Learning from Multi-Perspectives: A Multimodal Precipitation Nowcasting Framework

Precipitation nowcasting is a crucial spatio-temporal forecasting task with broad applications in weather warning, transportation, and agriculture. Existing deep learning based methods typically model precipitation evolution either from a radar-native perspective that captures signal-level motion and intensity dynamics, or from a visual perspective that extracts high-level morphological structures of precipitation system. Although these two perspectives are complementary, few works have integrated them into a unified framework. Moreover, recent works have introduced textual meteorological priors but only couple them with visual representations, leaving their potential synergy with radar-native dynamics largely unexplored. In this paper, we propose PreMM, a multimodal precipitation nowcasting framework that jointly models radar-native dynamics, visual representations, and textual semantic priors. PreMM consists of a Radar Dynamics Learner (RDL) that explicitly decouples radar evolution into motion and intensity components, and enhances them with frequency-domain and textual priors; a Visual Perception Learner (VPL) that reconstructs radar observations into visual representations with frequency and periodicity cues, and leverages a pre-trained vision-language encoder to capture precipitation structures; a Text Prior Generator (TPG) that produces meteorological semantic priors; and a Multimodal Fusion Network (MFN) that integrates these complementary representations for future radar prediction. Extensive experiments on multiple precipitation nowcasting benchmarks demonstrate that PreMM consistently outperforms state-of-the-art methods.