CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting
arXiv:2510.20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerical weather prediction (NWP) systems and data-driven models. We present CSU-PCAST, a deep learning-based ensemble forecasting framework for global precipitation prediction. The model is trained using ERA5 atmospheric and surface variables at 0.25{\deg} resolution with precipitation labels from NASA's IMERG dataset. CSU-PCAST uses 57 prognostic variables and static geographical fields to predict both
Advances in AI, particularly deep learning and transformer models, are enabling more sophisticated and accurate prediction systems for complex natural phenomena like weather.
Improved medium-range precipitation forecasting is critical for risk management in agriculture, disaster preparedness, and water resource allocation globally.
The ability to predict precipitation more accurately further ahead in time provides better lead times for mitigating climate-related risks and optimizing resource use.
- · Agriculture sector
- · Insurance companies
- · Water resource management agencies
- · AI/ML model developers
- · Regions unprepared for extreme weather events
- · Traditional forecasting methodologies
More precise warnings and resource allocation for impending weather events due to improved forecast accuracy.
Reduced economic losses from climate-related disasters and optimized agricultural yields in variable environments.
Enhanced resilience of critical infrastructure and food systems, impacting global geopolitical stability regarding resource security.
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Read at arXiv cs.LG