The Benefits of Multianalysis and Poor Man’s Ensembles

Abstract

A new approach to probabilistic forecasting is proposed, based on the generation of an ensemble of equally likely analyses of the current state of the atmosphere. The rationale behind this approach is to mimic a poor man?s ensemble, which combines the deterministic forecasts from national meteorological services around the world. The multianalysis ensemble aims to generate a series of forecasts that are both as skillful as each other and the control forecast. This produces an ensemble mean forecast that is superior not only to the ensemble members, but to the control forecast in the short range even for slowly varying parameters, such as 500-hPa height. This is something that it is not possible with traditional ensemble methods, which perturb a central analysis. The results herein show that the multianalysis ensemble is more skillful than the Met Office?s high-resolution forecast by 4.5% over the first 3 days (on average as measured for RMSE). Similar results are found for different verification scores and various regions of the globe. In contrast, the ensemble mean for the ensemble currently run by the Met Office performs 1.5% worse than the high-resolution forecast (similar results are found for the ECMWF ensemble). It is argued that the multianalysis approach is therefore superior to current ensemble methods. The multianalysis results were achieved with a two-member ensemble: the forecast from a high-resolution model plus a low-resolution perturbed model. It may be possible to achieve greater improvements with a larger ensemble.