2020ECAHM-scikit-downscale.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-downscale: an open source Python package for scalable climate downscaling\n",
    "\n",
    "Joseph Hamman (jhamman@ucar.edu) and Julia Kent (jkent@ucar.edu)\n",
    "\n",
    "NCAR, Boulder, CO, USA\n",
    "\n",
    "-------\n",
    "\n",
    "This notebook was developed for the [2020 EarthCube All Hands Meeting](https://www.earthcube.org/EC2020). The development of Scikit-downscale done in conjunction with the development of the [Pangeo Project](http://pangeo.io/) and was supported by the following awards:\n",
    "\n",
    "- [NSF-GEO-AGS 1928374: EarthCube Data Capabilities: Collaborative Proposal: Jupyter meets the Earth: Enabling discovery in geoscience through interactive computing at scale](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740633)\n",
    "- [NSF-OIA 1937136: Convergence Accelerator Phase I (RAISE): Knowledge Open Network Queries for Research (KONQUER)](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937136)\n",
    "\n",
    "ECAHM 2020 ID: 143\n",
    "\n",
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "Climate data from Earth System Models (ESMs) are increasingly being used to study the impacts of climate change on a broad range of biogeophysical systems (forest fire, flood, fisheries, etc.) and human systems (water resources, power grids, etc.). Before this data can be used to study many of these systems, post-processing steps commonly referred to as bias correction and statistical downscaling must be performed. “Bias correction” is used to correct persistent biases in climate model output and “statistical downscaling” is used to increase the spatiotemporal resolution of the model output (i.e. from 1 deg to 1/16th deg grid boxes). For our purposes, we’ll refer to both parts as “downscaling”.\n",
    "\n",
    "In the past few decades, the applications community has developed a plethora of downscaling methods. Many of these methods are ad-hoc collections of processing routines while others target very specific applications. The proliferation of downscaling methods has left the climate applications community with an overwhelming body of research to sort through without much in the form of synthesis guilding method selection or applicability.\n",
    "\n",
    "Motivated by the pressing socio-environmental challenges of climate change – and with the learnings from previous downscaling efforts (e.g. Gutmann et al. 2014, Lanzante et al. 2018) in mind – we have begun working on a community-centered open framework for climate downscaling: [scikit-downscale](https://scikit-downscale.readthedocs.io/en/latest/). We believe that the community will benefit from the presence of a well-designed open source downscaling toolbox with standard interfaces alongside a repository of benchmark data to test and evaluate new and existing downscaling methods.\n",
    "\n",
    "In this notebook, we provide an overview of the scikit-downscale project, detailing how it can be used to downscale a range of surface climate variables such as surface air temperature and precipitation. We also highlight how scikit-downscale framework is being used to compare exisiting methods and how it can be extended to support the development of new downscaling methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Scikit-downscale\n",
    "Scikit-downscale is a new open source Python project. Within Scikit-downscale, we are been curating a collection of new and existing downscaling methods within a common framework. Key features of Scikit-downscale are:\n",
    "\n",
    "- A high-level interface modeled after the popular `fit` / `predict` pattern found in many machine learning packages ([Scikit-learn](https://scikit-learn.org/stable/index.html), [Tensorflow](https://www.tensorflow.org/guide/keras), etc.),\n",
    "- Uses [Xarray](http://xarray.pydata.org/en/stable/) and [Pandas](https://pandas.pydata.org/) data structures and utilities for handling of labeled datasets,\n",
    "- Utilities for automatic parallelization of pointwisde downscaling models,\n",
    "- Common interface for pointwise and spatial (or global) downscaling models, and\n",
    "- Extensible, allowing the creation of new downscaling methods through composition.\n",
    "\n",
    "Scikit-downscale's source code is available on [GitHub](https://github.com/jhamman/scikit-downscale)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Pointwise Models\n",
    "We define pointwise methods as those that only use local information during the downscaling process. They can be often represented as a general linear model and fit independently across each point in the study domain. Examples of existing pointwise methods are:\n",
    "\n",
    "- BCSD_[Temperature, Precipitation]: Wood et al. (2004)\n",
    "- ARRM: Stoner et al. (2012)\n",
    "- (Hybrid) Delta Method (e.g. Hamlet et al. (2010)\n",
    "- [GARD](https://github.com/NCAR/GARD): Gutmann et al (in prep). \n",
    "\n",
    "Because pointwise methods can be written as a stand-alone linear model, Scikit-downscale implements these models as a Scikit-learn [LinearModel](https://scikit-learn.org/stable/modules/linear_model.html) or [Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). By building directly on Scikit-learn, we inherit a well defined model API and the ability to interoperate with a robust ecosystem utilities for model evaluation and optimization (e.g. grid-search). Perhaps more importantly, this structure also allows us to compare methods at a high-level of granularity (single spatial point) before deploying them on large domain problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "\n",
    "***Begin interactive demo***\n",
    "\n",
    "-------\n",
    "\n",
    "From here forward in this notebook, we'll jump back and forth between Python and text cells to describe how scikit-downscale works.\n",
    "\n",
    "This first cell just imports some libraries and get's things setup for our analysis to come."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # sklearn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from utils import get_sample_data\n",
    "\n",
    "sns.set(style='darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've imported a few libraries, let's open a sample dataset from a single point in North America. We'll use this data to explore Scikit-downscale and its existing functionality. You'll notice there are two groups of data, `training` and `targets`. The `training` data is meant to represent data from a typical climate model and the `targets` data is meant to represent our \"observations\". For the purpose of this demonstration, we've choosen training data sampled from a regional climate model (WRF) run at 50km resolution over North America. The observations are sampled from the nearest 1/16th grid cell in Livneh et al, 2013.\n",
    "\n",
    "We have choosen to use the `tmax` variable (daily maximum surface air temperature) for demonstration purposes. With a small amount of effort, an interested reader could swap `tmax` for `pcp` and test these methods on precipitation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "             training               targets          \n",
       "                 tmax           pcp    tmax       pcp\n",
       "time                                                 \n",
       "1950-01-01        NaN           NaN   -0.22  5.608394\n",
       "1950-01-02        NaN           NaN   -4.54  2.919726\n",
       "1950-01-03        NaN           NaN   -7.87  3.066762\n",
       "1950-01-04        NaN           NaN   -5.08  4.684164\n",
       "1950-01-05        NaN           NaN   -0.79  4.295568\n",
       "...               ...           ...     ...       ...\n",
       "2015-11-26   7.657013  0.000000e+00     NaN       NaN\n",
       "2015-11-27   7.687256  0.000000e+00     NaN       NaN\n",
       "2015-11-28  10.480835  0.000000e+00     NaN       NaN\n",
       "2015-11-29  11.728516  0.000000e+00     NaN       NaN\n",
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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load sample data\n",
    "training = get_sample_data('training')\n",
    "targets = get_sample_data('targets')\n",
    "\n",
    "# print a table of the training/targets data\n",
    "display(pd.concat({'training': training, 'targets': targets}, axis=1))\n",
    "\n",
    "# make a plot of the temperature and precipitation data\n",
    "fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(8, 6), sharex=True)\n",
    "time_slice = slice('1990-01-01', '1990-12-31')\n",
    "\n",
    "# plot-temperature\n",
    "training[time_slice]['tmax'].plot(ax=axes[0], label='training')\n",
    "targets[time_slice]['tmax'].plot(ax=axes[0], label='targets')\n",
    "axes[0].legend()\n",
    "axes[0].set_ylabel('Temperature [C]')\n",
    "\n",
    "# plot-precipitation\n",
    "training[time_slice]['pcp'].plot(ax=axes[1])\n",
    "targets[time_slice]['pcp'].plot(ax=axes[1])\n",
    "_ = axes[1].set_ylabel('Precipitation [mm/day]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Models as cattle, not pets\n",
    "\n",
    "As we mentioned above, Scikit-downscale utilizes a similiar API to that of Scikit-learn for its pointwise models. This means we can build collections of models that may be quite different internally, but operate the same at the API level. Importantly, this means that all downscaling methods have two common API methods: `fit`, which trains the model given _training_ and _targets_ data, and `predict` which uses the fit model to perform the downscaling opperation. This is perhaps the most important feature of Scikit-downscale, the ability to test and compare arbitrary combinations of models under a common interface. This allows us to try many combinations of models and parameters, choosing only the best combinations. The following pseudo-code block describe the workflow common to all scikit-downscale models:\n",
    "\n",
    "```python\n",
    "from skdownscale.pointwise_models import MyModel\n",
    "\n",
    "...\n",
    "# load and pre-process input data (X and y)\n",
    "...\n",
    "\n",
    "model = MyModel(**parameters)\n",
    "model.fit(X_train, y)\n",
    "predictions = model.predict(X_predict)\n",
    "\n",
    "...\n",
    "# evaluate and/or save predictions\n",
    "...\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "In the cell below, we'll create nine different downscaling models, some from Scikit-downscale and some from Scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from skdownscale.pointwise_models import PureAnalog, AnalogRegression\n",
    "from skdownscale.pointwise_models import BcsdTemperature, BcsdPrecipitation\n",
    "\n",
    "\n",
    "models = {\n",
    "    'GARD: PureAnalog-best-1': PureAnalog(kind='best_analog', n_analogs=1),\n",
    "    'GARD: PureAnalog-sample-10': PureAnalog(kind='sample_analogs', n_analogs=10),\n",
    "    'GARD: PureAnalog-weight-10': PureAnalog(kind='weight_analogs', n_analogs=10),\n",
    "    'GARD: PureAnalog-weight-100': PureAnalog(kind='weight_analogs', n_analogs=100),\n",
    "    'GARD: PureAnalog-mean-10': PureAnalog(kind='mean_analogs', n_analogs=10),\n",
    "    'GARD: AnalogRegression-100': AnalogRegression(n_analogs=100),\n",
    "    'GARD: LinearRegression': LinearRegression(),\n",
    "    'BCSD: BcsdTemperature': BcsdTemperature(return_anoms=False),\n",
    "    'Sklearn: RandomForestRegressor': RandomForestRegressor(random_state=0)\n",
    "}\n",
    "\n",
    "train_slice = slice('1980-01-01', '1989-12-31')\n",
    "predict_slice = slice('1990-01-01', '1999-12-31')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've created a collection of models, we want to train the models on the same input data. We do this by looping through our dictionary of models and calling the `fit` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract training / prediction data\n",
    "X_train = training[['tmax']][train_slice]\n",
    "y_train = targets[['tmax']][train_slice]\n",
    "X_predict = training[['tmax']][predict_slice]\n",
    "\n",
    "# Fit all models\n",
    "for key, model in models.items():\n",
    "    model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like that, we fit nine downscaling models. Now we want to use those models to downscale/bias-correct our data. For the sake of easy comparison, we'll use a different part of the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GARD: PureAnalog-best-1</th>\n",
       "      <th>GARD: PureAnalog-sample-10</th>\n",
       "      <th>GARD: PureAnalog-weight-10</th>\n",
       "      <th>GARD: PureAnalog-weight-100</th>\n",
       "      <th>GARD: PureAnalog-mean-10</th>\n",
       "      <th>GARD: AnalogRegression-100</th>\n",
       "      <th>GARD: LinearRegression</th>\n",
       "      <th>BCSD: BcsdTemperature</th>\n",
       "      <th>Sklearn: RandomForestRegressor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>4.50</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.375299</td>\n",
       "      <td>5.697786</td>\n",
       "      <td>5.895</td>\n",
       "      <td>5.931445</td>\n",
       "      <td>5.781472</td>\n",
       "      <td>4.528703</td>\n",
       "      <td>5.2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6.13</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.543398</td>\n",
       "      <td>3.264698</td>\n",
       "      <td>2.561</td>\n",
       "      <td>2.515919</td>\n",
       "      <td>2.524322</td>\n",
       "      <td>-1.584749</td>\n",
       "      <td>4.6935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>5.46</td>\n",
       "      <td>3.04</td>\n",
       "      <td>4.963575</td>\n",
       "      <td>4.933534</td>\n",
       "      <td>4.692</td>\n",
       "      <td>4.862730</td>\n",
       "      <td>4.944167</td>\n",
       "      <td>2.848937</td>\n",
       "      <td>4.6736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>8.57</td>\n",
       "      <td>5.90</td>\n",
       "      <td>8.369125</td>\n",
       "      <td>8.239455</td>\n",
       "      <td>7.340</td>\n",
       "      <td>7.255379</td>\n",
       "      <td>7.107427</td>\n",
       "      <td>6.687826</td>\n",
       "      <td>8.4134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>5.67</td>\n",
       "      <td>7.03</td>\n",
       "      <td>7.424970</td>\n",
       "      <td>7.583703</td>\n",
       "      <td>7.705</td>\n",
       "      <td>7.711861</td>\n",
       "      <td>7.878299</td>\n",
       "      <td>8.296425</td>\n",
       "      <td>6.5789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GARD: PureAnalog-best-1  GARD: PureAnalog-sample-10  \\\n",
       "time                                                              \n",
       "1990-01-01                     4.50                        5.67   \n",
       "1990-01-02                     6.13                        3.55   \n",
       "1990-01-03                     5.46                        3.04   \n",
       "1990-01-04                     8.57                        5.90   \n",
       "1990-01-05                     5.67                        7.03   \n",
       "\n",
       "            GARD: PureAnalog-weight-10  GARD: PureAnalog-weight-100  \\\n",
       "time                                                                  \n",
       "1990-01-01                    5.375299                     5.697786   \n",
       "1990-01-02                    3.543398                     3.264698   \n",
       "1990-01-03                    4.963575                     4.933534   \n",
       "1990-01-04                    8.369125                     8.239455   \n",
       "1990-01-05                    7.424970                     7.583703   \n",
       "\n",
       "            GARD: PureAnalog-mean-10  GARD: AnalogRegression-100  \\\n",
       "time                                                               \n",
       "1990-01-01                     5.895                    5.931445   \n",
       "1990-01-02                     2.561                    2.515919   \n",
       "1990-01-03                     4.692                    4.862730   \n",
       "1990-01-04                     7.340                    7.255379   \n",
       "1990-01-05                     7.705                    7.711861   \n",
       "\n",
       "            GARD: LinearRegression  BCSD: BcsdTemperature  \\\n",
       "time                                                        \n",
       "1990-01-01                5.781472               4.528703   \n",
       "1990-01-02                2.524322              -1.584749   \n",
       "1990-01-03                4.944167               2.848937   \n",
       "1990-01-04                7.107427               6.687826   \n",
       "1990-01-05                7.878299               8.296425   \n",
       "\n",
       "            Sklearn: RandomForestRegressor  \n",
       "time                                        \n",
       "1990-01-01                          5.2024  \n",
       "1990-01-02                          4.6935  \n",
       "1990-01-03                          4.6736  \n",
       "1990-01-04                          8.4134  \n",
       "1990-01-05                          6.5789  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# store predicted results in this dataframe\n",
    "predict_df = pd.DataFrame(index = X_predict.index)\n",
    "\n",
    "for key, model in models.items():\n",
    "    predict_df[key] = model.predict(X_predict)\n",
    "\n",
    "# show a table of the predicted data\n",
    "display(predict_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's do some sample analysis on our predicted data. First, we'll look at a timeseries of all the downscaled timeseries for the first year of the prediction period. In the figure below, the `target` (truth) data is shown in black, the original (pre-correction) data is shown in grey, and each of the downscaled data timeseries is shown in a different color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3.5))\n",
    "targets['tmax'][time_slice].plot(ax=ax, label='target', c='k', lw=1, alpha=0.75, legend=True, zorder=10)\n",
    "X_predict['tmax'][time_slice].plot(label='original', c='grey', ax=ax, alpha=0.75, legend=True)\n",
    "predict_df[time_slice].plot(ax=ax, lw=0.75)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "_ = ax.set_ylabel('Temperature [C]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, its difficult to tell which of the nine downscaling methods performed best from our plot above. We may want to evaluate our predictions using a standard statistical score, such as $r^2$. Those results are easily computed below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-best-1</th>\n",
       "      <td>0.820281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-sample-10</th>\n",
       "      <td>0.820977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCSD: BcsdTemperature</th>\n",
       "      <td>0.858258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sklearn: RandomForestRegressor</th>\n",
       "      <td>0.864160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-weight-10</th>\n",
       "      <td>0.881287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-weight-100</th>\n",
       "      <td>0.892049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: PureAnalog-mean-10</th>\n",
       "      <td>0.899297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: AnalogRegression-100</th>\n",
       "      <td>0.906217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARD: LinearRegression</th>\n",
       "      <td>0.906316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                r2_score\n",
       "GARD: PureAnalog-best-1         0.820281\n",
       "GARD: PureAnalog-sample-10      0.820977\n",
       "BCSD: BcsdTemperature           0.858258\n",
       "Sklearn: RandomForestRegressor  0.864160\n",
       "GARD: PureAnalog-weight-10      0.881287\n",
       "GARD: PureAnalog-weight-100     0.892049\n",
       "GARD: PureAnalog-mean-10        0.899297\n",
       "GARD: AnalogRegression-100      0.906217\n",
       "GARD: LinearRegression          0.906316"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate r2\n",
    "score = (predict_df.corrwith(targets.tmax[predict_slice]) **2).sort_values().to_frame('r2_score')\n",
    "display(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of our downscaling methods seem to be doing fairly well. The timeseries and statistics above shows that all our methods are producing generally resonable results. However, we are often interested in how our models do at predicting extreme events. We can quickly look into those aspects of our results using the `qq` plots below. There you'll see that the models diverge in some interesting ways. For example, while the `LinearRegression` method has the highest $r^2$ score, it seems to have trouble capturing extreme heat events. Whereas many of the analog methods, as well as the `RandomForestRegressor`, perform much better on the tails of the distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import prob_plots\n",
    "\n",
    "fig = prob_plots(X_predict, targets['tmax'], predict_df[score.index.values], shape=(3, 3), figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we've shown how easy it is to fit, predict, and evaluate scikit-downscale models. The seamless interoperability of these models clearly facilitates a workflow that enables a deeper level of model evaluation that is otherwise possible in the downscaling world. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Tailor-made methods in a common framework\n",
    "\n",
    "In the section above, we showed how it is possible to use scikit-downscale to bias-correct a timeseries of daily maximum air temperature using an arbitrary collection of linear models. Some of those models were general machine learning methods (e.g. `LinearRegression` or `RandomForestRegressor`) while others were tailor-made methods developed specifically for downscaling (e.g. `BCSDTemperature`). In this section, we walk through how new pointwise methods can be added to the scikit-downscale framework, highlighting the Z-Score method along the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.1 Z-Score Method\n",
    "\n",
    "Z-Score bias correction is a good technique for target variables with Gaussian probability distributions, such as zonal wind speed.\n",
    "\n",
    "In essence the technique:\n",
    "\n",
    "1. Finds the mean  \n",
    "$$\\overline{x} = \\sum_{i=0}^N \\frac{x_i}{N}$$ \n",
    "and standard deviation \n",
    "$$\\sigma = \\sqrt{\\frac{\\sum_{i=0}^N |x_i - \\overline{x}|^2}{N-1}}$$ \n",
    "of target (measured) data and training (historical modeled) data. \n",
    "\n",
    "2. Compares the difference between the statistical values to produce a shift \n",
    "$$shift = \\overline{x_{target}} - \\overline{x_{training}}$$ \n",
    "and scale parameter \n",
    "$$scale = \\sigma_{target} \\div \\sigma_{training}$$ \n",
    "\n",
    "3. Applies these paramaters to the future model data to be corrected to get a new mean\n",
    "$$\\overline{x_{corrected}} = \\overline{x_{future}} + shift$$\n",
    "and new standard deviation\n",
    "$$\\sigma_{corrected} = \\sigma_{future} \\times scale$$\n",
    "\n",
    "4. Calculates the corrected values\n",
    "$$x_{corrected_{i}} = z_i \\times \\sigma_{corrected} + \\overline{x_{corrected}}$$\n",
    "from the future model's z-score values\n",
    "$$z_i = \\frac{x_i-\\overline{x}}{\\sigma}$$\n",
    "\n",
    "In practice, if the wind was on average 3 m/s faster on the first of July in the models compared to the measurements, we would adjust the modeled data for all July 1sts in the future modeled dataset to be 3 m/s faster. And similarly for scaling the standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.2 Building the ZScoreRegressor Class\n",
    "\n",
    "Scikit-downscale's pointwise all implement Scikit-learn's `fit`/`predict` API. Each new downscaler must implement a minimum of three class methods: `__init__`, `fit`, `predict`. \n",
    "\n",
    "```python\n",
    "class AbstractDownscaler(object):\n",
    "    \n",
    "    def __init__(self):\n",
    "        ...\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        ...\n",
    "        return self\n",
    "    \n",
    "    def predict(X):\n",
    "        ...\n",
    "        return y_hat\n",
    "```\n",
    "\n",
    "Ommitting some of the complexity in the full implementation (which can be found in the [full implementation on GitHub](https://github.com/jhamman/scikit-downscale/blob/master/skdownscale/pointwise_models/zscore.py)), we demonstrate how the `ZScoreRegressor` was built:\n",
    "\n",
    "First, we define our `__init__` method, allowing users to specify specific options (in this case `window_width`):\n",
    "```python\n",
    "class ZScoreRegressor(object):\n",
    "    \n",
    "    def __init__(self, window_width=31):\n",
    "        self.window_width = window_width\n",
    "```\n",
    "\n",
    "Next, we define our `fit` method, \n",
    "\n",
    "```python\n",
    "    def fit(self, X, y):\n",
    "        X_mean, X_std = _calc_stats(X.squeeze(), self.window_width)\n",
    "        y_mean, y_std = _calc_stats(y.squeeze(), self.window_width)\n",
    "        \n",
    "        self.stats_dict_ = {\n",
    "            \"X_mean\": X_mean,\n",
    "            \"X_std\": X_std,\n",
    "            \"y_mean\": y_mean,\n",
    "            \"y_std\": y_std,\n",
    "        }\n",
    "\n",
    "        shift, scale = _get_params(X_mean, X_std, y_mean, y_std)\n",
    "\n",
    "        self.shift_ = shift\n",
    "        self.scale_ = scale\n",
    "        return self\n",
    "```\n",
    "\n",
    "Finally, we define our `predict` method,\n",
    "\n",
    "```python\n",
    "    def predict(self, X):\n",
    "\n",
    "        fut_mean, fut_std, fut_zscore = _get_fut_stats(X.squeeze(), self.window_width)\n",
    "        shift_expanded, scale_expanded = _expand_params(X.squeeze(), self.shift_, self.scale_)\n",
    "\n",
    "        fut_mean_corrected, fut_std_corrected = _correct_fut_stats(\n",
    "            fut_mean, fut_std, shift_expanded, scale_expanded\n",
    "        )\n",
    "\n",
    "        self.fut_stats_dict_ = {\n",
    "            \"meani\": fut_mean,\n",
    "            \"stdi\": fut_std,\n",
    "            \"meanf\": fut_mean_corrected,\n",
    "            \"stdf\": fut_std_corrected,\n",
    "        }\n",
    "\n",
    "        fut_corrected = (fut_zscore * fut_std_corrected) + fut_mean_corrected\n",
    "\n",
    "        return fut_corrected.to_frame(name)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skdownscale.pointwise_models import ZScoreRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a small dataset\n",
    "training = get_sample_data('wind-hist')\n",
    "target = get_sample_data('wind-obs')\n",
    "future = get_sample_data('wind-rcp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bias correction using ZScoreRegresssor\n",
    "zscore = ZScoreRegressor()\n",
    "zscore.fit(training, target)\n",
    "fit_stats = zscore.fit_stats_dict_\n",
    "out = zscore.predict(future)\n",
    "predict_stats = zscore.predict_stats_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the datasets\n",
    "from utils import zscore_ds_plot\n",
    "\n",
    "zscore_ds_plot(training, target, future, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import zscore_correction_plot\n",
    "\n",
    "zscore_correction_plot(zscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Automatic Parallelization\n",
    "\n",
    "In the examples above, we have performed downscaling on sample data sourced from individual points. In many downscaling workflows, however, users will want to apply pointwise methods at all points in their study domain. For this use case, scikit-downscale provides a high-level wrapper class: `PointWiseDownscaler`.\n",
    "\n",
    "In the example below, we'll use the `BCSDTemperature` model, along with the `PointWiseDownscaler` wrapper, to downscale daily maximum surface air temperature from CMIP6 for all point in a subset of the Pacific Norwest. We'll use a local [Dask Cluster](https://dask.org/) to distribute the computation among our available processors. Though not the point of this example, we also use [intake-esm](https://intake-esm.readthedocs.io/en/latest/) to access [CMIP6](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) data stored on [Google Cloud Storage](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6).\n",
    "\n",
    "**Data:**\n",
    "  - Training / Prediction data: NASA-GISS-E2 historical data from CMIP6\n",
    "  - Targets: [GridMet](http://www.climatologylab.org/gridmet.html) daily maximum surface air temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "train_slice = slice('1980', '1982')  # train time range\n",
    "holdout_slice = slice('1990', '1991')  # prediction time range\n",
    "\n",
    "# bounding box of downscaling region\n",
    "lon_slice = slice(-124.8, -120.0) \n",
    "lat_slice = slice(50, 45)\n",
    "\n",
    "# chunk shape for dask execution (time must be contiguous, ie -1)\n",
    "chunks = {'lat': 10, 'lon': 10, 'time': -1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Start a Dask Cluster. Xarray and the `PointWiseDownscaler` will make use of this cluster when it comes time to load input data and train/predict downscaling models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:41711</li>\n",
       "  <li><b>Dashboard: </b><a href='/user/jhamman/proxy/8787/status' target='_blank'>/user/jhamman/proxy/8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>4</li>\n",
       "  <li><b>Memory: </b>25.77 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:41711' processes=4 threads=4, memory=25.77 GB>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2. Load our target data. \n",
    "\n",
    "Here we use xarray directly to load a collection of OpenDAP endpoints. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air_temperature&#x27; (time: 1096, lat: 106, lon: 115)&gt;\n",
       "dask.array&lt;xarray-&lt;this-array&gt;, shape=(1096, 106, 115), dtype=float32, chunksize=(1096, 10, 10), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
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       "  * lon      (lon) float64 -124.8 -124.7 -124.7 -124.6 ... -120.1 -120.1 -120.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air_temperature'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1096</li><li><span class='xr-has-index'>lat</span>: 106</li><li><span class='xr-has-index'>lon</span>: 115</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f2320833-20cf-4ab3-a83c-8b3dd9c0e4be' class='xr-array-in' type='checkbox' ><label for='section-f2320833-20cf-4ab3-a83c-8b3dd9c0e4be' 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=(1096, 10, 10), meta=np.ndarray&gt;</span></div><pre class='xr-array-data'><table>\n",
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       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.588235,70.588235 119.449907,70.588235 119.449907,108.999188 80.588235,108.999188\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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       "  <text x=\"100.019071\" y=\"128.999188\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >115</text>\n",
       "  <text x=\"139.449907\" y=\"89.793712\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.449907,89.793712)\">106</text>\n",
       "  <text x=\"35.294118\" y=\"93.705071\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,93.705071)\">1096</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></pre></div></li><li class='xr-section-item'><input id='section-b8392143-782c-40d1-a48b-b45cf9de75d3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8392143-782c-40d1-a48b-b45cf9de75d3' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 1982-12-31</div><input id='attrs-fdafd49c-6b0b-4c81-b47a-a5d8948db092' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdafd49c-6b0b-4c81-b47a-a5d8948db092' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fffe2f6e-0b44-405c-b63f-092213f44f4a' class='xr-var-data-in' type='checkbox'><label for='data-fffe2f6e-0b44-405c-b63f-092213f44f4a' 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><pre class='xr-var-data'>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T00:00:00.000000000&#x27;, ..., &#x27;1982-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-12-30T00:00:00.000000000&#x27;, &#x27;1982-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.4 49.36 49.32 ... 45.07 45.03</div><input id='attrs-c1d2a1d7-00d5-4a39-baff-91c2748efe7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1d2a1d7-00d5-4a39-baff-91c2748efe7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e90fc4d2-e0bf-4047-b7d2-b0927b17787e' class='xr-var-data-in' type='checkbox'><label for='data-e90fc4d2-e0bf-4047-b7d2-b0927b17787e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>description :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>585</dd></dl></div><pre class='xr-var-data'>array([49.4     , 49.358333, 49.316667, 49.275   , 49.233333, 49.191667,\n",
       "       49.15    , 49.108333, 49.066667, 49.025   , 48.983333, 48.941667,\n",
       "       48.9     , 48.858333, 48.816667, 48.775   , 48.733333, 48.691667,\n",
       "       48.65    , 48.608333, 48.566667, 48.525   , 48.483333, 48.441667,\n",
       "       48.4     , 48.358333, 48.316667, 48.275   , 48.233333, 48.191667,\n",
       "       48.15    , 48.108333, 48.066667, 48.025   , 47.983333, 47.941667,\n",
       "       47.9     , 47.858333, 47.816667, 47.775   , 47.733333, 47.691667,\n",
       "       47.65    , 47.608333, 47.566667, 47.525   , 47.483333, 47.441667,\n",
       "       47.4     , 47.358333, 47.316667, 47.275   , 47.233333, 47.191667,\n",
       "       47.15    , 47.108333, 47.066667, 47.025   , 46.983333, 46.941667,\n",
       "       46.9     , 46.858333, 46.816667, 46.775   , 46.733333, 46.691667,\n",
       "       46.65    , 46.608333, 46.566667, 46.525   , 46.483333, 46.441667,\n",
       "       46.4     , 46.358333, 46.316667, 46.275   , 46.233333, 46.191667,\n",
       "       46.15    , 46.108333, 46.066667, 46.025   , 45.983333, 45.941667,\n",
       "       45.9     , 45.858333, 45.816667, 45.775   , 45.733333, 45.691667,\n",
       "       45.65    , 45.608333, 45.566667, 45.525   , 45.483333, 45.441667,\n",
       "       45.4     , 45.358333, 45.316667, 45.275   , 45.233333, 45.191667,\n",
       "       45.15    , 45.108333, 45.066667, 45.025   ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.8 -124.7 ... -120.1 -120.0</div><input id='attrs-62328956-bc96-432b-abdf-b609fe5a9799' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62328956-bc96-432b-abdf-b609fe5a9799' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-465081cf-2a90-4a8c-9c36-547040aa831d' class='xr-var-data-in' type='checkbox'><label for='data-465081cf-2a90-4a8c-9c36-547040aa831d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>description :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>1386</dd></dl></div><pre class='xr-var-data'>array([-124.766667, -124.725   , -124.683333, -124.641667, -124.6     ,\n",
       "       -124.558333, -124.516667, -124.475   , -124.433333, -124.391667,\n",
       "       -124.35    , -124.308333, -124.266667, -124.225   , -124.183333,\n",
       "       -124.141667, -124.1     , -124.058333, -124.016667, -123.975   ,\n",
       "       -123.933333, -123.891667, -123.85    , -123.808333, -123.766667,\n",
       "       -123.725   , -123.683333, -123.641667, -123.6     , -123.558333,\n",
       "       -123.516667, -123.475   , -123.433333, -123.391667, -123.35    ,\n",
       "       -123.308333, -123.266667, -123.225   , -123.183333, -123.141667,\n",
       "       -123.1     , -123.058333, -123.016667, -122.975   , -122.933333,\n",
       "       -122.891667, -122.85    , -122.808333, -122.766667, -122.725   ,\n",
       "       -122.683333, -122.641667, -122.6     , -122.558333, -122.516667,\n",
       "       -122.475   , -122.433333, -122.391667, -122.35    , -122.308333,\n",
       "       -122.266667, -122.225   , -122.183333, -122.141667, -122.1     ,\n",
       "       -122.058333, -122.016667, -121.975   , -121.933333, -121.891667,\n",
       "       -121.85    , -121.808333, -121.766667, -121.725   , -121.683333,\n",
       "       -121.641667, -121.6     , -121.558333, -121.516667, -121.475   ,\n",
       "       -121.433333, -121.391667, -121.35    , -121.308333, -121.266667,\n",
       "       -121.225   , -121.183333, -121.141667, -121.1     , -121.058333,\n",
       "       -121.016667, -120.975   , -120.933333, -120.891667, -120.85    ,\n",
       "       -120.808333, -120.766667, -120.725   , -120.683333, -120.641667,\n",
       "       -120.6     , -120.558333, -120.516667, -120.475   , -120.433333,\n",
       "       -120.391667, -120.35    , -120.308333, -120.266667, -120.225   ,\n",
       "       -120.183333, -120.141667, -120.1     , -120.058333, -120.016667])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-35de08e7-516c-416e-833e-b43609fb24c3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-35de08e7-516c-416e-833e-b43609fb24c3' 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 'air_temperature' (time: 1096, lat: 106, lon: 115)>\n",
       "dask.array<xarray-<this-array>, shape=(1096, 106, 115), dtype=float32, chunksize=(1096, 10, 10), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-01-02 ... 1982-12-31\n",
       "  * lat      (lat) float64 49.4 49.36 49.32 49.28 ... 45.15 45.11 45.07 45.03\n",
       "  * lon      (lon) float64 -124.8 -124.7 -124.7 -124.6 ... -120.1 -120.1 -120.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbffec07610>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "fnames = [f'http://thredds.northwestknowledge.net:8080/thredds/dodsC/MET/tmmx/tmmx_{year}.nc'\n",
    "          for year in range(int(train_slice.start), int(train_slice.stop) + 1)]\n",
    "# open the data and cleanup a bit of metadata\n",
    "obs = xr.open_mfdataset(fnames, engine='pydap', concat_dim='day').rename({'day': 'time'}).drop('crs')\n",
    "\n",
    "obs_subset = obs['air_temperature'].sel(time=train_slice, lon=lon_slice, lat=lat_slice).resample(time='1d').mean().load(scheduler='threads').chunk(chunks)\n",
    "\n",
    "# display\n",
    "display(obs_subset)\n",
    "obs_subset.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: Load our training/prediction data.\n",
    "\n",
    "Here we use `intake-esm` to access a single Xarray dataset from the Pangeo's Google Cloud CMIP6 data catalog. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020.6.11'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import intake_esm\n",
    "intake_esm.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
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       "</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",
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       "</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, 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;tasmax&#x27; (time: 1096, lat: 106, lon: 115)&gt;\n",
       "dask.array&lt;xarray-&lt;this-array&gt;, shape=(1096, 106, 115), dtype=float32, chunksize=(1096, 10, 10), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-01-02 ... 1982-12-31\n",
       "  * lat      (lat) float64 49.4 49.36 49.32 49.28 ... 45.15 45.11 45.07 45.03\n",
       "  * lon      (lon) float64 -124.8 -124.7 -124.7 -124.6 ... -120.1 -120.1 -120.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tasmax'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1096</li><li><span class='xr-has-index'>lat</span>: 106</li><li><span class='xr-has-index'>lon</span>: 115</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8a8d47ac-1783-40ab-8c40-21960cbafc4c' class='xr-array-in' type='checkbox' ><label for='section-8a8d47ac-1783-40ab-8c40-21960cbafc4c' 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=(1096, 10, 10), meta=np.ndarray&gt;</span></div><pre 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> 53.44 MB </td> <td> 438.40 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1096, 106, 115) </td> <td> (1096, 10, 10) </td></tr>\n",
       "    <tr><th> Count </th><td> 133 Tasks </td><td> 132 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "  <text x=\"35.294118\" y=\"93.705071\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,93.705071)\">1096</text>\n",
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       "</table></pre></div></li><li class='xr-section-item'><input id='section-8a9ce94e-2f0c-493b-ad6f-17a2856e305c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a9ce94e-2f0c-493b-ad6f-17a2856e305c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 1982-12-31</div><input id='attrs-7da91e89-039a-418d-8138-7499d28caa48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7da91e89-039a-418d-8138-7499d28caa48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ffcee16-046f-4e14-b147-40340bce968f' class='xr-var-data-in' type='checkbox'><label for='data-6ffcee16-046f-4e14-b147-40340bce968f' 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><pre class='xr-var-data'>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T00:00:00.000000000&#x27;, ..., &#x27;1982-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-12-30T00:00:00.000000000&#x27;, &#x27;1982-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.4 49.36 49.32 ... 45.07 45.03</div><input id='attrs-c716bf2f-d383-4c25-b198-111f589fe893' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c716bf2f-d383-4c25-b198-111f589fe893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf9e6c01-38e7-4ba3-ba79-a8829ff17902' class='xr-var-data-in' type='checkbox'><label for='data-cf9e6c01-38e7-4ba3-ba79-a8829ff17902' 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><pre class='xr-var-data'>array([49.4     , 49.358333, 49.316667, 49.275   , 49.233333, 49.191667,\n",
       "       49.15    , 49.108333, 49.066667, 49.025   , 48.983333, 48.941667,\n",
       "       48.9     , 48.858333, 48.816667, 48.775   , 48.733333, 48.691667,\n",
       "       48.65    , 48.608333, 48.566667, 48.525   , 48.483333, 48.441667,\n",
       "       48.4     , 48.358333, 48.316667, 48.275   , 48.233333, 48.191667,\n",
       "       48.15    , 48.108333, 48.066667, 48.025   , 47.983333, 47.941667,\n",
       "       47.9     , 47.858333, 47.816667, 47.775   , 47.733333, 47.691667,\n",
       "       47.65    , 47.608333, 47.566667, 47.525   , 47.483333, 47.441667,\n",
       "       47.4     , 47.358333, 47.316667, 47.275   , 47.233333, 47.191667,\n",
       "       47.15    , 47.108333, 47.066667, 47.025   , 46.983333, 46.941667,\n",
       "       46.9     , 46.858333, 46.816667, 46.775   , 46.733333, 46.691667,\n",
       "       46.65    , 46.608333, 46.566667, 46.525   , 46.483333, 46.441667,\n",
       "       46.4     , 46.358333, 46.316667, 46.275   , 46.233333, 46.191667,\n",
       "       46.15    , 46.108333, 46.066667, 46.025   , 45.983333, 45.941667,\n",
       "       45.9     , 45.858333, 45.816667, 45.775   , 45.733333, 45.691667,\n",
       "       45.65    , 45.608333, 45.566667, 45.525   , 45.483333, 45.441667,\n",
       "       45.4     , 45.358333, 45.316667, 45.275   , 45.233333, 45.191667,\n",
       "       45.15    , 45.108333, 45.066667, 45.025   ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.8 -124.7 ... -120.1 -120.0</div><input id='attrs-d441f26e-f931-48f0-b133-321483c89d61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d441f26e-f931-48f0-b133-321483c89d61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700d18a2-b880-4330-aaa9-319b478a4b1f' class='xr-var-data-in' type='checkbox'><label for='data-700d18a2-b880-4330-aaa9-319b478a4b1f' 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><pre class='xr-var-data'>array([-124.766667, -124.725   , -124.683333, -124.641667, -124.6     ,\n",
       "       -124.558333, -124.516667, -124.475   , -124.433333, -124.391667,\n",
       "       -124.35    , -124.308333, -124.266667, -124.225   , -124.183333,\n",
       "       -124.141667, -124.1     , -124.058333, -124.016667, -123.975   ,\n",
       "       -123.933333, -123.891667, -123.85    , -123.808333, -123.766667,\n",
       "       -123.725   , -123.683333, -123.641667, -123.6     , -123.558333,\n",
       "       -123.516667, -123.475   , -123.433333, -123.391667, -123.35    ,\n",
       "       -123.308333, -123.266667, -123.225   , -123.183333, -123.141667,\n",
       "       -123.1     , -123.058333, -123.016667, -122.975   , -122.933333,\n",
       "       -122.891667, -122.85    , -122.808333, -122.766667, -122.725   ,\n",
       "       -122.683333, -122.641667, -122.6     , -122.558333, -122.516667,\n",
       "       -122.475   , -122.433333, -122.391667, -122.35    , -122.308333,\n",
       "       -122.266667, -122.225   , -122.183333, -122.141667, -122.1     ,\n",
       "       -122.058333, -122.016667, -121.975   , -121.933333, -121.891667,\n",
       "       -121.85    , -121.808333, -121.766667, -121.725   , -121.683333,\n",
       "       -121.641667, -121.6     , -121.558333, -121.516667, -121.475   ,\n",
       "       -121.433333, -121.391667, -121.35    , -121.308333, -121.266667,\n",
       "       -121.225   , -121.183333, -121.141667, -121.1     , -121.058333,\n",
       "       -121.016667, -120.975   , -120.933333, -120.891667, -120.85    ,\n",
       "       -120.808333, -120.766667, -120.725   , -120.683333, -120.641667,\n",
       "       -120.6     , -120.558333, -120.516667, -120.475   , -120.433333,\n",
       "       -120.391667, -120.35    , -120.308333, -120.266667, -120.225   ,\n",
       "       -120.183333, -120.141667, -120.1     , -120.058333, -120.016667])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-f9a70d40-0adb-4605-be6d-ecfd67a51bf1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f9a70d40-0adb-4605-be6d-ecfd67a51bf1' 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 'tasmax' (time: 1096, lat: 106, lon: 115)>\n",
       "dask.array<xarray-<this-array>, shape=(1096, 106, 115), dtype=float32, chunksize=(1096, 10, 10), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-01-02 ... 1982-12-31\n",
       "  * lat      (lat) float64 49.4 49.36 49.32 49.28 ... 45.15 45.11 45.07 45.03\n",
       "  * lon      (lon) float64 -124.8 -124.7 -124.7 -124.6 ... -120.1 -120.1 -120.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbffe6eff70>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import intake\n",
    "\n",
    "# search the cmip6 catalog\n",
    "col = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")\n",
    "cat = col.search(experiment_id=['historical', 'ssp585'], table_id='day', variable_id='tasmax',\n",
    "                 grid_label='gn')\n",
    "\n",
    "# access the data and do some cleanup\n",
    "ds_model = cat['CMIP.NASA-GISS.GISS-E2-1-G.historical.day.gn'].to_dask().squeeze(drop=True).drop(['height', 'lat_bnds', 'lon_bnds', 'time_bnds'])\n",
    "ds_model.lon.values[ds_model.lon.values > 180] -= 360\n",
    "ds_model = ds_model.roll(lon=72, roll_coords=True)\n",
    "\n",
    "# regional subsets, ready for downscaling\n",
    "train_subset = ds_model['tasmax'].sel(time=train_slice).interp_like(obs_subset.isel(time=0, drop=True), method='linear')\n",
    "train_subset['time'] = train_subset.indexes['time'].to_datetimeindex()\n",
    "train_subset = train_subset.resample(time='1d').mean().load(scheduler='threads').chunk(chunks)\n",
    "\n",
    "holdout_subset = ds_model['tasmax'].sel(time=holdout_slice).interp_like(obs_subset.isel(time=0, drop=True), method='linear')\n",
    "holdout_subset['time'] = holdout_subset.indexes['time'].to_datetimeindex()\n",
    "holdout_subset = holdout_subset.resample(time='1d').mean().load(scheduler='threads').chunk(chunks)\n",
    "\n",
    "# display\n",
    "display(train_subset)\n",
    "train_subset.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4. Now that we have loaded our training and target data, we can move on to fit our BcsdTemperature model at each x/y point in our domain. This is where the `PointWiseDownscaler` comes in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<skdownscale.PointWiseDownscaler>\n",
       "  Fit Status: False\n",
       "  Model:\n",
       "    BcsdTemperature(return_anoms=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skdownscale.pointwise_models import PointWiseDownscaler\n",
    "from dask.diagnostics import ProgressBar\n",
    "\n",
    "model = PointWiseDownscaler(BcsdTemperature(return_anoms=False))\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5. We fit the `PointWiseDownscaler`, passing it data in Xarray data structures (our regional subsets from above). This opperation is lazy and return immediately. Under the hood, we can see that `PointWiseDownscaler._models` is an `Xarray.DataArray` of `BcsdTemperature` models.\n",
    "\n",
    "<div class=\"alert-warning\">\n",
    "Note: The following two cells may take a few minutes, or longer, to complete depending on how many cores your computer has, and your internet connection.     \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<skdownscale.PointWiseDownscaler>\n",
       "  Fit Status: True\n",
       "  Model:\n",
       "    BcsdTemperature(return_anoms=False)"
      ]
     },
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     "output_type": "display_data"
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       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"130.608696\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >115</text>\n",
       "  <text x=\"140.000000\" y=\"55.304348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,55.304348)\">106</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></pre></div></li><li class='xr-section-item'><input id='section-4c1f1611-2ead-4f92-a310-4edc56dbe971' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c1f1611-2ead-4f92-a310-4edc56dbe971' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.4 49.36 49.32 ... 45.07 45.03</div><input id='attrs-b65cac5a-8114-4edb-8bfa-c2dd86f99885' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b65cac5a-8114-4edb-8bfa-c2dd86f99885' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2bc6cab-b1b7-4de9-8b10-a9d358971654' class='xr-var-data-in' type='checkbox'><label for='data-b2bc6cab-b1b7-4de9-8b10-a9d358971654' 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><pre class='xr-var-data'>array([49.4     , 49.358333, 49.316667, 49.275   , 49.233333, 49.191667,\n",
       "       49.15    , 49.108333, 49.066667, 49.025   , 48.983333, 48.941667,\n",
       "       48.9     , 48.858333, 48.816667, 48.775   , 48.733333, 48.691667,\n",
       "       48.65    , 48.608333, 48.566667, 48.525   , 48.483333, 48.441667,\n",
       "       48.4     , 48.358333, 48.316667, 48.275   , 48.233333, 48.191667,\n",
       "       48.15    , 48.108333, 48.066667, 48.025   , 47.983333, 47.941667,\n",
       "       47.9     , 47.858333, 47.816667, 47.775   , 47.733333, 47.691667,\n",
       "       47.65    , 47.608333, 47.566667, 47.525   , 47.483333, 47.441667,\n",
       "       47.4     , 47.358333, 47.316667, 47.275   , 47.233333, 47.191667,\n",
       "       47.15    , 47.108333, 47.066667, 47.025   , 46.983333, 46.941667,\n",
       "       46.9     , 46.858333, 46.816667, 46.775   , 46.733333, 46.691667,\n",
       "       46.65    , 46.608333, 46.566667, 46.525   , 46.483333, 46.441667,\n",
       "       46.4     , 46.358333, 46.316667, 46.275   , 46.233333, 46.191667,\n",
       "       46.15    , 46.108333, 46.066667, 46.025   , 45.983333, 45.941667,\n",
       "       45.9     , 45.858333, 45.816667, 45.775   , 45.733333, 45.691667,\n",
       "       45.65    , 45.608333, 45.566667, 45.525   , 45.483333, 45.441667,\n",
       "       45.4     , 45.358333, 45.316667, 45.275   , 45.233333, 45.191667,\n",
       "       45.15    , 45.108333, 45.066667, 45.025   ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.8 -124.7 ... -120.1 -120.0</div><input id='attrs-76fdc59f-8fd4-4149-ac71-9cacb1187713' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76fdc59f-8fd4-4149-ac71-9cacb1187713' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8b997ba-2e46-40c9-9504-8f55406e75e0' class='xr-var-data-in' type='checkbox'><label for='data-d8b997ba-2e46-40c9-9504-8f55406e75e0' 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><pre class='xr-var-data'>array([-124.766667, -124.725   , -124.683333, -124.641667, -124.6     ,\n",
       "       -124.558333, -124.516667, -124.475   , -124.433333, -124.391667,\n",
       "       -124.35    , -124.308333, -124.266667, -124.225   , -124.183333,\n",
       "       -124.141667, -124.1     , -124.058333, -124.016667, -123.975   ,\n",
       "       -123.933333, -123.891667, -123.85    , -123.808333, -123.766667,\n",
       "       -123.725   , -123.683333, -123.641667, -123.6     , -123.558333,\n",
       "       -123.516667, -123.475   , -123.433333, -123.391667, -123.35    ,\n",
       "       -123.308333, -123.266667, -123.225   , -123.183333, -123.141667,\n",
       "       -123.1     , -123.058333, -123.016667, -122.975   , -122.933333,\n",
       "       -122.891667, -122.85    , -122.808333, -122.766667, -122.725   ,\n",
       "       -122.683333, -122.641667, -122.6     , -122.558333, -122.516667,\n",
       "       -122.475   , -122.433333, -122.391667, -122.35    , -122.308333,\n",
       "       -122.266667, -122.225   , -122.183333, -122.141667, -122.1     ,\n",
       "       -122.058333, -122.016667, -121.975   , -121.933333, -121.891667,\n",
       "       -121.85    , -121.808333, -121.766667, -121.725   , -121.683333,\n",
       "       -121.641667, -121.6     , -121.558333, -121.516667, -121.475   ,\n",
       "       -121.433333, -121.391667, -121.35    , -121.308333, -121.266667,\n",
       "       -121.225   , -121.183333, -121.141667, -121.1     , -121.058333,\n",
       "       -121.016667, -120.975   , -120.933333, -120.891667, -120.85    ,\n",
       "       -120.808333, -120.766667, -120.725   , -120.683333, -120.641667,\n",
       "       -120.6     , -120.558333, -120.516667, -120.475   , -120.433333,\n",
       "       -120.391667, -120.35    , -120.308333, -120.266667, -120.225   ,\n",
       "       -120.183333, -120.141667, -120.1     , -120.058333, -120.016667])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c5ae9491-891f-4ee5-bc4c-09080f8ff077' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c5ae9491-891f-4ee5-bc4c-09080f8ff077' 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 'tasmax' (lat: 106, lon: 115)>\n",
       "dask.array<_fit_wrapper-723171662d2ffd5ed708bc9f7d1b3a95-<this, shape=(106, 115), dtype=object, chunksize=(10, 10), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 49.4 49.36 49.32 49.28 ... 45.15 45.11 45.07 45.03\n",
       "  * lon      (lon) float64 -124.8 -124.7 -124.7 -124.6 ... -120.1 -120.1 -120.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.fit(train_subset, obs_subset)\n",
    "display(model, model._models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 6. Finally, we can use our model to complete the downscaling workflow using the `predict` method along with our `holdout_subset` of CMIP6 data. We call the `.load()` method to eagerly compute the data. We end by plotting a map of downscaled data over our study area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "  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 (time: 730, lat: 106, lon: 115)&gt;\n",
       "array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 272.9068 , 272.03818,\n",
       "         271.74667],\n",
       "        [      nan,       nan,       nan, ..., 272.58118, 271.9899 ,\n",
       "         271.59863],\n",
       "        [      nan,       nan,       nan, ..., 272.38995, 271.89896,\n",
       "         271.48502]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 272.2721 , 271.3799 ,\n",
       "         271.10745],\n",
       "        [      nan,       nan,       nan, ..., 271.8796 , 271.28778,\n",
       "         270.87622],\n",
       "        [      nan,       nan,       nan, ..., 271.8478 , 271.35623,\n",
       "         270.78433]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 270.06155, 269.17004,\n",
       "         268.7786 ],\n",
       "        [      nan,       nan,       nan, ..., 269.6548 , 269.1635 ,\n",
       "         268.67227],\n",
       "        [      nan,       nan,       nan, ..., 269.63492, 269.057  ,\n",
       "         268.65283]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 277.1879 , 276.33917,\n",
       "         276.03476],\n",
       "        [      nan,       nan,       nan, ..., 276.86758, 276.36328,\n",
       "         275.859  ],\n",
       "        [      nan,       nan,       nan, ..., 276.79156, 276.2874 ,\n",
       "         275.82758]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 277.70404, 277.39084,\n",
       "         276.97433],\n",
       "        [      nan,       nan,       nan, ..., 277.34024, 276.8421 ,\n",
       "         276.762  ],\n",
       "        [      nan,       nan,       nan, ..., 277.26825, 276.48682,\n",
       "         276.36444]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 281.16882, 280.37064,\n",
       "         280.07254],\n",
       "        [      nan,       nan,       nan, ..., 280.88696, 280.38916,\n",
       "         279.8914 ],\n",
       "        [      nan,       nan,       nan, ..., 280.80515, 280.2077 ,\n",
       "         279.81027]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 49.4 49.36 49.32 49.28 ... 45.15 45.11 45.07 45.03\n",
       "  * time     (time) datetime64[ns] 1990-01-01 1990-01-02 ... 1991-12-31\n",
       "  * lon      (lon) float64 -124.8 -124.7 -124.7 -124.6 ... -120.1 -120.1 -120.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 730</li><li><span class='xr-has-index'>lat</span>: 106</li><li><span class='xr-has-index'>lon</span>: 115</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-db0fbd2a-0328-4853-887e-e5d6d2df130e' class='xr-array-in' type='checkbox' ><label for='section-db0fbd2a-0328-4853-887e-e5d6d2df130e' 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 ... 280.30255 280.80515 280.2077 279.81027</span></div><pre class='xr-array-data'>array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 272.9068 , 272.03818,\n",
       "         271.74667],\n",
       "        [      nan,       nan,       nan, ..., 272.58118, 271.9899 ,\n",
       "         271.59863],\n",
       "        [      nan,       nan,       nan, ..., 272.38995, 271.89896,\n",
       "         271.48502]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 272.2721 , 271.3799 ,\n",
       "         271.10745],\n",
       "        [      nan,       nan,       nan, ..., 271.8796 , 271.28778,\n",
       "         270.87622],\n",
       "        [      nan,       nan,       nan, ..., 271.8478 , 271.35623,\n",
       "         270.78433]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 270.06155, 269.17004,\n",
       "         268.7786 ],\n",
       "        [      nan,       nan,       nan, ..., 269.6548 , 269.1635 ,\n",
       "         268.67227],\n",
       "        [      nan,       nan,       nan, ..., 269.63492, 269.057  ,\n",
       "         268.65283]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 277.1879 , 276.33917,\n",
       "         276.03476],\n",
       "        [      nan,       nan,       nan, ..., 276.86758, 276.36328,\n",
       "         275.859  ],\n",
       "        [      nan,       nan,       nan, ..., 276.79156, 276.2874 ,\n",
       "         275.82758]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 277.70404, 277.39084,\n",
       "         276.97433],\n",
       "        [      nan,       nan,       nan, ..., 277.34024, 276.8421 ,\n",
       "         276.762  ],\n",
       "        [      nan,       nan,       nan, ..., 277.26825, 276.48682,\n",
       "         276.36444]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 281.16882, 280.37064,\n",
       "         280.07254],\n",
       "        [      nan,       nan,       nan, ..., 280.88696, 280.38916,\n",
       "         279.8914 ],\n",
       "        [      nan,       nan,       nan, ..., 280.80515, 280.2077 ,\n",
       "         279.81027]]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-d6517db1-9b5a-4653-bedc-74e286dabbce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6517db1-9b5a-4653-bedc-74e286dabbce' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.4 49.36 49.32 ... 45.07 45.03</div><input id='attrs-f0e94deb-8d80-4dc7-b606-d8d0813ed58e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0e94deb-8d80-4dc7-b606-d8d0813ed58e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-640f00dc-6edf-4ea3-a822-5610bbb3c685' class='xr-var-data-in' type='checkbox'><label for='data-640f00dc-6edf-4ea3-a822-5610bbb3c685' 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><pre class='xr-var-data'>array([49.4     , 49.358333, 49.316667, 49.275   , 49.233333, 49.191667,\n",
       "       49.15    , 49.108333, 49.066667, 49.025   , 48.983333, 48.941667,\n",
       "       48.9     , 48.858333, 48.816667, 48.775   , 48.733333, 48.691667,\n",
       "       48.65    , 48.608333, 48.566667, 48.525   , 48.483333, 48.441667,\n",
       "       48.4     , 48.358333, 48.316667, 48.275   , 48.233333, 48.191667,\n",
       "       48.15    , 48.108333, 48.066667, 48.025   , 47.983333, 47.941667,\n",
       "       47.9     , 47.858333, 47.816667, 47.775   , 47.733333, 47.691667,\n",
       "       47.65    , 47.608333, 47.566667, 47.525   , 47.483333, 47.441667,\n",
       "       47.4     , 47.358333, 47.316667, 47.275   , 47.233333, 47.191667,\n",
       "       47.15    , 47.108333, 47.066667, 47.025   , 46.983333, 46.941667,\n",
       "       46.9     , 46.858333, 46.816667, 46.775   , 46.733333, 46.691667,\n",
       "       46.65    , 46.608333, 46.566667, 46.525   , 46.483333, 46.441667,\n",
       "       46.4     , 46.358333, 46.316667, 46.275   , 46.233333, 46.191667,\n",
       "       46.15    , 46.108333, 46.066667, 46.025   , 45.983333, 45.941667,\n",
       "       45.9     , 45.858333, 45.816667, 45.775   , 45.733333, 45.691667,\n",
       "       45.65    , 45.608333, 45.566667, 45.525   , 45.483333, 45.441667,\n",
       "       45.4     , 45.358333, 45.316667, 45.275   , 45.233333, 45.191667,\n",
       "       45.15    , 45.108333, 45.066667, 45.025   ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-01-01 ... 1991-12-31</div><input id='attrs-01b5ab71-4714-405d-8a2c-c5d0c00163e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01b5ab71-4714-405d-8a2c-c5d0c00163e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bafe22b-cd26-4d22-a4e2-a8bf63daf250' class='xr-var-data-in' type='checkbox'><label for='data-4bafe22b-cd26-4d22-a4e2-a8bf63daf250' 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><pre class='xr-var-data'>array([&#x27;1990-01-01T00:00:00.000000000&#x27;, &#x27;1990-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-01-03T00:00:00.000000000&#x27;, ..., &#x27;1991-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-12-30T00:00:00.000000000&#x27;, &#x27;1991-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.8 -124.7 ... -120.1 -120.0</div><input id='attrs-749569fa-3cc4-476b-ba23-fc7297f0cb7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-749569fa-3cc4-476b-ba23-fc7297f0cb7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e45550b-bd0f-4bf1-bd30-72bb27813fdc' class='xr-var-data-in' type='checkbox'><label for='data-2e45550b-bd0f-4bf1-bd30-72bb27813fdc' 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><pre class='xr-var-data'>array([-124.766667, -124.725   , -124.683333, -124.641667, -124.6     ,\n",
       "       -124.558333, -124.516667, -124.475   , -124.433333, -124.391667,\n",
       "       -124.35    , -124.308333, -124.266667, -124.225   , -124.183333,\n",
       "       -124.141667, -124.1     , -124.058333, -124.016667, -123.975   ,\n",
       "       -123.933333, -123.891667, -123.85    , -123.808333, -123.766667,\n",
       "       -123.725   , -123.683333, -123.641667, -123.6     , -123.558333,\n",
       "       -123.516667, -123.475   , -123.433333, -123.391667, -123.35    ,\n",
       "       -123.308333, -123.266667, -123.225   , -123.183333, -123.141667,\n",
       "       -123.1     , -123.058333, -123.016667, -122.975   , -122.933333,\n",
       "       -122.891667, -122.85    , -122.808333, -122.766667, -122.725   ,\n",
       "       -122.683333, -122.641667, -122.6     , -122.558333, -122.516667,\n",
       "       -122.475   , -122.433333, -122.391667, -122.35    , -122.308333,\n",
       "       -122.266667, -122.225   , -122.183333, -122.141667, -122.1     ,\n",
       "       -122.058333, -122.016667, -121.975   , -121.933333, -121.891667,\n",
       "       -121.85    , -121.808333, -121.766667, -121.725   , -121.683333,\n",
       "       -121.641667, -121.6     , -121.558333, -121.516667, -121.475   ,\n",
       "       -121.433333, -121.391667, -121.35    , -121.308333, -121.266667,\n",
       "       -121.225   , -121.183333, -121.141667, -121.1     , -121.058333,\n",
       "       -121.016667, -120.975   , -120.933333, -120.891667, -120.85    ,\n",
       "       -120.808333, -120.766667, -120.725   , -120.683333, -120.641667,\n",
       "       -120.6     , -120.558333, -120.516667, -120.475   , -120.433333,\n",
       "       -120.391667, -120.35    , -120.308333, -120.266667, -120.225   ,\n",
       "       -120.183333, -120.141667, -120.1     , -120.058333, -120.016667])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-d9b2d023-82ab-4d04-9813-54561ef55eef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d9b2d023-82ab-4d04-9813-54561ef55eef' 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 (time: 730, lat: 106, lon: 115)>\n",
       "array([[[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 272.9068 , 272.03818,\n",
       "         271.74667],\n",
       "        [      nan,       nan,       nan, ..., 272.58118, 271.9899 ,\n",
       "         271.59863],\n",
       "        [      nan,       nan,       nan, ..., 272.38995, 271.89896,\n",
       "         271.48502]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 272.2721 , 271.3799 ,\n",
       "         271.10745],\n",
       "        [      nan,       nan,       nan, ..., 271.8796 , 271.28778,\n",
       "         270.87622],\n",
       "        [      nan,       nan,       nan, ..., 271.8478 , 271.35623,\n",
       "         270.78433]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 270.06155, 269.17004,\n",
       "         268.7786 ],\n",
       "        [      nan,       nan,       nan, ..., 269.6548 , 269.1635 ,\n",
       "         268.67227],\n",
       "        [      nan,       nan,       nan, ..., 269.63492, 269.057  ,\n",
       "         268.65283]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 277.1879 , 276.33917,\n",
       "         276.03476],\n",
       "        [      nan,       nan,       nan, ..., 276.86758, 276.36328,\n",
       "         275.859  ],\n",
       "        [      nan,       nan,       nan, ..., 276.79156, 276.2874 ,\n",
       "         275.82758]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 277.70404, 277.39084,\n",
       "         276.97433],\n",
       "        [      nan,       nan,       nan, ..., 277.34024, 276.8421 ,\n",
       "         276.762  ],\n",
       "        [      nan,       nan,       nan, ..., 277.26825, 276.48682,\n",
       "         276.36444]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ..., 281.16882, 280.37064,\n",
       "         280.07254],\n",
       "        [      nan,       nan,       nan, ..., 280.88696, 280.38916,\n",
       "         279.8914 ],\n",
       "        [      nan,       nan,       nan, ..., 280.80515, 280.2077 ,\n",
       "         279.81027]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 49.4 49.36 49.32 49.28 ... 45.15 45.11 45.07 45.03\n",
       "  * time     (time) datetime64[ns] 1990-01-01 1990-01-02 ... 1991-12-31\n",
       "  * lon      (lon) float64 -124.8 -124.7 -124.7 -124.6 ... -120.1 -120.1 -120.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbffcad1be0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = model.predict(holdout_subset).load()\n",
    "display(predicted)\n",
    "predicted.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Spatial Models\n",
    "Spatial models is a second class of downscaling methods that use information from the full study domain to form relationships between observations and ESM data. Scikit-downscale implements these models as as SpatialDownscaler. Beyond providing fit and predict methods that accept Xarray objects, the internal layout of these methods is intentionally unspecified. We are currently working on wrapping a few popular spatial downscaling models such as:\n",
    "\n",
    "- [MACA: Multivariate Adaptive Constructed Analogs](http://www.climatologylab.org/maca.html), Abatzoglou and Brown (2012)\n",
    "- [LOCA: Localized Constructed Analogs](http://loca.ucsd.edu/), Pierce, Cayan, and Thrasher (2014)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Discussion\n",
    "\n",
    "### 3.1 Benchmark Applications\n",
    "Its likely that one of the reasons we haven’t seen strong consensus develop around particularl downscaling methodologies is the abscense of widely available benchamrk applications to test methods against eachother. While Scikit-downscale will not solve this problem on its own, we hope the ability to implemnt downscaling applications within a common framework will enable a more robust benchmarking inititive that previously possible.\n",
    "\n",
    "### 3.2 Call for Participation\n",
    "The Scikit-downscale effort is just getting started. With the recent release of [CMIP6](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6), we expect a surge of interest in downscaled climate data. There are clear opportunities for involvement from climate impacts practicioneers, computer scientists with an interest in machine learning for climate applications, and climate scientists alike. Please reach out if you are interested in participating in any way.\n",
    "\n",
    "## References\n",
    "\n",
    "1. Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131.\n",
    "1. Abatzoglou J.T. and Brown T.J. A comparison of statistical downscaling methods suited for wildfire applications, International Journal of Climatology (2012), 32, 772-780\n",
    "1. Gutmann, E., Pruitt, T., Clark, M. P., Brekke, L., Arnold, J. R., Raff, D. A., and Rasmussen, R. M. ( 2014), An intercomparison of statistical downscaling methods used for water resource assessments in the United States, Water Resour. Res., 50, 7167– 7186, doi:10.1002/2014WR015559.\n",
    "1. Gutmann, E., J. Hamman, M. Clark, T. Eidhammer, A. Wood, J. Arnold, K. Nowak (in prep), Evaluating the effect of statistical downscaling methodological choices in a common framework. To be submitted to JGR-Atomspheres.\n",
    "1. Hamlet, A.F., Salathé, E.P., and Carrasco, P., 2010. Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning studies. A report prepared by the Climate Impact Group for Columbia Basin Climate Change Scenario Project, University of Washington, Seattle, WA.\n",
    "http://www.hydro.washington.edu/2860/products/sites/r7climate/study_report/CBCCSP_chap4_gcm_draft_20100111.pdf\n",
    "1. Lanzante JR, KW Dixon, MJ Nath, CE Whitlock, and D Adams-Smith (2018): Some Pitfalls in Statistical Downscaling of Future Climate. Bulletin of the American Meteorological Society. DOI: 0.1175/BAMS-D-17-0046.1.\n",
    "1. Livneh B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, 9384–9392.\n",
    "1. Pierce, D. W., D. R. Cayan, and B. L. Thrasher, 2014: Statistical downscaling using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, volume 15, page 2558-2585\n",
    "1. Stoner, A., K. Hayhoe, and X. Yang (2012), An asynchronous regional regression model for statistical downscaling of daily climate variables, Int. J. Climatol., 33, 2473–2494, doi:10.1002/joc.3603.\n",
    "1. Wood, A., L. Leung, V. Sridhar, and D. Lettenmaier (2004), Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Clim. Change, 62, 189–216.\n",
    "\n",
    "## License\n",
    "\n",
    "This notebook is licensed under [CC-BY](https://creativecommons.org/licenses/by/4.0/). Scikit-downscale is licensed under [Apache License 2.0](https://github.com/jhamman/scikit-downscale/blob/master/LICENSE)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:notebook] *",
   "language": "python",
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