Keywords: LOWESS regression, robust weighted regression, VIP score, maximum temperatures, minimum temperatures, downscaling models, arid regions, India, partial least squares, PLS regression, global warming
Robust weighted regression as a downscaling tool in temperature projections
Downscaling models are developed using robust version of locally weighted regression smoothing scatter plots technique (LOWESS) regression approach for obtaining projections of mean monthly maximum and minimum temperatures (Tmax and Tmin) to Pichola watershed in an arid region in India. Variable Importance in the Projection (VIP) score from Partial Least Squares (PLSs) regression is used to select the variables. A comparison is also done with LOWESS regression approach. The results show that an increasing trend is observed for Tmax and Tmin for A1B, A2 and Bl scenarios whereas no trend is discerned with the COMMIT.