On the impact of re‐centring initial conditions for ensemble forecasts

Abstract

Many weather forecasting centres generate the initial conditions for their ensemble prediction system by adding a set of perturbations to a single analysis. At the European Centre for Medium‐Range Weather Forecasts (ECMWF), initial conditions from an ensemble of data assimilations (EDA) are re‐centred on the deterministic high‐resolution analysis. This study assesses how the re‐centring step affects the ensemble forecasts and what benefits starting directly from the perturbed EDA members may bring. Numerical experiments are based on suitably modified configurations of the ECMWF medium‐range ensemble.Re‐centring leads to an artificial increase of the climatological variance of the initial conditions for variables and spatial scales that are not well constrained in the analysis. For those variables and spatial scales, the EDA variance can reach a similar magnitude as the climatological variance. Re‐centring on the unperturbed member of the EDA leads to a degradation of the ensemble skill in comparison to starting directly from the perturbed EDA members. Depending on the amplitude of the EDA spread and which scales are less constrained, different lead times are affected. Furthermore, the re‐centring step increases the jumpiness of the ensemble forecasts in general and alters the precipitation frequency during the first 12 h of the forecasts.It also becomes apparent from our results that the benefit for the ECMWF ensemble of starting directly from EDA initial conditions is sensitive to several factors: the difference in analysis accuracy between the unperturbed EDA control and the high‐resolution analysis, the number of EDA members, and the EDA spread. At present, the forecast skill still benefits from centring the ensemble initial conditions on the high‐resolution analysis, but future changes to the EDA may change the balance.