Abstract
AbstractWith 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.
Highlights
Precipitation is a key process in the hydrological cycle
This study shows that our bias-adjusted PERSIANN-CCS (Adj-CCS) estimates are worse in representing observed rainfall than those products, including the PGFv3 [Chaney et al, 2014; Peng et al, 2016], CHIRPSv2 [Funk et al, 2015], TRMM Multisatellite Precipitation Analysis (TMPA) 3B42v7 [Huffman et al, 2010], and MSWEPv1.1 [Beck et al, 2016]
The monthly estimates for Adj-CCS are improved with great decreases in absolute BIAS and root-mean-square errors (RMSEs) by on average 90% and 53%, respectively
Summary
Precipitation is a key process in the hydrological cycle. The quality of precipitation data is of high importance to hydrologists, as precipitation uncertainty is the most influential cause of uncertainty in hydrological simulation [Moradkhani and Sorooshian, 2008]. Satellite-based Precipitation Estimates (SPE) are becoming a popular alternative for measuring precipitation, over those gauge-sparse areas It can provide highresolution and global coverage SPE products based on remotely sensed information from geostationary Earth-orbiting (GEO) or/and low Earth-orbiting satellites [Hong et al, 2004; Hsu et al, 1997, 1999; Huffman et al, 2010, 2007, 2014; Joyce et al, 2004; Kubota et al, 2007; Mitchell et al, 2004; Sorooshian et al, 2000]. These SPE products are subject to considerable biases [AghaKouchak et al, 2011; Li et al, 2013; YANG ET AL
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