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This pull request modernizes the weather forecasting sample by replacing the custom PyTorch FCN model with a pipeline that integrates Google Earth Engine, BigQuery ML (TimesFM 2.5), Google DeepMind's WeatherNext 3, and Gemini 3.8 Flash for multimodal storm risk nowcasting. The changes include new scripts for data ingestion, time-series loading via Apache Beam, zero-shot forecasting, multimodal analysis, and visualization, alongside updated documentation and comprehensive tests. Feedback on the changes highlights a critical issue in fetch_weather_patch.py where raw netCDF scale factors and offsets are incorrectly applied to already-calibrated GOES thermal IR bands, which would corrupt downstream calculations and visualizations.
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… Earth Engine, Apache Beam, BigQuery ML AI.FORECAST, and Gemini 3.8 Flash Problem: The legacy people-and-planet-ai/weather-forecasting sample spanned 45 files across four separate notebooks, four duplicate test harnesses, and a custom PyTorch/HuggingFace Cloud Run serving container with checked-in model weights (serving/model/pytorch_model.bin). It relied on the deprecated NASA/GPM_L3/IMERG_V06 Earth Engine collection, hardcoded NOAA/GOES/16/MCMIPF (which ceased operation in April 2025 in favor of GOES-19), used legacy Vertex AI custom training and gcloud AI Platform commands, and included an outdated user feedback form. Solution: - Consolidate the multi-notebook workflow into a single end-to-end tutorial notebook (README.ipynb) orchestrating standalone, single-task Python modules aligned with JoTa task_rules.yaml and DevSite Dataflow Python ML references. - Add fetch_weather_patch.py to query calibrated half-hourly precipitation from NASA GPM IMERG V07 (NASA/GPM_L3/IMERG_V07), cloud-top brightness and moisture bands from NOAA GOES-19 (NOAA/GOES/19/MCMIPF) and GOES-16 (NOAA/GOES/16/MCMIPF), and terrain elevation from MERIT DEM (MERIT/DEM/v1_0_3), featuring Hurricane Melissa (Oct 2025, Jamaica) and Hurricane Helene (Sept 2024, Southern Appalachians) case studies. - Add load_weather_time_series.py (dataflow_weather_load_time_series) to transform and load hourly satellite observations into BigQuery using Apache Beam. - Add forecast_precipitation.py (bigquery_weather_forecast_precipitation) for zero-shot multi-hour precipitation nowcasting using BigQuery ML AI.FORECAST with TimesFM 2.5. - Add analyze_storm_risk.py (googlegenaisdk_weather_analyze_storm_risk) for structured multimodal flood-risk synthesis combining multi-sensor satellite composites and forecasted precipitation curves using the Google Gen AI SDK (gemini-3.8-flash on global). - Document scaling to global medium-range ensemble forecasts using Google DeepMind WeatherNext 3 in Earth Engine and BigQuery. - Remove legacy notebooks, serving scaffolding, binary weights, static PNGs, and outdated feedback forms, and add fast offline unit tests in test_weather_forecasting.py.
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