Satellite and gauge rainfall merging using geographically weighted regression
- 1State Key Labratory of Water Resources and Hydraulic Engineering & Science, Nanjing Hydraulic Research Institute, Nanjing, 210029, China
- 2Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China
- 3School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
Keywords: Satellite rainfall, rainfall merging, geographically weighted regression, CMORPH
Abstract. A residual-based rainfall merging scheme using geographically weighted regression (GWR) has been proposed. This method is capable of simultaneously blending various satellite rainfall data with gauge measurements and could describe the non-stationary influences of geographical and terrain factors on rainfall spatial distribution. Using this new method, an experimental study on merging daily rainfall from the Climate Prediction Center Morphing dataset (CMOROH) and gauge measurements was conducted for the Ganjiang River basin, in Southeast China. We investigated the capability of the merging scheme for daily rainfall estimation under different gauge density. Results showed that under the condition of sparse gauge density the merging rainfall scheme is remarkably superior to the interpolation using just gauge data.