| GPM近实时反演数据对河南省2021年“7·20”极端暴雨的比较分析 |
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| 引用本文： | 胡庆芳,张野,李伶杰,王银堂,杨汉波,牛凯杰,李哲.GPM近实时反演数据对河南省2021年“7·20”极端暴雨的比较分析[J].水科学进展,2022,33(4):567-580. |
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| 作者姓名： | 胡庆芳 张野 李伶杰 王银堂 杨汉波 牛凯杰 李哲 |
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| 作者单位： | 1.南京水利科学研究院水文水资源与水利工程科学国家重点实验室, 江苏 南京 210029 |
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| 基金项目： | 国家重点研发计划资助项目2021YFC3000104国家自然科学基金资助项目52009081 |
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| 摘 要： | 近实时卫星降水反演数据具有覆盖范围广、空间连续性和时效性较强以及开放获取等优势, 是重要的全球性降水资料。针对2021年河南省“7·20”极端暴雨, 基于116个地面气象站观测资料及其空间插值数据, 综合解析了4种GPM近实时卫星降水数据(IMERG early、IMERG late、GSMaP NOW和GSMaP Gauge NOW)对极端强降水事件的表征能力。结果发现:①IMERG early、IMERG late对站网累积雨量的低估程度在20%左右, GSMaP NOW和GSMaP Gauge NOW的高估程度分别达到了约35%和70%, 但2种GSMaP数据更易探测到500 mm以上的累积雨量。②在雨量过程方面, 4种GPM数据对小时降水事件均具有较强的分类辨识能力, 但未捕捉到主要雨峰过程, 定量误差较突出, 与地面降水量之间表现出显著的负相关关系。GPM降水数据对小时雨量低于10 mm的降水事件以高估为主；对于小时雨量超过30mm的降水事件以低估为主, 甚至存在普遍低估。③在空间格局方面, 4种GPM数据的精度指标均具有较强的时间波动性, IMERG数据的空间相关系数和体积临界成功指数等指标总体上优于GSMaP数据, 但后者对较高量级的降水事件更敏感。④2种IMERG数据中, IMERG late相对IMERG early的精度具有较明显改善；2种GSMaP数据中, GSMaP Gauge NOW相对GSMaP NOW提高了对较高量级雨量的探测能力, 但也明显增大了较低量级雨量的定量估计误差。本文研究深化了对GPM近实时卫星降水数据性能的认识, 为完善GPM降水反演算法、提升其对极端降水的监测能力提供了重要反馈信息。
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| 关 键 词： | 卫星近实时降水 极端降水 GPM IMERG GSMaP “7·20”暴雨 河南省 |
| 收稿时间： | 2021-11-05 |
Comparative evaluation of GPM near-real-time precipitation products during the 20 July 2021 extreme rainfall event in Henan Province |
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| Institution: | 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China2.Yangtze River Conservation and Green Development Research Institute, Nanjing 210098, China3.Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China4.Department of Electrical and Computer Engineering, Colorado State University, Fort Collins CO 80523, USA |
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| Abstract: | Near-real-time satellite precipitation retrievals have the advantages of wide coverage, spatial continuity, short latency and open access, serving as an important precipitation data source that is globally available. For the 20 July 2021 extreme rainfall event in Henan Province, the performance of four GPM near-real-time satellite precipitation products—IMERG early, IMERG late, GSMaP NOW and GSMaP Gauge NOW—is comprehensively evaluated in this study. Based on the observations of 116 ground meteorological gauges and the interpolated rainfall fields, their skills to characterize heavy rainfall are compared.①The results show that the underestimation of accumulated rainfall by IMERG early and IMERG late is about 20%, while GSMaP NOW and GSMaP Gauge NOW overestimates it by about 35% and 70% respectively. However, the latter two are found to be easier to detect accumulated rainfall of above 500mm. ②In terms of rainfall process, the four GPM datasets are able to detect hourly precipitation events, but fail to capture major rainfall peaks. The estimation error of hourly rain rate is prominent, and it is negatively correlated with the ground observed rainfall magnitude. Specifically, the four products tend to overestimate hourly rainfall event that is less than 10 mm/h and underestimate rainfall event exceeding 30 mm/h. ③As for spatial pattern, the evaluation metrics of all the datasets show strong temporal fluctuations. The spatial correlation coefficient and volume critical success index of IMERG data are generally better than those of GSMaP data, but the latter is more sensitive to high-magnitude precipitation events. ④For IMERG products, it is found that the accuracy of IMERG late is greatly improved upon IMERG early; for GSMaP products, GSMaP Gauge NOW has a better detection skill of high-intensity rainfall compared against GSMaP NOW, but it also shows an increased estimation error of low-magnitude rainfall. This study has deepened the understanding of the performance of various GPM near-real-time satellite precipitation products, and provided critical feedbacks for improving the GPM-era satellite precipitation retrieval algorithms and enhancing their skills to monitor extreme precipitation. |
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