基于 TRMM 的多卫星降水量估算中的哪些误差成分在官方偏差调整后会在中国大陆上空减少?系统性还是随机性?

Z. Shen, Bin Yong, Hao Wu
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摘要

气候学校准算法(CCA)和卫星-测站组合(SG)是热带降雨测量任务(TRMM)多卫星降水分析(TMPA)中对卫星降水估算(SPE)进行的两种官方偏差调整。CCA 专为近实时 SPE 而设计,而 SG 程序则是将纯 SPE 与测站观测数据合并的最后一步。本研究探讨了 CCA 和 SG 对 TMPA SPEs 系统误差和随机误差的影响。以中国基于测站的日降水量分析(CGDPA)为基准,将CCA前后的TMPA第7版近实时产品(RT_UC、RT_C)和研究产品TMPA 3B42(V7)的误差分解为系统误差和随机误差。经 CCA 校正后,除青藏高原和天山山脉外,中国大陆 RT_C 相对 RT_UC 减少了系统误差。但是,CCA 不仅没有帮助减少随机误差,反而加剧了随机误差。另一方面,由于直接加入了全球降水气候学中心(GPCC)的同步测站数据,SG合并比CCA校准更能有效减少SPE的系统误差。我们还发现,在海拔相对较高的地区,SG 合并可减少纯 SPE 的随机误差。尽管地形复杂地区的 V7 随机误差较低,但 SG 却不利地增加了中国东南部地区的随机误差。本文所报告的结果可能会对从 TMPA 中提取的 CCA 和 SG 技术的效果提供有价值的见解,并有可能推动未来 SPE 偏差调整算法的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?
Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.
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