Uncertainty growth and forecast reliability during extratropical cyclogenesis

Abstract

Abstract. In global numerical weather prediction, the strongest contribution to ensemble variance growth over the first few days is at synoptic scales. Hence it is particularly important to ensure that this synoptic-scale variance is reliable. Here we focus on wintertime synoptic-scale growth in the North Atlantic storm track. In the 12 h background forecasts of the Ensemble of Data Assimilations (EDA) from the European Centre for Medium-Range Weather Forecasts (ECMWF), we find that initial variance growth at synoptic scales tends to be organized in particular flow situations, such as during the deepening of cyclones (cyclogenesis). Both baroclinic and diabatic aspects may be involved in the overall growth rate. However, evaluation of reliability through use of an extended error–spread equation indicates that the ECMWF ensemble forecast, which is initialized from the EDA but with additional singular vector perturbations, appears to have too much variance at a lead time of 2 d and that this over-spread is associated with cyclogenesis situations. Comparison of variance growth rates and reliability with other forecast systems within The International Grand Global Ensemble (TIGGE) archive indicates some sensitivity to the model or its initialization. For the ECMWF ensemble forecast, sensitivity experiments suggest that a large part of the total day-2 spread in cyclogenesis cases is associated with the growth of EDA uncertainty, but up to 25 % can be associated with the additional singular vector perturbations to the initial conditions and up to 25 % with the representation of model uncertainty. The sensitivities of spread to resolution, the explicit representation of convection, and the assimilation of local observations are also considered. The study raises the question of whether the EDA now successfully represents initial uncertainty (and the enhanced growth rates associated with cyclogenesis) to the extent that singular vector perturbations could be reduced in magnitude to improve storm track reliability. This would leave a more seamless forecast system, allowing short-range diagnostics to better help improve the model and model-uncertainty representation, which could be beneficial throughout the forecast range.