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Bias adjustment of satellite‐based precipitation estimation using gauge observations: A case study in Chile
Author(s) -
Yang Zhongwen,
Hsu Kuolin,
Sorooshian Soroosh,
Xu Xinyi,
Braithwaite Dan,
Verbist Koen M. J.
Publication year - 2016
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2015jd024540
Subject(s) - satellite , precipitation , rain gauge , weighting , environmental science , nonparametric statistics , quantile , gauge (firearms) , climatology , mean squared error , meteorology , computer science , statistics , remote sensing , mathematics , geography , geology , medicine , archaeology , aerospace engineering , engineering , radiology
Satellite‐based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground‐based observations are limited. However, existing satellite‐based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN‐CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM‐GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN‐CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN‐CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root‐mean‐square errors and mean biases. The systematic biases of the PERSIANN‐CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground‐based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.