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Add Met Office example notebooks #311

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138 changes: 138 additions & 0 deletions datasets/met-office/met-office-global-deterministic-height.ipynb
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Original file line number Diff line number Diff line change
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{
"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
}
138 changes: 138 additions & 0 deletions datasets/met-office/met-office-global-deterministic-near-surface.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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
}
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