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arXiv CS.AI·1d ago·5 min read
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models
TL;DR
WeatherBridge is a novel adapter that transforms initial states from numerical weather prediction systems to match the ERA5 dataset, enhancing the performance of pretrained weather forecasting models. It achieves a median reduction of 10.2% in forecast error across various scenarios.
✦ Why It Matters
Implement WeatherBridge to improve your weather forecasting models using diverse NWP inputs without retraining.
Key Takeaways
- Utilize WeatherBridge to adapt NWP data for improved forecasts.
- Leverage Swin-UNet for structured residual corrections in weather models.
- Combine latitude-weighted reconstruction with ORA for optimal state adaptation.
- Achieve significant RMSE reductions in weather predictions across various scenarios.
How It Works
WeatherBridge employs a Swin-UNet architecture to create a mapping from heterogeneous NWP initial states to the ERA5 reference states. It predicts structured residual corrections and integrates source embeddings at multiple spatial scales, allowing the model to effectively capture both shared atmospheric structures and specific discrepancies from different data sources.