RainSeer：基于物理引导建模的细粒度降雨重建
RainSeer: Fine-Grained Rainfall Reconstruction via Physics-Guided Modeling
AI总结：
RainSeer提出物理引导的两阶段深度学习框架，利用雷达结构先验和因果时空注意力，在韩国和法国数据集上实现高分辨率降雨重建，MAE降低超13.31%。
摘要：
重建高分辨率降雨场对于洪水预报、水文建模和气候分析至关重要。然而，现有的空间插值方法——无论是基于自动气象站（AWS）测量，还是通过卫星/雷达观测增强——常常过度平滑关键结构，无法捕捉急剧的转变和局地极端情况。我们提出了RainSeer，一种结构感知的重建框架，将雷达反射率重新解释为物理基础的结构先验——捕捉降雨何时、何地以及如何发展。然而，这一转变引入了两个基本挑战：（i）将高分辨率体积雷达场转换为稀疏的点级降雨观测，以及（ii）弥合高空水凝物与地面降水之间的物理脱节。RainSeer通过一个物理信息的两阶段架构来解决这些问题：一个结构到点映射器通过双向映射将中尺度雷达结构投影到局地地面降雨，进行空间对齐；一个地理感知降雨解码器通过因果时空注意力机制捕捉水凝物在下落、融化和蒸发过程中的语义转变。我们在两个公共数据集——RAIN-F（韩国，2017-2019）和MeteoNet（法国，2016-2018）上评估了RainSeer，并观察到相对于最先进的基线模型的一致改进，平均绝对误差（MAE）降低了超过13.31%，并在重建降雨场的结构保真度方面显著增强。
英文摘要：
Reconstructing high-resolution rainfall fields is essential for flood forecasting, hydrological modeling, and climate analysis. However, existing spatial interpolation methods-whether based on automatic weather station (AWS) measurements or enhanced with satellite/radar observations often over-smooth critical structures, failing to capture sharp transitions and localized extremes. We introduce RainSeer, a structure-aware reconstruction framework that reinterprets radar reflectivity as a physically grounded structural prior-capturing when, where, and how rain develops. This shift, however, introduces two fundamental challenges: (i) translating high-resolution volumetric radar fields into sparse point-wise rainfall observations, and (ii) bridging the physical disconnect between aloft hydro-meteors and ground-level precipitation. RainSeer addresses these through a physics-informed two-stage architecture: a Structure-to-Point Mapper performs spatial alignment by projecting mesoscale radar structures into localized ground-level rainfall, through a bidirectional mapping, and a Geo-Aware Rain Decoder captures the semantic transformation of hydro-meteors through descent, melting, and evaporation via a causal spatiotemporal attention mechanism. We evaluate RainSeer on two public datasets-RAIN-F (Korea, 2017-2019) and MeteoNet (France, 2016-2018)-and observe consistent improvements over state-of-the-art baselines, reducing MAE by over 13.31% and significantly enhancing structural fidelity in reconstructed rainfall fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
