Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations

2025-11-01

AI Summary

To address the low accuracy of nowcasting heavy rainfall in urban areas—caused by insufficient spatial coverage of rain gauges and the inability of single-source radar data to capture the rapid onset and short duration of intense precipitation—this paper proposes a multimodal deep learning model that fuses radar reflectivity images with meteorological satellite imagery. The model enables collaborative spatiotemporal modeling of precipitation evolution and adaptive integration of heterogeneous observational data. Evaluated on the 2021 flood event in North Rhine-Westphalia, Germany, it achieves a 4% and 3% improvement in Critical Success Index (CSI) for heavy and extreme rainfall, respectively, at a 5-minute lead time, while maintaining superior forecast skill at longer lead times. The core contribution lies in a novel cross-platform remote sensing data fusion architecture specifically designed for very-short-term heavy precipitation forecasting, significantly enhancing the capability for high-resolution early warning of urban extreme rainfall.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The increasing frequency of heavy rainfall events, which are a major cause of urban flooding, underscores the urgent need for accurate precipitation forecasting - particularly in urban areas where localized events often go undetected by ground-based sensors. In Germany, only 17.3% of hourly heavy rain events between 2001 and 2018 were recorded by rain gauges, highlighting the limitations of traditional monitoring systems. Radar data are another source that effectively tracks ongoing precipitation; however, forecasting the development of heavy rain using radar alone remains challenging due to the brief and unpredictable nature of such events. Our focus is on evaluating the effectiveness of fusing satellite and radar data for nowcasting. We develop a multimodal nowcasting model that combines both radar and satellite imagery for predicting precipitation at lead times of 5, 15, and 30 minutes. We demonstrate that this multimodal strategy significantly outperforms radar-only approaches. Experimental results show that integrating satellite data improves prediction accuracy, particularly for intense precipitation. The proposed model increases the Critical Success Index for heavy rain by 4% and for violent rain by 3% at a 5-minute lead time. Moreover, it maintains higher predictive skill at longer lead times, where radar-only performance declines. A qualitative analysis of the severe flooding event in the state of North Rhine-Westphalia, Germany in 2021 further illustrates the superior performance of the multimodal model. Unlike the radar-only model, which captures general precipitation patterns, the multimodal model yields more detailed and accurate forecasts for regions affected by heavy rain. This improved precision enables timely, reliable, life-saving warnings. Implementation available at https://github.com/RamaKassoumeh/Multimodal_heavy_rain

Problem

• Improving heavy rain nowcasting accuracy using multimodal data
• Overcoming radar-only limitations by integrating satellite observations
• Enhancing short-term precipitation forecasts for urban flood prevention

Innovation

• Combining radar and satellite data for nowcasting
• Multimodal model improves heavy rain prediction accuracy
• Integrating satellite imagery enhances short-term precipitation forecasts