Improvement of ensemble reliability with a new dressing kernel

Abstract

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 x 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..