International Journal of Environmental Science and Development

Abstract—This study proposes an innovative approach for statistical downscaling of rainfall based on scaling property of meteorological variables. The reanalysis data of five dominant meteorological variables mean sea level pressure, relative humidity, surface temperature, wind velocity (zonal and meridional components) extracted from National Centre for Environmental Prediction (NCEP) are used as predictors to project monthly rainfall of Kerala meteorological subdivision in India. The multiscale decomposition of predictor dataset of the region and the monthly rainfall of a specific grid point is performed simultaneously by employing the Multivariate Empirical Mode Decomposition (MEMD) technique. The individual modes are predicted by fitting stepwise linear regression (SLR) by considering the potential predictors based on p-value statistics. Subsequent addition of the predicted modes gives the monthly rainfall. The method is demonstrated by a specific grid point of Chalakkudi river basin in Kerala, India. The method is found to be superior over the linear regression and M5 model tree based transfer function approaches. Further, the MEMD-SLR hybrid model is used for rainfall projections of the state of Kerala under three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) provided by Canadian Centre for Climate Modeling and Analysis (CCCMa).

Index Terms—Downscaling, Kerala, rainfall, MEMD.

S. Adarsh is with TKM College of Engineering Kollam 691005, India (e-mail: adarsh_lce@ yahoo.co.in).

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Cite: S. Adarsh, "Finer Scale Rainfall Projections for Kerala Meteorological Subdivision, India Based on Multivariate Empirical Mode Decomposition," International Journal of Environmental Science and Development vol. 7, no. 12, pp. 896-901, 2016.