结合地理上下文与物理引导正则化的事件感知时空降水预报
Event-Aware Spatiotemporal Precipitation Forecasting with Geographic Context and Physics-Guided Regularization
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中文总结 AI 辅助
该研究开发了结合地理上下文、事件感知学习与物理引导正则化的时空降水预报框架，利用ERA5数据验证了各组件对提升降水预报效果的作用。
中文摘要 AI 辅助
逐小时降水预报面临多项独特统计挑战，包括预报因子与降水的空间异质关系、降水分布严重失衡，以及随预报时效增加降水事件预报技能逐步下降。我们开发了一种时空预报框架，通过三个互补组件应对这些挑战：显式地理表示、针对失衡降水的事件感知学习，以及源自大气水分收支的弱非对称正则化。物理信息被视为非对称约束而非额外预报目标，旨在抑制不符合物理规律的降水衰减，同时不取代数据驱动的预报。使用ERA5数据在区域、扩大域和空间子集设置下开展的实验显示，显式地理信息可提升空间场预报效果，而事件感知学习在检测中雨和大雨事件时提供最稳定的增益。物理正则化具有更具选择性的效果，主要减少扩大域上的系统性低估，同时在空间子集实验中改善较长时效的降水事件预报。这些结果表明，物理引导的益处取决于可用数据 regime，且在数据驱动的降水信息随预报时效增加而恶化时最为显著。
英文摘要
Hourly precipitation forecasting involves several distinct statistical challenges, including spatially varying predictor-precipitation relationships, a strongly imbalanced precipitation distribution, and progressive degradation of precipitation event skill with increasing lead time. We develop a spatiotemporal forecasting framework that addresses these challenges through three complementary components: explicit geographic representation, event-aware learning for imbalanced precipitation, and weak asymmetric regularization derived from the atmospheric water budget. The physical information is treated as an asymmetric constraint rather than as an additional prediction target, designed to discourage physically unsupported precipitation attenuation without replacing the data-driven forecast. Experiments using ERA5 data across regional, enlarged domain, and spatial subset settings show that explicit geographic information improves spatial field prediction, while event-aware learning provides the most consistent gains in detecting moderate and heavy precipitation events. Physical regularization has a more selective effect, mainly reducing systematic underprediction over the enlarged domain while improving longer lead precipitation event prediction in the spatial subset experiment. These results indicate that the benefit of physical guidance depends on the available data regime and becomes most apparent when data-driven precipitation information deteriorates with increasing lead time.
发表机构
- Beijing Institute of Technology（北京理工大学）
- Tangshan Research Institute, Beijing Institute of Technology（北京理工大学唐山研究院）
- King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。
