Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP

Mozheng Wei,Zoltan Toth,Richard Wobus,Yuejian Zhu,Craig H Bishop,Xuguang Wang

doi:10.1111/j.1600-0870.2006.00159.x

Abstract

The initial perturbations used for the operational global ensemble prediction system of the National Centers for Environmental Prediction are generated through the breeding method with a regional rescaling mechanism. Limitations of the system include the use of a climatologically fixed estimate of the analysis error variance and the lack of an orthogonalization in the breeding procedure. The Ensemble Transform Kalman Filter (ETKF) method is a natural extension of the concept of breeding and, as shown byWang and Bishop, can be used to generate ensemble perturbations that can potentially ameliorate these shortcomings. In the present paper, a spherical simplex 10-member ETKF ensemble, using the actual distribution and error characteristics of real-time observations and an innovation-based inflation, is tested and compared with a 5-pair breeding ensemble in an operational environment.The experimental results indicate only minor differences between the performances of the operational breeding and the experimental ETKF ensemble and only minor differences to Wang and Bishop’s earlier comparison studies. As for the ETKF method, the initial perturbation variance is found to respond to temporal changes in the observational network in the North Pacific. In other regions, however, 10 ETKF perturbations do not appear to be enough to distinguish spatial variations in observational network density. As expected, the whitening effect of the ETKF together with the use of the simplex algorithm that centres a set of quasi-orthogonal perturbations around the best analysis field leads to a significantly higher number of degrees of freedom as compared to the use of paired initial perturbations in operations. As a new result, the perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations, a feature that can be important in practical applications. Potential additional benefits of the ETKF and Ensemble Transform methods when using more ensemble members and a more appropriate inflation scheme will be explored in follow-up studies.

Highlights

It is well known that the atmosphere is chaotic, and its predictability is severely limited by both initial and model-related errors
One of the main attractions of using an Ensemble Transform Kalman Filter (ETKF) ensemble generation is that it allows ensemble variance to reflect the impact of variations in observational density on analysis and forecast error variance, provided the ensemble is large enough
We have carried out experiments with two ensemble forecast systems based on two different techniques for generating initial perturbations: ETKF and breeding

Summary

Introduction

It is well known that the atmosphere is chaotic, and its predictability is severely limited by both initial and model-related errors. A feasible way to improve a single, deterministic forecast is to use ensemble forecasting. Ensemble forecasts start from a set of different states that are approximated using a finite sample of initial perturbations. The nature of the best method to generate these initial perturbations for an ensemble forecasting system is still under investigation. At the European Center for Medium-Range Weather Forecasts (ECMWF), singular vectors (SVs) are used to identify the Another method is the perturbed observation (PO) approach developed at the Meteorological Service of Canada (MSC) (Houtekamer et al, 1996; Houtekamer and Mitchell, 1998).

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