Add Oya estimation dataset and catalog to the public catalog on earth engine.

catalog/global_precipitation_nowcast/catalog.jsonnet

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

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

examples/global_precipitation_nowcast/projects_global_precipitation_nowcast_assets_global_estimation.js

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

examples/global_precipitation_nowcast/projects_global_precipitation_nowcast_assets_global_estimation_preview.js

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

owners/global-precipitation-nowcast.jsonnet

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