融合雷达与气象站数据，用注意力机制精准预测短时降雨。
M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention
- 用气象站时序数据作查询，专注抓取雷达中的降水特征。
- 在三个100km×100km区域测试中，精度和检测能力显著提升。
- 适合需要高精度短时降雨预报的气象与防灾系统使用。
准确及时的短时降雨预报对防灾减灾和水资源管理至关重要。尽管深度学习取得进展，但有效利用多源异构气象数据仍是挑战。本文提出M3R，一种气象引导的多模态注意力模型，直接结合NEXRAD雷达影像与个人气象站（PWS）观测数据，通过完整的时序对齐流程处理异构数据。基于专用的多模态注意力机制，M3R将气象站时间序列作为查询，选择性关注雷达空间特征，实现对降水信号的精准提取。在三个以雷达站为中心的100km×100km区域上的实验表明，M3R优于现有方法，在准确性、效率和降水检测能力上均有显著提升。本工作为多模态降水短时预报建立了新基准，并为业务化天气预报系统提供了实用工具。代码已公开于https://github.com/Sanjeev97/M3Rain。
原文摘要 · Abstract (English)
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
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