ABSTRACT Precipitation nowcasting plays a critical role in agriculture, transportation, urban planning, and emergency response. With the growing emphasis on sustainable climate resilience, AI‐driven approaches have become vital for enhancing the accuracy and timeliness of meteorological forecasting. However, most existing deep learning models rely on a single data source, employ fixed‐scale feature extraction, and lack effective spatio‐temporal attention mechanisms. To address these issues, we propose a multi‐source data fusion Transformer, termed FusionFormer, for precipitation nowcasting. The FusionFormer integrates a Multi‐Source Fused Embedding (MSFE) module to effectively capture both fine‐grained local features and large‐scale patterns. It utilises parallel multi‐size convolutional kernels and multi‐resolution branches. Additionally, a Multi‐scale Spatio‐Temporal Attention (MSTA) module dynamically identifies the movement trajectory and intensity variations of precipitation systems. We also constructed a high‐resolution, spatiotemporally aligned multi‐source meteorological dataset for Shandong Province. Experiments on this dataset demonstrate that FusionFormer outperforms state‐of‐the‐art methods in both objective and subjective evaluations.
Sun et al. (Mon,) studied this question.