notebooks/access-analyse_temperature.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4ea1def-a0b9-4130-9343-dd53fc4c77d3",
   "metadata": {},
   "source": [
    "# Accessing data on Object Store from any platform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e738cd-2b6f-474c-b269-30ebbd48f88d",
   "metadata": {},
   "source": [
    "## Objective: The purpose of this notebook is to demonstrate how to access data stored on Object Storage.\n",
    "### This notebook will work on three different platforms (DataLabs, JASMIN Notebook Service and Google Colab). To use this notebook please select the following python environments:\n",
    "- DataLabs: Python 3 (ipykernel)\n",
    "- JASMIN Notebook Service: Python 3 + Jaspy\n",
    "- Google Colab*: Default environment\n",
    "*For running this notebook in Google Colab, please remember to create a copy of the notebook before running it. Then the associated catalogue yaml (catalogue_temperature.yaml) file should be uploaded to the file system, otherwise the notebook will fail."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59778028-179e-4ac9-a3b3-7c4bfa05708b",
   "metadata": {},
   "source": [
    "## Install the required packages\n",
    "### Install the required packages using the following command after selecting the basic python3 envionments listed in the cell above based on the platform you are using. Although we do not set up virtual environment for this notebook, if needed, please read the following links for more details on setting up virtual environments on the different platforms:\n",
    "- DataLabs: https://datalab-docs.datalabs.ceh.ac.uk/tutorials/getting-started-jupyter/create-conda-environment.html\n",
    "- JASMIN Notebook Service: https://help.jasmin.ac.uk/article/5084-creating-a-virtual-environment-in-the-jasmin-notebooks-service\n",
    "- Google Colab: https://colab.research.google.com/drive/1Vg005uKhgt-ZwyQ11-BGceqY0q07qIzF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba44868a-8e67-4489-9253-fdf866a3bbfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "!pip install s3fs zarr intake intake-xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c662e77a-6941-4192-aabf-dc7e93888e08",
   "metadata": {},
   "source": [
    "##  Import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c717597-94d8-4cc2-a43b-d984667b6bde",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.0' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "/opt/conda/lib/python3.9/site-packages/pandas/core/arrays/masked.py:62: UserWarning: Pandas requires version '1.3.4' or newer of 'bottleneck' (version '1.3.2' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import intake\n",
    "import intake_xarray\n",
    "import s3fs\n",
    "import xarray as xr\n",
    "from datetime import datetime\n",
    "import io\n",
    "import json\n",
    "import requests\n",
    "import zipfile\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a62618-b340-47f6-ac90-516c775c1076",
   "metadata": {},
   "source": [
    "## Accessing Station Observations\n",
    "### We will be accessing observed variable of Daily Maximum Temperature Air (TA_MAX) from 2016--2022 for one of the COSMOS station (ALIC1) directly from COSMOS API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f95e0fe-c687-4571-b7ea-7c7ede3bb33b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-written functions for accessing COSMOS data.\n",
    "# Please see https://cosmos-api.ceh.ac.uk/python_examples for code examples\n",
    "# Please see https://cosmos-api.ceh.ac.uk/docs for more details\n",
    "\n",
    "\n",
    "def get_api_response(url, csv=False):\n",
    "    \"\"\"Helper function to send request to API and get the response\n",
    "\n",
    "    :param str url: The URL of the API request\n",
    "    :param bool csv: Whether this is a CSV request. Default False.\n",
    "    :return: API response\n",
    "    \"\"\"\n",
    "    # Send request and read response\n",
    "    print(url)\n",
    "    response = requests.get(url)\n",
    "\n",
    "    if csv:\n",
    "        return response\n",
    "    else:\n",
    "        # Decode from JSON to Python dictionary\n",
    "        return json.loads(response.content)\n",
    "\n",
    "\n",
    "def get_collection_parameter_info(params):\n",
    "    \"\"\"A function for wrangling the collection information into a more visually appealing format!\"\"\"\n",
    "    df = pd.DataFrame.from_dict(params)\n",
    "    df = df.T[[\"label\", \"description\", \"unit\", \"sensorInfo\"]]\n",
    "\n",
    "    df[\"unit_symbol\"] = df[\"unit\"].apply(lambda x: x[\"symbol\"][\"value\"])\n",
    "    df[\"unit_label\"] = df[\"unit\"].apply(lambda x: x[\"label\"])\n",
    "    df[\"sensor_depth\"] = df[\"sensorInfo\"].apply(\n",
    "        lambda x: None if pd.isna(x) else x[\"sensor_depth\"][\"value\"]\n",
    "    )\n",
    "\n",
    "    df = df.drop([\"sensorInfo\", \"unit\"], axis=1)\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "def format_datetime(dt):\n",
    "    return dt.strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n",
    "\n",
    "\n",
    "def read_json_collection_data(json_response):\n",
    "    \"\"\"Wrangle the response JSON from a COSMOS-API data collection request into a more usable format - in this case a Pandas Dataframe\n",
    "\n",
    "    :param dict json_response: The JSON response dictionary returned from a COSMOS-API data collection request\n",
    "    :return: Dataframe of data\n",
    "    :rtype: pd.DataFrame\n",
    "    \"\"\"\n",
    "    # The response is a list of dictionaries, one for each requested site\n",
    "\n",
    "    # You can choose how you want to build your dataframes.  Here, I'm just loading all stations into one big dataframe.\n",
    "    # But you could modify this for your own use cases.  For example you might want to build a dictionary of {site_id: dataframe}\n",
    "    # to keep site data separate, etc.\n",
    "    master_df = pd.DataFrame()\n",
    "\n",
    "    for site_data in resp[\"coverages\"]:\n",
    "        # Read the site ID\n",
    "        site_id = site_data[\"dct:identifier\"]\n",
    "\n",
    "        # Read the time stamps of each data point\n",
    "        time_values = pd.DatetimeIndex(site_data[\"domain\"][\"axes\"][\"t\"][\"values\"])\n",
    "\n",
    "        # Now read the values for each requested parameter at each of the time stamps\n",
    "        param_values = {\n",
    "            param_name: param_data[\"values\"]\n",
    "            for param_name, param_data in site_data[\"ranges\"].items()\n",
    "        }\n",
    "\n",
    "        # And put everything into a dataframe\n",
    "        site_df = pd.DataFrame.from_dict(param_values)\n",
    "        site_df[\"datetime\"] = time_values\n",
    "        site_df[\"site_id\"] = site_id\n",
    "\n",
    "        site_df = site_df.set_index([\"datetime\", \"site_id\"])\n",
    "        master_df = pd.concat([master_df, site_df])\n",
    "\n",
    "    return master_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9429ffa-f17d-46d6-b0c9-ba70ecf7176d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to extract \"ta_max\" parameter for COSMOS station \"ALIC1\" over the period of 2016 -- 2022\n",
    "start_date = format_datetime(datetime(2016, 1, 1))\n",
    "end_date = format_datetime(datetime(2022, 12, 31))\n",
    "query_date_range = f\"{start_date}/{end_date}\"\n",
    "param_name = [\n",
    "    \"ta_max\",\n",
    "]\n",
    "site_nm = \"ALIC1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a9d6405-f4a7-4774-aad8-76a155e4e92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://cosmos-api.ceh.ac.uk/collections/1D/locations\n"
     ]
    },
    {
     "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>site_name</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>altitude</th>\n",
       "      <th>bulk_density</th>\n",
       "      <th>bulk_density_sd</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lattice_water</th>\n",
       "      <th>lattice_water_sd</th>\n",
       "      <th>soil_organic_carbon</th>\n",
       "      <th>soil_organic_carbon_sd</th>\n",
       "      <th>soil_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALIC1</th>\n",
       "      <td>Alice Holt</td>\n",
       "      <td>[51.153551, -0.858232]</td>\n",
       "      <td>2015-03-06T13:30:00Z</td>\n",
       "      <td>2023-11-16T00:00:00Z</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>None</td>\n",
       "      <td>Broadleaf woodland</td>\n",
       "      <td>0.025</td>\n",
       "      <td>None</td>\n",
       "      <td>0.042</td>\n",
       "      <td>None</td>\n",
       "      <td>Mineral soil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        site_name             coordinates            start_date  \\\n",
       "ALIC1  Alice Holt  [51.153551, -0.858232]  2015-03-06T13:30:00Z   \n",
       "\n",
       "                   end_date altitude bulk_density bulk_density_sd  \\\n",
       "ALIC1  2023-11-16T00:00:00Z     80.0         0.84            None   \n",
       "\n",
       "               land_cover lattice_water lattice_water_sd soil_organic_carbon  \\\n",
       "ALIC1  Broadleaf woodland         0.025             None               0.042   \n",
       "\n",
       "      soil_organic_carbon_sd     soil_type  \n",
       "ALIC1                   None  Mineral soil  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First we get the metadata for the COSMOS station\n",
    "BASE_URL = \"https://cosmos-api.ceh.ac.uk\"\n",
    "site_info_url = f\"{BASE_URL}/collections/1D/locations\"\n",
    "site_info_response = get_api_response(site_info_url)\n",
    "\n",
    "site_info = {}\n",
    "for site in site_info_response[\"features\"]:\n",
    "    site_id = site[\"id\"]\n",
    "    site_name = site[\"properties\"][\"label\"]\n",
    "    coordinates = site[\"geometry\"][\"coordinates\"]\n",
    "    date_range = site[\"properties\"][\"datetime\"]\n",
    "    start_date, end_date = date_range.split(\"/\")\n",
    "\n",
    "    other_info = site[\"properties\"][\"siteInfo\"]\n",
    "    other_info = {key: d[\"value\"] for key, d in other_info.items()}\n",
    "\n",
    "    site_info[site_id] = {\n",
    "        \"site_name\": site_name,\n",
    "        \"coordinates\": coordinates,\n",
    "        \"start_date\": start_date,\n",
    "        \"end_date\": end_date,\n",
    "    } | other_info\n",
    "\n",
    "site_info_df = pd.DataFrame.from_dict(site_info).T\n",
    "s_df = site_info_df[site_info_df.index == site_nm]\n",
    "s_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35ca582f-63a5-4117-9032-c6cd19dccdda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COMOS Site ALIC1 Latitude: 51.153551 Longitude: -0.858232\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1224/733139797.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  site_latitude = s_df[\"coordinates\"][0][0]\n",
      "/tmp/ipykernel_1224/733139797.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  site_longitude = s_df[\"coordinates\"][0][1]\n"
     ]
    }
   ],
   "source": [
    "# Extracting the COSMOS station latitude and longitude from the whole metadata list\n",
    "# COSMOS station latitude and longitude is required to calculate the nearest grid point on the CHESS grid to extract corresponding model data\n",
    "site_latitude = s_df[\"coordinates\"][0][0]\n",
    "site_longitude = s_df[\"coordinates\"][0][1]\n",
    "print(\n",
    "    \"COMOS Site \"\n",
    "    + site_nm\n",
    "    + \" Latitude: \"\n",
    "    + str(site_latitude)\n",
    "    + \" Longitude: \"\n",
    "    + str(site_longitude)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9730e8c6-a4e1-41ea-9d0e-7203a1d2a8ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://cosmos-api.ceh.ac.uk/collections/1D/locations/ALIC1?datetime=2016-01-01T00:00:00Z/2022-12-31T00:00:00Z&parameter-name=ta_max\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>site_id</th>\n",
       "      <th>ta_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>2022-12-27 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>2022-12-28 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>2022-12-29 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>2022-12-30 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>12.6</td>\n",
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       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>2022-12-31 00:00:00+00:00</td>\n",
       "      <td>ALIC1</td>\n",
       "      <td>13.4</td>\n",
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      "text/plain": [
       "                      datetime site_id  ta_max\n",
       "0    2016-01-01 00:00:00+00:00   ALIC1     8.6\n",
       "1    2016-01-02 00:00:00+00:00   ALIC1    10.7\n",
       "2    2016-01-03 00:00:00+00:00   ALIC1     8.9\n",
       "3    2016-01-04 00:00:00+00:00   ALIC1     9.7\n",
       "4    2016-01-05 00:00:00+00:00   ALIC1     8.2\n",
       "...                        ...     ...     ...\n",
       "2552 2022-12-27 00:00:00+00:00   ALIC1    10.3\n",
       "2553 2022-12-28 00:00:00+00:00   ALIC1    11.4\n",
       "2554 2022-12-29 00:00:00+00:00   ALIC1     9.7\n",
       "2555 2022-12-30 00:00:00+00:00   ALIC1    12.6\n",
       "2556 2022-12-31 00:00:00+00:00   ALIC1    13.4\n",
       "\n",
       "[2557 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2557, 3)\n"
     ]
    }
   ],
   "source": [
    "# Extracting COSMOS TA_MAX data for the station over the required period into a pandas dataframe\n",
    "query_url = f'{BASE_URL}/collections/1D/locations/{site_nm}?datetime={query_date_range}&parameter-name={\",\".join(param_name)}'\n",
    "resp = get_api_response(query_url)\n",
    "df = read_json_collection_data(resp)\n",
    "df = df.reset_index()\n",
    "display(df)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62ab39e1-e9ff-4c84-a1a8-615852f3180e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ta_max</th>\n",
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       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>7.479724</td>\n",
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       "      <td>8.883418</td>\n",
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       "      <td>10.689401</td>\n",
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       "      <td>17.071429</td>\n",
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      ],
      "text/plain": [
       "             ta_max\n",
       "datetime           \n",
       "1          7.479724\n",
       "2          8.883418\n",
       "3         10.689401\n",
       "4         13.544762\n",
       "5         17.071429\n",
       "6         19.841905\n",
       "7         21.749462\n",
       "8         21.487097\n",
       "9         18.777143\n",
       "10        14.853456\n",
       "11        10.685238\n",
       "12         8.682488"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating monthly climatological values of TA_MAX for the station over 2016--2022\n",
    "df_site = (\n",
    "    df.groupby(pd.PeriodIndex(df[\"datetime\"], freq=\"M\"))[\"ta_max\"].mean().reset_index()\n",
    ")\n",
    "df_site[\"datetime\"] = df_site.datetime.dt.to_timestamp()\n",
    "df_site = df_site.groupby(df_site[\"datetime\"].dt.month).mean(\"ta_max\")\n",
    "df_site"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e447fa5-2f4c-4bfb-a776-b3eb6d86b47f",
   "metadata": {},
   "source": [
    "## Accessing Model Data from JASMIN Object Store\n",
    "### In this notebook we will be accessing CHESS-SCAPE data that has been stored in the JASMIN Object Store tenancy and made open READ access to all. For more information about CHESS-SCAPE data, please see https://catalogue.ceda.ac.uk/uuid/8194b416cbee482b89e0dfbe17c5786c. We will be extracting the Daily Maximum Surface Air Temperature (TASMAX) at the grid point nearest to the COSMOS Station ALIC1 for the period 2016--2022 for all ensemble members available. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52a28b4-0beb-458a-adc1-9376d3560cd5",
   "metadata": {},
   "source": [
    "### Exploring the bucket with s3fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dfa0f727-e1ba-4da8-ab47-92e45226f279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ens01-year100kmchunk/hurs_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/huss_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/pr_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/psurf_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/rlds_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/rsds_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/sfcWind_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/tmax_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/tmean_01_year100km.zarr',\n",
       " 'ens01-year100kmchunk/tmin_01_year100km.zarr']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# s3fs is a python package that allows you to not only read the data but also explore the tenancy (chess-scape-o)\n",
    "# Here we will be using s3fs to list the bucket and not read the data, we read the data using intake package shown below\n",
    "# For more information please see: https://pypi.org/project/s3fs/\n",
    "s3 = s3fs.S3FileSystem(anon=True, client_kwargs={'endpoint_url': \"https://chess-scape-o.s3-ext.jc.rl.ac.uk\"})\n",
    "s3.ls('s3://ens01-year100kmchunk/')\n",
    "# In the output you see that within in the chess-scape-o tenancy, a bucket called ens01-year100kmchunk\n",
    "# has 10 different zarr files for different 10 different variables. This is for a single chunk type tested."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669c664d-4100-440a-8e76-445921842a5a",
   "metadata": {},
   "source": [
    "### Setup Intake Catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e7b1140-74cc-45cb-999b-d1d89f42c8db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/yaml": "catalogue:\n  args:\n    path: ./catalogue.yaml\n  description: ''\n  driver: intake.catalog.local.YAMLFileCatalog\n  metadata: {}\n",
      "text/plain": [
       "catalogue:\n",
       "  args:\n",
       "    path: ./catalogue.yaml\n",
       "  description: ''\n",
       "  driver: intake.catalog.local.YAMLFileCatalog\n",
       "  metadata: {}\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "catalogue"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The intake catalogue defines the endpoints that should be used to access specific datasets on the object storage.\n",
    "# For any dataset that is not open READ access, you would also need to provide required credentials\n",
    "catalogue = intake.open_catalog(\"./catalogue.yaml\")\n",
    "catalogue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b59b9bd-0739-4200-bfc3-d4f6f4e83684",
   "metadata": {},
   "source": [
    "### Accessing data for the ensemble members and the associated metadata\n",
    "#### We have not activated any Dask Cluster for this notebook. This notebook will run without any Dask Cluster activated. However, if a Dask Cluster is available on the platform you are using, you may initalise a dask client and the rest of the notebook will run as the same. For using Dask Client on the different platforms, please see the links below:\n",
    "- DataLabs: https://datalab-docs.datalabs.ceh.ac.uk/tutorials/datalabs-dask-cluster/index.html\n",
    "- JASMIN Notebook Service: https://github.com/cedadev/jasmin-daskgateway\n",
    "- Google Colab: https://saturncloud.io/docs/using-saturn-cloud/external-connect/colab_external_connect/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd3612d8-058d-44b6-a49d-7327409d2f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "/opt/conda/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.1\n",
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       "  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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (y: 1057, x: 656, time: 36000)\n",
       "Coordinates:\n",
       "  * time     (time) object 1980-12-01 12:00:00 ... 2080-11-30 12:00:00\n",
       "  * x        (x) float32 500.0 1.5e+03 2.5e+03 ... 6.535e+05 6.545e+05 6.555e+05\n",
       "  * y        (y) float32 500.0 1.5e+03 2.5e+03 ... 1.054e+06 1.056e+06 1.056e+06\n",
       "Data variables:\n",
       "    lat      (y, x) float32 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
       "    lon      (y, x) float32 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
       "    tasmax   (time, y, x) float32 dask.array&lt;chunksize=(360, 100, 100), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.8 (https://mpimet.mpg.de...\n",
       "    CDO:          Climate Data Operators version 1.9.8 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    NCO:          4.7.3\n",
       "    contact:      emrobi@ceh.ac.uk\n",
       "    history:      Fri Mar 19 15:18:41 2021: cdo --sortname -add /gws/nopw/j04...\n",
       "    institution:  CEH Wallingford - NERC\n",
       "    project:      UK-SCAPE: SPEED</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4f5dc801-3f83-4a42-811f-455377136b21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4f5dc801-3f83-4a42-811f-455377136b21' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1057</li><li><span class='xr-has-index'>x</span>: 656</li><li><span class='xr-has-index'>time</span>: 36000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f0965b0-08a3-46be-9b72-040d870e1c9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f0965b0-08a3-46be-9b72-040d870e1c9e' 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'>object</div><div class='xr-var-preview xr-preview'>1980-12-01 12:00:00 ... 2080-11-...</div><input id='attrs-ac1dbe4d-cebf-4c6e-a012-2645bf9fd771' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac1dbe4d-cebf-4c6e-a012-2645bf9fd771' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34406692-10e5-4079-bbcc-854725fb5c16' class='xr-var-data-in' type='checkbox'><label for='data-34406692-10e5-4079-bbcc-854725fb5c16' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(1980, 12, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1980, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1980, 12, 3, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.Datetime360Day(2080, 11, 28, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2080, 11, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2080, 11, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 6.555e+05</div><input id='attrs-48f72b56-7a44-45d7-b378-9a85ad8681d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48f72b56-7a44-45d7-b378-9a85ad8681d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1f35c44-b6b8-46a0-971e-a8f0933d2e90' class='xr-var-data-in' type='checkbox'><label for='data-d1f35c44-b6b8-46a0-971e-a8f0933d2e90' 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>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000e+02, 1.500e+03, 2.500e+03, ..., 6.535e+05, 6.545e+05, 6.555e+05],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.056e+06</div><input id='attrs-99b188df-4969-4646-a92b-f51f43c94310' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99b188df-4969-4646-a92b-f51f43c94310' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8911221f-5847-4152-830d-6fd2d127656c' class='xr-var-data-in' type='checkbox'><label for='data-8911221f-5847-4152-830d-6fd2d127656c' 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>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e+02, 1.5000e+03, 2.5000e+03, ..., 1.0545e+06, 1.0555e+06,\n",
       "       1.0565e+06], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d80d04e-2cec-4321-a328-177266239197' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3d80d04e-2cec-4321-a328-177266239197' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-5fd89160-5742-4b29-985a-62f014c0ec99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fd89160-5742-4b29-985a-62f014c0ec99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0bdf485-b9b8-44ea-a8a0-5f6f001ead1d' class='xr-var-data-in' type='checkbox'><label for='data-f0bdf485-b9b8-44ea-a8a0-5f6f001ead1d' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.65 MiB </td> <td> 39.06 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1057, 656) </td> <td> (100, 100) </td></tr>\n",
       "    <tr><th> Count </th><td> 78 Tasks </td><td> 77 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-776208e6-329a-4be3-b426-e038de40a93a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-776208e6-329a-4be3-b426-e038de40a93a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bc0f8de-a286-473a-b67e-f0f259337539' class='xr-var-data-in' type='checkbox'><label for='data-9bc0f8de-a286-473a-b67e-f0f259337539' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "             1980-12-04 12:00:00, 1980-12-05 12:00:00, 1980-12-06 12:00:00,\n",
       "             1980-12-07 12:00:00, 1980-12-08 12:00:00, 1980-12-09 12:00:00,\n",
       "             1980-12-10 12:00:00,\n",
       "             ...\n",
       "             2080-11-21 12:00:00, 2080-11-22 12:00:00, 2080-11-23 12:00:00,\n",
       "             2080-11-24 12:00:00, 2080-11-25 12:00:00, 2080-11-26 12:00:00,\n",
       "             2080-11-27 12:00:00, 2080-11-28 12:00:00, 2080-11-29 12:00:00,\n",
       "             2080-11-30 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=36000, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84b50e04-2dab-4bcc-8e40-ae13e9c721a0' class='xr-index-data-in' type='checkbox'/><label for='index-84b50e04-2dab-4bcc-8e40-ae13e9c721a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   500.0,   1500.0,   2500.0,   3500.0,   4500.0,   5500.0,   6500.0,\n",
       "         7500.0,   8500.0,   9500.0,\n",
       "       ...\n",
       "       646500.0, 647500.0, 648500.0, 649500.0, 650500.0, 651500.0, 652500.0,\n",
       "       653500.0, 654500.0, 655500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;, length=656))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac1d4ce5-93db-4004-994b-1704379a3b8c' class='xr-index-data-in' type='checkbox'/><label for='index-ac1d4ce5-93db-4004-994b-1704379a3b8c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    500.0,    1500.0,    2500.0,    3500.0,    4500.0,    5500.0,\n",
       "          6500.0,    7500.0,    8500.0,    9500.0,\n",
       "       ...\n",
       "       1047500.0, 1048500.0, 1049500.0, 1050500.0, 1051500.0, 1052500.0,\n",
       "       1053500.0, 1054500.0, 1055500.0, 1056500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;, length=1057))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aeb4114e-0505-4e5c-af51-ee299f274802' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aeb4114e-0505-4e5c-af51-ee299f274802' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>NCO :</span></dt><dd>4.7.3</dd><dt><span>contact :</span></dt><dd>emrobi@ceh.ac.uk</dd><dt><span>history :</span></dt><dd>Fri Mar 19 15:18:41 2021: cdo --sortname -add /gws/nopw/j04/hydro_jules/data/uk/driving_data/ukcp18_1km/rcp85/01/daily/tasmax/ukcp18_rcp85_land-rcm_uk_1km_01_v20190731_tasmax_198012.nc /work/scratch-nopw/emrobi/UKCP18/ukcp18_1km/tmp/delta_85_01_198012.nc /work/scratch-nopw/emrobi/UKCP18/ukcp18_1km/rcp85_bias_corrected/01/daily/tasmax/ukcp18_rcp85_land-rcm_uk_1km_01_v20190731_tasmax_bias_corrected_198012.nc\n",
       "Thu Feb 27 20:18:21 2020: ncatted -a _FillValue,tasmax,o,f,-1e20 /work/scratch/emrobi/UKCP18/land-rcm/uk/1km/rcp85/01/v20190731/ukcp18_rcp85_land-rcm_uk_1km_01_v20190731_tasmax_198012.nc</dd><dt><span>institution :</span></dt><dd>CEH Wallingford - NERC</dd><dt><span>project :</span></dt><dd>UK-SCAPE: SPEED</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (y: 1057, x: 656, time: 36000)\n",
       "Coordinates:\n",
       "  * time     (time) object 1980-12-01 12:00:00 ... 2080-11-30 12:00:00\n",
       "  * x        (x) float32 500.0 1.5e+03 2.5e+03 ... 6.535e+05 6.545e+05 6.555e+05\n",
       "  * y        (y) float32 500.0 1.5e+03 2.5e+03 ... 1.054e+06 1.056e+06 1.056e+06\n",
       "Data variables:\n",
       "    lat      (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "    lon      (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "    tasmax   (time, y, x) float32 dask.array<chunksize=(360, 100, 100), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.8 (https://mpimet.mpg.de...\n",
       "    CDO:          Climate Data Operators version 1.9.8 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    NCO:          4.7.3\n",
       "    contact:      emrobi@ceh.ac.uk\n",
       "    history:      Fri Mar 19 15:18:41 2021: cdo --sortname -add /gws/nopw/j04...\n",
       "    institution:  CEH Wallingford - NERC\n",
       "    project:      UK-SCAPE: SPEED"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are accessing TASMAX for the Ensemble member #01 from the catalogue\n",
    "# We are using the mid-sized chunk type for the dataset\n",
    "# For more information about different chunk sizes and their performance, please see the GitHub README.\n",
    "chess_data_01 = catalogue.e01_tmax_year100km.to_dask()\n",
    "chess_data_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cc750e7-4d4f-4bc6-a433-05f490751b0d",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (y: 1057, x: 656, time: 36000)\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
       "    lon      (y, x) float32 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
       "  * time     (time) object 1980-12-01 12:00:00 ... 2080-11-30 12:00:00\n",
       "  * x        (x) float32 500.0 1.5e+03 2.5e+03 ... 6.535e+05 6.545e+05 6.555e+05\n",
       "  * y        (y) float32 500.0 1.5e+03 2.5e+03 ... 1.054e+06 1.056e+06 1.056e+06\n",
       "Data variables:\n",
       "    tasmax   (time, y, x) float32 dask.array&lt;chunksize=(360, 100, 100), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.8 (https://mpimet.mpg.de...\n",
       "    CDO:          Climate Data Operators version 1.9.8 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    NCO:          4.7.3\n",
       "    contact:      emrobi@ceh.ac.uk\n",
       "    history:      Fri Mar 19 15:18:41 2021: cdo --sortname -add /gws/nopw/j04...\n",
       "    institution:  CEH Wallingford - NERC\n",
       "    project:      UK-SCAPE: SPEED</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6891bfb1-1374-435e-93ea-2e74b4a1654c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6891bfb1-1374-435e-93ea-2e74b4a1654c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1057</li><li><span class='xr-has-index'>x</span>: 656</li><li><span class='xr-has-index'>time</span>: 36000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ae932909-17a3-481e-90f0-4811d2df02db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae932909-17a3-481e-90f0-4811d2df02db' class='xr-section-summary' >Coordinates: <span>(5)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-a2be9f5e-22a7-4050-8400-98ebe2629aba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2be9f5e-22a7-4050-8400-98ebe2629aba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8812173-75d0-4850-a641-700924c91ad9' class='xr-var-data-in' type='checkbox'><label for='data-b8812173-75d0-4850-a641-700924c91ad9' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-48734194-ead1-4f18-a104-a7850e66b5e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48734194-ead1-4f18-a104-a7850e66b5e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a08169-031d-49ae-815b-06d79a0480da' class='xr-var-data-in' type='checkbox'><label for='data-d9a08169-031d-49ae-815b-06d79a0480da' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <!-- Text -->\n",
       "  <text x=\"37.237465\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >656</text>\n",
       "  <text x=\"94.474929\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,94.474929,60.000000)\">1057</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-12-01 12:00:00 ... 2080-11-...</div><input id='attrs-58de9f3d-507e-4187-8b25-452914472f52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58de9f3d-507e-4187-8b25-452914472f52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e830da3-c271-4232-a25c-4c2621715ebb' class='xr-var-data-in' type='checkbox'><label for='data-8e830da3-c271-4232-a25c-4c2621715ebb' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(1980, 12, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1980, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1980, 12, 3, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.Datetime360Day(2080, 11, 28, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2080, 11, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2080, 11, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 6.555e+05</div><input id='attrs-9261fc3e-a22a-485a-b467-227858978dd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9261fc3e-a22a-485a-b467-227858978dd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dea68c7-faf0-4dcb-a707-6605d316de82' class='xr-var-data-in' type='checkbox'><label for='data-7dea68c7-faf0-4dcb-a707-6605d316de82' 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>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000e+02, 1.500e+03, 2.500e+03, ..., 6.535e+05, 6.545e+05, 6.555e+05],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.056e+06</div><input id='attrs-5a9f3dbe-89a1-47be-8fea-d9bece158348' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a9f3dbe-89a1-47be-8fea-d9bece158348' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d56fce7-8c10-41e2-b0c2-6b301284e25d' class='xr-var-data-in' type='checkbox'><label for='data-6d56fce7-8c10-41e2-b0c2-6b301284e25d' 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>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e+02, 1.5000e+03, 2.5000e+03, ..., 1.0545e+06, 1.0555e+06,\n",
       "       1.0565e+06], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-212f740e-5231-4f04-9651-6cafbd485a7b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-212f740e-5231-4f04-9651-6cafbd485a7b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-fa4be390-c50e-4bc2-a928-88204bdd3e0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa4be390-c50e-4bc2-a928-88204bdd3e0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a93e30b-43d8-4522-bc4c-101c554f9a26' class='xr-var-data-in' type='checkbox'><label for='data-8a93e30b-43d8-4522-bc4c-101c554f9a26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Maximum air temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 92.99 GiB </td> <td> 13.73 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (36000, 1057, 656) </td> <td> (360, 100, 100) </td></tr>\n",
       "    <tr><th> Count </th><td> 7701 Tasks </td><td> 7700 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9a9831e-7eef-4eb8-8832-a40ffe26dc94' class='xr-section-summary-in' type='checkbox'  ><label for='section-a9a9831e-7eef-4eb8-8832-a40ffe26dc94' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4a222254-5752-4942-ac0a-3e09d448e349' class='xr-index-data-in' type='checkbox'/><label for='index-4a222254-5752-4942-ac0a-3e09d448e349' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1980-12-01 12:00:00, 1980-12-02 12:00:00, 1980-12-03 12:00:00,\n",
       "             1980-12-04 12:00:00, 1980-12-05 12:00:00, 1980-12-06 12:00:00,\n",
       "             1980-12-07 12:00:00, 1980-12-08 12:00:00, 1980-12-09 12:00:00,\n",
       "             1980-12-10 12:00:00,\n",
       "             ...\n",
       "             2080-11-21 12:00:00, 2080-11-22 12:00:00, 2080-11-23 12:00:00,\n",
       "             2080-11-24 12:00:00, 2080-11-25 12:00:00, 2080-11-26 12:00:00,\n",
       "             2080-11-27 12:00:00, 2080-11-28 12:00:00, 2080-11-29 12:00:00,\n",
       "             2080-11-30 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=36000, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37bfe568-49d9-4229-ae44-cee002b08cdc' class='xr-index-data-in' type='checkbox'/><label for='index-37bfe568-49d9-4229-ae44-cee002b08cdc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   500.0,   1500.0,   2500.0,   3500.0,   4500.0,   5500.0,   6500.0,\n",
       "         7500.0,   8500.0,   9500.0,\n",
       "       ...\n",
       "       646500.0, 647500.0, 648500.0, 649500.0, 650500.0, 651500.0, 652500.0,\n",
       "       653500.0, 654500.0, 655500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;, length=656))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b09b53ca-2cd1-43a6-915d-daf31a3c884d' class='xr-index-data-in' type='checkbox'/><label for='index-b09b53ca-2cd1-43a6-915d-daf31a3c884d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    500.0,    1500.0,    2500.0,    3500.0,    4500.0,    5500.0,\n",
       "          6500.0,    7500.0,    8500.0,    9500.0,\n",
       "       ...\n",
       "       1047500.0, 1048500.0, 1049500.0, 1050500.0, 1051500.0, 1052500.0,\n",
       "       1053500.0, 1054500.0, 1055500.0, 1056500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;, length=1057))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e5db237-80fb-44e1-8ce8-791f3601b37d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e5db237-80fb-44e1-8ce8-791f3601b37d' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>NCO :</span></dt><dd>4.7.3</dd><dt><span>contact :</span></dt><dd>emrobi@ceh.ac.uk</dd><dt><span>history :</span></dt><dd>Fri Mar 19 15:18:41 2021: cdo --sortname -add /gws/nopw/j04/hydro_jules/data/uk/driving_data/ukcp18_1km/rcp85/01/daily/tasmax/ukcp18_rcp85_land-rcm_uk_1km_01_v20190731_tasmax_198012.nc /work/scratch-nopw/emrobi/UKCP18/ukcp18_1km/tmp/delta_85_01_198012.nc /work/scratch-nopw/emrobi/UKCP18/ukcp18_1km/rcp85_bias_corrected/01/daily/tasmax/ukcp18_rcp85_land-rcm_uk_1km_01_v20190731_tasmax_bias_corrected_198012.nc\n",
       "Thu Feb 27 20:18:21 2020: ncatted -a _FillValue,tasmax,o,f,-1e20 /work/scratch/emrobi/UKCP18/land-rcm/uk/1km/rcp85/01/v20190731/ukcp18_rcp85_land-rcm_uk_1km_01_v20190731_tasmax_198012.nc</dd><dt><span>institution :</span></dt><dd>CEH Wallingford - NERC</dd><dt><span>project :</span></dt><dd>UK-SCAPE: SPEED</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (y: 1057, x: 656, time: 36000)\n",
       "Coordinates:\n",
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       "    lon      (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "  * time     (time) object 1980-12-01 12:00:00 ... 2080-11-30 12:00:00\n",
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       "  * y        (y) float32 500.0 1.5e+03 2.5e+03 ... 1.054e+06 1.056e+06 1.056e+06\n",
       "Data variables:\n",
       "    tasmax   (time, y, x) float32 dask.array<chunksize=(360, 100, 100), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.8 (https://mpimet.mpg.de...\n",
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       "    contact:      emrobi@ceh.ac.uk\n",
       "    history:      Fri Mar 19 15:18:41 2021: cdo --sortname -add /gws/nopw/j04...\n",
       "    institution:  CEH Wallingford - NERC\n",
       "    project:      UK-SCAPE: SPEED"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHESS-SCAPE is on the British National Grid with Easting and Northing Coordinates.\n",
    "# We also set the latitude and longitude as coordinates\n",
    "chess_data_01 = chess_data_01.set_coords((\"lat\", \"lon\"))\n",
    "chess_data_01"
   ]
  },
  {
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   "execution_count": 13,
   "id": "ccca7cf2-e13d-4a52-af53-2f4d5b01b0ac",
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   "outputs": [
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       "    long_name:      Maximum air temperature\n",
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       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-11835e1a-6abc-4aa3-b717-f19218b1cc9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11835e1a-6abc-4aa3-b717-f19218b1cc9d' class='xr-section-summary' >Coordinates: <span>(5)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-5d00eb82-8f6f-43f3-b7e2-77a4df9393c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d00eb82-8f6f-43f3-b7e2-77a4df9393c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c89fe14-ce22-4405-a626-7696ad08d6a6' class='xr-var-data-in' type='checkbox'><label for='data-8c89fe14-ce22-4405-a626-7696ad08d6a6' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-3e2673e9-a380-4719-afc0-87acf233fbd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e2673e9-a380-4719-afc0-87acf233fbd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-906b8c27-09f8-40c7-a555-9581e4f52076' class='xr-var-data-in' type='checkbox'><label for='data-906b8c27-09f8-40c7-a555-9581e4f52076' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "  <tbody>\n",
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       "    <tr><th> Shape </th><td> (1057, 656) </td> <td> (100, 100) </td></tr>\n",
       "    <tr><th> Count </th><td> 78 Tasks </td><td> 77 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2016-01-01 12:00:00 ... 2022-12-...</div><input id='attrs-f302f50f-9c4f-41bc-bf0b-e0efb68a64be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f302f50f-9c4f-41bc-bf0b-e0efb68a64be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00dda694-6f1c-4737-87bb-1b690022adfc' class='xr-var-data-in' type='checkbox'><label for='data-00dda694-6f1c-4737-87bb-1b690022adfc' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2016, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2016, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2016, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.Datetime360Day(2022, 12, 28, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2022, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2022, 12, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 6.555e+05</div><input id='attrs-d08f856a-d1c8-446e-9889-f5676a32d308' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d08f856a-d1c8-446e-9889-f5676a32d308' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4345b55c-27db-4689-8e53-83f2a1724c0f' class='xr-var-data-in' type='checkbox'><label for='data-4345b55c-27db-4689-8e53-83f2a1724c0f' 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>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000e+02, 1.500e+03, 2.500e+03, ..., 6.535e+05, 6.545e+05, 6.555e+05],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.056e+06</div><input id='attrs-b543ec62-ec2d-4b5f-a06e-f69ff6eed7ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b543ec62-ec2d-4b5f-a06e-f69ff6eed7ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25fa0663-3db1-421b-8d5b-87ee54957675' class='xr-var-data-in' type='checkbox'><label for='data-25fa0663-3db1-421b-8d5b-87ee54957675' 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>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e+02, 1.5000e+03, 2.5000e+03, ..., 1.0545e+06, 1.0555e+06,\n",
       "       1.0565e+06], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5a1c438-6b5a-405c-ac04-75507182443a' class='xr-section-summary-in' type='checkbox'  ><label for='section-b5a1c438-6b5a-405c-ac04-75507182443a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-40c88526-1042-4c7a-94f3-1cb26267cdb6' class='xr-index-data-in' type='checkbox'/><label for='index-40c88526-1042-4c7a-94f3-1cb26267cdb6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2016-01-01 12:00:00, 2016-01-02 12:00:00, 2016-01-03 12:00:00,\n",
       "             2016-01-04 12:00:00, 2016-01-05 12:00:00, 2016-01-06 12:00:00,\n",
       "             2016-01-07 12:00:00, 2016-01-08 12:00:00, 2016-01-09 12:00:00,\n",
       "             2016-01-10 12:00:00,\n",
       "             ...\n",
       "             2022-12-21 12:00:00, 2022-12-22 12:00:00, 2022-12-23 12:00:00,\n",
       "             2022-12-24 12:00:00, 2022-12-25 12:00:00, 2022-12-26 12:00:00,\n",
       "             2022-12-27 12:00:00, 2022-12-28 12:00:00, 2022-12-29 12:00:00,\n",
       "             2022-12-30 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=2520, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37a46b04-097e-482c-9a70-873bff3ef40d' class='xr-index-data-in' type='checkbox'/><label for='index-37a46b04-097e-482c-9a70-873bff3ef40d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   500.0,   1500.0,   2500.0,   3500.0,   4500.0,   5500.0,   6500.0,\n",
       "         7500.0,   8500.0,   9500.0,\n",
       "       ...\n",
       "       646500.0, 647500.0, 648500.0, 649500.0, 650500.0, 651500.0, 652500.0,\n",
       "       653500.0, 654500.0, 655500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;, length=656))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e71908ac-e284-430c-8397-5e8bea30c1d3' class='xr-index-data-in' type='checkbox'/><label for='index-e71908ac-e284-430c-8397-5e8bea30c1d3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    500.0,    1500.0,    2500.0,    3500.0,    4500.0,    5500.0,\n",
       "          6500.0,    7500.0,    8500.0,    9500.0,\n",
       "       ...\n",
       "       1047500.0, 1048500.0, 1049500.0, 1050500.0, 1051500.0, 1052500.0,\n",
       "       1053500.0, 1054500.0, 1055500.0, 1056500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;, length=1057))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c40859b6-fb37-40a2-bd66-5cbc8faa76fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c40859b6-fb37-40a2-bd66-5cbc8faa76fb' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Maximum air temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tasmax' (time: 2520, y: 1057, x: 656)>\n",
       "dask.array<getitem, shape=(2520, 1057, 656), dtype=float32, chunksize=(360, 100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "    lon      (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "  * time     (time) object 2016-01-01 12:00:00 ... 2022-12-30 12:00:00\n",
       "  * x        (x) float32 500.0 1.5e+03 2.5e+03 ... 6.535e+05 6.545e+05 6.555e+05\n",
       "  * y        (y) float32 500.0 1.5e+03 2.5e+03 ... 1.054e+06 1.056e+06 1.056e+06\n",
       "Attributes:\n",
       "    long_name:      Maximum air temperature\n",
       "    standard_name:  air_temperature\n",
       "    units:          K"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing for the time period 2016--2022\n",
    "chess_data_01 = chess_data_01[\"tasmax\"].sel(time=slice(\"2016-01-01\", \"2022-12-30\"))\n",
    "chess_data_01"
   ]
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   "cell_type": "code",
   "execution_count": 14,
   "id": "2e212822-7feb-4442-8707-802119fb3cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting data for the other ensemble members\n",
    "# Ensemble member #04\n",
    "chess_data_04 = catalogue.e04_tmax_year100km.to_dask()\n",
    "chess_data_04 = chess_data_04.set_coords((\"lat\", \"lon\"))\n",
    "chess_data_04 = chess_data_04[\"tasmax\"].sel(time=slice(\"2016-01-01\", \"2022-12-30\"))\n",
    "\n",
    "# Ensemble member #06\n",
    "chess_data_06 = catalogue.e06_tmax_year100km.to_dask()\n",
    "chess_data_06 = chess_data_06.set_coords((\"lat\", \"lon\"))\n",
    "chess_data_06 = chess_data_06[\"tasmax\"].sel(time=slice(\"2016-01-01\", \"2022-12-30\"))\n",
    "\n",
    "# Ensemble member #15\n",
    "chess_data_15 = catalogue.e15_tmax_year100km.to_dask()\n",
    "chess_data_15 = chess_data_15.set_coords((\"lat\", \"lon\"))\n",
    "chess_data_15 = chess_data_15[\"tasmax\"].sel(time=slice(\"2016-01-01\", \"2022-12-30\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7899fb0-968f-4874-a300-e6406ba67da0",
   "metadata": {},
   "source": [
    "### Deriving the Observed Station nearest grid point on the Gridded Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "164cdd80-3ff7-4b97-8993-ca1a30b9016e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to derive the data for the nearest grid point to the station lat lon\n",
    "def find_chess_tile(lat, lon, latlon_ref):\n",
    "    \"\"\"\n",
    "    Created by Doran Khamis (dorkha@ceh.ac.uk)\n",
    "    Function to calculate the nearest grid point\n",
    "    of a given lat lon value within a gridded dataset\n",
    "    The input data is the latitude, longitude of the station\n",
    "    and the grid reference (latlon_ref) of the gridded dataset\n",
    "    The function returns the y and x index for the gridded dataset\n",
    "    which can be used to derive the nearest grid point\n",
    "    This function assumes equal length lat/lon vectors in latlon_ref\n",
    "    \"\"\"\n",
    "    dist_diff = np.sqrt(\n",
    "        np.square(latlon_ref.lat.values - lat) + np.square(latlon_ref.lon.values - lon)\n",
    "    )\n",
    "    chesstile_yx = np.where(dist_diff == np.min(dist_diff))\n",
    "    return chesstile_yx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57e6fa80-4ebd-4ef2-85ff-32e650d0beca",
   "metadata": {},
   "outputs": [
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       "    lon      (y, x) float32 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
       "    time     object 2016-01-01 12:00:00\n",
       "  * x        (x) float32 500.0 1.5e+03 2.5e+03 ... 6.535e+05 6.545e+05 6.555e+05\n",
       "  * y        (y) float32 500.0 1.5e+03 2.5e+03 ... 1.054e+06 1.056e+06 1.056e+06\n",
       "Attributes:\n",
       "    long_name:      Maximum air temperature\n",
       "    standard_name:  air_temperature\n",
       "    units:          K</pre><div class='xr-wrap' style='display:none'><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'>y</span>: 1057</li><li><span class='xr-has-index'>x</span>: 656</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3b57c05b-428b-43d8-81b3-fc4a80cc59e4' class='xr-array-in' type='checkbox' checked><label for='section-3b57c05b-428b-43d8-81b3-fc4a80cc59e4' 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=(100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.65 MiB </td> <td> 39.06 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1057, 656) </td> <td> (100, 100) </td></tr>\n",
       "    <tr><th> Count </th><td> 8394 Tasks </td><td> 77 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-dc67e701-68ce-494a-9b7d-257f41c72265' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc67e701-68ce-494a-9b7d-257f41c72265' class='xr-section-summary' >Coordinates: <span>(5)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-650a4a75-218c-4895-b491-408b513318ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-650a4a75-218c-4895-b491-408b513318ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8df30f9-8b02-4c6e-a0ab-c294eef895d2' class='xr-var-data-in' type='checkbox'><label for='data-a8df30f9-8b02-4c6e-a0ab-c294eef895d2' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.65 MiB </td> <td> 39.06 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1057, 656) </td> <td> (100, 100) </td></tr>\n",
       "    <tr><th> Count </th><td> 78 Tasks </td><td> 77 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-b081de57-9ed5-4fb6-a853-6c79dcd6d9c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b081de57-9ed5-4fb6-a853-6c79dcd6d9c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdec922e-7b9d-483c-8232-e7fce14916f4' class='xr-var-data-in' type='checkbox'><label for='data-cdec922e-7b9d-483c-8232-e7fce14916f4' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.65 MiB </td> <td> 39.06 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1057, 656) </td> <td> (100, 100) </td></tr>\n",
       "    <tr><th> Count </th><td> 78 Tasks </td><td> 77 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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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2016-01-01 12:00:00</div><input id='attrs-9b30e5bc-eb2c-40e0-9341-89303d8b4510' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b30e5bc-eb2c-40e0-9341-89303d8b4510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38ac1851-b585-46a5-bd76-503bf99455e8' class='xr-var-data-in' type='checkbox'><label for='data-38ac1851-b585-46a5-bd76-503bf99455e8' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(cftime.Datetime360Day(2016, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 6.555e+05</div><input id='attrs-d7da1e3b-2256-4146-a958-29e9794ec943' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7da1e3b-2256-4146-a958-29e9794ec943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70533c76-0a7b-4e9e-9bc1-7a66cd6148b6' class='xr-var-data-in' type='checkbox'><label for='data-70533c76-0a7b-4e9e-9bc1-7a66cd6148b6' 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>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000e+02, 1.500e+03, 2.500e+03, ..., 6.535e+05, 6.545e+05, 6.555e+05],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.056e+06</div><input id='attrs-6d8c95d9-c731-45fc-b3f2-1da62a6ad04e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d8c95d9-c731-45fc-b3f2-1da62a6ad04e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4205e34e-9d51-46c9-9896-3aca7223fe89' class='xr-var-data-in' type='checkbox'><label for='data-4205e34e-9d51-46c9-9896-3aca7223fe89' 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>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e+02, 1.5000e+03, 2.5000e+03, ..., 1.0545e+06, 1.0555e+06,\n",
       "       1.0565e+06], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6cdafed-f068-44fa-b15b-975172080606' class='xr-section-summary-in' type='checkbox'  ><label for='section-d6cdafed-f068-44fa-b15b-975172080606' class='xr-section-summary' >Indexes: <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-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6c0c4a2-1a2b-4cbb-902b-74990f75ec66' class='xr-index-data-in' type='checkbox'/><label for='index-e6c0c4a2-1a2b-4cbb-902b-74990f75ec66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   500.0,   1500.0,   2500.0,   3500.0,   4500.0,   5500.0,   6500.0,\n",
       "         7500.0,   8500.0,   9500.0,\n",
       "       ...\n",
       "       646500.0, 647500.0, 648500.0, 649500.0, 650500.0, 651500.0, 652500.0,\n",
       "       653500.0, 654500.0, 655500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;, length=656))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a237d6a6-25e4-47f4-8d97-d4280f922c80' class='xr-index-data-in' type='checkbox'/><label for='index-a237d6a6-25e4-47f4-8d97-d4280f922c80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    500.0,    1500.0,    2500.0,    3500.0,    4500.0,    5500.0,\n",
       "          6500.0,    7500.0,    8500.0,    9500.0,\n",
       "       ...\n",
       "       1047500.0, 1048500.0, 1049500.0, 1050500.0, 1051500.0, 1052500.0,\n",
       "       1053500.0, 1054500.0, 1055500.0, 1056500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;, length=1057))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c03c2197-c9d2-4707-afc1-b31b2f2e42a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c03c2197-c9d2-4707-afc1-b31b2f2e42a1' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Maximum air temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tasmax' (y: 1057, x: 656)>\n",
       "dask.array<getitem, shape=(1057, 656), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "    lon      (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "    time     object 2016-01-01 12:00:00\n",
       "  * x        (x) float32 500.0 1.5e+03 2.5e+03 ... 6.535e+05 6.545e+05 6.555e+05\n",
       "  * y        (y) float32 500.0 1.5e+03 2.5e+03 ... 1.054e+06 1.056e+06 1.056e+06\n",
       "Attributes:\n",
       "    long_name:      Maximum air temperature\n",
       "    standard_name:  air_temperature\n",
       "    units:          K"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We create a temporary CHESS-SCAPE gridded dataset array\n",
    "chess_tmp = chess_data_01[0, :, :]\n",
    "chess_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "98cd3f5f-41ae-47c4-9e97-736477f71b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[140] [479]\n"
     ]
    }
   ],
   "source": [
    "# Extracting the x and y indices which point to the nearest grid point of the COSMOS station\n",
    "y, x = find_chess_tile(site_latitude, site_longitude, chess_tmp)\n",
    "print(y, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b530c3f6-842f-43ac-b45d-f66a25f260ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting the temporary array\n",
    "del chess_tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebea9028-9c63-430e-9273-91493dad1196",
   "metadata": {},
   "source": [
    "### Extracting the model ensemble data for the grid point nearest to the observed station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fc2ea110-a52f-4bb5-9024-65c0cd5f9d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating arrays for day, month and year from the time index\n",
    "day = np.array([i.day for i in chess_data_01.time.values])\n",
    "month = np.array([i.month for i in chess_data_01.time.values])\n",
    "year = np.array([i.year for i in chess_data_01.time.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55714bc0-6a8f-448b-ab31-91153ac6394c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indexing the CHESS-SCAPE data with the x and y coordinates nearest to the observed station\n",
    "ens = [\"ENS01\", \"ENS04\", \"ENS06\", \"ENS15\"]\n",
    "chess_site_data = np.zeros((len(ens), len(day)))\n",
    "chess_site_data[0, :] = chess_data_01[:, y, x].squeeze().values\n",
    "chess_site_data[1, :] = chess_data_04[:, y, x].squeeze().values\n",
    "chess_site_data[2, :] = chess_data_06[:, y, x].squeeze().values\n",
    "chess_site_data[3, :] = chess_data_15[:, y, x].squeeze().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0bf8f675-c693-43e2-a588-d7e43db09ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting CHESS-SCAPE temperature from Kelvin to deg Celsius\n",
    "chess_site_data = chess_site_data - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2810088c-8974-4ad8-8de7-c5d24754ee78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>ENS01</th>\n",
       "      <th>ENS04</th>\n",
       "      <th>ENS06</th>\n",
       "      <th>ENS15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.469476</td>\n",
       "      <td>10.387781</td>\n",
       "      <td>2.881372</td>\n",
       "      <td>9.451196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.242853</td>\n",
       "      <td>8.467340</td>\n",
       "      <td>6.806024</td>\n",
       "      <td>10.261072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.037408</td>\n",
       "      <td>4.452875</td>\n",
       "      <td>5.930597</td>\n",
       "      <td>8.856165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.358728</td>\n",
       "      <td>4.522943</td>\n",
       "      <td>4.312189</td>\n",
       "      <td>6.318750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.984888</td>\n",
       "      <td>2.625421</td>\n",
       "      <td>1.373346</td>\n",
       "      <td>7.547205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>11.695123</td>\n",
       "      <td>7.517450</td>\n",
       "      <td>10.040674</td>\n",
       "      <td>6.743341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.335809</td>\n",
       "      <td>11.334253</td>\n",
       "      <td>9.427026</td>\n",
       "      <td>7.944208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2517</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10.932184</td>\n",
       "      <td>8.553369</td>\n",
       "      <td>5.844202</td>\n",
       "      <td>7.643549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2518</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.518945</td>\n",
       "      <td>7.154962</td>\n",
       "      <td>7.586847</td>\n",
       "      <td>5.023523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.549493</td>\n",
       "      <td>7.390710</td>\n",
       "      <td>10.485040</td>\n",
       "      <td>5.607843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2520 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        YEAR  MONTH   DAY      ENS01      ENS04      ENS06      ENS15\n",
       "0     2016.0    1.0   1.0   8.469476  10.387781   2.881372   9.451196\n",
       "1     2016.0    1.0   2.0   9.242853   8.467340   6.806024  10.261072\n",
       "2     2016.0    1.0   3.0  10.037408   4.452875   5.930597   8.856165\n",
       "3     2016.0    1.0   4.0   6.358728   4.522943   4.312189   6.318750\n",
       "4     2016.0    1.0   5.0   6.984888   2.625421   1.373346   7.547205\n",
       "...      ...    ...   ...        ...        ...        ...        ...\n",
       "2515  2022.0   12.0  26.0  11.695123   7.517450  10.040674   6.743341\n",
       "2516  2022.0   12.0  27.0   7.335809  11.334253   9.427026   7.944208\n",
       "2517  2022.0   12.0  28.0  10.932184   8.553369   5.844202   7.643549\n",
       "2518  2022.0   12.0  29.0  10.518945   7.154962   7.586847   5.023523\n",
       "2519  2022.0   12.0  30.0  10.549493   7.390710  10.485040   5.607843\n",
       "\n",
       "[2520 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a pandas dataframe for CHESS-SCAPE ensemble TASMAX\n",
    "f = np.vstack((year, month, day, chess_site_data))\n",
    "df = pd.DataFrame(f.T, columns=[\"YEAR\", \"MONTH\", \"DAY\"] + ens)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2240eed-e862-4ef4-b265-64a74ac4f0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENS01</th>\n",
       "      <th>ENS04</th>\n",
       "      <th>ENS06</th>\n",
       "      <th>ENS15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>8.199085</td>\n",
       "      <td>7.225493</td>\n",
       "      <td>7.370226</td>\n",
       "      <td>6.386664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>7.908752</td>\n",
       "      <td>7.396607</td>\n",
       "      <td>6.743253</td>\n",
       "      <td>7.388288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>10.210987</td>\n",
       "      <td>10.479619</td>\n",
       "      <td>9.844518</td>\n",
       "      <td>10.104133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>12.819470</td>\n",
       "      <td>13.400659</td>\n",
       "      <td>12.973615</td>\n",
       "      <td>12.981180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>16.253889</td>\n",
       "      <td>17.441104</td>\n",
       "      <td>16.428872</td>\n",
       "      <td>16.286755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>20.577827</td>\n",
       "      <td>19.185257</td>\n",
       "      <td>20.710894</td>\n",
       "      <td>21.569696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>23.490857</td>\n",
       "      <td>21.457086</td>\n",
       "      <td>25.016401</td>\n",
       "      <td>23.415034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>23.007554</td>\n",
       "      <td>21.511500</td>\n",
       "      <td>25.052005</td>\n",
       "      <td>22.698149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>20.052419</td>\n",
       "      <td>19.290494</td>\n",
       "      <td>20.566005</td>\n",
       "      <td>19.663535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>14.997889</td>\n",
       "      <td>14.631403</td>\n",
       "      <td>15.084459</td>\n",
       "      <td>15.343498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>11.058280</td>\n",
       "      <td>10.899864</td>\n",
       "      <td>11.144928</td>\n",
       "      <td>10.397077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>8.552765</td>\n",
       "      <td>8.235628</td>\n",
       "      <td>8.761400</td>\n",
       "      <td>7.872899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ENS01      ENS04      ENS06      ENS15\n",
       "MONTH                                            \n",
       "1.0     8.199085   7.225493   7.370226   6.386664\n",
       "2.0     7.908752   7.396607   6.743253   7.388288\n",
       "3.0    10.210987  10.479619   9.844518  10.104133\n",
       "4.0    12.819470  13.400659  12.973615  12.981180\n",
       "5.0    16.253889  17.441104  16.428872  16.286755\n",
       "6.0    20.577827  19.185257  20.710894  21.569696\n",
       "7.0    23.490857  21.457086  25.016401  23.415034\n",
       "8.0    23.007554  21.511500  25.052005  22.698149\n",
       "9.0    20.052419  19.290494  20.566005  19.663535\n",
       "10.0   14.997889  14.631403  15.084459  15.343498\n",
       "11.0   11.058280  10.899864  11.144928  10.397077\n",
       "12.0    8.552765   8.235628   8.761400   7.872899"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating monthly climatology of TASMAX for all the ensemble members\n",
    "df_model = df.groupby([\"YEAR\", \"MONTH\"])[ens].mean()\n",
    "df_model = df_model.groupby([\"MONTH\"])[ens].mean()\n",
    "df_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a74cf1d-0e6e-4eaf-95b7-d2f54a5467a2",
   "metadata": {},
   "source": [
    "### Comparing observations against modelled ensemble projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5f1326ef-b00b-4157-95c1-6f1bb9042897",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For plotting the data in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4edce09f-9dbf-4561-a168-7e36dbf88db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of months\n",
    "months = [\n",
    "    \"JAN\",\n",
    "    \"FEB\",\n",
    "    \"MAR\",\n",
    "    \"APR\",\n",
    "    \"MAY\",\n",
    "    \"JUN\",\n",
    "    \"JUL\",\n",
    "    \"AUG\",\n",
    "    \"SEP\",\n",
    "    \"OCT\",\n",
    "    \"NOV\",\n",
    "    \"DEC\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "45499c97-d399-4f49-82bc-c08dc2969668",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating model ensemble mean, minimum and maximum\n",
    "df_model_max = df_model.max(axis=1)\n",
    "df_model_min = df_model.min(axis=1)\n",
    "df_model_mn = df_model.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b338c195-f48d-4e7b-b4b7-29c573222766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting monthly climatology of Daily Maximum Air Temperature from COSMOS station ALIC1 and nearest grid point on CHESS-SCAPE averaged over 2016--2022\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "plt.plot(months, df_site.values, color=\"k\", lw=3, label=\"OBSERVED\")\n",
    "plt.plot(months, df_model_mn.values, color=\"b\", ls=\"--\", lw=2, label=\"MODEL MEAN\")\n",
    "plt.fill_between(\n",
    "    months,\n",
    "    df_model_min.values,\n",
    "    df_model_max.values,\n",
    "    color=\"b\",\n",
    "    alpha=0.3,\n",
    "    label=\"MODEL SPREAD\",\n",
    ")\n",
    "plt.ylabel(\"Daily Maximum Air Temperature ($^\\circ$C)\", fontsize=15)\n",
    "plt.yticks(np.arange(7, 26, 2), fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.legend(loc=\"upper left\", fontsize=15)\n",
    "plt.title(site_nm + \" - Monthly Climatology 2016 - 2022\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2acb35c-e750-439a-bcd0-2ee1b324a185",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}