Improving Medium Range Severe Weather Prediction through Transformer Post-processing of AI Weather Forecasts

• URL: http://arxiv.org/abs/2505.11750v2
• Date: Tue, 20 May 2025 17:42:26 GMT
• Title: Improving Medium Range Severe Weather Prediction through Transformer Post-processing of AI Weather Forecasts
• Authors: Zhanxiang Hua, Ryan Sobash, David John Gagne II, Yingkai Sha, Alexandra Anderson-Frey,
• Abstract summary: This study introduces a novel approach leveraging decoder-only transformer networks to post-process AI-based weather forecasts.<n>Our method treats forecast lead times as sequential tokens'', enabling the transformer to learn complex temporal relationships.<n>Results demonstrate that the transformer-based post-processing significantly enhances forecast skill compared to dense neural networks.
• Score: 39.58317527488534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the skill of medium-range (1-8 day) severe weather prediction is crucial for mitigating societal impacts. This study introduces a novel approach leveraging decoder-only transformer networks to post-process AI-based weather forecasts, specifically from the Pangu-Weather model, for improved severe weather guidance. Unlike traditional post-processing methods that use a dense neural network to predict the probability of severe weather using discrete forecast samples, our method treats forecast lead times as sequential ``tokens'', enabling the transformer to learn complex temporal relationships within the evolving atmospheric state. We compare this approach against post-processing of the Global Forecast System (GFS) using both a traditional dense neural network and our transformer, as well as configurations that exclude convective parameters to fairly evaluate the impact of using the Pangu-Weather AI model. Results demonstrate that the transformer-based post-processing significantly enhances forecast skill compared to dense neural networks. Furthermore, AI-driven forecasts, particularly Pangu-Weather initialized from high resolution analysis, exhibit superior performance to GFS in the medium-range, even without explicit convective parameters. Our approach offers improved accuracy, and reliability, which also provides interpretability through feature attribution analysis, advancing medium-range severe weather prediction capabilities.

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