The local ETKF and SKEB: Upgrades to the MOGREPS short‐range ensemble prediction system

Abstract

ABSTRACT:The Met Office has been routinely running a short-range global and regional ensemble prediction system (EPS) since the summer of 2005. This article describes a major upgrade to the global ensemble, which affected both the initial condition and model uncertainty perturbations applied in that ensemble. The change to the initial condition perturbations is to allow localization within the ensemble transform Kalman filter (ETKF). This enables better specification of the ensemble spread as a function of location around the globe. The change to the model uncertainty perturbations is the addition of a stochastic kinetic energy backscatter scheme (SKEB). This adds vorticity perturbations to the forecast in order to counteract the damping of small-scale features introduced by the semi-Lagrangian advection scheme. Verification of ensemble forecasts is presented for the global ensemble system. It is shown that the localization of the ETKF gives a distribution of the spread as a function of latitude that better matches the forecast error of the ensemble mean. The SKEB scheme has a substantial effect on the power spectrum of the kinetic energy, and with the scheme a shallowing of the spectral slope is seen in the tail. A k −5/3 slope is seen at wavelengths shorter than 1000 km and this better agrees with the observed spectrum. The local ETKF significantly improves forecasts at all lead times over a number of variables. The SKEB scheme increases the rate of growth of ensemble spread in some variables, and improves forecast skill at short lead times.

Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office

“…The operational EPS at the Met Office is called MOGREPS; the Met Office Global and Regional Ensemble Prediction System (Bowler et al, 2008). The global component of this system (MOGREPS-G) is based on the ensemble transform Kalman filter (ETKF) of Wang et al (2004), and for the experiments described in this paper included the horizontal localization scheme described in Bowler et al (2009), the stochastic kinetic energy backscatter (SKEB) scheme described in Tennant et al (2011), and the level-dependent inflation scheme described in Flowerdew and Bowler (2012). One of the important design features of MOGREPS is its focus on short-range forecast errors, giving hope of reasonable covariance estimates at the times required by the hybrid system.…”

Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

“…Shutts (2005) introduced stochastic perturbations to backscatter a portion of the kinetic energy that is normally dissipated by parametrized processes, resulting in an improvement of probabilistic measures of forecast skill. Other parametrizations based on this concept have been tested by Bowler et al (2009), Berner et al (2009) and Tennant et al (2011). Berner et al (2009) found that stochastic kinetic energy backscatter leads to improved rainfall forecasts in the ECMWF ensemble forecast system, and Bowler (2009) and Tennant et al (2011) reported an improvement of the growth rate of the ensemble spread of some variables, and an improved forecast skill at short lead times.…”

Tests of different flavours of EnKF on a simple model

“…In order to improve this link, one would envisage making a number of improvements to the ensemble initialization, which should make the perturbations more suited for use within data assimilation. The Met Office ensemble uses an ETKF with broad horizontal localization (Bowler et al, 2009;Flowerdew and Bowler, 2011) and it does not have vertical covariance localization. The tuning of the ensemble spread is quite sophisticated Bowler, 2011, 2012) but otherwise the ensemble initialization is quite basic.…”