| 摘要: |
| 基于比差分传播相移（[KDP]）的降水估计算法[R(KDP)]相较于传统基于水平反射率因子[(ZH)]的[算法R(ZH)]的表现更优。在雷达实际运行中，由于随机误差和后向散射相位（backscattering phase）的影响，可能出现负的[KDP]。运用一种基于变分的雷达定量降水估计混合算法(V-RQPE)。该算法用变分拟合方法重构差分相位（[ΦDP]），用一种新的稳健的边界条件求解方法，在消除随机误差的同时获得非负的[KDP]，进而进行降水估计。随后我们使用2017年5月7日广州S波段雷达的回波数据和地面雨量站观测数据进行验证，同时使用了六种不同的算法进行对比，结果显示，在1小时累计降水估计中，V-RQPE表现最好，在24小时累计降水估计中，V-RQPE和基于变分拟合的[KDP]的降水估计算法(R-VKDP)表现最好，实验结果表明变分拟合方法对雷达降水估计能力有显著提升。 |
| 关键词: 变分拟合 雷达定量降水估计 比差分传播相移 边界条件 |
| DOI：Doi：10.16032/j.issn.1004-4965.2022.036 |
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| A VARIATIONAL APPROACH FOR RETRIEVING QUANTITATIVE PRECIPITATION WITH S-BAND DUAL-POLARIZATION RADAR |
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LIU Chenshuai1,2,3,4, ZHANG Asi5, CHEN Sheng6
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1. School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong 519082, China;2. Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai, Guangdong 519082, China;3. Key Laboratory of Tropical Atmosphere-Ocean System Sun Yat-sen University, Ministry of Education, Zhuhai, Guangdong 519082, China;4. Southern Laboratory of Ocean Science and Engineering, Zhuhai, Guangdong 519082, China;5.Guangdong Meteorological Observatory, Guangzhou 510641, China;6.Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
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| Abstract: |
| Compared with the traditional radar quantitative precipitation estimation (QPE) algorithm [R(ZH)] based on reflectivity factor ([ZH]), the radar QPE algorithm based on specific differential phase shift ([KDP]) performs better. In the actual operation of radar, the random error and backscattering phase observed in the differential phase ([ΦDP]) result in negative [KDP] estimates. In this study, an improved variational hybrid radar QPE algorithm (V-RQPE) is used to quantitatively estimate the rainfall rate (R) from the [KDP]. In this algorithm, the [ΦDP] is reconstructed by using the improved variational approach based on a new robust boundary condition solution method. The reconstructed [ΦDP] may eliminate the random error and obtain the non-negative [KDP] at the same time. The approach is assessed with a real rainfall case on May 7, 2017 observed by an operational S-band dual-polarization radar in Guangzhou and compared with the other five different algorithms. The results show that: (1) V-RQPE performs best for the 1-hour cumulative precipitation estimation; (2) the quantitative estimate of R with the [KDP] derived from the optimized [ΦDP] based on variational approached (R-VKDP) demonstrates best performance for the 24-hour cumulative precipitation estimation; (3) the experimental results indicate that the variational approach to reconstruct the [ΦDP ]can help significantly improve radar precipitation estimation. |
| Key words: variational approach quantitative precipitation estimation specific differential phase shift boundary condition |
