New Statistical Methods for Precipitation Bias Correction Applied to WRF Model Simulations in the Antisana Region, Ecuador

The Ecuadorian Andes are characterized by a complex spatiotemporal variability of precipitation. Global circulation models do not have sufficient horizontal resolution to realistically simulate the complex Andean climate and in situ meteorological data are sparse; thus, a high-resolution gridded precipitation product is needed for hydrological purposes. The region of interest is situated in the center of Ecuador and covers three climatic influences: the Amazon basin, the Andes, and the Pacific coast. Therefore, regional climate models are essential tools to simulate the local climate with high spatiotemporal resolution; this study is based on simulations from the Weather Research and Forecasting (WRF) Model. The WRF Model is able to reproduce a realistic precipitation variability in terms of the diurnal cycle and seasonal cycle compared to observations and satellite products; however, it generated some nonnegligible bias in the region of interest. We propose two new methods for precipitation bias correction of the WRF precipitation simulations based on in situ observations. One method consists of modeling the precipitation bias with a Gaussian process metamodel. The other method is a spatial adaptation of the cumulative distribution function transform approach, called CDF-t, based on Voronoi diagrams. The methods are compared in terms of precipitation occurrence and intensity criteria using a cross-validation leave-one-out framework. In terms of both criteria, the Gaussian process metamodel approach yields better results. However, in the upper parts of the Andes (>2000 m), the spatial CDF-t method seems to better preserve the spatial WRF physical patterns.