Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment
We propose ScaleCast, a regional forecasting framework that addresses these challenges through Global-Regional Alignment.
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Key points
- Kilometer-scale regional weather forecasting is essential for local weather warnings and weather-sensitive decisions.
- Pretrained global weather models offer an efficient source of large-scale forecasts, motivating their reuse to guide high-resolution regional prediction.
- However, this coupling requires aligning global and regional representations across different grids and integrating global guidance with local interactions to advance regional states.
- Experiments using ERA5 global analyses on a 0.25-degree grid and CERRA regional reanalysis at 5.5 km spacing demonstrate improved regional forecasts across surface and upper-air variables, with a single trained model supporting multiple global forecast drivers (i.e., Pangu-Weather, GraphCast, and HRES) without specific retraining.
Sources (1)
- [1]Learning Kilometer-Scale Weather Prediction with Global-Regional AlignmentarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:43 PM
We propose ScaleCast, a regional forecasting framework that addresses these challenges through Global-Regional Alignment.
Kilometer-scale regional weather forecasting is essential for local weather warnings and weather-sensitive decisions.
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