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add Met Office example notebooks
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‎datasets/met-office/met-office-global-deterministic-height.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fbf471b1",
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"metadata": {},
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"source": [
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"# Accessing Global Height data from Microsoft Planetary Computer"
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]
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},
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{
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"cell_type": "markdown",
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"id": "941120d0",
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"metadata": {},
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"source": [
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"Set-up the pystac client to access the Microsoft Planetary Computer catalog"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4bafd899",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pystac_client import Client\n",
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"import planetary_computer\n",
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"\n",
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"catalog = Client.open(\n",
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" \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
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" modifier=planetary_computer.sign_inplace,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b5a6a858",
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"metadata": {},
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"source": [
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"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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2132d393",
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"metadata": {},
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"outputs": [],
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"source": [
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"collections = [\"met-office-global-deterministic-height\"]\n",
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"asset_id = \"cloud_amount_on_height_levels\"\n",
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"datacube_extension_filters = {\n",
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" \"op\": \"and\",\n",
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" \"args\": [\n",
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" {\n",
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" \"op\": \"=\",\n",
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" \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2026-01-14T12:00:00Z\" ]\n",
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" },\n",
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" {\n",
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" \"op\": \"=\",\n",
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" \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0144H00M\" ]\n",
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" }\n",
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" ]\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dec7c74b",
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"metadata": {},
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"source": [
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"Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "edb71afa",
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"metadata": {},
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"outputs": [],
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"source": [
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"search = catalog.search(\n",
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" collections=collections,\n",
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" filter_lang= \"cql2-json\",\n",
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" filter=datacube_extension_filters\n",
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")\n",
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"\n",
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"items = search.item_collection()\n",
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"asset_url = items.items[0].assets[asset_id].href"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee73ba3d",
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"metadata": {},
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"source": [
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"Example usage: Plot NetCDF data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fbc72d2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fsspec\n",
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"import xarray as xr\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
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"plt.figure(figsize=(10, 5))\n",
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"example_netcdf[\"cloud_volume_fraction_in_atmosphere_layer\"].plot()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎datasets/met-office/met-office-global-deterministic-near-surface.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fbf471b1",
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"metadata": {},
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"source": [
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"# Accessing Global Surface data from Microsoft Planetary Computer"
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]
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},
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{
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"cell_type": "markdown",
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"id": "941120d0",
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"metadata": {},
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"source": [
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"Set-up the pystac client to access the Microsoft Planetary Computer catalog"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4bafd899",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pystac_client import Client\n",
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"import planetary_computer\n",
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"\n",
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"catalog = Client.open(\n",
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" \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
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" modifier=planetary_computer.sign_inplace,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b5a6a858",
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"metadata": {},
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"source": [
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"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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2132d393",
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"metadata": {},
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"outputs": [],
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"source": [
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"collections = [\"met-office-global-deterministic-near-surface\"]\n",
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"asset_id = \"temperature_at_surface\"\n",
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"datacube_extension_filters = {\n",
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" \"op\": \"and\",\n",
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" \"args\": [\n",
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" {\n",
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" \"op\": \"=\",\n",
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" \"args\": [ { \"property\": \"forecast:reference_datetime\" }, \"2025-12-05T12:00:00Z\" ]\n",
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" },\n",
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" {\n",
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" \"op\": \"=\",\n",
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" \"args\": [ { \"property\": \"forecast:horizon\" }, \"PT0120H00M\" ]\n",
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" }\n",
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" ]\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dec7c74b",
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"metadata": {},
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"source": [
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"Search Planetary Computer catalog for STAC items and retrieve STAC Asset URL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "edb71afa",
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"metadata": {},
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"outputs": [],
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"source": [
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"search = catalog.search(\n",
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" collections=collections,\n",
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" filter_lang= \"cql2-json\",\n",
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" filter=datacube_extension_filters\n",
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")\n",
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"\n",
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"items = search.item_collection()\n",
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"asset_url = items.items[0].assets[asset_id].href"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee73ba3d",
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"metadata": {},
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"source": [
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"Example usage: Plot NetCDF data on a map"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fbc72d2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fsspec\n",
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"import xarray as xr\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
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"plt.figure(figsize=(10, 5))\n",
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"example_netcdf[\"surface_temperature\"].plot()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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