Improvement of ensemble reliability with a new dressing kernel

A new method of combining dynamical and statistical ensembles for the purpose of improving ensemble reliability for underdispersive ensembles is introduced. The method involves adding independent sets of N random four-dimensional ‘dressing ’ perturbations to each of the K members of a dynamical ensemble forecast to obtain an N × K dressed ensemble. The new method mathematically constrains the stochastic process used to generate the statistical dressing perturbations so that it removes seasonally averaged errors in the second moment measures for originally underdispersive ensembles. A random-number generator experiment and an experiment with the ensemble transform Kalman filter (ETKF) ensemble generation scheme show that the previously proposed ‘bestmember’ dressing method fails to reliably predict the second moment of the distribution of forecast errors, whereas the new dressing method reliably predicts this second moment. After being dressed with the second moment constraint method, the ETKF ensemble is more skilful than the undressed ensemble. The ETKF ensemble postprocessed with the new dressing method is applied for probabilistic forecasts of cooling degree-days (CDD) for Boston. It is shown that the new kernel’s ability to account for temporally correlated forecast errors results in ensemble forecasts of CDDs with reliable spread, whereas the best-member method leads to an underdispersive ensemble of CDD forecasts.