RainSeer: Fine-Grained Rainfall Reconstruction via Physics-Guided Modeling

• URL: http://arxiv.org/abs/2510.02414v2
• Date: Tue, 07 Oct 2025 01:44:40 GMT
• Title: RainSeer: Fine-Grained Rainfall Reconstruction via Physics-Guided Modeling
• Authors: Lin Chen, Jun Chen, Minghui Qiu, Shuxin Zhong, Binghong Chen, Kaishun Wu,
• Abstract summary: RainSeer is a structure-aware reconstruction framework that reinterprets radar reflectivity as a physically grounded structural prior-capture.<n>RainSeer addresses two fundamental challenges: translating high-resolution radar fields into sparse point-wise rainfall observations, and bridging the physical disconnect between aloft hydro-meteors and ground-level precipitation.<n>We observe consistent improvements over state-of-the-art baselines, reducing MAE by over 13.31% and significantly enhancing structural fidelity in reconstructed rainfall fields.
• Score: 23.446919642417694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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