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Merging high‐resolution satellite‐based precipitation fields and point‐scale rain gauge measurements—A case study in Chile
Author(s) -
Yang Zhongwen,
Hsu Kuolin,
Sorooshian Soroosh,
Xu Xinyi,
Braithwaite Dan,
Zhang Yuan,
Verbist Koen M. J.
Publication year - 2017
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2016jd026177
Subject(s) - satellite , precipitation , rain gauge , weighting , environmental science , mean squared error , gauge (firearms) , inverse distance weighting , remote sensing , ground truth , scale (ratio) , calibration , meteorology , computer science , statistics , mathematics , artificial intelligence , geography , medicine , archaeology , aerospace engineering , multivariate interpolation , engineering , bilinear interpolation , radiology , cartography
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 mean biases that were consistently improved from the original PERSIANN‐CCS estimates. The cross‐validation evidences that the framework is effective in providing high‐quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates.