diff --git a/datasets/met-office/met-office-global-deterministic-height.ipynb b/datasets/met-office/met-office-global-deterministic-height.ipynb
new file mode 100644
index 0000000..d179b1c
--- /dev/null
+++ b/datasets/met-office/met-office-global-deterministic-height.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing Global Height data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2132d393",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-global-deterministic-height\"]\n",
+    "asset_id = \"cloud_amount_on_height_levels\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2026-01-14T12:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0144H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec7c74b",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee73ba3d",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbc72d2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"cloud_volume_fraction_in_atmosphere_layer\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/datasets/met-office/met-office-global-deterministic-near-surface.ipynb b/datasets/met-office/met-office-global-deterministic-near-surface.ipynb
new file mode 100644
index 0000000..b188188
--- /dev/null
+++ b/datasets/met-office/met-office-global-deterministic-near-surface.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing Global Surface data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2132d393",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-global-deterministic-near-surface\"]\n",
+    "asset_id = \"temperature_at_surface\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2025-12-05T12:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0120H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec7c74b",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee73ba3d",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data on a map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbc72d2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"surface_temperature\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/datasets/met-office/met-office-global-deterministic-pressure.ipynb b/datasets/met-office/met-office-global-deterministic-pressure.ipynb
new file mode 100644
index 0000000..1b2abfe
--- /dev/null
+++ b/datasets/met-office/met-office-global-deterministic-pressure.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing Global Pressure data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2132d393",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-global-deterministic-pressure\"]\n",
+    "asset_id = \"wind_speed_on_pressure_levels\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2025-12-05T12:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0135H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec7c74b",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee73ba3d",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbc72d2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"cloud_volume_fraction_in_atmosphere_layer\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/datasets/met-office/met-office-global-deterministic-whole-atmosphere.ipynb b/datasets/met-office/met-office-global-deterministic-whole-atmosphere.ipynb
new file mode 100644
index 0000000..812ea10
--- /dev/null
+++ b/datasets/met-office/met-office-global-deterministic-whole-atmosphere.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing Global Whole Atmosphere data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2132d393",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-global-deterministic-whole-atmosphere\"]\n",
+    "asset_id = \"CAPE_most_unstable_below_500hPa\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2025-12-05T12:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0081H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec7c74b",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee73ba3d",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbc72d2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"atmosphere_convective_available_potential_energy\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/datasets/met-office/met-office-uk-deterministic-height.ipynb b/datasets/met-office/met-office-uk-deterministic-height.ipynb
new file mode 100644
index 0000000..f248922
--- /dev/null
+++ b/datasets/met-office/met-office-uk-deterministic-height.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing UK Model Height data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cab8be1",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f95ecac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-uk-deterministic-height\"]\n",
+    "asset_id = \"wind_speed_on_height_levels\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2025-12-05T18:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0032H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56d27e19",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data on a map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45613dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"wind_speed\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/datasets/met-office/met-office-uk-deterministic-near-surface.ipynb b/datasets/met-office/met-office-uk-deterministic-near-surface.ipynb
new file mode 100644
index 0000000..84fbcaa
--- /dev/null
+++ b/datasets/met-office/met-office-uk-deterministic-near-surface.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing UK Model Surface data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cab8be1",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f95ecac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-uk-deterministic-near-surface\"]\n",
+    "asset_id = \"temperature_at_surface\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2025-12-05T18:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0001H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56d27e19",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data on a map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45613dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"surface_temperature\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/datasets/met-office/met-office-uk-deterministic-pressure.ipynb b/datasets/met-office/met-office-uk-deterministic-pressure.ipynb
new file mode 100644
index 0000000..17d5ee7
--- /dev/null
+++ b/datasets/met-office/met-office-uk-deterministic-pressure.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing UK Model Pressure Level data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cab8be1",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f95ecac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-uk-deterministic-pressure\"]\n",
+    "asset_id = \"wet_bulb_potential_temperature_on_pressure_levels\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2026-01-14T12:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0052H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56d27e19",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data on a map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45613dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"wet_bulb_potential_temperature\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/datasets/met-office/met-office-uk-deterministic-whole-atmosphere.ipynb b/datasets/met-office/met-office-uk-deterministic-whole-atmosphere.ipynb
new file mode 100644
index 0000000..f8bde1f
--- /dev/null
+++ b/datasets/met-office/met-office-uk-deterministic-whole-atmosphere.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbf471b1",
+   "metadata": {},
+   "source": [
+    "# Accessing UK Model Whole Atmosphere data from Microsoft Planetary Computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "941120d0",
+   "metadata": {},
+   "source": [
+    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bafd899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "import planetary_computer\n",
+    "\n",
+    "catalog = Client.open(\n",
+    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
+    "    modifier=planetary_computer.sign_inplace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cab8be1",
+   "metadata": {},
+   "source": [
+    "Define collection and assets to retrieve and construct [STAC API filters](https://github.com/stac-api-extensions/filter) for efficient query performance against Planetary Computer API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f95ecac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collections = [\"met-office-uk-deterministic-whole-atmosphere\"]\n",
+    "asset_id = \"lightning_flash_accumulation-PT01H\"\n",
+    "datacube_extension_filters = {\n",
+    "    \"op\": \"and\",\n",
+    "    \"args\": [\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2025-12-05T18:00:00Z\" ]\n",
+    "        },\n",
+    "        {\n",
+    "            \"op\": \"=\",\n",
+    "            \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0037H00M\" ]\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a6a858",
+   "metadata": {},
+   "source": [
+    "Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb71afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = catalog.search(\n",
+    "    collections=collections,\n",
+    "    filter_lang= \"cql2-json\",\n",
+    "    filter=datacube_extension_filters\n",
+    ")\n",
+    "\n",
+    "items = search.item_collection()\n",
+    "asset_url = items.items[0].assets[asset_id].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56d27e19",
+   "metadata": {},
+   "source": [
+    "Example usage: Plot NetCDF data on a map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45613dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "example_netcdf[\"number_of_lightning_flashes_per_unit_area\"].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}