Flow-dependent versus flow-independent initial perturbations for ensemble prediction

Ensemble prediction relies on a faithful representation of initial uncertainties in a forecasting system. Early research on initial perturbation methods tested random perturbations by adding ‘white noise’ to the analysis. Here, an alternative kind of random perturbations is introduced by using the difference between two randomly chosen atmospheric states (i.e. analyses). It yields perturbations (random field, RF, perturbations) in approximate flow balance.

The RF method is compared with the operational singular vector based ensemble at European Centre for Medium Range Weather Forecasts (ECMWF) and the ensemble transform (ET) method. All three methods have been implemented on the ECMWFIFS-model with resolution T_L255L40. The properties of the different perturbation methods have been investigated both by comparing the dynamical properties and the quality of the ensembles in terms of different skill scores. The results show that the RF perturbations initially have the same dynamical properties as the natural variability of the atmosphere. After a day of integration, the perturbations from all three methods converge. The skill scores indicate a statistically significant advantage for the RF method for the first 2–3 d for the most of the evaluated parameters. For the medium range (3–8 d), the differences are very small.