Statistical downscaling of precipitation and temperature using sparse Bayesian learning, multiple linear regression and genetic programming frameworks

Abstract

This study attempted to investigate two approaches to downscale temperature and four approaches to downscale precipitation. The first approach was an implementation of multiple linear regression (MLR) in the form of backward stepwise regression. The second approach applied canonical correlation analysis (CCA) with a sparse Bayesian learning (SBL) approach called relevance vector machine (RVM). For precipitation downscaling, two additional approaches which combined genetic programming (GP) as the predictor processing method with sparse Bayesian learning (SBLGP) and multiple linear regression (MLRGP) were also presented. The results showed that SBL outperformed MLR in downscaling temperature. For all stations, temperature was better downscaled than precipitation. For precipitation downscaling, the SBLGP approach outperformed all other approaches. MLRGP, on the other hand, did not bring about much improvement in the results and was in many cases outperformed by MLR.