Statistical Downscaling Using Local Polynomial Regression for Rainfall Predictions – A Case Study

This article presents a methodology for statistical downscaling using local polynomial regression for obtaining the future projections of rainfall in a catchment. Local polynomial regression offers a method to catch the nonlinearities in the input–output relationship compared to traditional regression by identifying the nearest neighbors of the predictor point for a specified band width. It fits a low degree polynomial model to the subset of the data at each point by weighted least squares. The local regression fit is complete when the regression function values are calculated for all the data points. A smooth curve through the data points is obtained by this method. Mean sea level pressure, geopotential height 500 mb, air temperature, relative humidity and wind speed are identified as the potential predictors for predicting the rainfall. Monthly data on the predictors for nine grid points around the study area are obtained from National Centre for Environmental Prediction (NCEP)/ National Centre for Atmospheric Research (NCAR) Re-analysis data. The model was applied to forecast the rainfall in the catchment of Idukky reservoir in Kerala, India. The model performance was compared with that of multiple linear regression and artificial neural network models. It is seen that the local polynomial regression model gives a better performance in forecasting the rainfall. The new methodology adopted is computationally simple, easy to implement and it captures the linear and non linear features in the data set preserving the dynamics of the atmosphere and the properties of the historical series. Copyright Springer Science+Business Media Dordrecht 2016