arXiv:2605.24009v1 Announce Type: cross Abstract: Convective available potential energy (CAPE) is an important variable for forecasting severe weather and understanding deep convection and precipitation. The latest versions of the Global Forecast System (GFS) and related Global Ensemble Forecast System (GEFS) have exhibited a bias towards underestimating CAPE values during the summertime. We train an artificial intelligence (AI) diffusion model to improve the skill and uncertainty quantification of afternoon 6-hour lead time ensemble forecasts over the United States. Our model takes a GFS CAPE
The increasing sophistication of AI models, specifically diffusion models, allows for more accurate and nuanced weather forecasting, addressing historical biases in existing systems.
Improved severe weather forecasting has significant economic and humanitarian implications, enhancing preparedness and mitigating losses across various sectors.
The accuracy and uncertainty quantification of short-term severe weather forecasts are enhanced, potentially leading to more reliable risk assessment and resource allocation.
- · Meteorological agencies
- · Agriculture industry
- · Insurance companies
- · Emergency services
- · Sectors reliant on outdated forecasting models
More precise warnings for severe weather events reduce property damage and save lives.
Economic benefits accrue from better planning in weather-sensitive industries like transportation and energy.
The application of advanced AI in scientific domains accelerates, potentially leading to breakthroughs in other complex systems modeling.
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Read at arXiv cs.LG