融合多源数据提升暴雨短时预报精度
Towards a Spatiotemporal Fusion Approach to Precipitation Nowcasting
- 采用时空深度模型融合气象站、雷达与再分析数据
- 一小时预报暴雨（>25mm/h）F1达0.2033
- 适合气象预报与城市防灾研究者参考
随着各类传感器、数值模式和再分析产品气象数据日益丰富，高效的数据融合方法对提升天气预报和水文气象研究至关重要。本文提出一种针对里约热内卢大都会区的降水短时预报数据融合方法，整合地面气象站、雨量计、ERA5再分析数据及GFS数值天气预报数据。采用时空深度学习架构STConvS2S，基于9×11网格的结构化数据集，研究时间跨度为2011年1月至2024年10月。评估了三种地面站点系统的融合效果，最优融合模型在提前一小时预测强降水事件（>25 mm/h）时达到F1分数0.2033。通过消融实验分析各站点网络贡献，并提出一种结合GFS与现场观测的优化推理策略。
原文摘要 · Abstract (English)
With the increasing availability of meteorological data from various sensors, numerical models and reanalysis products, the need for efficient data integration methods has become paramount for improving weather forecasts and hydrometeorological studies. In this work, we propose a data fusion approach for precipitation nowcasting by integrating data from meteorological and rain gauge stations in Rio de Janeiro metropolitan area with ERA5 reanalysis data and GFS numerical weather prediction. We employ the spatiotemporal deep learning architecture called STConvS2S, leveraging a structured dataset covering a 9 x 11 grid. The study spans from January 2011 to October 2024, and we evaluate the impact of integrating three surface station systems. Among the tested configurations, the fusion-based model achieves an F1-score of 0.2033 for forecasting heavy precipitation events (greater than 25 mm/h) at a one-hour lead time. Additionally, we present an ablation study to assess the contribution of each station network and propose a refined inference strategy for precipitation nowcasting, integrating the GFS numerical weather prediction (NWP) data with in-situ observations.
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