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M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention

arXiv 2604.15377 ↗First posted 15 Apr 2026cs.LG cs.CV cs.MMcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 15 Apr 2026.

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16 of 24 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2021ViViT: A Video Vision Transformercited by 1,620
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  7. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  8. 2017Deep Learning for Precipitation Nowcasting: A Benchmark and A New Modelcited by 182
  9. 2022Earthformer: Exploring Space-Time Transformers for Earth System Forecastingcited by 152
  10. 2023PreDiff: Precipitation Nowcasting with Latent Diffusion Modelscited by 98
  11. 2023DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcastingcited by 61
  12. 2019Machine Learning for Precipitation Nowcasting from Radar Imagescited by 56
4 more
  1. 2023MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformercited by 29
  2. 2024An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictionscited by 11
  3. 2024Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Datacited by 3
  4. 2026Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecastingcited by 1

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