Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

Abstract

ABSTRACT Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies. The wide-used statistical downscaling model (SDSM) software tools use multi-linear regression to extract linear relations between large-scale and local climate variables and then produce high-resolution climate maps from sparse climate observations. The latest machine learning techniques (e.g. SRCNN, SRGAN) can extract nonlinear links, but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance. In this study, we proposed a novel hybrid RBF (Radial Basis Function) network by embedding several RBF networks into new RBF networks. Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling. To test the performance of our model, we generated high-resolution precipitation, air temperature and humidity maps from 34 meteorological stations in Bangladesh. In terms of three statistical indicators, the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression (MLR), Kriging interpolation or a pure RBF network.