Parameter variations in prediction skill optimization at ECMWF

P Ollinaho,H Haario,M Laine,M Leutbecher,H Järvinen,P Bechtold,A Solonen

doi:10.5194/npg-20-1001-2013

Abstract

Abstract. Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for Medium-Range 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 function-based 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.

Highlights

Long-term improvements in numerical weather prediction models (NWP) originate from dedicated research to improve the representation of atmospheric phenomena across all spatial and temporal scales
The main remaining questions are as follows: (i) are the convergence properties of the ensemble prediction and parameter estimation system (EPPES) algorithm in the low-dimensional parameter space preserved as the model state space becomes very highdimensional, (ii) do the stochastic model physics perturbations affect the estimation process detrimentally, and (iii) is it possible to formulate a target criterion such that the parameter estimation results in a genuine and universally acceptable model improvement? This paper explores questions (i) and (ii), while question (iii) remains a topic for further research and is only briefly discussed here
In this paper we present experimentation using the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), including their Ensemble Prediction System (EPS)

Summary

Introduction

Long-term improvements in numerical weather prediction models (NWP) originate from dedicated research to improve the representation of atmospheric phenomena across all spatial and temporal scales This involves a slow but steady development process that gradually improves the predictive skill of NWP models and reduces their systematic errors (Simmons and Hollingsworth, 2002). Short-term developments are typically incremental, such as refinements to existing modeling schemes, or the introduction of new observing system components. These are aimed to be implemented as new model releases within a time frame of some months and are seen as gradual small steps between model generations.

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Discussion

Conclusion