M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention

2026-04-15

AI Summary

This study addresses the challenge of fusing multi-source heterogeneous meteorological data by proposing the M3R architecture, which introduces a novel multimodal attention mechanism guided by meteorological information. Specifically, it treats time-series observations from personal weather stations as queries and NEXRAD radar images as spatial key-value pairs to precisely focus on and efficiently integrate precipitation features. A dedicated temporal alignment pipeline is developed to harmonize asynchronous radar imagery and station measurements, thereby significantly enhancing both accuracy and timeliness in nowcasting rainfall. Experimental results across three 100 km × 100 km regions demonstrate that M3R consistently outperforms existing methods in forecast precision, computational efficiency, and detection capability for light precipitation, establishing a new benchmark for operational short-term forecasting systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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

Problem

• rainfall nowcasting
• multimodal data fusion
• meteorological data
• precipitation prediction
• heterogeneous data alignment

Innovation

• multimodal attention
• rainfall nowcasting
• meteorological data fusion
• NEXRAD radar
• time series alignment