A Comparative Study of Multi-Model Ensemble Forecasting Accuracy between Equal- and Variant-Weight Techniques

Abstract

Previous studies on multi-model ensemble forecasting mainly focused on the weight allocation of each model, but did not discuss how to suppress the reduction of ensemble forecasting accuracy when adding poorer models. Based on a variant weight (VW) method and the equal weight (EW) method, this study explored this topic through theoretical and real case analyses. A theoretical proof is made, showing that this VW method can improve the forecasting accuracy of a multi-model ensemble, in the case of either the same models combination or adding an even worse model into the original multi-model ensemble, compared to the EW method. Comparative multi-model ensemble forecasting experiments against a real case between the VW and EW methods show that the forecasting accuracy of a multi-model ensemble applying the VW method is better than that of each individual model (including the model from the European Centre for Medium-Range Weather Forecasts). The 2 m temperature forecasting applying the VW method is superior to that applying the EW method for all the multi-model ensembles. Both theoretical proof and numerical experiments show that an improved forecast, better than a best model, is generally possible.

Probabilistic projections of granular energy technology diffusion at subnational level

“…Based on the performance of the 12 S-curve models, we assign weights that are individual for each municipality and use the weights to combine the probabilistic projections. We derive the calculation of weights from methods of ensemble weather forecasting where the use of inverse error variance outperforms equal weighting ( 67 , 68 ). The mean squared WIS acts as the error variance in our case.…”

Forecasting the Ensemble Hydrograph of the Reservoir Inflow based on Post-Processed TIGGE Precipitation Forecasts in a Coupled Atmospheric-Hydrological System

“…In recent decades, some researchers have combined the output of several NWP models to increase their forecast quality, known as the multi-model ensemble system [25,26]. Wei et al (2022) compared the forecasting accuracy of equal and variant weight techniques to create a multi-model ensemble forecast provided by the National Meteorological Information Center of China. They concluded that the variant weight method produced more accurate temperature forecasts [27].…”

“…Wei et al (2022) compared the forecasting accuracy of equal and variant weight techniques to create a multi-model ensemble forecast provided by the National Meteorological Information Center of China. They concluded that the variant weight method produced more accurate temperature forecasts [27]. Medina et al (2018) developed a multi-model ensemble system to forecast reference evapotranspiration by combining the models of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the United Kingdom Meteorological Office (UKMO) using the linear regression model.…”