Merging high‐resolution satellite‐based precipitation fields and point‐scale rain gauge measurements—A case study in Chile

作者：Zhongwen Yang, Kuolin Hsu, Soroosh Sorooshian, Xinyi Xu, Dan Braithwaite, Yuan Zhang, Koen Verbist · 发表于：Journal of Geophysical Research Atmospheres · 年份：2017 · DOI：10.1002/2016jd026177 · 被引用次数：85 · 研究领域：Precipitation Measurement and Analysis、Meteorological Phenomena and Simulations、Climate variability and models

Abstract With high spatial‐temporal resolution, Satellite‐based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground‐based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high‐quality precipitation estimates. An inverse‐root‐mean‐square‐error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009–2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high‐resolution‐precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root‐mean‐square errors, and absolute me...