Multimodel Prediction of Monsoon Rain Using Dynamical Model Selection

Abstract

In this paper, dynamical-model-selection-based multimodel ensemble (DMS-MME) technique is developed for skill improvement of monsoon rain prediction in the medium range (i.e., 24-120 h ahead). The data set consists of 24-120 h daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.). The DMS-MME forecasts are constructed during the monsoon months (JJAS) for the years 2008-13 over the Indian mainland. For the training and verification purposes, India Meteorological Department rainfall is used. The forecast skill of the DMS-MME model has been compared with the performance of individual models and regression-based MME model. Further, to remove the nonnormality of rainfall distribution, square-root and logarithmic transfer functions are used for normalizing the precipitation data. The impact of these transfer functions on the forecast skill of the DMS-MME model has been assessed. The forecast skill of the proposed model is evaluated using the standard statistical measures. DMS-MME forecasts carries higher skill in terms of verification scores compared with the MME forecasts up to 120 h. It has been found that using the DMS-MME approach with a square-root transfer function (SDMS-MME) gives the best results. SDMS-MME outperforms the operational models and the regression-based MME at all forecast steps.