CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
arXiv:2510.09484v3 Announce Type: replace Abstract: Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models. At such high resolutions, machine learning approaches for weather prediction increasingly rely on ensemble methods to produce probabilistic forecasts. However, existing machine learning LAMs are not scalable due to relying on computationally costly diffusion models or inefficient graph neural networks. We tackle this by introducing a new hybrid CNN/GNN architecture, tailored to the LAM weat
The increasing computational power and advancements in machine learning architectures are making sophisticated regional weather models more feasible and necessary to handle climate complexities.
Improved probabilistic regional weather forecasting can enhance preparedness for extreme weather events, optimize resource allocation, and support critical infrastructure planning, directly impacting economic and social stability.
The development of more scalable and efficient machine learning models for limited-area weather forecasting reduces dependency on computationally expensive traditional methods, opening doors for broader adoption and finer-grained predictions.
- · Regional planning authorities
- · Agricultural sector
- · Energy grid operators
- · Logistics and transportation
- · Legacy weather forecasting model developers
- · Sectors reliant on imprecise weather data
More accurate and localized weather predictions become widely available, reducing disaster impact and improving operational efficiencies.
Economic advantages accrue to regions and industries that can best leverage these granular forecasts, potentially leading to new weather-data-driven services.
The success of hybrid CNN/GNN architectures in weather forecasting could accelerate their application in other complex, spatiotemporal modeling domains, further advancing AI capabilities.
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Read at arXiv cs.LG