diff --git a/docs/notebooks/phenology_modelling_tensors.ipynb b/docs/notebooks/phenology_modelling_tensors.ipynb
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+{
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+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b70984c6-1364-4a0d-b60e-79bc6d62c114",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import xarray as xr\n",
+    "import yaml\n",
+    "\n",
+    "from pcse.base import ParameterProvider\n",
+    "from pcse.input import YAMLCropDataProvider\n",
+    "from pcse.util import wind10to2\n",
+    "from diffwofost.physical_models.base.weather import TensorWeatherDataProvider\n",
+    "from diffwofost.physical_models.config import Configuration\n",
+    "from diffwofost.physical_models.crop.phenology import DVS_Phenology\n",
+    "from diffwofost.physical_models.engine import Engine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6965950-1f37-4582-8430-84611d89fe0b",
+   "metadata": {},
+   "source": [
+    "# Phenology simulation with diffWOFOST\n",
+    "\n",
+    "This notebook re-implements the simulation of phenology illustrated in [this notebook](https://github.com/artofmodelling/phenomodelling/blob/main/phenology_modelling.ipynb) with diffWOFOST. \n",
+    "\n",
+    "We first clone the repository hosting the phenomodelling notebook, as it includes the required data as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2470b32-d875-4e51-91dd-c74a3524430a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'phenomodelling'...\n",
+      "remote: Enumerating objects: 93, done.\u001b[K\n",
+      "remote: Counting objects: 100% (93/93), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
+      "remote: Total 93 (delta 69), reused 79 (delta 58), pack-reused 0 (from 0)\u001b[K\n",
+      "Receiving objects: 100% (93/93), 18.79 MiB | 20.38 MiB/s, done.\n",
+      "Resolving deltas: 100% (69/69), done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!if [ ! -d phenomodelling ] ; then git clone https://github.com/artofmodelling/phenomodelling.git ; fi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "254d54c0-2b08-4797-8dcb-496a636e673c",
+   "metadata": {},
+   "source": [
+    "Let's load the phenological observations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d8578c37-962c-43ea-a2c3-65fea78c9052",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_observations():\n",
+    "    \"\"\"Load observation data.\"\"\"\n",
+    "    df = pd.read_excel(\"phenomodelling/data/pheno_obs_1784.xlsx\", sheet_name=\"data\")\n",
+    "    return df[df.VALID]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "43a31fc7-127e-4bd8-a5b0-fbee317c8798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = load_observations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0169b9a5-1193-4693-933f-84e329200566",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\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>ObjectID</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>DOS</th>\n",
+       "      <th>DOA</th>\n",
+       "      <th>DOM</th>\n",
+       "      <th>DOH</th>\n",
+       "      <th>VALID</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Spring Barley_2010_-0.7_53.87_86565</td>\n",
+       "      <td>-0.700067</td>\n",
+       "      <td>53.869820</td>\n",
+       "      <td>2010-03-19</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>2010-08-19</td>\n",
+       "      <td>2010-10-15</td>\n",
+       "      <td>True</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spring Barley_2010_-2.588_55.51_86548</td>\n",
+       "      <td>-2.587734</td>\n",
+       "      <td>55.510104</td>\n",
+       "      <td>2010-04-12</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>2010-08-18</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Spring Barley_2011_-2.988_52.28_89360</td>\n",
+       "      <td>-2.988059</td>\n",
+       "      <td>52.279508</td>\n",
+       "      <td>2011-03-22</td>\n",
+       "      <td>2011-06-16</td>\n",
+       "      <td>2011-08-21</td>\n",
+       "      <td>2011-08-23</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spring Barley_2011_-3.144_55.727_86748</td>\n",
+       "      <td>-3.143778</td>\n",
+       "      <td>55.726719</td>\n",
+       "      <td>2011-04-18</td>\n",
+       "      <td>2011-07-23</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Spring Barley_2012_-3.046_55.919_86978</td>\n",
+       "      <td>-3.046074</td>\n",
+       "      <td>55.919286</td>\n",
+       "      <td>2012-03-02</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>2012-08-27</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 ObjectID  Longitude   Latitude        DOS  \\\n",
+       "0     Spring Barley_2010_-0.7_53.87_86565  -0.700067  53.869820 2010-03-19   \n",
+       "1   Spring Barley_2010_-2.588_55.51_86548  -2.587734  55.510104 2010-04-12   \n",
+       "2   Spring Barley_2011_-2.988_52.28_89360  -2.988059  52.279508 2011-03-22   \n",
+       "3  Spring Barley_2011_-3.144_55.727_86748  -3.143778  55.726719 2011-04-18   \n",
+       "5  Spring Barley_2012_-3.046_55.919_86978  -3.046074  55.919286 2012-03-02   \n",
+       "\n",
+       "         DOA        DOM        DOH  VALID  \n",
+       "0        NaT 2010-08-19 2010-10-15   True  \n",
+       "1        NaT 2010-08-18        NaT   True  \n",
+       "2 2011-06-16 2011-08-21 2011-08-23   True  \n",
+       "3 2011-07-23        NaT        NaT   True  \n",
+       "5        NaT 2012-08-27        NaT   True  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observations.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "637119c3-5df3-4f83-b56e-7bbec00b4e9c",
+   "metadata": {},
+   "source": [
+    "We then load the weather data corresponding to the observations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7e801ad-33e2-433e-8dfa-0c187950df82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variable_renaming = [\n",
+    "    (\"temperature_max\", \"TMAX\", None),\n",
+    "    (\"temperature_min\", \"TMIN\", None),\n",
+    "    (\"temperature_avg\",  \"TEMP\", None),\n",
+    "    (\"vapourpressure\", \"VAP\", None),\n",
+    "    (\"windspeed\", \"WIND\", wind10to2),\n",
+    "    (\"precipitation\", \"RAIN\", lambda x: x/10.),\n",
+    "    (\"radiation\", \"IRRAD\", lambda x: x*1000.),\n",
+    "    (\"snowdepth\", \"SNOWDEPTH\", None),\n",
+    "    (\"day\", \"DAY\", None)\n",
+    "]\n",
+    "\n",
+    "def load_weather_data_file(object_id):\n",
+    "    \"\"\"Load weather data for a given location from the corresponding JSON file.\"\"\"\n",
+    "\n",
+    "    with open(f\"phenomodelling/data/meteo/{object_id}.json\") as f:\n",
+    "        d = json.load(f)\n",
+    "\n",
+    "    data = d[\"data\"]\n",
+    "\n",
+    "    df = pd.DataFrame(data[\"weather_variables\"])\n",
+    "\n",
+    "    # Parse location information\n",
+    "    location_info = data[\"location_info\"]\n",
+    "    lon = float(location_info[\"grid_agera5_longitude\"])\n",
+    "    lat = float(location_info[\"grid_agera5_latitude\"])\n",
+    "    elev = float(location_info[\"grid_agera5_elevation\"])\n",
+    "\n",
+    "    # Rename columns and apply unit corrections\n",
+    "    rename_cols = {old_name: new_name for old_name, new_name, _ in variable_renaming}\n",
+    "    convert_cols = {new_name: conversion for _, new_name, conversion in\n",
+    "                    variable_renaming if conversion is not None}\n",
+    "    df = df.rename(columns=rename_cols)\n",
+    "    for column, conv in convert_cols.items():\n",
+    "        df[column] = df[column].apply(conv)\n",
+    "\n",
+    "    df.DAY = pd.to_datetime(df.DAY)\n",
+    "    return df, lon, lat, elev\n",
+    "\n",
+    "\n",
+    "def load_weather_data(object_ids):\n",
+    "    \"\"\"Load weather data for all locations.\"\"\"\n",
+    "    dfs = []\n",
+    "    nlocations = len(object_ids)\n",
+    "    lon = np.zeros(nlocations)\n",
+    "    lat = np.zeros(nlocations)\n",
+    "    elev = np.zeros(nlocations)\n",
+    "\n",
+    "    # Load data for all locations\n",
+    "    for n, object_id in enumerate(object_ids):\n",
+    "        df, lon[n], lat[n], elev[n] = load_weather_data_file(object_id=object_id)\n",
+    "        df = df.set_index(\"DAY\")\n",
+    "        dfs.append(df)\n",
+    "\n",
+    "    # Merge data in a dataset\n",
+    "    ds = xr.concat(\n",
+    "        [xr.Dataset.from_dataframe(df) for df in dfs],\n",
+    "        dim=\"location\"\n",
+    "    )\n",
+    "\n",
+    "    # Add location information to the dataset\n",
+    "    ds.coords[\"LON\"] = xr.DataArray(lon, dims=\"location\")\n",
+    "    ds.coords[\"LAT\"] = xr.DataArray(lat, dims=\"location\")\n",
+    "    ds[\"ELEV\"] = xr.DataArray(elev, dims=\"location\")\n",
+    "    return ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "25c03f13-cfcf-45c2-a868-4aaad24cee95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_data = load_weather_data(observations[\"ObjectID\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
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+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\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",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 15MB\n",
+       "Dimensions:    (location: 41, DAY: 5234)\n",
+       "Coordinates:\n",
+       "  * DAY        (DAY) datetime64[us] 42kB 2010-01-01 2010-01-02 ... 2024-04-30\n",
+       "    LON        (location) float64 328B -0.75 -2.55 -2.95 ... -7.05 -8.35 -7.05\n",
+       "    LAT        (location) float64 328B 53.85 55.55 52.25 ... 52.35 52.15 52.55\n",
+       "Dimensions without coordinates: location\n",
+       "Data variables:\n",
+       "    idgrid     (location, DAY) int64 2MB 5178592 5178592 ... 5131729 5131729\n",
+       "    TMAX       (location, DAY) float64 2MB 0.7736 2.148 1.086 ... 13.99 11.49\n",
+       "    TMIN       (location, DAY) float64 2MB -2.119 -2.754 -2.795 ... 6.517 7.682\n",
+       "    TEMP       (location, DAY) float64 2MB -0.1971 -0.02729 ... 10.36 8.895\n",
+       "    VAP        (location, DAY) float64 2MB 5.374 5.225 4.916 ... 10.3 10.01\n",
+       "    WIND       (location, DAY) float64 2MB 1.937 1.993 1.323 ... 2.364 2.041\n",
+       "    RAIN       (location, DAY) float64 2MB 0.118 0.174 0.015 ... 0.729 0.79\n",
+       "    IRRAD      (location, DAY) float64 2MB 2.159e+06 2.064e+06 ... 1.232e+07\n",
+       "    SNOWDEPTH  (location, DAY) float64 2MB 0.1 0.1 0.1 0.1 ... 1.76 7.29 7.9\n",
+       "    ELEV       (location) float64 328B 1.7 231.3 265.8 ... 67.6 105.7 113.8</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-ce47039d-09c5-4500-a3d0-960b9037a6d0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ce47039d-09c5-4500-a3d0-960b9037a6d0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>location</span>: 41</li><li><span class='xr-has-index'>DAY</span>: 5234</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ba934ef9-6e23-4fac-b978-5e8740e5d42c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba934ef9-6e23-4fac-b978-5e8740e5d42c' 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'>DAY</span></div><div class='xr-var-dims'>(DAY)</div><div class='xr-var-dtype'>datetime64[us]</div><div class='xr-var-preview xr-preview'>2010-01-01 ... 2024-04-30</div><input id='attrs-7c0869e6-40e1-4346-b299-c8e8e58f52fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c0869e6-40e1-4346-b299-c8e8e58f52fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed176284-5920-4366-8b45-984e738cfdc3' class='xr-var-data-in' type='checkbox'><label for='data-ed176284-5920-4366-8b45-984e738cfdc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000&#x27;, &#x27;2010-01-02T00:00:00.000000&#x27;,\n",
+       "       &#x27;2010-01-03T00:00:00.000000&#x27;, ..., &#x27;2024-04-28T00:00:00.000000&#x27;,\n",
+       "       &#x27;2024-04-29T00:00:00.000000&#x27;, &#x27;2024-04-30T00:00:00.000000&#x27;],\n",
+       "      shape=(5234,), dtype=&#x27;datetime64[us]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LON</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.75 -2.55 -2.95 ... -8.35 -7.05</div><input id='attrs-9f1125d5-ccb7-4885-a65d-aef03767251a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f1125d5-ccb7-4885-a65d-aef03767251a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ab0c25-3653-4780-9ad8-111341d50f81' class='xr-var-data-in' type='checkbox'><label for='data-84ab0c25-3653-4780-9ad8-111341d50f81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.75, -2.55, -2.95, -3.15, -3.05, -6.95, -6.95, -6.95, -8.45,\n",
+       "       -2.95, -6.65, -7.05, -8.45, -6.55, -6.55, -6.65, -7.05, -0.45,\n",
+       "       -0.45, -3.05, -6.55, -6.95, -6.95, -8.35, -8.35, -0.65, -0.65,\n",
+       "       -8.35, -8.35, -8.35, -6.55, -6.85, -6.85, -7.35, -8.35, -8.35,\n",
+       "       -2.85, -6.75, -7.05, -8.35, -7.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LAT</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.85 55.55 52.25 ... 52.15 52.55</div><input id='attrs-5f1ac81b-56ac-41db-81a2-4101beed862a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f1ac81b-56ac-41db-81a2-4101beed862a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6185ee9-950b-4111-ad51-a2842cca0020' class='xr-var-data-in' type='checkbox'><label for='data-e6185ee9-950b-4111-ad51-a2842cca0020' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([53.85, 55.55, 52.25, 55.75, 55.95, 52.75, 52.75, 52.75, 51.95,\n",
+       "       52.25, 52.35, 53.15, 51.95, 53.85, 53.85, 52.35, 53.35, 54.15,\n",
+       "       54.15, 55.95, 52.45, 52.75, 52.75, 51.85, 51.85, 54.25, 54.25,\n",
+       "       51.85, 51.85, 51.85, 52.45, 52.45, 52.85, 52.25, 51.85, 51.85,\n",
+       "       53.45, 53.45, 52.35, 52.15, 52.55])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4be2114-5daa-465d-8a19-c1d32eebb520' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4be2114-5daa-465d-8a19-c1d32eebb520' class='xr-section-summary' >Data variables: <span>(10)</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>idgrid</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5178592 5178592 ... 5131729 5131729</div><input id='attrs-dd6c2aa3-6e9c-46e1-bc9c-aecf72e654ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd6c2aa3-6e9c-46e1-bc9c-aecf72e654ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-809b0c23-1001-498d-b887-7e70a8f07a01' class='xr-var-data-in' type='checkbox'><label for='data-809b0c23-1001-498d-b887-7e70a8f07a01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5178592, 5178592, 5178592, ..., 5178592, 5178592, 5178592],\n",
+       "       [5239774, 5239774, 5239774, ..., 5239774, 5239774, 5239774],\n",
+       "       [5120970, 5120970, 5120970, ..., 5120970, 5120970, 5120970],\n",
+       "       ...,\n",
+       "       [5124529, 5124529, 5124529, ..., 5124529, 5124529, 5124529],\n",
+       "       [5117316, 5117316, 5117316, ..., 5117316, 5117316, 5117316],\n",
+       "       [5131729, 5131729, 5131729, ..., 5131729, 5131729, 5131729]],\n",
+       "      shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TMAX</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7736 2.148 1.086 ... 13.99 11.49</div><input id='attrs-184d3f8e-5b3c-47d3-82d6-5c9b3c50c52e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-184d3f8e-5b3c-47d3-82d6-5c9b3c50c52e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95026b40-666e-4082-8fc5-88c8ad2681df' class='xr-var-data-in' type='checkbox'><label for='data-95026b40-666e-4082-8fc5-88c8ad2681df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.77364502,  2.14815674,  1.08629761, ...,  9.37841225,\n",
+       "        14.2517643 , 17.61257362],\n",
+       "       [ 1.22210083,  1.28609619, -2.94180908, ...,  8.78734779,\n",
+       "        11.94722939, 16.31035805],\n",
+       "       [ 0.0979248 ,  2.57381592, -1.43955078, ..., 10.49483681,\n",
+       "        11.15062904, 12.18261147],\n",
+       "       ...,\n",
+       "       [ 0.21044312,  1.26674805,  2.08187256, ..., 11.69561195,\n",
+       "        13.25551796, 11.17742348],\n",
+       "       [ 0.30184326,  1.70424805,  2.68413696, ..., 12.37413979,\n",
+       "        13.03182411, 11.50362587],\n",
+       "       [ 0.95940552,  2.03325806,  2.61321411, ..., 12.06679726,\n",
+       "        13.99041176, 11.48577309]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TMIN</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.119 -2.754 ... 6.517 7.682</div><input id='attrs-4ea646ec-d362-4650-ab2d-c5234a9b114a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ea646ec-d362-4650-ab2d-c5234a9b114a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fecf41c-2e4a-450c-9464-8377681744d1' class='xr-var-data-in' type='checkbox'><label for='data-5fecf41c-2e4a-450c-9464-8377681744d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2.11932983, -2.75372925, -2.79468384, ...,  4.73729849,\n",
+       "         4.18276358,  7.3583312 ],\n",
+       "       [-2.96271362, -2.81851807, -6.15775146, ...,  0.74193728,\n",
+       "         4.42369986,  8.22887611],\n",
+       "       [-5.38562622, -3.21442261, -1.21362915, ...,  3.84740591,\n",
+       "         5.32778311,  7.9989562 ],\n",
+       "       ...,\n",
+       "       [-2.36435547, -3.15354004, -0.99640503, ...,  1.72716677,\n",
+       "         7.2857604 ,  7.93786001],\n",
+       "       [-3.278479  , -5.63849487, -3.5138916 , ...,  1.57015383,\n",
+       "         6.82500601,  6.09072256],\n",
+       "       [-2.3118042 , -3.675177  , -0.96686401, ...,  0.55477297,\n",
+       "         6.51723623,  7.68181753]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1971 -0.02729 ... 10.36 8.895</div><input id='attrs-1cf6b911-dcca-484a-a0ba-b42ea74a85ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cf6b911-dcca-484a-a0ba-b42ea74a85ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b7832ff-027c-44cc-ab59-572b2f133c59' class='xr-var-data-in' type='checkbox'><label for='data-1b7832ff-027c-44cc-ab59-572b2f133c59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.19708862, -0.02728882, -1.03253784, ...,  6.69909048,\n",
+       "        10.63796425, 12.98208046],\n",
+       "       [-0.07138672, -0.14060059, -3.76407471, ...,  6.23961782,\n",
+       "         9.79189491, 12.27517128],\n",
+       "       [-2.99884644,  0.15270386, -3.41138306, ...,  7.20964956,\n",
+       "         9.29143715,  9.70000648],\n",
+       "       ...,\n",
+       "       [-2.11109009, -1.05179443,  0.41240845, ...,  8.01662636,\n",
+       "        10.29281044,  8.96691322],\n",
+       "       [-3.22662964, -2.21387329,  0.15493164, ...,  7.93804312,\n",
+       "         9.70952797,  8.20989418],\n",
+       "       [-2.08374634, -0.98465576,  0.17266235, ...,  7.78856802,\n",
+       "        10.356287  ,  8.89507484]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VAP</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.374 5.225 4.916 ... 10.3 10.01</div><input id='attrs-5a1a3616-3d82-4e88-a324-992c742a910f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a1a3616-3d82-4e88-a324-992c742a910f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-516754d0-f137-49cb-a80f-4b8106df2956' class='xr-var-data-in' type='checkbox'><label for='data-516754d0-f137-49cb-a80f-4b8106df2956' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 5.3741937,  5.224797 ,  4.9156227, ...,  7.9185696,  8.836268 ,\n",
+       "        10.113685 ],\n",
+       "       [ 5.437059 ,  5.316069 ,  4.0548415, ...,  7.413932 ,  9.442195 ,\n",
+       "         9.915615 ],\n",
+       "       [ 4.3110085,  5.569859 ,  4.3789773, ...,  6.961126 ,  9.617224 ,\n",
+       "         9.987006 ],\n",
+       "       ...,\n",
+       "       [ 4.6997905,  5.132631 ,  5.1134405, ...,  8.098891 , 10.182598 ,\n",
+       "         9.852199 ],\n",
+       "       [ 4.4934597,  4.728412 ,  5.1612597, ...,  8.45564  , 10.093262 ,\n",
+       "         9.304883 ],\n",
+       "       [ 4.7090244,  5.156093 ,  5.10678  , ...,  8.025369 , 10.304367 ,\n",
+       "        10.012968 ]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WIND</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.937 1.993 1.323 ... 2.364 2.041</div><input id='attrs-17362c57-a1bb-4442-9041-801e58bd4586' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17362c57-a1bb-4442-9041-801e58bd4586' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4162f9bb-0136-472b-bfce-a318f1d2dfe4' class='xr-var-data-in' type='checkbox'><label for='data-4162f9bb-0136-472b-bfce-a318f1d2dfe4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.9371287 , 1.99332511, 1.32325049, ..., 2.8983657 , 3.82200641,\n",
+       "        2.78087683],\n",
+       "       [2.02688602, 1.8389302 , 1.06215333, ..., 2.11776088, 2.88372705,\n",
+       "        2.29005174],\n",
+       "       [1.66144577, 2.01727396, 1.77893929, ..., 2.52533252, 3.74228834,\n",
+       "        2.81388145],\n",
+       "       ...,\n",
+       "       [1.55674444, 1.54112948, 2.27653791, ..., 1.91186303, 2.74404256,\n",
+       "        2.3355086 ],\n",
+       "       [0.71611088, 0.95807629, 2.10276873, ..., 1.6409808 , 1.91836438,\n",
+       "        1.65649844],\n",
+       "       [1.18897464, 1.36283319, 1.78050589, ..., 1.6038922 , 2.36391888,\n",
+       "        2.04107266]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAIN</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.118 0.174 0.015 ... 0.729 0.79</div><input id='attrs-6d5eb966-6b9a-463c-b9cf-d1669816f624' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d5eb966-6b9a-463c-b9cf-d1669816f624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba317cf-ca4a-43a7-88fb-3817958119ca' class='xr-var-data-in' type='checkbox'><label for='data-0ba317cf-ca4a-43a7-88fb-3817958119ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.180e-01, 1.740e-01, 1.500e-02, ..., 1.103e+00, 7.900e-02,\n",
+       "        8.000e-03],\n",
+       "       [3.050e-01, 3.800e-01, 6.500e-02, ..., 4.810e-01, 2.910e-01,\n",
+       "        4.000e-03],\n",
+       "       [3.400e-02, 2.700e-02, 2.400e-02, ..., 6.800e-02, 5.610e-01,\n",
+       "        5.800e-01],\n",
+       "       ...,\n",
+       "       [9.000e-03, 0.000e+00, 7.300e-02, ..., 1.400e-01, 5.920e-01,\n",
+       "        8.090e-01],\n",
+       "       [0.000e+00, 0.000e+00, 5.000e-03, ..., 3.540e-01, 1.083e+00,\n",
+       "        8.680e-01],\n",
+       "       [1.000e-03, 2.000e-03, 1.900e-02, ..., 1.760e-01, 7.290e-01,\n",
+       "        7.900e-01]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IRRAD</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.159e+06 2.064e+06 ... 1.232e+07</div><input id='attrs-c2271317-d446-451c-84a1-d8dd01a132f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2271317-d446-451c-84a1-d8dd01a132f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-883ff466-d86b-4782-8ee1-e9cd55e8acf7' class='xr-var-data-in' type='checkbox'><label for='data-883ff466-d86b-4782-8ee1-e9cd55e8acf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 2159343.25,  2063740.25,  2729216.75, ...,  5192460.  ,\n",
+       "        16301062.  , 22794882.  ],\n",
+       "       [ 1275975.  ,  1368785.5 ,  1989175.5 , ...,  7099020.  ,\n",
+       "        12030338.  , 17767868.  ],\n",
+       "       [ 3546270.75,  3314364.  ,  3282052.75, ..., 16541505.  ,\n",
+       "        12027731.  ,  7107505.  ],\n",
+       "       ...,\n",
+       "       [ 3597375.5 ,  3643948.  ,  2683589.  , ..., 19336200.  ,\n",
+       "        16013287.  , 12514414.  ],\n",
+       "       [ 3283565.25,  3586318.  ,  3227391.  , ..., 18045610.  ,\n",
+       "        16106447.  , 13772944.  ],\n",
+       "       [ 3514418.5 ,  3477644.25,  2768004.5 , ..., 19368212.  ,\n",
+       "        16092497.  , 12316835.  ]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWDEPTH</span></div><div class='xr-var-dims'>(location, DAY)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1 0.1 0.1 0.1 ... 1.76 7.29 7.9</div><input id='attrs-ac84ea8e-dcc7-4532-8ba0-2229cf8aa8b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac84ea8e-dcc7-4532-8ba0-2229cf8aa8b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2eb2830-eaff-411e-a49e-4ca0362f8d5d' class='xr-var-data-in' type='checkbox'><label for='data-e2eb2830-eaff-411e-a49e-4ca0362f8d5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.1 ,  0.1 ,  0.1 , ..., 11.03,  0.79,  0.08],\n",
+       "       [ 2.4 ,  2.8 ,  3.2 , ...,  4.81,  2.91,  0.04],\n",
+       "       [ 0.7 ,  0.8 ,  1.  , ...,  0.68,  5.61,  5.8 ],\n",
+       "       ...,\n",
+       "       [ 0.1 ,  0.1 ,  0.1 , ...,  1.4 ,  5.92,  8.09],\n",
+       "       [ 0.2 ,  0.1 ,  0.  , ...,  3.54, 10.83,  8.68],\n",
+       "       [ 0.3 ,  0.3 ,  0.1 , ...,  1.76,  7.29,  7.9 ]], shape=(41, 5234))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ELEV</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.7 231.3 265.8 ... 105.7 113.8</div><input id='attrs-8619f2d2-4906-48b8-95e6-15defe0330c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8619f2d2-4906-48b8-95e6-15defe0330c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bab261df-b808-4100-bf8f-323f2d4cea98' class='xr-var-data-in' type='checkbox'><label for='data-bab261df-b808-4100-bf8f-323f2d4cea98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1.7, 231.3, 265.8, 299.4, 136.6, 113.8, 113.8, 113.8, 105.7,\n",
+       "       265.8,  48.4, 101.3, 105.7,  61.4,  61.4,  48.4,  97.7,  58.9,\n",
+       "        58.9, 136.6,  19. , 113.8, 113.8,  38.9,  38.9,  83.9,  83.9,\n",
+       "        38.9,  38.9,  38.9,  19. ,  67.6, 118.7,  96.8,  38.9,  38.9,\n",
+       "        50.4,  83.1,  67.6, 105.7, 113.8])</pre></div></li></ul></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 15MB\n",
+       "Dimensions:    (location: 41, DAY: 5234)\n",
+       "Coordinates:\n",
+       "  * DAY        (DAY) datetime64[us] 42kB 2010-01-01 2010-01-02 ... 2024-04-30\n",
+       "    LON        (location) float64 328B -0.75 -2.55 -2.95 ... -7.05 -8.35 -7.05\n",
+       "    LAT        (location) float64 328B 53.85 55.55 52.25 ... 52.35 52.15 52.55\n",
+       "Dimensions without coordinates: location\n",
+       "Data variables:\n",
+       "    idgrid     (location, DAY) int64 2MB 5178592 5178592 ... 5131729 5131729\n",
+       "    TMAX       (location, DAY) float64 2MB 0.7736 2.148 1.086 ... 13.99 11.49\n",
+       "    TMIN       (location, DAY) float64 2MB -2.119 -2.754 -2.795 ... 6.517 7.682\n",
+       "    TEMP       (location, DAY) float64 2MB -0.1971 -0.02729 ... 10.36 8.895\n",
+       "    VAP        (location, DAY) float64 2MB 5.374 5.225 4.916 ... 10.3 10.01\n",
+       "    WIND       (location, DAY) float64 2MB 1.937 1.993 1.323 ... 2.364 2.041\n",
+       "    RAIN       (location, DAY) float64 2MB 0.118 0.174 0.015 ... 0.729 0.79\n",
+       "    IRRAD      (location, DAY) float64 2MB 2.159e+06 2.064e+06 ... 1.232e+07\n",
+       "    SNOWDEPTH  (location, DAY) float64 2MB 0.1 0.1 0.1 0.1 ... 1.76 7.29 7.9\n",
+       "    ELEV       (location) float64 328B 1.7 231.3 265.8 ... 67.6 105.7 113.8"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c21a3bc5-df35-401d-afc9-349e58ae7804",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x11a5b9550>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weather_data[\"TEMP\"].plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5651df6-940a-4399-a978-af139d2801c7",
+   "metadata": {},
+   "source": [
+    "We preprocess the weather data by computing the daylength from latitude and day of the year:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "52cf3acd-c53d-48dd-b366-169ac27078af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def doy(day):\n",
+    "    \"\"\"Converts a date or datetime object to day-of-year (Jan 1st = doy 1).\"\"\"\n",
+    "    # Check if day is a date or datetime object\n",
+    "    if isinstance(day, (datetime.date, datetime.datetime)):\n",
+    "        return day.timetuple().tm_yday\n",
+    "    elif isinstance(day, np.ndarray):\n",
+    "        day_series = pd.Series(np.ravel(day), copy=False)\n",
+    "        day_of_year = day_series.dt.day_of_year.values\n",
+    "        return day_of_year.reshape(day.shape)\n",
+    "    else:\n",
+    "        raise TypeError(f\"Cannot calculate day-of-year from object of type: {type(day)}\")\n",
+    "\n",
+    "\n",
+    "def daylength(day, latitude, angle=-4, outer=True):\n",
+    "    \"\"\"Numpy-vectorized daylength calculation for a given day, latitude and base angle.\"\"\"\n",
+    "    # if outer, all possible combinations of day and latitude are considered\n",
+    "    if outer:\n",
+    "        multiply = np.multiply.outer\n",
+    "    else:\n",
+    "        multiply = np.multiply\n",
+    "\n",
+    "    # Check for range of latitude and convert to radians\n",
+    "    if (abs(latitude) > 90.0).any():\n",
+    "        msg = \"Latitude not between -90 and 90\"\n",
+    "        raise RuntimeError(msg)\n",
+    "    latitude_rad = np.radians(latitude)\n",
+    "\n",
+    "    # Calculate day-of-year from date object day\n",
+    "    iday = doy(day)\n",
+    "\n",
+    "    # calculate daylength\n",
+    "    dec = -np.asin(np.sin(np.radians(23.45)) * np.cos(2.0 * np.pi * (iday + 10.0) / 365.0))\n",
+    "    sinld = multiply(np.sin(latitude_rad), np.sin(dec))\n",
+    "    cosld = multiply(np.cos(latitude_rad), np.cos(dec))\n",
+    "    aob = (-np.sin(np.radians(angle)) + sinld) / cosld\n",
+    "\n",
+    "    # daylength — replace scalar if/elif/else with torch.where for batched support\n",
+    "    aob_clipped = np.clip(aob, -1.0, 1.0)\n",
+    "    daylp = 12.0 * (1.0 + 2.0 * np.asin(aob_clipped) / np.pi)\n",
+    "    daylp = np.where(aob < -1.0, 0.0, daylp)\n",
+    "    daylp = np.where(aob > 1.0, 24.0, daylp)\n",
+    "    return daylp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "055abe62-6a57-4579-90ba-9638ccfb426f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_data[\"DAYLENGTH\"] = xr.DataArray(\n",
+    "    daylength(weather_data.DAY.data, weather_data.LAT.data),\n",
+    "    dims=(\"location\", \"DAY\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "09d1317d-71e2-4165-afa6-70cfd43bdb6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x11a647aa0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weather_data[\"DAYLENGTH\"].plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f602922d-9f2b-4fed-9f6d-fd92d62500d1",
+   "metadata": {},
+   "source": [
+    "For all locations, simulations consider a selected number of days (here 300) that follow the date of sowing (DOS):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d927320f-e0c8-4f23-8804-6a0f26d08e88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NDAYS = 300  # Max simulated days\n",
+    "DOS = xr.DataArray(observations.DOS.values, dims=\"location\")\n",
+    "delta = np.array(NDAYS, np.dtype('timedelta64[D]'))\n",
+    "mask = (weather_data[\"DAY\"] >= DOS) & (weather_data[\"DAY\"] < DOS + delta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfab21d9-255a-4447-9b00-3e7b66ba650c",
+   "metadata": {},
+   "source": [
+    "All the remaining weather data can be masked out, e.g.:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b6c243d9-f6c9-4942-b7aa-6187b299f254",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x11a81e000>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TEMP = weather_data[\"TEMP\"].where(mask)\n",
+    "TEMP.plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ea3bde6-7542-44c7-a9a6-901efcde1e33",
+   "metadata": {},
+   "source": [
+    "We can thus extract drop these masked values from the dataset, effectively setting the zero along the time axis on the DOS: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6cd8ca53-60bd-4105-bd3b-1d374221b12e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_days(data, day, dos):\n",
+    "    \"\"\"Extract days from DOS to DOS + delta.\"\"\"\n",
+    "    indices = np.where((day >= dos) & (day < dos + delta))\n",
+    "    return np.take(data, indices)\n",
+    "\n",
+    "selected = xr.apply_ufunc(\n",
+    "    extract_days,\n",
+    "    weather_data[[\"TEMP\", \"DAYLENGTH\"]],\n",
+    "    weather_data[\"DAY\"],\n",
+    "    DOS,\n",
+    "    input_core_dims=[[\"DAY\"], [\"DAY\"], []],\n",
+    "    output_core_dims=[[\"day\"]],\n",
+    "    vectorize=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "035753d8-f551-40e2-8723-0425abb52b78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x11a8d0890>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "selected[\"TEMP\"].plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6740afc8-9724-4f5d-bb7c-c3d859e7ff40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x11a99bd10>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "selected[\"DAYLENGTH\"].plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "218079e6-839e-492d-87fc-6b6b74e566b7",
+   "metadata": {},
+   "source": [
+    "We now assign fictitious date labels to the time axis: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ab2946a9-83e7-436f-8aa7-16bce0dc0eed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "START_DATE = \"1900-01-01\"\n",
+    "selected[\"day\"] = (\"day\", pd.date_range(START_DATE, periods=NDAYS))\n",
+    "selected = selected.set_coords(\"day\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "655e0ac9-5717-4e4c-a63e-aa896731630f",
+   "metadata": {},
+   "source": [
+    "And define an derived class to be able to use easily access tensor weather data in diffWOFOST: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9d994dd9-6577-4e8b-84a8-7042e888b0ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class XarrayWeatherDataProvider(TensorWeatherDataProvider):\n",
+    "    def _get_variable_shape(self):\n",
+    "        return self.store.isel(day=0).TEMP.shape\n",
+    "\n",
+    "    def _get_variables(self, day):\n",
+    "        store = self.store.sel(day=str(day))\n",
+    "        vars = {k: store[k].data for k in (\"TEMP\", \"DAYLENGTH\")}\n",
+    "        return vars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "36d83679-2c36-4feb-9c76-ba511d3bf9a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wdp = XarrayWeatherDataProvider(store=selected, meteo_range_checks=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1cb7878-d41a-4998-8634-2f05ea19672e",
+   "metadata": {},
+   "source": [
+    "We now define the other required elements to run a simulation, i.e. the parameter provider:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b056dd2f-4c18-440b-8ff1-6eaf6b46f447",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pcse.base import ParameterProvider\n",
+    "from pcse.input import YAMLCropDataProvider\n",
+    "\n",
+    "cropd = YAMLCropDataProvider()\n",
+    "params = ParameterProvider(cropdata=cropd)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36cfa81b-435c-4c36-940e-d4aae9e8977f",
+   "metadata": {},
+   "source": [
+    "the model configuration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "da57da36-7d89-4fb6-a330-3aab41481ea1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phenology_config = Configuration(\n",
+    "    CROP=DVS_Phenology,\n",
+    "    OUTPUT_VARS=[\"DVS\", \"STAGE\", \"DOS\", \"DOA\", \"DOM\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9034830-b3ca-45e5-aba2-42ab4143b93a",
+   "metadata": {},
+   "source": [
+    "and the agromanagement (note that we use the fictitious starting date here):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "22caa4eb-7df1-467f-b854-255cc16c4255",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agro_yaml = f\"\"\"\n",
+    "- {START_DATE}:\n",
+    "    CropCalendar:\n",
+    "        crop_name: barley\n",
+    "        variety_name: Spring_barley_301\n",
+    "        crop_start_date: {START_DATE}\n",
+    "        crop_start_type: sowing\n",
+    "        crop_end_date:\n",
+    "        crop_end_type: maturity\n",
+    "        max_duration: 600\n",
+    "    TimedEvents:\n",
+    "    StateEvents:\n",
+    "\"\"\"\n",
+    "agro = yaml.safe_load(agro_yaml)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2f62f30-e14a-49bc-a558-160b60ab5e7e",
+   "metadata": {},
+   "source": [
+    "We can finally setup the engine and run the phenology simulation for all locations in one execution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3bceb237-599d-4302-ba7f-4efd86dbc430",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "engine = Engine(config=phenology_config)\n",
+    "engine.setup(\n",
+    "    parameterprovider=params,\n",
+    "    weatherdataprovider=wdp,\n",
+    "    agromanagement=agro,\n",
+    ")\n",
+    "engine.run_till_terminate()\n",
+    "out = engine.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21ca8755-4983-49b2-81ba-e6d433b68298",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "Visualizing the output (different colors refer to different locations):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "072c0330-b9ea-4496-8d9a-2ced5e3f8c3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'day')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(torch.stack([el[\"STAGE\"] for el in out]))\n",
+    "plt.ylabel(\"STAGE\")\n",
+    "plt.xlabel(\"day\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5c7718b-dbbd-4f47-87df-d2b2bd51866c",
+   "metadata": {},
+   "source": [
+    "From the last time step of the simulation, we can extract the simulated dates of anthesis (DOA) and dates of maturity (DOM), and compute the corresponding deltas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "687b5570-00c2-4716-b302-5dbdacd58fde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final = out[-1] # last time step of simulation\n",
+    "days_to_anthesis_sim = np.asarray(final[\"DOA\"] - final[\"DOS\"])\n",
+    "days_to_maturity_sim = np.asarray(final[\"DOM\"] - final[\"DOS\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6a50011-7a4d-4ffd-83d7-4e30b79eb188",
+   "metadata": {},
+   "source": [
+    "Some post-processing of observations, e.g. to estimated maturity from harvest by subtracting 7 days:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c6b13423-90bc-4408-ad43-3d7c355d9c7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Estimate maturity from harvest if DOM is null\n",
+    "days_to_harvest = 7\n",
+    "ix = pd.isna(observations.DOM)\n",
+    "observations.loc[ix, \"DOM\"] = observations.loc[ix,\"DOH\"] - pd.Timedelta(days_to_harvest, unit=\"days\")\n",
+    "days_to_anthesis_obs = (observations.DOA - observations.DOS).dt.days\n",
+    "days_to_maturity_obs = (observations.DOM - observations.DOS).dt.days"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9dc76f5-7b89-4a2e-9584-73186592783f",
+   "metadata": {},
+   "source": [
+    "Visualize the comparison of observations with simulations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7bb9fabb-7cc2-4ec2-8290-06212c227922",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(figsize=(8,8))\n",
+    "ix = pd.isna(days_to_anthesis_obs)\n",
+    "axes.scatter(days_to_anthesis_obs[~ix], days_to_anthesis_sim[~ix], label=\"Days2Anthesis\", color='blue', marker=\"o\")\n",
+    "ix = pd.isna(days_to_maturity_obs)\n",
+    "axes.scatter(days_to_maturity_obs[~ix], days_to_maturity_sim[~ix], label=\"Days2Maturity\", color='orange', marker=\"o\")\n",
+    "limits = [40, 200]\n",
+    "axes.set_xlabel(\"Observed days to stage\")\n",
+    "axes.set_ylabel(\"Simulated days to stage\")\n",
+    "axes.set_xlim(limits)\n",
+    "axes.set_ylim(limits)\n",
+    "axes.plot(limits, limits, marker=\"\", linestyle=\"--\", color='black')\n",
+    "r = fig.legend(loc=2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "diffWOFOST",
+   "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": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/diffwofost/physical_models/base/weather.py b/src/diffwofost/physical_models/base/weather.py
new file mode 100644
index 0000000..074bae8
--- /dev/null
+++ b/src/diffwofost/physical_models/base/weather.py
@@ -0,0 +1,104 @@
+from abc import ABC
+from abc import abstractmethod
+from dataclasses import dataclass
+from diffwofost.physical_models.config import ComputeConfig
+from diffwofost.physical_models.utils import _broadcast_to
+
+
+@dataclass(frozen=True)
+class WeatherVariable:
+    unit: str
+    range: tuple[float, float]
+
+
+# Units and ranges for meteorological variables
+WEATHER_VARIABLES = {
+    "LAT": WeatherVariable("Degrees", (-90.0, 90.0)),
+    "LON": WeatherVariable("Degrees", (-180.0, 180.0)),
+    "ELEV": WeatherVariable("m", (-300, 6000)),
+    "IRRAD": WeatherVariable("J/m2/day", (0.0, 40e6)),
+    "TMIN": WeatherVariable("Celsius", (-50.0, 60.0)),
+    "TMAX": WeatherVariable("Celsius", (-50.0, 60.0)),
+    "VAP": WeatherVariable(
+        "hPa", (0.06, 199.3)
+    ),  # computed as sat. vapour pressure at -50, 60 Celsius  # noqa: E501
+    "RAIN": WeatherVariable("cm/day", (0, 25)),
+    "E0": WeatherVariable("cm/day", (0.0, 2.5)),
+    "ES0": WeatherVariable("cm/day", (0.0, 2.5)),
+    "ET0": WeatherVariable("cm/day", (0.0, 2.5)),
+    "SNOWDEPTH": WeatherVariable("cm", (0.0, 250.0)),
+    "TEMP": WeatherVariable("Celsius", (-50.0, 60.0)),
+    "TMINRA": WeatherVariable("Celsius", (-50.0, 60.0)),
+    "WIND": WeatherVariable("m/s", (0.0, 100.0)),
+}
+
+
+class TensorWeatherDataProvider(ABC):
+    def __init__(self, store, meteo_range_checks=True):
+        self._shape = None
+
+        # Optionally carry out range checks for the weather variables
+        if meteo_range_checks:
+            self._meteo_range_check(store)
+
+        # Extract weather from store
+        self.store = store
+        self.shape = self._get_variable_shape()
+
+    @abstractmethod
+    def _get_variable_shape(self):
+        """Determine the shape of the weather variables, excluding the time dimension.
+
+        Returns:
+            tuple: Base shape of the weather variables.
+        """
+        pass
+
+    @abstractmethod
+    def _get_variables(self, day):
+        """Extract the available weather variables for the given date from the store.
+
+        Args:
+            day (datetime.date): date
+
+        Returns:
+            dict: Collection of available variables as extracted from the store.
+        """
+        pass
+
+    def __call__(self, day):
+        """Get the available weather variables on a given date.
+
+        Args:
+            day (datetime.date): date
+
+        Returns:
+            dict: Collection of available weather variables as `torch.Tensor`
+        """
+        vars = {}
+        for key, var in self._get_variables(day).items():
+            vars[key] = _broadcast_to(
+                var,
+                shape=self.shape,
+                dtype=ComputeConfig.get_dtype(),
+                device=ComputeConfig.get_device(),
+            )
+        return vars
+
+    @property
+    def shape(self):
+        """Base shape of the weather variables.
+
+        This is the shape of the data variables excluding the time dimension.
+        """
+        return self._shape
+
+    @shape.setter
+    def shape(self, shape):
+        if self.shape and self.shape != shape:
+            raise ValueError(f"Container shape already set to {self.shape}")
+        self._shape = shape
+
+    def _meteo_range_check(self, store):
+        """Check whether entries in the store fit acceptable ranges."""
+        raise NotImplementedError("Range checks have to be implemented.")
diff --git a/src/diffwofost/physical_models/crop/phenology.py b/src/diffwofost/physical_models/crop/phenology.py
index 2205686..9fd621c 100644
--- a/src/diffwofost/physical_models/crop/phenology.py
+++ b/src/diffwofost/physical_models/crop/phenology.py
@@ -182,7 +182,7 @@ def calc_rates(self, day, drv):
         VERNBASE = params.VERNBASE
         DVS = self.kiosk["DVS"]
 
-        TEMP = _get_drv(drv.TEMP, self.params.shape, self.dtype, self.device)
+        TEMP = _get_drv(drv["TEMP"], self.params.shape, self.dtype, self.device)
 
         # Operate elementwise only on elements not yet vernalised
         not_vernalised = ~self.states.ISVERNALISED
@@ -505,8 +505,11 @@ def calc_rates(self, day, drv):
 
         # Day length sensitivity
         # daylength returns a Tensor directly; broadcast to parameter shape.
-        DAYLP = daylength(day, drv.LAT, dtype=self.dtype, device=self.device)
-        DAYLP_t = _broadcast_to(DAYLP, p.shape, dtype=self.dtype, device=self.device)
+        if "DAYLENGTH" in drv:
+            DAYLP_t = _get_drv(drv["DAYLENGTH"], p.shape, self.dtype, self.device)
+        else:
+            DAYLP = daylength(day, drv["LAT"], dtype=self.dtype, device=self.device)
+            DAYLP_t = _broadcast_to(DAYLP, p.shape, dtype=self.dtype, device=self.device)
         # Compute DVRED conditionally based on IDSL >= 1
         safe_den = p.DLO - p.DLC
         safe_den = safe_den.sign() * torch.maximum(torch.abs(safe_den), self._epsilon)
@@ -526,7 +529,7 @@ def calc_rates(self, day, drv):
                 self._ones,
             )
 
-        TEMP = _get_drv(drv.TEMP, p.shape, self.dtype, self.device)
+        TEMP = _get_drv(drv["TEMP"], p.shape, self.dtype, self.device)
 
         # Initialize all rate variables
         r.DTSUME = self._zeros
diff --git a/src/diffwofost/physical_models/engine.py b/src/diffwofost/physical_models/engine.py
index ef7a010..94b603c 100644
--- a/src/diffwofost/physical_models/engine.py
+++ b/src/diffwofost/physical_models/engine.py
@@ -12,6 +12,7 @@
 from pcse.base import BaseEngine
 from pcse.engine import Engine as PcseEngine
 from pcse.timer import Timer
+from pcse.traitlets import Any
 from pcse.traitlets import Instance
 from diffwofost.physical_models.config import Configuration
 from diffwofost.physical_models.variablekiosk import VariableKiosk
@@ -26,6 +27,7 @@ class Engine(PcseEngine):
     """
 
     mconf = Instance(Configuration)
+    weatherdataprovider = Any
 
     def __init__(
         self,
@@ -103,7 +105,8 @@ def setup(
         self._reset_runtime_state()
 
         self.parameterprovider = parameterprovider
-        self._shape = _get_params_shape(self.parameterprovider)
+        self.weatherdataprovider = weatherdataprovider
+        self._shape = _get_shape(self.parameterprovider, self.weatherdataprovider)
 
         # Variable kiosk for registering and publishing variables
         self.kiosk = VariableKiosk(external_states)
@@ -140,6 +143,10 @@ def setup(
         self.calc_rates(self.day, self.drv)
         return self
 
+    def _get_driving_variables(self, day):
+        """Get driving variables, compute derived properties and return it."""
+        return self.weatherdataprovider(day)
+
     def _on_CROP_START(
         self, day, crop_name=None, variety_name=None, crop_start_type=None, crop_end_type=None
     ):
@@ -189,6 +196,30 @@ def _finish_cropsimulation(self, day):
             gc.collect()
 
 
+def _get_shape(parameterprovider, weatherdataprovider):
+    """Infer common tensor shape from the data providers.
+
+    Args:
+        parameterprovider: Parameter provider.
+        weatherdataprovider: Weather data provider.
+
+    Raises:
+        ValueError: If non-matching shapes are found for the data providers.
+
+    Returns:
+        tuple: Shared tensor shape.
+    """
+    params_shape = _get_params_shape(parameterprovider)
+    weather_shape = getattr(weatherdataprovider, "shape", ())
+    if not params_shape and not weather_shape:
+        if params_shape != weather_shape:
+            raise ValueError(
+                "Non-matching shapes between parameter and weather data: "
+                f"{params_shape} and {weather_shape}"
+            )
+    return params_shape or weather_shape
+
+
 def _get_params_shape(parameterprovider):
     """Infer the common tensor batch shape from a parameter provider.