectorize=vectorize,\n",
    "                dask=dask,\n",
    "                join=join,\n",
    "                input_core_dims=[[dim], [dim], [dim]],\n",
    "                output_core_dims=[[dim]],\n",
    "                kwargs={\"modified\": False},\n",
    "            )\n",
    "\n",
    "        elif method == \"modified_quantile\":\n",
    "            corrected = xr.apply_ufunc(\n",
    "                quantile_correction,\n",
    "                self.obs_data,\n",
    "                self.mod_data,\n",
    "                self.sce_data,\n",
    "                vectorize=vectorize,\n",
    "                dask=dask,\n",
    "                join=join,\n",
    "                input_core_dims=[[dim], [dim], [dim]],\n",
    "                output_core_dims=[[dim]],\n",
    "                kwargs={\"modified\": True},\n",
    "            )\n",
    "\n",
    "        else:\n",
    "            raise Exception(\"Specify correct method for bias correction.\")\n",
    "        self.corrected = corrected\n",
    "        return self.corrected\n",
    "\n",
    "    def _set_dtype(self):\n",
    "        aa = self.mod_data\n",
    "        if isinstance(aa, xr.Dataset):\n",
    "            dtype = aa[list(aa.data_vars)[0]].dtype\n",
    "            # print('No `dtype` chosen. Input is Dataset. \\\n",
    "            # Defaults to %s' % dtype)\n",
    "        elif isinstance(aa, xr.DataArray):\n",
    "            dtype = aa.dtype\n",
    "        return dtype"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast.sst.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "08605b88-e1a6-46df-8669-1d3b6da0595d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<defs>\n",
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       "</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",
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       "\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",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "  display: grid;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (member: 10, init: 360)\n",
       "Coordinates:\n",
       "  * init     (init) datetime64[ns] 1985-01-01 1985-02-01 ... 2014-12-01\n",
       "    L        float64 0.0\n",
       "    model    &lt;U7 &#x27;CanCM4i&#x27;\n",
       "  * member   (member) float32 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0\n",
       "Data variables:\n",
       "    sst      (member, init) float64 25.7 25.9 26.58 27.18 ... 27.19 27.44 27.51</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-770dfcfe-f315-4548-9401-05d26a6db460' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-770dfcfe-f315-4548-9401-05d26a6db460' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>member</span>: 10</li><li><span class='xr-has-index'>init</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-db2413a9-8d9c-4c37-9eac-9245467fc0c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db2413a9-8d9c-4c37-9eac-9245467fc0c9' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>1985-01-01 ... 2014-12-01</div><input id='attrs-e52343a9-841a-444c-818d-2e25428dfa27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e52343a9-841a-444c-818d-2e25428dfa27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09e8ee21-b9f4-439b-be86-4706b3418c43' class='xr-var-data-in' type='checkbox'><label for='data-09e8ee21-b9f4-439b-be86-4706b3418c43' 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;1985-01-01T00:00:00.000000000&#x27;, &#x27;1985-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-03-01T00:00:00.000000000&#x27;, ..., &#x27;2014-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-01T00:00:00.000000000&#x27;, &#x27;2014-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>L</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-4eede5e4-44ae-4096-a9f6-b2b221383bf1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4eede5e4-44ae-4096-a9f6-b2b221383bf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f40d13cf-b284-4117-88e7-ef003bb39e65' class='xr-var-data-in' type='checkbox'><label for='data-f40d13cf-b284-4117-88e7-ef003bb39e65' 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(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;CanCM4i&#x27;</div><input id='attrs-9fdff0d7-e7e8-4f47-8f7c-5a8f115083a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fdff0d7-e7e8-4f47-8f7c-5a8f115083a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2f282aa-c7a6-4a67-8686-83dae0704b70' class='xr-var-data-in' type='checkbox'><label for='data-c2f282aa-c7a6-4a67-8686-83dae0704b70' 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;CanCM4i&#x27;, dtype=&#x27;&lt;U7&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member</span></div><div class='xr-var-dims'>(member)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 8.0 9.0 10.0</div><input id='attrs-e9c167f3-1dc7-4f3a-b5a4-15c3b01a976e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9c167f3-1dc7-4f3a-b5a4-15c3b01a976e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44f0c8e0-7b95-4905-ba1f-f48c35c22fe6' class='xr-var-data-in' type='checkbox'><label for='data-44f0c8e0-7b95-4905-ba1f-f48c35c22fe6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4232439d-4e5b-4904-91c0-5e469e19c5b2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4232439d-4e5b-4904-91c0-5e469e19c5b2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(member, init)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>25.7 25.9 26.58 ... 27.44 27.51</div><input id='attrs-c60bd3ae-b544-44f1-883c-e923ab4b5de8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c60bd3ae-b544-44f1-883c-e923ab4b5de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62b4ed53-fd02-4c85-b46f-5ddf8f7cc13d' class='xr-var-data-in' type='checkbox'><label for='data-62b4ed53-fd02-4c85-b46f-5ddf8f7cc13d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[25.70362239, 25.90488136, 26.58206638, ..., 27.18172369,\n",
       "        27.46625198, 27.49227491],\n",
       "       [25.77754475, 25.85395912, 26.61966413, ..., 27.21426676,\n",
       "        27.43577696, 27.50389918],\n",
       "       [25.70295246, 25.89930817, 26.6216554 , ..., 27.19699557,\n",
       "        27.45195649, 27.5168339 ],\n",
       "       ...,\n",
       "       [25.78233834, 25.88878836, 26.62440432, ..., 27.19424666,\n",
       "        27.43101007, 27.54907048],\n",
       "       [25.70008373, 25.88097319, 26.57498991, ..., 27.19983235,\n",
       "        27.43662723, 27.49581357],\n",
       "       [25.70523854, 25.88829729, 26.623916  , ..., 27.19473497,\n",
       "        27.43705139, 27.51411179]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46cee3cc-f7c0-4b22-89f7-5f93e1d568f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46cee3cc-f7c0-4b22-89f7-5f93e1d568f5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (member: 10, init: 360)\n",
       "Coordinates:\n",
       "  * init     (init) datetime64[ns] 1985-01-01 1985-02-01 ... 2014-12-01\n",
       "    L        float64 0.0\n",
       "    model    <U7 'CanCM4i'\n",
       "  * member   (member) float32 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0\n",
       "Data variables:\n",
       "    sst      (member, init) float64 25.7 25.9 26.58 27.18 ... 27.19 27.44 27.51"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XBiasCorrection(observation, forecast, forecast, dim='init').correct(method='normal_mapping')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1f69435a-011f-4d14-8d97-897a37ae312c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x10e3d9df0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XtZLoHNPYf2DybnfvvvIUvWOjvvGzyxC722sB2FDfza48+Pp81NkbswA02UU7/182KVmTR9luI+5vz3aX7yTJTq5UmdbRQ600gDdAbf9EWjbS/KIFdM6rcr9F9jtXuyOklyW0Fyx4CNKFV7fJm5lVlu94NTOrKN/xamZWcaVr8mZm1RThgbzNzCorEK3SvWvMzCrLd7yamVVUlbpQjuXKgqQ3SLpd0m2SrpG0ZhxxmJkNlh5rsNw0DVY9SkknA68HNkXE9wI14MdWOw4zs6WUeZzXpaZpMK7mmjqwVlITWAfcM6Y4zMwOEAHNilx4XfWafETcDbwDuBPYBjwYEf+wcDlJF0vaLGnz9u3bVztMMzuMdW6GWm6aBuNorjkWeClwKvBo4AhJP7lwuYi4IiI2RcSmDRs2rHaYZnaYq0pzzTiuHDwf+EZEbI+IJnAt8P1jiMPMbKBO7xrX5A/OncAzJa2TJOB5wNYxxGFmtqhR9K6RdIqkGyRtzT0KL8nzz5H0BUlbcrP0M1ZqP1b9wmtE3CTpI8AtQAv4EnDFasdhZrao0dXUW8BlEXGLpCOBmyVdD7wd+J2I+HtJF+b3zx3FBhcaS++aiHgz8OZxbNvMbDlBGk/hkMuJ2EbqYEJE7Ja0FTg5b+KovNjRrGAPQ9/xama2wErc8SppI3AucBNwKfBJSe8gNZuv2HXJ6bhly8xslQ154fX4TlfvPF08qCxJ64GPApdGxC7gF4E3RMQpwBuAq1ZqP1yTNzNb4BEMGrIjIjYttYCkBinBXx0R1+bZrwEuya//ErjyYGNdjmvyZmYDjKKffO5BeBWwNSLe2ffRPcB/yq/PB7428h3IXJM3M1soRtYmfx7wauBWSVvyvMuBi4A/klQH9gEDm3lGwUneAOh2JAhol+nkLghmihYAjVo7XY3K1E7LlLNld17Z6C8w/dxtlangZllwbGMfkHotSKmwKHt/SO1SFHl+Q+3u/DVqUsuLNcuCZqRnipSh7m/R2hy01uVN9z9yRAH5j7VZ1rqDM7f7ek5IaTGAsg50din6yhL0hcSe5iwAM0W7u4+U6h7H/mUhPQslbSuIgFqRZvTXBqMUUUvzVfa2rWbZO/YL8k5nexFCucwIQaectmCGwTr7uaDM2aIJQEHJUbX0f1ZTUND7v24opY5GtDiySMvc2z6KWl5mbznDjuaRKZ56pDjzQYiZOmq1cgwlRC633aZolr39z+eYovf/E7XesS3mS8paOgDRqKGyF9+hCuj9vx5KORE3csAR7vq+Q97AEJzkzcwW8EDeZmYVF07yZmbVNS0PIFuOk7yZ2QIxuguvY+ckb2Z2ANEewYXXSeAkb2Y2gNvkzcwqaiWeXTMuTvJmZgtF796Gaeckb2Y2gHvXmJlVVOA2eTOzClP38R7TzknezGwA1+TNzCoqwknezKzS3IXSzKzC3IXSzKyiAlH6sQZmZtVVkYq8k7yZ2QF84dXMrOIqUpV3kjczG8A1eTOzCnPvGquWfEKrAPVVYFplDYC5dp3o721Q9P4C1EorqAQafYsomCnavfd5IzNFq7euekPZ1/rKbEaNdv6kGTX25VpVm4Kaym75kYuPAjoVL7XSfgCoLVAq97g1e6mR1m2oRRlFtxwG1dqU9ynvWzmTXs/U26xvzAGwtzVDvcgLRW/5qB1YHKTaYVHEfn2wO6/LUinezvGYS3GXswW1fangUNq/zvY6+wYQfbfh510jaqDOf4H6Fi8XxJrX7Y8rHfe003vLGR5V2w1Auy/7lQT3t9d338/37fjR9b2p/HqARMykdFPsfAiKHGC9juqN3ut22Y1ftcj71TuetV10z9VYsD80RpfOItj/fJ9iTvJmZgO4Jm9mVmUVSfLL/h6RVJP0qdUIxsxsMoiI5adpsGySj4g2sFfS0asQj5nZZIghpikwbHPNPuBWSdcDD3VmRsTrVyQqM7NxOgxvhvq7PJmZHR4OpyQfER+StBZ4bER89VA3KukY4Erge0k/el4bEf98qOWamY3MlDTHLGeojqCSXgxsAT6R358j6WOHsN0/Aj4REU8Gzga2HkJZZmajV5E2+WF7+78FeAawEyAitgCnHswGJR0F/EfgqlzWfETsPJiyzMxWRJCaa5abpsCwSb4VEQ8umHew32OPB7YDH5D0JUlXSjriIMsyM1sRaQjApadpMGySv03SjwM1SadJejfw+YPcZh14GvDeiDiX1FvnjQsXknSxpM2SNm/fvv0gN2VmdpBKLT9NgWGT/OuApwJzwDXALuDSg9zmXcBdEXFTfv8RUtLfT0RcERGbImLThg0bDnJTZmYHR7H8NA2G7V2zF/hNSb+X3sbug91gRNwr6duSnpR76jwP+MrBlmdmNnJTdGF1OUMleUlPB94PHJnfP0jq9njzQW73dcDVkmaAfwd+5iDLMTNbAdNzYXU5w94MdRXwSxHxOQBJzwY+AJx1MBvNvXM2Hcy6ZmaroiI1+WHb5Hd3EjxARNwIHHSTjZnZxBtBP3lJp0i6QdJWSbdLuiTP/wtJW/L0TUlblinngC7rg+YNsmRNXlLngugXJf0p6aJrAK8EPjPMBszMpk4wqt4zLeCyiLhF0pHAzZKuj4hXdhaQ9AfAwi7qC32UAzuofAT4vuUCWK655g8WvH9z3+uK/JgxMzvQKHrPRMQ2YFt+vVvSVuBkcmcTSQJeAZw/MAbpyaSejUdLennfR0cBa4aJYckkHxE/MEwhZmaVM1ySP17S5r73V0TEFYMWlLQROBe4qW/2c4DvRMTXFin/ScAPAccAL+6bvxu4aJgAh+1dcwzwU8DG/nX8qGEzO8ztiIhlO5FIWk9qcrk0Inb1ffQqUjP4QBFxHXCdpGcd7EMch+1d83HgC8CtkEdCNjOrsFHd7CSpQUrwV0fEtX3z68DLGaJdHXiZpNuBh0kPijyb9IXx58utOGySXxMRvzrkslYRa2tN6kUbgEbR7n69qw2QLkopovvHoP6v/3b6fE9rpltWLS8wX9ap19LrCCiK3oqNvL0ja/uo5d/LD7bXcUzRuQg2TyMFgBSUuX9YqLf96DurVQJFKmdvq7Hf/s0WLQDKvv7QKun1jw5Qq/e6s/+tdsHOubUA1IuSfe16N4jOqiq7h4hCgTqvi6BQ0KilfdjTnKWW979WK2nnbZQNKOv5GLeCyPsfSp91Y8obLNuiqKX9LGol0TkWBUSt97roJK6CdEkwz+990FNTcITmAGiHaOfOeOtUp8wHo0DM5P+PGdrsK9Lya9Rkb3s2HaM9BUWrjZppOWYasDsHWOvr4FcUtNalY9laR3cfEEQ9xVfOKMUL3WPS1R5x/XME/eRzm/tVwNaIeOeCj58P3BERdw1R1A9GxH+R9DLSUwN+FLgBWDbJD9uF8sOSLpJ0kqTjOtOQ65qZTZfOl/py0/LOA14NnN/XZfLC/NmPsURTzQKdr/YLgWsi4oEh1xu6Jj8P/D7wm/QuRwTpiZJmZpUzot41N9L9TXfAZz/9CIr6mKQ7SM01vyRpA2lY1mUNm+R/FXhiROx4BEGZmU2vyeokfgvwXuBu4DdIY3IM1YQ+bHPN7cDegwrNzGwaTdbIUG+KiDuBZwEXAP8DWNjGP9CwNfk2sEXSDaTHDQPuQmlm1TSBjxLOV615EWksjuskvWWYFYdN8n+dJzOzw8NkDQpyd360zPOB35M0y5AtMcM+T/5DktYCj83PgDczq7QJq8m/Angh8I6I2CnpJODXh1lxqG8CSS8GtpA64SPpHEkfO7hYzcymwAS1yUfE3oi4tvP4g4jYFhH/MMy6w154fQvwDGBn3sAWYKjHXJqZTZ0hhv6bsJr+ooZtk29FxIPSfm1UU7KLZmYHoSIZbtgkf5ukHwdqkk4DXg98fuXCMjMbs4ok+WGba15HeqbxHPC/SA+4v2SlgjIzG7eqNNcMm+Sfkqc66UH1LwX+ZaWCMjMbuwm68Hoohm2uuRr4NeA2/KhhM6u6KaqpL2fYJL89Iv5mRSMxM5skh1mSf7OkK4F/ZP/HGly7+CpmZlPsMEvyPwM8mfRM405zTQBO8mZWOeLwa645OyLOXNFIzMwmRSwY6WyKDdu75guSnrKikZiZTZLDrHfNs4HXSPoGqU1eQETEWSsWmZnZOE1JEl/OsEn+hSsahY1fZ7zqku7A0nNlnbW1eWD/wa7LOkQeNJpS3UGWQb0BoRW0S7GmlkaLfrjdYE3R7JbRaqcfkRKUZe8HZTtvp9kZfRo4qbGTh8o82DVQ5N/RUuzXbtoZ4JmyN5h3eya6AzLPtevdQcDTNtIKhaK7P9H/eNkQZSMPjt1St8wyxJp62q/5di9Oil48pdhvIOiI3r8qgvlWrbftvFyEur+t1aabZKImilYe+Lwu8vjj+w1wXSsC5Y2XraJbTtFa5Im5fcet/xgWCtYo/T/tK3sDn89Fg2Y+AHO0Wd/XCLCuSOfI7nKWWi5sd7mGRmeg9NlIg5Lnx6LETB01ctll9EbsrhXU9+ad0wzKgUctUFO949I5V+t9A6dHQDna9pXDqk0+Ir610oGYmU2UwynJm5kdVip04dVJ3sxsENfkzcyq67BqkzczO+w4yZuZVdQU9YNfjpO8mdkCottTc+oNe8fryEmqSfqSpL8dVwxmZotRufw0DcaW5EkjS20d4/bNzBZXkccajCXJS3oM8CLgynFs38xsWU7yh+RdwH9hiVGmJF0sabOkzdu3b1+1wMzMOiNDHU5jvI6MpB8C7ouIm5daLiKuiIhNEbFpw4YNqxSdmVlWkZr8OHrXnAe8RNKFpEHBj5L05xHxk2OIxcxsoGm5sLqcVa/JR8RvRMRjImIj8GPAp53gzWzSVKW5xv3kzcwWmqLmmOWMNclHxGeAz4wzBjOzgZzkzcyqqUoDeY/zZigzs8k1gt41kk6RdIOkrZJul3RJ32evk/TVPP/tK7EL4Jq8mdmBAlSOpCrfAi6LiFskHQncLOl64ETgpcBZETEn6YRRbGwQJ3kzswFG0VwTEduAbfn1bklbgZOBi4C3RcRc/uy+Q9/aYG6uMTMbZMQ3Q0naCJwL3AScDjxH0k2S/knS00cW9wKuyRsAandeQLtM3/1ra/MUuTrTLGtQS69r81DW04NY2/W+M71kv+ezNuptykgzZosWjbyRMoQ6yylQ3kYZooy07XXFfLecfdGg1lk+6C5TK4Iyxx01KNq9ZaKWiy/V/WNsFG1q+Q6XY2p7mS1aaRkFaqcNqNV/UKJXDQqIzv4XJQW9mPt13naPZ1YrIsfTOVRpwXX1eepFmqki9k8cuawooD3bq49F37HoLK8iqHXKqUW3mFhYjRuUmBbc9NPIB2FdMUeZD8C+aLBG8wvXBKCZN3KE5rm3PAaAo2sPc/fcsSmeplC7JDr/iSWocwJEgNL6US+IWv6/fRjI55YU3f2Iou8mJUF7Jq8rUczMDIzvYA1Zkz9e0ua+91dExBUHlCWtBz4KXBoRuyTVgWOBZwJPB/6PpMdHxMgv9zrJm5kNMly63RERm5ZaQFKDlOCvjohr8+y7gGtzUv+ipBI4Hhj5g7rcXGNmttCIHlCm9JPlKmBrRLyz76O/Bs7Py5wOzAA7Rr4fuCZvZnYAMbJn15wHvBq4VdKWPO9y4P3A+yXdBswDr1mJphpwkjczG2wEOTcibmTxkQRX5ZldTvJmZgNU5Y5XJ3kzs4X8gDIzs2qryvPkneTNzAZwkjczq6pgJBdeJ4GTvJnZAL7wamZWZU7yZmbVVKVBQ5zkzcwWinCbvJlZlbl3jZlZhbm5xsysqgIYzfB/Y+ckb2Y2SDVyvJO8mdkgbq4xM6sy964xM6uocO8aM7PKSjdDuSZvFRKd0X5LUeTGyLmy3n09W2t1ly0bfcv3j3kjUEvd8iJ6ZZUUPNha2120M58QRdH7Yyojrb+7vYY1RbM7v5kXeShq7CsbALRLEbXetst673U3LkV3JOM19RbtHPj21lHM5RUiRHttp9pW9BpjQ6jVK7OYS4VKsK9d75a5t5XiUa1X9Ysa+zXqdvYr3WMjlD/b05xdtFWgu29AMZ/KVtkrViXdi4MCynLAkM3qW6ZvXfpqqVFP+wRQL0r2lrNp34omZT5ezaixL2by62AumrmYYF80umW1836WIdbX93Xn1+ZK1Gz3bTQHUq+hIh/XuVZvviDaOai+/xIEeROohNp83zFv9c7RkXBN3sysulyTNzOrKo8MZWZWZYF8M5SZWYW5ucbMrKLchdLMrOIqUpMf0OdqZUk6RdINkrZKul3SJasdg5nZsmKIaQqMoybfAi6LiFskHQncLOn6iPjKGGIxMxvIXSgPUkRsA7bl17slbQVOBpzkzWwyBNB2kj9kkjYC5wI3jTMOM7N+IipTk1/1NvkOSeuBjwKXRsSuAZ9fLGmzpM3bt29f/QDN7PDWGed1qWkKjCXJS2qQEvzVEXHtoGUi4oqI2BQRmzZs2LC6AZqZVSTJr3pzjSQBVwFbI+Kdq719M7NlBZV5QNk4avLnAa8Gzpe0JU8XjiEOM7NFKWLZaRqMo3fNjez/gFozswkTUFajKu87Xs3MFgqmps19OU7yZmaDVKMi7yRvZjbItLS5L2ds/eTNzCbaCLpQLvasLklvkXT3anQ+cU3ezGyhCGiPpL1m4LO68md/GBHvGMVGluIkb2Y2yAiaa5Z4VteqcZI3AJTPZ5VQL1INZr6sM1u0AGiXRe/RqgFq5+XbUDby/AKiyAvlSlA9j7xQ76wAzBRt2mXqRRsllOWBPWobatPI6zyqtoc1Ut5E+4BlAdQC1XJ4otsQqbagndYtQzzYXgfA0bW9NHJsUlDbm1YoWp0C8o5Gr8zI5bfbBevqzXyMaswUKaZyvrZ/A2jeL6mXLKT0vsgFr2/MUcvHrGyr17lYUDTT/KINUfRi6g5mIYiZ6MbU3U4AucxQb/CLKHq7JvrmC2j1/g/2lrMAbGBXd98eKmcp8n/qrAqKvl7QR2gegH3R4KjaPgB2l2up5X0s5kVZU9p5IOoFarV6B6QjAvJ+hnrnZJTqHnv64lYroJbXLxaUNQojbpNf8Kyu84BfkfRTwGZSbf+7I91g5jZ5M7OFAihj+QmO7zxjK08XDypuwLO63gs8ATiHVNP/g5XaFdfkzcwOEOln5vJ2RMSmpRYY9KyuiPhO3+fvA/72EIJdkmvyZmaDjKZ3zcBndUk6qW+xlwG3jTz+zDV5M7OFglH1ruk8q+tWSVvyvMuBV0k6J2/pm8DPj2JjgzjJm5kNMpreNYs9q+vjh1z4kJzkzcwOMD3Pi1+Ok7yZ2UKBn0JpZlZprsmbmVXVyB5rMHZO8mZmCwXEcP3kJ56TvJnZIKWba8zMqstt8mZmFRUe49XMrNpckzczq6og2oMfaz1tnOTNzBbqPGq4ApzkzcwGcRdKM7NqCiBckzczq6gYetCQieckb2Y2QFVq8oop6CYkaTvwrREVdzywY0RlHapJigUcz1ImKRZwPEt5XERsOJQCJH2CtE/L2RERLzyUba20qUjyoyRp83JjMq6WSYoFHM9SJikWcDw2PI/xamZWYU7yZmYVdjgm+SvGHUCfSYoFHM9SJikWcDw2pMOuTd7M7HByONbkzcwOG07yZmYVVskkL8k3eS1BksYdQ79JikfSxPxN+Dy2UZiYE3oUJD1L0vuAp09ALOdIukjS94w7FgBJT5X0XICYgAsxks6Q9KxJiEfSmZIuy7GM/V72STqPYfLOZXtkKnPhVdJFwOuB/wl8AGhGxKo/EFpSA3gPsAnYCswBV0TETasdS46nyPGcD9wJ3ARcFxGbJRWrndQkHQ28A3gGsD3H84GI+LfVjGNBTB8DLgAuiIjPSKqN49zJsUzEeZxjmahz2Q5OlWryjwV+MyLeGxH7xvWHAZwJHB0R3xcRP0k6xuO83fsYYD1wBvATwP3AZZLWj6nW+uukysXZwM8DjwI2jiEOJNXyy88CfwT8LkBEtMfYbDMp5zHA9zJZ57IdhKlN8pKeJum0/Ppo0gn5RUnnS/qkpMslvTx/vqJtvjmW0/PbNvAKSUfn7T8TeJ6kc1cjlryNUyWtyW+PA74fWBcR24GPAg8Av7zK8azNb98H/DZARHyd9CV05krHsCCW2fy2zPt/QY7rPkk/l2MrV/HYzObXxzHG87gvns65I8Z8Ltuhm7oLO5JOJf2EPA4oJP1mRHxK0i7gz4GvkH7qHgf8tqSvR8S/rlIsvxUR10t6e47hBcBlwOnAiyVdFhH/byViyfFsBN4LzAA7Jb0pIr4i6bPArwJvBbYB1wIXSXp0RNyzivFcHhFfzZ/NRMQ88DDw9ZWKYalYgP8XESHpy8C3STX5ayRdALwhIu5axXh+KyK2SrofuBq4nVU6jxeJ500RcYuk/wr8CfA8VvFcttGZipr8ghrDrwFbIuJZwF8DF+X5bwLOBu6JiOsi4gPAx4GXrmIsP5fn/wapDfNHIuLDwLuAbwDnjTKWReK5KSKeB9wA/I6kpwAfBJ4p6fER0QK+A+wD1i4sb4Xjeaukp+bPOs0QJ5MS7Mh7tiwXC3B6rkWfAJxKas46ETghIu7qa85Z6Xg+DfxurjS8mfTLZkXP42Xi6fxfnR4RbyJ94az4uWwrYyqSPLAGuiflQ0Azzz8auE3SGRFxJ3Al8KN9650AfH6VY3lK7i0yB7wSICLuJyWzr4w4lv54Or/Kbs/bfA/p4uaPAfcA/wK8PX92G/C4HONqx/Pjkk7I7d5PBB6IiC9J+kXgTZKOWcVYfpp0jrSAL5KuXZwPPFbSWSvQHr5YPP8D+D7gYtLF6CuBH+lbbyXO46XieU+O57X5i3cf8Ir82Uqey7YCJjrJS3qBpOuB35f0ipw8bwROk/Ql4IWkJqcPSzo/In4D+Iakt0n6Aumn7u2rGEsN+JCkHwQ+AVwg6R2SPkf6Mvj3UcSySDwtUlv7uZLOlnQ2cBuphlojNUU8RtK7Jd1Gej7/g6NqV30E8TyOdLEV4PHA0yXdALwE+N8RsXOVYrmdlKyOBv4S2BQRPx8Rt5CuGRxyHAcRz2OBx0bE5cCdK3EeP4J4bgNOITXPfBy4UNLvr8S5bCssIiZyAp5I6l73UuBc4H8Bv5Y/exJwbd+ybwLek18fBTwZ+MExxfLbwB/m1+eQepC8bIWPzTXALwFH5mPxt6QvoE051kvzeieSLsK+ZMzxvD6v9xOk5PL8McXyv4Ff7Fu3AIoxH5s3rNR5fBDxXAP8ykqey55Wfhp7APsF0/dHlhPA/+z77LWk2tWJwAZSl7cz8mfPBj5C7vc/IbGMOlksFc/P5ng25PeP7/vsl4Gfy69X6/gMG09tgmKZtGMzCfH8SiceT9M7TUxzjaSfAe4iXRADuBV4Vb7qD9Ag/UR8K7Cb9BP29ZIuAf4U+NSExRIjbApZLp46qYfKH+b338jrXUz6I74FRndn6QjjOeQ27wofm0mI57WdeGyKjftbJp/L60m9Uy4hnVRPzvPfRfrJ+H9J3SPPBP4eOIJ0c8/rgA8Bz6xiLAcRz98BJ+bPLyVdbH16VeOZpFgcj6dJncYeQDeQdMEJ4G3AX+TXNVIt+dn5/Sk5kc4cLrE8wng+CMzm9+sOh3gmKRbH42kSp4lpronUBRJSLeNUSRdE+jn/YETcmD/7BVK3xRW91XuSYnmE8ewldQckIvYeDvFMUiyOxybSuL9lBk2kq/j/1Pf+GcB1pK5c33O4xuJ4picWx+NpUqaJewql8pMRJX2EdAv+HOlC5tciPevksIzF8UxPLI7HJsnENNd05BNxHekuv1cBd0bEJ8ZxIk5SLI5nemJxPDZJJvUBZb9E6g3wgohYiVvvpzUWcDzTEgs4HpsAE9dcA72fluOOAyYrFnA8S5mkWMDx2GSYyCRvZmajMXFt8mZmNjpO8mZmFeYkb2ZWYU7yZmYV5iRvE03SsiMiSbpSaZhDlMZuNbPMvWusUiTtiYj1447DbFK4Jm8TTdKe/O9zJX1G0kck3SHp6s7z+vP8TZLeBqyVtEXS1WMN3GxCOMnbNDmX9Kzzp5DGhz2v/8OIeCPwcEScExE/sfrhmU0eJ3mbJl+MiLvyXZtbgI3jDcds8jnJ2zTpf95Km8l99pLZxHCSt6ppSmqMOwizSeEkb1VzBfBlX3g1S9yF0syswlyTNzOrMCd5M7MKc5I3M6swJ3kzswpzkjczqzAneTOzCnOSNzOrMCd5M7MK+/+U62+DJPliLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "XBiasCorrection(observation.sel(init=slice('1985','1990')), forecast.sel(init=slice('1985','1990')), forecast.sel(init=slice('1990','1999')), dim='init').correct(method='basic_quantile', join='outer').sst.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "133b7fe9-9ade-4225-9030-61bd84e2cb67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:climpred-dev] *",
   "language": "python",
   "name": "conda-env-climpred-dev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
No results found