Improving ensemble extreme precipitation forecasts using generative artificial intelligence
<|reference_start|>Improving ensemble extreme precipitation forecasts using generative artificial intelligence: An ensemble post-processing method is developed to improve the probabilistic forecasts of extreme precipitation events across the conterminous United States (CONUS). The method combines a 3-D Vision Transformer (ViT) for bias correction with a Latent Diffusion Model (LDM), a generative Artificial Intelligence (AI) method, to post-process 6-hourly precipitation ensemble forecasts and produce an enlarged generative ensemble that contains spatiotemporally consistent precipitation trajectories. These trajectories are expected to improve the characterization of extreme precipitation events and offer skillful multi-day accumulated and 6-hourly precipitation guidance. The method is tested using the Global Ensemble Forecast System (GEFS) precipitation forecasts out to day 6 and is verified against the Climate-Calibrated Precipitation Analysis (CCPA) data. Verification results indicate that the method generated skillful ensemble members with improved Continuous Ranked Probabilistic Skill Scores (CRPSSs) and Brier Skill Scores (BSSs) over the raw operational GEFS and a multivariate statistical post-processing baseline. It showed skillful and reliable probabilities for events at extreme precipitation thresholds. Explainability studies were further conducted, which revealed the decision-making process of the method and confirmed its effectiveness on ensemble member generation. This work introduces a novel, generative-AI-based approach to address the limitation of small numerical ensembles and the need for larger ensembles to identify extreme precipitation events.<|reference_end|>
@article{sha2024improving,
    title={Improving ensemble extreme precipitation forecasts using generative
  artificial intelligence},
    author={Yingkai Sha, Ryan A. Sobash, David John Gagne II},
    journal={arXiv preprint arXiv:2407.04882},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04882},
    primaryClass={physics.ao-ph cs.AI}
}