Comparison analysis of six purely satellite-derived global precipitation estimates
Comparison analysis of six purely satellite-derived global precipitation estimates
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六种纯卫星全球降水估算的比较分析
DOI：
10.1016/j.jhydrol.2019.124376
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发表时间：
2020-02-01
影响因子：
6.4
通讯作者：
Zhang, Jianyun
中科院分区：
文献类型：
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作者：
Chen, Hanqing;Yong, Bin;Zhang, Jianyun
我们对六个纯粹的卫星降水估计（即，IMERG-晚期、IMERG-早期、GSMaP-NRT、GSMaP-MVK、TMPA-RT和PERSIANN-CCS）。结果表明，IMERG-Late在6种评价产品中表现出最好的性能，而GSMaP-NRT和GSMaP-MVK的性能最差。6种卫星产品的均方根误差（RMSE）与降水强度的对数均呈幂函数关系。结果表明，在32 mm/d（或8 mm/h）以上的降雨事件中，各产品的RMSE占相应降雨强度的30%以上，这可能会对卫星降水模拟的山洪灾害的探测能力和预报产生重大影响。此外，IMERG和GSMaP都高估了小雨发生的比例，并且在小时（或日）时间尺度上RMSE值超过0.5 mm（或2 mm）的小雨（0.2-0.4 mm/h或1-2 mm/天）中也显示出相对较大的误差。在误差分析方面，我们将除TMPA-RT外的5种产品的总偏差分解为小时和0.1度分辨率下的命中偏差、未命中偏差和错误偏差，发现错误偏差是这5种产品在半湿润地区冷季的主要误差源，而命中偏差在GSMaP套件中所占的比例不可忽略。在大部分湿润地区，降水缺失是PERSIANN-CCS两季的主要误差源，同时也是其他四种产品的主要误差源之一。我们希望本研究的结果不仅为算法开发人员提供一些有价值的反馈，以改善基于GPS的卫星降水反演，但也为全球各地的数据用户提供一些指导。
We executed a comprehensive evaluation and intercomparison between six purely satellite-derived precipitation estimates (i.e., IMERG-Late, IMERG-Early, GSMaP-NRT, GSMaP-MVK, TMPA-RT and PERSIANN-CCS) at global and regional scales for the period from February 2017 to January 2019. The results show that IMERG-Late exhibits the best performance among six evaluated products, while the worst performance was found in GSMaP-NRT and GSMaP-MVK. The root mean squared error (RMSE) has a power function to the logarithm of precipitation intensity in all six satellite products. On the basis of our findings, the RMSE of all products in rainfall events with intensity exceeding 32 mm/day (or 8 mm/h) accounts for beyond 30% of the corresponding precipitation intensity, which might result in a significant impact on the detectability and forecast of flash floods simulated by satellite precipitation. Additionally, both IMERG and GSMaP overestimate the proportions of light rainfall occurrences, and also display relatively larger errors in light precipitation (0.2-0.4 mm/h or 1-2 mm/day) with the RMSE values exceeding 0.5 mm (or 2 mm) at hourly (or daily) time scale. As for the error analysis, we decomposed the total bias of each product into hits, misses and false biases at hourly and 0.1 degrees resolution over mainland China except for TMPA-RT. We found that the false bias is the dominated error sources for these five products in cold season over semi-humid areas despite that the hit bias accounts for a non-negligible proportion for GSMaP suite. The missed precipitation is the dominated error sources of PERSIANN-CCS both in two seasons over most of humid regions, and meanwhile is one of major error sources for other four products. We expect that the findings of this study not only provide some valuable feedbacks for algorithm developers to improve the GPM-based satellite precipitation retrievals, but also provide some guidance for data users across the world.