Integration of Exponential Weighted Moving Average Chart in Ensemble of Precipitation of Multiple Global Climate Models (GCMs)

Global Climate Models (GCMs) are globally accepted simulations used for modeling and forecasting of precipitation and other parameters of climate. However, discrepancies among GCMs reduce the scope of individual models. In this regard, a multimodel ensemble provides a more accurate picture than individual models. However, the presence of extreme values and outliers in GCMs makes ensemble inappropriate for statistical analysis and reanalysis. In this article, we propose a novel weighting scheme that integrates the Exponentially Weighted Moving Average (EWMA) chart to obtain optimum weights for ensemble means of multiple GCMs – EWMA-based Ensemble (EWMABE). To investigate the validity of EWMABE, we used precipitation data of eighteen GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this study, the performance of EWMABE is compared with Multi-Model Simple Averaging Ensemble (MMSAE). The comparative statistics show that the EWMABE scheme has significantly improved the multimodel estimates. These results suggests the potential candidacy of EWMABE for the estimation of multimodel ensemble.