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Add Oya estimation dataset and catalog to the public catalog on earth engine.
for brempong PiperOrigin-RevId: 828446459
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‎catalog/global_precipitation_nowcast/catalog.jsonnet‎

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local id = 'global_precipitation_nowcast';
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local subdir = id;
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local description = importstr 'description.md';
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local ee_const = import 'earthengine_const.libsonnet';
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local ee = import 'earthengine.libsonnet';
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local basename = 'catalog';
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local base_filename = basename + '.json';
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local base_url = ee_const.catalog_base + subdir + '/';
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local parent_url = ee_const.catalog_base + 'catalog.json';
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local self_url = base_url + base_filename;
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{
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stac_version: ee_const.stac_version,
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type: ee_const.stac_type.catalog,
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id: id,
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title: id,
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description: description,
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links: [
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ee.link.root(),
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ee.link.parent(parent_url),
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ee.link.self_link(self_url),
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ee.link.child_collection('projects_global_precipitation_nowcast_assets_global_estimation', base_url),
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],
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}

‎catalog/global_precipitation_nowcast/description.md‎

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Dataset of Quasi-Global Precipitation Estimates.

‎catalog/global_precipitation_nowcast/projects_global_precipitation_nowcast_assets_global_estimation.jsonnet‎

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local id = 'projects/global_precipitation_nowcast/assets/global_estimation';
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local subdir = 'global_precipitation_nowcast';
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local ee_const = import 'earthengine_const.libsonnet';
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local ee = import 'earthengine.libsonnet';
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local spdx = import 'spdx.libsonnet';
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local units = import 'units.libsonnet';
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local license = spdx.cc_by_4_0;
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local basename = std.strReplace(id, '/', '_');
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local self_ee_catalog_url = ee_const.ee_catalog_url + basename;
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{
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'gee:user_uploaded': true,
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id: id,
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title: 'Oya: Quasi-Global Precipitation Estimates',
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description: |||
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Oya is a quasi-global, high-resolution precipitation estimation dataset
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derived from geostationary (GEO) satellite observations.
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The Oya model utilizes the full spectrum of visible and infrared (VIS-IR)
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channels from a constellation of GEO satellites — including GOES-16/18,
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Meteosat-9/10, and Himawari-8/9 — to generate precipitation estimates
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covering 60°N to 60°S.
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To address the inherent data imbalance between rain and no-rain events,
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Oya employs a two-stage deep learning approach. It combines two U-Net
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models: one specialized for precipitation detection and another for
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Quantitative Precipitation Estimation (QPE). The models are
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trained using high-resolution GPM Combined Radar-Radiometer Algorithm
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(CORRA) v07 data as ground truth and are pre-trained on IMERG-Final
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retrievals to enhance robustness.
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Oya demonstrates superior performance compared to existing operational
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GEO-based baselines, such as the PERSIANN DynamicInfrared Rain Rate
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(PDIR-Now) and the Convective Rainfall Rate (CRR), across all precipitation
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intensities. It also outperforms Passive Microwave (PMW) based products like
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the Integrated Multisatellite Retrievals for GPM (IMERG) Early and is
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competitive with research-grade products like IMERG Final, which has a
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3.5-month latency. This dataset includes a half-hourly historical record
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generated from 2004 with a spatial resolution of 5km.
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Accurate precipitation data is critical for applications in agriculture,
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water resource management, and disaster preparedness, particularly in
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regions where ground-based observation networks are sparse.
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For more details, see the associated paper: "Oya: Deep Learning for Accurate
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Global Precipitation Estimation".
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|||,
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'gee:categories': ['precipitation'],
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keywords: [
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'weather',
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'climate',
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'geophysical',
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'gpm',
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'precipitation',
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// 'half_hourly',
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],
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providers: [
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ee.producer_provider('Google', 'https://research.google/'),
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ee.host_provider(self_ee_catalog_url),
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],
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extent: ee.extent_global('2004-01-01T00:00:00Z', null),
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// Summaries contain additional information specific to the dataset type.
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summaries: {
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gsd: [5000],
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'eo:bands': [
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{
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name: 'precipitation',
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description: 'Precipitation estimate',
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'gee:units': 'mm/hr'
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},
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],
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'gee:visualizations': [
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{
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display_name: 'Precipitation retrieval (mm/hr)',
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// Do not use too many significant digits.
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lookat: {lon: 0, lat: 0, zoom: 3},
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image_visualization: {
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band_vis: {
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min: [0],
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max: [150],
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bands: ['precipitation'],
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}
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},
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},
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],
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'gee:schema': [
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{
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name: 'ingestion_time_utc',
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description: 'The time of ingestion.',
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type: ee_const.var_type.string,
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},
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],
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},
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'gee:interval': {
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type: 'cadence',
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unit: 'minute',
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interval: 30,
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},
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'gee:terms_of_use': ee.gee_terms_of_use(license),
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'gee:status': 'beta',
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'gee:type': ee_const.gee_type.image_collection,
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license: license.id,
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links: ee.standardLinks(subdir, id),
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type: ee_const.stac_type.collection,
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stac_version: ee_const.stac_version,
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stac_extensions: [
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ee_const.ext_eo,
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],
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}

‎examples/global_precipitation_nowcast/projects_global_precipitation_nowcast_assets_global_estimation.js‎

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var imageCollection = ee.ImageCollection(
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"projects/global-precipitation-nowcast/assets/global_estimation"
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);
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// Select a single estimate.
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var singleEstimate = imageCollection.filterDate('2022-12-30T12-00').first();
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// mask to remove 0 values
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var masked = singleEstimate.selfMask();
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// Display on map.
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var visParams = {
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min: 0,
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max: 15,
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palette: [
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'000096','0064ff', '00b4ff', '33db80', '9beb4a',
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'ffeb00', 'ffb300', 'ff6400', 'eb1e00', 'af0000'
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]
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};
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Map.addLayer(
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masked,
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visParams,
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"Preciptation retrieval for 2022-12-30T12-00 in mm/hr"
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);

‎examples/global_precipitation_nowcast/projects_global_precipitation_nowcast_assets_global_estimation_preview.js‎

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var imageCollection = ee.ImageCollection(
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"projects/global-precipitation-nowcast/assets/global_estimation"
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);
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// Select a single estimate.
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var single_estimate = imageCollection.filterDate('2022-12-30T12-00').first();
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// mask to remove 0 values
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var masked = single_estimate.selfMask();
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var lon = 8;
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var lat = 2;
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Map.setCenter(lon, lat, 2);
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// Degrees in EPSG:4326
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var lon_delta = 50;
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var lat_delta = 50;
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// Width and height of the thumbnail image.
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var pixels = 256;
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var areaOfInterest = ee.Geometry.Rectangle(
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[lon - lon_delta, lat - lat_delta, lon + lon_delta, lat + lat_delta], null,
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false);
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// Display on map.
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var visParams = {
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min: 0,
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max: 15,
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palette: [
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'000096','0064ff', '00b4ff', '33db80', '9beb4a',
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'ffeb00', 'ffb300', 'ff6400', 'eb1e00', 'af0000'
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]
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};
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var imageVisualized = masked.visualize(visParams);
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var countryOutlines = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
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var outlinesStyle = {
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fillColor: 'FFFFFF',
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color: '000000',
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width: 2,
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};
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var countries = countryOutlines.style(outlinesStyle);
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var landWaterBackground = countries.visualize();
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// Combine the precipitation visualization with the country outlines.
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var combinedVisual = landWaterBackground.blend(imageVisualized);
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Map.addLayer(combinedVisual, {}, 'Precipitation with country boundaries');
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var imageParams = {
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dimensions: [pixels, pixels],
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region: areaOfInterest,
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crs: 'EPSG:4326',
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format: 'png',
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};
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print(ui.Thumbnail({image: combinedVisual, params: imageParams}));

‎owners/global-precipitation-nowcast.jsonnet‎

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{
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"id": "global_precipitation_nowcast",
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"homeBucket": "projects/global_precipitation_nowcast",
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"name": "Oya",
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"description": "Oya is a quasi-global, high-resolution precipitation estimation dataset derived from geostationary (GEO) satellite observations.",
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"type": "PUBLISHER",
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"link": "https://research.google",
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"contactDisplay": "oya-team@google.com",
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"contactLink": "mailto:oya-team@google.com"
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}

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