Xie Wenhao, Yi Shanzhen, Leng Chuang, Xia Defeng, Li Mingli, Zhong Zewen, Ye Jianfeng
Key Laboratory of Digital Watershed of Hubei Province, School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Center for Operation and Management on Watershed Hub, China Three Gorges Corporation, Yichang, 443134, China.
Sci Rep. 2022 May 16;12(1):8085. doi: 10.1038/s41598-022-12307-0.
Evaluating the accuracy of the satellite and reanalysis precipitation products is very important for understanding their uncertainties and potential applications. However, because of underestimation existing in commonly used evaluation benchmark, gauge precipitation data, it is necessary to investigate the influence of systematic errors in gauge data on the performance evaluation of satellite and reanalysis precipitation datasets. Daily satellite-based IMERG and model-based ERA5-Land, together with gauge precipitation data, were collected with the period from 2005 to 2016 over China in this study. Daily corrections for precipitation biases from wind-induced undercatch, wetting loss, and trace error were made for gauge measurements. A set of metrics, including relative bias, Kling-Gupta efficiency, frequency bias, and critical success index, were used to evaluate and intercompare the performances of IMERG and ERA5-Land against original and bias-corrected gauge data in different locations, years, seasons, climatic zones, classes of precipitation events, and precipitation phases. The results have shown that: After removing the bias in gauge data, the relative biases of IMERG and ERA5-Land both significantly decline. The noticeable changes of their accuracy occur and vary with different locations, years, seasons, climatic zones, and precipitation phases. Furthermore, the frequency biases of IMERG and ERA5-Land rise in no precipitation events and decline in light, moderate, heavy, and extreme precipitation events. The detection capability of IMERG and ERA5-Land in no and light precipitation events is also obviously affected. Therefore, this study has demonstrated the significant influence of systematic gauge precipitation errors on the assessment of IMERG and ERA5-Land and reinforces the necessity to remove negative bias in gauge data before using it as the benchmark.
评估卫星和再分析降水产品的准确性对于了解其不确定性和潜在应用非常重要。然而，由于常用的评估基准——雨量计降水数据存在低估现象，有必要研究雨量计数据中的系统误差对卫星和再分析降水数据集性能评估的影响。本研究收集了2005年至2016年期间中国境内基于卫星的每日IMERG数据、基于模型的每日ERA5-Land数据以及雨量计降水数据。对雨量计测量的降水偏差进行了每日校正，校正内容包括风致截留不足、湿润损失和微量误差。使用了一组指标，包括相对偏差、克林-古普塔效率、频率偏差和临界成功指数，来评估和相互比较IMERG和ERA5-Land在不同地点、年份、季节、气候区、降水事件类别和降水阶段相对于原始和偏差校正后的雨量计数据的性能。结果表明：去除雨量计数据中的偏差后，IMERG和ERA5-Land的相对偏差均显著下降。它们的准确性发生了显著变化，并且随不同地点、年份、季节、气候区和降水阶段而变化。此外，IMERG和ERA5-Land在无降水事件中的频率偏差上升，而在小雨、中雨、大雨和极端降水事件中下降。IMERG和ERA5-Land在无降水和小雨事件中的探测能力也受到明显影响。因此，本研究证明了系统的雨量计降水误差对IMERG和ERA5-Land评估的重大影响，并强化了在将雨量计数据用作基准之前消除其负偏差的必要性。
