Improving precipitation forecasts in an AI weather model using observational data

• URL: http://arxiv.org/abs/2609.03210v1
• Date: Wed, 02 Sep 2026 22:50:10 GMT
• Title: Improving precipitation forecasts in an AI weather model using observational data
• Abstract summary: Current global AIWP models are trained almost exclusively using one reanalysis dataset, ERA5.<n>Here we fine-tune a graph-transformer architecture with IMERG precipitation data at 0.25 resolution.<n>Our model exceeds the Brier skill score of state-of-the-art operational models on extreme rainfall prediction by 57% globally.
• Score: 20.61891646579683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence weather prediction (AIWP) systems now surpass state-of-the-art physical models for medium-range weather forecasting. Current global AIWP models are trained almost exclusively using one reanalysis dataset, ERA5, but it has known biases, particularly for precipitation. Here we fine-tune a graph-transformer architecture with IMERG precipitation data at 0.25° resolution. The resulting model improves medium-range continuous ranked probability scores by up to 19%, while also demonstrating superior skill for tropical storms and drizzle events. Our model exceeds the Brier skill score of state-of-the-art operational models on extreme rainfall prediction by 57% globally; however, a physics-based operational model remains more reliable for the heaviest precipitation events. Our results demonstrate that incorporating observations-based precipitation data directly into training can substantially improve precipitation forecasts.

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