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Abstract <p>Accurate precipitation monitoring serves as the cornerstone for extreme weather forecasting and hydrological modeling. However, single-source approaches systematically underestimate precipitation in complex terrain, severely constraining disaster preparedness capabilities. Current precipitation products have respective advantages and limitations. This study develops a spatially adaptive multi-source precipitation fusion framework that integrates ERA5, TRMM, and GPM through terrain-land use constrained weight allocation, systematically addressing surface environmental variability neglected by conventional uniform weighting approaches. The fusion method incorporates digital elevation models and land cover classifications into dynamic weight mechanisms, enabling physically-informed optimization, which adapts to varying observational advantages across different geographical regions. A comprehensive evaluation system featuring cosine similarity analysis assesses precipitation distribution characteristics alongside numerical accuracy, providing robust performance assessment independent of extreme outliers. These advances provide transformative foundation for refined precipitation monitoring, extreme weather early warning systems, and disaster risk management in topographically complex environments.</p>

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Terrain-constrained Multi-source Precipitation Fusion: A Spatially Adaptive Framework for Extreme Event Monitoring

  • 期刊论文

  • 发表日期：

    2026年3月1日

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  • Zhiyong Luo,
  • Jingzun Wu,
  • Qiyuan Yin,
  • Leiwen Chen,
  • Zhongda Liu,
  • Dongbin Hu,
  • Shuman Zhu

摘要

Abstract

Accurate precipitation monitoring serves as the cornerstone for extreme weather forecasting and hydrological modeling. However, single-source approaches systematically underestimate precipitation in complex terrain, severely constraining disaster preparedness capabilities. Current precipitation products have respective advantages and limitations. This study develops a spatially adaptive multi-source precipitation fusion framework that integrates ERA5, TRMM, and GPM through terrain-land use constrained weight allocation, systematically addressing surface environmental variability neglected by conventional uniform weighting approaches. The fusion method incorporates digital elevation models and land cover classifications into dynamic weight mechanisms, enabling physically-informed optimization, which adapts to varying observational advantages across different geographical regions. A comprehensive evaluation system featuring cosine similarity analysis assesses precipitation distribution characteristics alongside numerical accuracy, providing robust performance assessment independent of extreme outliers. These advances provide transformative foundation for refined precipitation monitoring, extreme weather early warning systems, and disaster risk management in topographically complex environments.