Dual-Polarization Radar Quantitative Precipitation Estimation (QPE): Principles, Operations, and Challenges
文献类型：期刊论文
| 作者 | Zhang, Zhe2,3; Zhao, Zhanfeng1,3; Qi, Youcun1,3; Xiong, Muqi1,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-10-31 |
| 卷号 | 17期号:21页码:3619 |
| 关键词 | dual-polarization radars quantitative precipitation estimation (QPE) drop size distribution (DSD) |
| DOI | 10.3390/rs17213619 |
| 产权排序 | 1 |
| 文献子类 | Review |
| 英文摘要 | Highlights What are the main findings? Dual-polarization radar significantly improves quantitative precipitation estimation (QPE) accuracy by utilizing polarimetric variables such as differential reflectivity (DR) and specific differential phase (DP), which provide essential microphysical information about precipitation particles. ZKAdvanced QPE methods-including composite, hydrometeor classification-based, and drop size distribution (DSD) retrieval approaches-effectively mitigate the uncertainties associated with traditional reflectivity-rain rate ((H)) estimators, especially under diverse precipitation types and conditions. What is the implication of the main finding? RZThe integration of dual-polarization radar data into operational QPE systems (e.g., MRMS in the U.S., SWAN in China, and the French radar network) enhances real-time precipitation monitoring and forecasting, supporting improved hydrological prediction and disaster preparedness. Despite progress, challenges remain in complex terrain, snow estimation, and the quality control of polarimetric variables, highlighting the need for continued research and development to achieve a higher accuracy and reliability in global precipitation measurement.Highlights What are the main findings? Dual-polarization radar significantly improves quantitative precipitation estimation (QPE) accuracy by utilizing polarimetric variables such as differential reflectivity (DR) and specific differential phase (DP), which provide essential microphysical information about precipitation particles. ZKAdvanced QPE methods-including composite, hydrometeor classification-based, and drop size distribution (DSD) retrieval approaches-effectively mitigate the uncertainties associated with traditional reflectivity-rain rate ((H)) estimators, especially under diverse precipitation types and conditions. What is the implication of the main finding? RZThe integration of dual-polarization radar data into operational QPE systems (e.g., MRMS in the U.S., SWAN in China, and the French radar network) enhances real-time precipitation monitoring and forecasting, supporting improved hydrological prediction and disaster preparedness. Despite progress, challenges remain in complex terrain, snow estimation, and the quality control of polarimetric variables, highlighting the need for continued research and development to achieve a higher accuracy and reliability in global precipitation measurement.Abstract Quantitative precipitation estimation (QPE) is one of the primary applications of weather radar. Over the last several decades, dual-polarization radars have significantly improved QPE accuracy by providing additional observational variables that offer more microphysical information about precipitation particles. In this work, we review QPE methods for dual-polarization radars and summarize their advantages and disadvantages from both theoretical and practical perspectives. The development paths and current status of operational QPE systems in the United States, China, and France are examined. We demonstrate how dual-polarization radars have improved QPE accuracy in these systems not only directly through the application of polarimetric QPE methods, but also indirectly through the more accurate identification of non-meteorological echoes, the mitigation of the partial blockage effect, and the detection of melting layers. The challenges are discussed for dual-polarization radar QPE, including the quality of polarimetric variables, QPE quality in complex terrain, estimation of surface precipitation with observations within or above the melting layer, and polarimetric QPE methods for snow. |
| URL标识 | 查看原文 |
| WOS关键词 | RAINDROP SIZE DISTRIBUTION ; PARTIAL BEAM BLOCKAGE ; UTILIZING SPECIFIC ATTENUATION ; DISTRIBUTION DSD RETRIEVAL ; REAL-TIME ALGORITHM ; Z-R RELATIONSHIPS ; POLARIMETRIC RADAR ; RAINFALL ESTIMATION ; DIFFERENTIAL PHASE ; WEATHER RADAR |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001613090800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217752]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Qi, Youcun |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Shenzhen Natl Climate Observ, Shenzhen 518040, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Zhe,Zhao, Zhanfeng,Qi, Youcun,et al. Dual-Polarization Radar Quantitative Precipitation Estimation (QPE): Principles, Operations, and Challenges[J]. REMOTE SENSING,2025,17(21):3619. |
| APA | Zhang, Zhe,Zhao, Zhanfeng,Qi, Youcun,&Xiong, Muqi.(2025).Dual-Polarization Radar Quantitative Precipitation Estimation (QPE): Principles, Operations, and Challenges.REMOTE SENSING,17(21),3619. |
| MLA | Zhang, Zhe,et al."Dual-Polarization Radar Quantitative Precipitation Estimation (QPE): Principles, Operations, and Challenges".REMOTE SENSING 17.21(2025):3619. |
入库方式： OAI收割
来源：地理科学与资源研究所
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