基于风云三号 F 星被动微波观测的水凝物反演算法研究

The study on a retrieval algorithm of multiple hydrometeors based on passive microwave observations from the FY-3F satellite

摘要

基于星载被动微波传感器获得的全球云与降水反演数据对于理解各种灾害性天气和气候变化的发生发展机理至关重要。针对我国新一代上午轨道业务星 FY-3F 搭载的微波成像仪-II 型 (Micro-Wave Radiation Imager-II，MWRI-II)，基于贝叶斯理论，结合具有完整代表性和准确性的先验数据库，构建了一套地表降水率及云液态水、雨水及冰水含量等多种水凝物廓线的反演算法，并进行了算法验证及反演结果评估。试验结果表明，该算法具有较高的准确性，各反演物理量与参考值的平均偏差在个位数量级。实际反演结果与全球卫星降水观测计划 (Global Precipitation Measurement Mission，GPM) 的戈达德廓线降水反演算法 (Goddard Profiling Algorithm，GPROF) 产品及 FY-3G 的多种水凝物廓线 (Multiple-Hydrometeor Profile，MHP) 产品的对比评估显示，在 2024 年 5-7 月期间、覆盖中国及其周边地区的统计平均结果中，三种数据的空间分布特征基本吻合，空间相关系数均在 0.60 以上，最高达 0.83。此外，与 MHP 产品的平均差异均在 10% 左右。该反演算法不依赖外部辅助数据，可为 FY-3F 卫星云降水业务化产品的构建提供技术支撑，并可推广至具有相近通道配置的其他风云系列卫星搭载的被动微波辐射计，这对于未来多星协同观测具有重要意义。

Abstract

Global cloud and precipitation data derived from satellite-based passive microwave sensors are essential for understanding the formation and evolution mechanisms of hazardous weather events and climate change. Focusing on Micro-Wave Radiation Imager-II (MWRI-II) onboard FY-3F, China’s new-generation morning-orbit operational meteorological satellite, a retrieval algorithm based on Bayesian theory and a highly representative and physically accurate prior database is developed. The algorithm retrieves surface precipitation rates and vertical profiles of multiple hydrometeors, including cloud liquid water, rainwater, and ice water, and is further validated and evaluated. The validation results based on simulations demonstrate that the proposed algorithm has very high accuracy, with the mean bias of the retrieved physical quantities and reference values of any hydrometeor being within 10 g·m^-2. The comparison of the retrieval results based on FY-3F measurements with FY-3G Multiple-Hydrometeor Profile (MHP) products and Goddard Profiling Algorithm (GPROF) products of Global Precipitation Measurement Mission (GPM) shows that for the statistical analysis over China and its surrounding regions during May–July 2024, the spatial distributions of the three datasets are generally consistent with correlation coefficients above 0.60, reaching up to 0.83. In addition, the relative biases between the retrieved parameters and those in the MHP product are all about 10%. Note that the algorithm does not rely on any other auxiliary data and can provide technical support for the operational deployment of the cloud-precipitation products for FY-3F, while also being applicable to passive microwave radiometers with similar channel configurations onboard other Fengyun satellites, thereby offering significant potential for future multi-satellite collaborative observation strategies.

关键词

多种水凝物反演 / FY-3F 卫星 / 被动微波 / MWRI-II

Key words

retrieval of multiple hydrometeors / FY-3F satellite / passive microwave / MWRI-II

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基金资助

国家自然科学基金气象联合基金重点项目(U2542205)

安徽省自然科学基金江淮气象联合基金项目(2408055UQ005)

中国气象局科技创新项目(CMAJBGS202502)