New approaches to postprocessing of multi‐model ensemble forecasts. (3rd September 2019) - Barnes, Clair; Brierley, Christopher M.; Chandler, Richard E.
Abstract : Ensemble weather forecasts often under‐represent uncertainty, leading to overconfidence in their predictions. Multi‐model forecasts combining several individual ensembles have been shown to display greater skill than single‐ensemble forecasts in predicting temperatures, but tend to retain some bias in their joint predictions. Established postprocessing techniques are able to correct bias and calibration issues in univariate forecasts, but are generally not designed to handle multivariate forecasts (of several variables or at several locations, say). We propose a flexible multivariate Bayesian postprocessing framework, based on a directed acyclic graph representing the relationships between the ensembles and the observed weather. The posterior forecast is inferred from available ensemble forecasts and an estimate of the shared discrepancy, obtained from a collection of past forecast–observation pairs. We also propose a novel approach to selecting an appropriate training set for estimation of the required correction, using synoptic‐scale analogues to obtain a regime‐dependent estimate of the adjustment. The proposed technique is applied to forecasts of surface temperature over the UK during the winter period from 2007 to 2013. Although the resulting parametric multivariate‐normal probabilistic forecasts are marginally less sharp than those of the leading competitor, they capture the spatial structure of the observations better than a correlation structure based on either the ensembles or climatology alone, and are robust to changes in the variables and spatial domain of the forecast, at a greatly reduced computational cost. Abstract : Multi‐model forecasts combining several ensembles have been shown to display greater skill than single‐ensemble forecasts, but require careful postprocessing. We propose a multivariate Bayesian postprocessing framework, based on a graphical representation of the relationships between the ensembles and the observed weather, alongside a novel approach to selecting an appropriate training set using synoptic‐scale analogues. The resulting forecasts capture the spatial structure of the observations well, and are robust to changes in the variables and spatial domain of the forecast.