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| 多源降水数据的不确定性解析与融合算法研究 |
| Uncertainty analysis and algorithms for multi-source precipitation data merging: a review |
| 投稿时间：2025-08-30 修订日期：2025-12-16 |
| DOI： |
| 中文关键词: 卫星遥感 不确定性分析 偏差校正 数据融合 |
| 英文关键词: satellite remote sensing uncertainty analysis bias correction data merging |
| 基金项目:国家重点基础研究发展计划（973计划）,国家自然科学基金项目（面上项目，重点项目，重大项目）,江苏省水利科技项目 |
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| 摘要点击次数: 14 |
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| 中文摘要: |
| 高精度、高分辨率的降水数据对流域水文过程模拟、水资源调配及洪涝干旱监测至关重要。卫星遥感与再分析系统能有效估算降水的时空分布，但其原始数据的不确定性高，难以满足水利等行业的应用需求，需通过校正与融合技术提升数据质量。传统多源降水融合算法多以高质量地面站网为基准，难以适用于地面观测站点稀疏区。近年来兴起的不确定性分析方法通过多源观测信息联合求解的方式，在不依赖地面观测的情况下系统评估各类降水数据的误差特征，并实现误差最小化的最优融合，有效缓解了传统数据融合方法对高密度站点的依赖，推动了地面观测稀疏区的降水融合研究。本文系统综述了多源降水数据的体系与特征，总结了主要的误差类型及其校正与融合方法，梳理了现有主流的不确定性分析算法，阐明了多源降水数据融合面临的困难和挑战，并对未来的研究方向进行了展望。本研究旨在促进降水融合技术领域的发展，对提高降水产品质量具有重要意义。 |
| 英文摘要: |
| High-precision, high-resolution precipitation data are critical for simulating river basin hydrological processes, managing water resources management, and monitoring floods and droughts. Satellite remote sensing and reanalysis systems can provide valuable spatiotemporal coverage; however, their raw estimates often exhibit large uncertainties, making it difficult to meet the application requirements of sectors like water resources. Data quality must be improved through bias correction and data merging, while traditional multi-source precipitation merging approaches, which rely heavily on dense gauge networks, are unsuitable for sparsely observed regions. Statistical uncertainty analysis offers an alternative by jointly evaluating multiple datasets with no much dependence on ground observations, quantifying their error characteristics, and minimizing overall errors to produce optimal merged estimates. This review summarizes major statistical uncertainty analysis methods, outlines common error sources and corresponding correction strategies, and highlights emerging challenges and future directions in multi-source precipitation merging. This study aims to provide more reliable precipitation estimates and holds significant importance for improving the quality of precipitation products to support water management and agricultural risk reduction. |
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