Creating Pseudo–Forecast Ensembles Statistically Using a Characterization of Displacements: A Pilot Study

Abstract

A feature-based statistical method is investigated as a method of generating pseudoensembles of numerical weather prediction forecasts. The goal is to enhance or dress a single dynamical forecast or an ensemble of dynamical forecasts with many realistic perturbations so as to represent better the forecast uncertainty. The feature calibration and alignment method (FCA) is used to characterize forecast differences and to generate the additional ensemble members. FCA is unique in decomposing forecast errors or differences into phase, bias, and residual error or difference components. In a pilot study using 500-hPa geopotential height data, pseudoensembles of weather forecasts are generated from one deterministic forecast and perturbations obtained by randomly sampling FCA displacements based on a priori statistics and applying these displacements to the original deterministic forecast. Comparison with actual dynamical ensembles of 500-hPa geopotential height generated by ECMWF show that important features of the dynamical ensemble, such as the spatial patterns of the ensemble mean and variance, can be approximated by the FCA pseudoensemble. Ensemble verification statistics are presented for the dynamic and FCA ensemble and compared with those of simpler statistically based pseudoensembles. Some limitations of the FCA ensembles are noted, and mitigation approaches are discussed, with a view toward applying the method to mesoscale forecasts for dispersion modeling.