A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System

Abstract

Understanding model error in state-of-the-art numerical weather prediction models and representing its impact on flow-dependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscale-propagating errors caused by unresolved subgrid-scale processes. For this purpose, stochastic streamfunction perturbations are generated by autoregressive processes in spectral space and injected into regions where numerical integration schemes and parameterizations in the model lead to excessive systematic kinetic energy loss. It is demonstrated how output from coarse-grained high-resolution models can be used to inform the parameters of such a scheme. The performance of the spectral backscatter scheme is evaluated in the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts. Its implementation in conjunction with reduced initial perturbations results in a better spread–error relationship, more realistic kinetic-energy spectra, a better representation of forecast-error growth, improved flow-dependent predictability, improved rainfall forecasts, and better probabilistic skill. The improvement is most pronounced in the tropics and for large-anomaly events. It is found that whereas a simplified scheme assuming a constant dissipation rate already has some positive impact, the best results are obtained for flow-dependent formulations of the unresolved processes.

Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts

“…Two stochastic tendency perturbation schemes aim to mimic model errors. These are the stochastic kinetic energy backscatter scheme (Berner et al, 2009) and the stochastically perturbed parametrization tendency scheme Palmer et al, 2009).…”

‘Superparameterization’ and statistical emulation in the Lorenz '96 system

“…In previous work on stochastic parameterizations, both with the Lorenz '96 system (Wilks, 2005) and in other settings Neelin, 2000, 2003;Berner et al, 2009), it has been found that serially independent random numbers are relatively ineffective at improving ensemble forecast performance, and that serially dependent (i.e. temporally coherent) random innovations yield better forecast ensembles.…”

“…The first introduction of stochastic parameterization into a realistic forecast model was described in the pioneering paper of Buizza et al (1999). Since that time stochastic parameterizations have been employed in operational settings, using weather-forecast and climate models from the European Centre for Medium-Range Weather Forecasts (Shutts, 2005;Berner et al, 2009Berner et al, , 2010, the UK Met Office (Bowler et al, 2009;Tennant et al, 2011), the Canadian Meteorological Centre (Charron et al, 2010) and in the USA (Hou et al, 2008;Teixeira and Reynolds, 2008). Stochastic parameterizations have in addition been used in research settings employing geographically explicit atmospheric models, in connection with the study of convection Neelin, 2000, 2003;Song et al, 2007), stratospheric dynamics (Piani et al, 2004) and tropical cyclone simulation (Snyder et al, 2011).…”

“…Stochastic parameterizations have in addition been used in research settings employing geographically explicit atmospheric models, in connection with the study of convection Neelin, 2000, 2003;Song et al, 2007), stratospheric dynamics (Piani et al, 2004) and tropical cyclone simulation (Snyder et al, 2011). Stochastic parameterizations appear to result in a broader range of potential behaviors becoming available to the model containing them, yielding more correct ensemble dispersion and therefore better calibrated probabilistic forecasts, among other improvements, both in realistic Lin and Neelin, 2003;Piani et al, 2004;Shutts, 2005;Song et al, 2007;Hou et al, 2008;Teixeira and Reynolds 2008;Berner et al, 2009Berner et al, , 2010Charron et al, 2010;Snyder et al 2011;Tennant et al 2011) and idealized (Wilks, 2005;Crommelin and VandenEijnden, 2008;) models.…”

Parameter variations in prediction skill optimization at ECMWF

“…These are complemented by perturbations based on initial-time singular vectors (Buizza et al, 2008;Isaksen et al, 2010). Uncertainty of the forecast model formulation is represented in these experiments by stochastically perturbing the tendencies generated by the parameterization schemes (Buizza et al, 1999;Palmer et al, 2009) and by a stochastic kinetic energy backscatter scheme that adds a stream function forcing to the momentum equation (Berner et al, 2009). …”