Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains challenging due to limitations in effectively leveraging diverse multimedia data sources. We introduce M3R, a Meteorology-informed MultiModal attention-based architecture for direct Rainfall prediction that synergistically combines visual NEXRAD radar imagery with numerical Personal Weather Station (PWS) measurements, using a comprehensive pipeline for temporal alignment of heterogeneous meteorological data. With specialized multimodal attention mechanisms, M3R novelly leverages weather station time series as queries to selectively attend to spatial radar features, enabling focused extraction of precipitation signatures. Experimental results for three spatial areas of 100 km * 100 km centered at NEXRAD radar stations demonstrate that M3R outperforms existing approaches, achieving substantial improvements in accuracy, efficiency, and precipitation detection capabilities. Our work establishes new benchmarks for multimedia-based precipitation nowcasting and provides practical tools for operational weather prediction systems. The source code is available at https://github.com/Sanjeev97/M3Rain

翻译：准确及时的降雨临近预报对于减灾和水资源管理至关重要。尽管深度学习近期取得了进展，但由于有效利用多样化多媒体数据源存在局限，降水预测仍具挑战性。我们提出M3R——一种基于气象信息的多模态注意力架构，用于直接降雨预测，该架构通过完整的多源异构气象数据时间对齐流程，将视觉NEXRAD雷达图像与数值化个人气象站（PWS）测量数据协同融合。凭借专用多模态注意力机制，M3R创新性地以气象站时间序列作为查询，选择性关注空间雷达特征，从而实现降水特征的聚焦提取。以NEXRAD雷达站为中心的三个100公里×100公里空间区域实验结果表明，M3R优于现有方法，在准确性、效率和降水探测能力方面均实现了显著提升。我们的工作为基于多媒体的降水临近预报建立了新基准，并为业务化天气预报系统提供了实用工具。源代码见https://github.com/Sanjeev97/M3Rain