Spatiotemporal Fusion of DEM-Corrected GNSS-PWV and Radar Reflectivity for Extreme Precipitation Nowcasting in Complex Terrain

Abstract

Short-term extreme precipitation nowcasting remains challenging because convective rainfall evolves through coupled moisture, dynamical, and microphysical processes that are only partially observed by any single sensor. Radar composite reflectivity (CR) directly describes hydrometeor structures, whereas global navigation satellite system (GNSS)-derived precipitable water vapor (PWV) provides complementary moisture information before and during precipitation development. In complex terrain, however, raw GNSS-derived PWV (GNSS-PWV) fields are affected by station elevation differences. In this study, DEM information is therefore used to support GNSS-PWV elevation reduction before model ingestion, while the neural network fuses DEM-corrected GNSS-PWV and radar CR as two dynamic input streams. This article proposes M2-CroT, a spatiotemporal fusion framework that combines DEM-corrected GNSS-PWV with radar CR for extreme precipitation nowcasting in complex terrain. The main contributions are: 1) a DEM-assisted elevation reduction method that improves the terrain consistency of GNSS-PWV grids before model ingestion; 2) a dual-branch Vision Transformer (ViT) feature extractor that captures long-range spatial dependencies from sparse moisture fields and dense radar imagery; and 3) a gated cross-attention module that adaptively regulates the contribution of moisture-background and reflectivity features during fusion. Experiments on real-world North China datasets show that the proposed model improves the Critical Success Index (CSI) for extreme precipitation events by 57% over the original Trajectory Gated Recurrent Unit (TrajGRU) radar-only baseline. These results support the value of combining DEM-assisted PWV preprocessing with adaptive multimodal fusion for high-resolution precipitation nowcasting in complex terrain.