Investigating the Evaluation Uncertainty for Satellite Precipitation Estimates Based on Two Different Ground Precipitation Observation Products
Investigating the Evaluation Uncertainty for Satellite Precipitation Estimates Based on Two Different Ground Precipitation Observation Products
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DOI：
10.1175/jhm-d-20-0103.1
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发表时间：
2020-11-01
影响因子：
3.8
通讯作者：
Hong, Yang
中科院分区：
文献类型：
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作者：
Chen, Hanqing;Yong, Bin;Hong, Yang
由标准参考本身引起的评估不确定性不利于算法开发者和数据使用者充分理解卫星降水产品（SPPs）的误差特征和性能。在这项研究中，气候降水中心统一（CPCU）的数据和合并降水分析（MPA）的数据被用作基准，调查的参考本身产生的卫星降水估计的评估不确定性。这里采用两个SPP，IMERG-Late和GSMaP-MVK。结果表明，利用两种不同的地面降水产品作为参考的方法可以有效地揭示潜在的评估不确定性。有趣的是，它被发现，评估结果是容易导致较大的不确定性在半湿润地区。此外，统计指标的评估不确定度与降雨强度密切相关，随着降雨强度的增加，评估不确定度有逐渐减小的趋势。此外，我们还发现，假警报率（FAR）和均方根误差（RMSE）分数的依赖性的雨量计的空间密度相对较低。小降水量（1- 5 mm日（-1））的相对偏差（RBIAS）和归一化均方根误差（NRMSE）分数都随着雨量计的空间密度而增加，这表明相对于中高降雨率，小降水量的评估容易引起不确定性。最后讨论了不同评分和不同雨强下所需的最小密度。这项研究有望为卫星定量降水估算界提供调查评估结果可靠性的标准。
The evaluation uncertainty caused by a standard reference itself is harmful to both algorithm developers and data users in substantially understanding the error features and the performance of satellite precipitation products (SPPs). In this study, the Climate Precipitation Center Unified (CPCU) data and the Merged Precipitation Analysis (MPA) data are used as the benchmark to investigate the evaluation uncertainties of satellite precipitation estimates generated by the reference itself. Two SPPs, IMERG-Late and GSMaP-MVK, are employed here. The results show that the approach using two different ground-based precipitation products as the references can effectively reveal the potential evaluation uncertainties. Interestingly, it is found that the evaluation results are prone to resulting in larger uncertainties over semihumid areas. Furthermore, evaluation uncertainty of statistical metrics is closely related to rainfall intensity in that it has a gradually decreasing tendency with increasing rainfall intensities. Additionally, we also found that the dependency of the false alarm ratio (FAR) and root-mean-square error (RMSE) scores on the spatial density of rain gauges is relatively low. Both relative bias (RBIAS) and normalized root-mean-square error (NRMSE) scores for light precipitation (1-5mm day(-1)) increase with the spatial density of the rain gauges, suggesting that the evaluation of light precipitation can easily cause uncertainties relative to medium-to-high rain rates. Finally, the minimum gauge density required for different scores and different rainfall intensities is discussed. This study is expected to provide criteria to investigate the reliability of evaluation results for the satellite quantitative precipitation estimation community.