发表机构
University of Illinois Urbana-Champaign; Sharif University of Technology（伊利诺伊大学厄巴纳-香槟分校; 谢里夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出GenONet，将DeepONet作为GAN的生成器，结合对抗训练与物理信息损失，实现了长时程高分辨率降水预报，在多数指标上优于基线模型。
AI 中文摘要
高分辨率临近降水预报对于减轻恶劣天气的影响至关重要，但由于风暴演变速度快，这项任务仍存在难度。深度学习模型在该任务中已展现出巨大潜力，但其预测技能往往会在较长预报时程中退化，导致预报结果越来越模糊，无法捕捉风暴系统复杂的非线性演变。为解决这些局限，我们提出了时空U-DeepONet（GenONet），这是一种专门设计用于长达3小时的长时程降水预报的新型架构，旨在生成清晰且符合物理规律的结果。GenONet的架构在本任务中首次将深度算子网络（DeepONet）用作生成对抗网络（GAN）框架内的生成器，DeepONet学习降水的连续时间动力学，确保在长预报时程中的稳定性；针对时空判别器的对抗训练促使模型生成清晰、连贯的预报结果；而源自水汽守恒方程的物理信息损失正则化器，在我们的 ablation 实验设置中提升了结果的物理合理性。定量评估显示，我们的模型在大多数指标上均取得了更高的分数，尤其是针对高强度事件和较长提前期的情况；定性来看，GenONet生成的预报结果结构连贯且能保持完整性，而基线模型则退化为模糊的图案。最后， ablation 研究证实了该物理信息损失的益处，凸显了将算子学习与对抗训练相结合的优势。
英文摘要
High-resolution precipitation nowcasting is critical for reducing the impacts of severe weather but remains difficult because of rapid storm evolution. Deep learning models have shown great promise for this task, but their predictive skill often deteriorates over longer forecast horizons. This leads to increasingly blurry forecasts that fail to capture the complex, non-linear evolution of storm systems. In order to address these limitations, we introduce Spatio-Temporal U-DeepONet (GenONet), a novel architecture for long-range precipitation forecasting up to 3 hours, specifically designed to produce sharp and physically consistent results. GenONet's architecture pioneers the use of a Deep Operator Network (DeepONet) as a generator within a Generative Adversarial Network (GAN) framework for this task. The DeepONet learns the continuous-time dynamics of precipitation, ensuring stability over long forecast horizons. Adversial training against a spatio-temporal discriminator compels the model to produce sharp, coherent forecasts, while a physics-informed loss regularizer, derived from the Moisture Conservation Equation, improves physical plausibility in our ablation setting. Quantitative evaluations show that our model achieves consistently higher scores on most of the metrics, especially for highintensity events and at longer lead times. Qualitatively, GenONet produces structurally coherent forecasts that maintain their integrity, whereas baseline models degrade into indistinct patterns. Finally, an ablation study confirms the benefit of this physics-informed loss, highlighting the strength of combining operator learning with adversarial training.
Comments28 pages, 11 Figures
