Parameter variations in prediction skill optimization at ECMWF

Abstract

Abstract. Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for MediumRange Weather Forecasts (ECMWF). We report the results of initial experimentation using importance sampling based on model parameter estimation methodology targeted for ensemble prediction systems, called the ensemble prediction and parameter estimation system (EPPES). The same methodology was earlier proven to be a viable concept in low-order ordinary differential equation systems, and in large-scale atmospheric general circulation models (ECHAM5). Here we show that prediction skill optimization is possible even in the context of a system that is (i) of very high dimensionality, and (ii) carefully tuned to very high skill. We concentrate on four closure parameters related to the parameterizations of sub-grid scale physical processes of convection and formation of convective precipitation. We launch standard ensembles of medium-range predictions such that each member uses different values of the four parameters, and make sequential statistical inferences about the parameter values. Our target criterion is the squared forecast error of the 500 hPa geopotential height at day three and day ten. The EPPES methodology is able to converge towards closure parameter values that optimize the target criterion. Therefore, we conclude that estimation and cost functionbased tuning of low-dimensional static model parameters is possible despite the very high dimensional state space, as well as the presence of stochastic noise due to initial state and physical tendency perturbations. The remaining question before EPPES can be considered as a generally applicable tool in model development is the correct formulation of the target criterion. The one used here is, in our view, very selective.Considering the multi-faceted question of improving forecast model performance, a more general target criterion should be developed. This is a topic of ongoing research.

Towards process‐level representation of model uncertainties: stochastically perturbed parametrizations in the ECMWF ensemble

“…Although parameter covariances for IFS are available from research experiments (Ollinaho et al , ), how to apply them effectively still remains an open question (as was indicated by the experiments of Christensen et al , ). We also note that multiple space and time correlation scales that differ from parameter to parameter are technically possible (according to the multiple scales in SPPT; Palmer et al , ).…”

Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

“…For the upper-troposphere zonal wind, the improvements (9 %) in skill are substantial when compared to other stateof-the-art stochastic perturbation schemes. In Ollinaho et al (2017) improvements by the SPP and SPPT scheme around 2.5 % are reported for zonal wind at 200 hPa. For 850 hPa zonal wind CRPS improvements induced by the SPP and SPPT scheme (1.8 %) are also comparable to the results presented here (1.7 %).…”

“…This technique was first applied in Bowler et al (2008) and further adapted for use in a convection-permitting ensemble by Baker et al (2014). The concept of stochastically perturbing parameters was further generalized in the stochastically perturbed parameterization (SPP) scheme (Ollinaho et al, 2013(Ollinaho et al, , 2017. SPP extends the concept of perturbing parameters to locally perturbing both parameters and variables inside the parameterizations.…”

Constraining stochastic parametrisation schemes using high‐resolution simulations

“…For example, Ollinaho et al . () describe the use of a Bayesian parameter estimation framework to quantify the uncertainty in four parameters within the convection parametrisation scheme, which was used by Christensen et al . () to develop a well‐constrained stochastically perturbed parameter scheme.…”