Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China
Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China
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DOI：
10.3390/rs8050440
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
B. Yong;Bo Chen-;Yudong Tian;Zhongbo Yu;Y. Hong
中科院分区：
文献类型：
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作者：
B. Yong;Bo Chen-;Yudong Tian;Zhongbo Yu;Y. Hong
热带降雨测量任务(TRMM)多卫星降水分析(TMPA)产品已得到广泛应用，但其在不同气候条件下的误差和不确定性特征仍需量化。在这项研究中，我们重点是系统地评估了大陆中国地区的TMPA的错误特征，并改进了错误成分分析程序。我们在每日尺度和0.25°×0.25°分辨率下对TMPA实时和研究产品套件进行了分析。我们的结果表明，总的来说，TMPA的误差分量表现出较强的区域和季节差异。对于湿润地区，夏季的误差主要来自命中偏差和少雨，而冬季的总误差主要来自少雨。对于半湿润和半干旱地区，两个实时TMPA产品的误差分量表现出明显的地形相关性。此外，漏报降水分量和虚假降水分量具有相似的季节变化，但它们相互抵消，导致总误差比单独分量小。对于干旱地区，虚假降水是反演中的主要问题，尤其是在冬季。另一方面，我们研究了两种规范校正方案，即用于实时TMPA的气候校准算法(CCA)和用于实时TMPA后的基于规范的调整(GA)。总体而言，我们的结果表明，CCA的向上调整缓解了TMPA对湿润地区的系统性低估，但同时也不利地增加了青藏高原和天山地区原有的正偏差。相比之下，GA技术可以显著改善局部区域的误差分量。此外，我们改进的误差分量分析发现，CCA和GA实际上也影响降雨率较低(特别是对于非湿润地区)以及较高降雨率时的命中偏差。最后，本研究建议下一步的努力将重点放在改善湿润地区的命中偏差、干旱地区的错误错误和冬季漏雪事件上。
The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter.