Mozheng Wei

A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems

Abstract The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following: the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts; a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; and for all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources of forecast ...

Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system

Since modern data assimilation (DA) involves the repetitive use of dynamical forecasts, errors in analyses share characteristics of those in short-range forecasts. Initial conditions for an ensemble prediction/forecast system (EPS or EFS) are expected to sample uncertainty in the analysis field. Ensemble forecasts with such initial conditions can therefore (a) be fed back to DA to reduce analysis uncertainty, as well as (b) sample forecast uncertainty related to initial conditions. Optimum performance of both DA and EFS requires a careful choice of initial ensemble perturbations. DA can be improved with an EFS that represents the dynamically conditioned part of forecast error covariance as accurately as possible, while an EFS can be improved by initial perturbations reflecting analysis error variance. Initial perturbation generation schemes that dynamically cycle ensemble perturbations reminiscent to how forecast errors are cycled in DA schemes may offer consistency between DA and EFS, and good performance for both. In this paper, we introduce an EFS based on the initial perturbations that are generated by the Ensemble Transform (ET) and ET with rescaling (ETR) methods to achieve this goal. Both ET and ETR are generalizations of the breeding method (BM). The results from ensemble systems based on BM, ET, ETR and the Ensemble Transform Kalman Filter (ETKF) method are experimentally compared in the context of ensemble forecast performance. Initial perturbations are centred around a 3D-VAR analysis, with a variance equal to that of estimated analysis errors. Of the four methods, the ETR method performed best in most probabilistic scores and in terms of the forecast error explained by the perturbations. All methods display very high time consistency between the analysis and forecast perturbations. It is expected that DA performance can be improved by the use of forecast error covariance from a dynamically cycled ensemble either with a variational DA approach (coupled with an ETR generation scheme), or with an ETKF-type DA scheme.

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

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.

A new measure of ensemble performance: Perturbation versus error correlation analysis (PECA)

Abstract Most existing ensemble forecast verification statistics are influenced by the quality of not only the ensemble generation scheme, but also the forecast model and the analysis scheme. In this study, a new tool called perturbation versus error correlation analysis (PECA) is introduced that lessens the influence of the initial errors that affect the quality of the analysis. PECA evaluates the ensemble perturbations, instead of the forecasts themselves, by measuring their ability to explain forecast error variance. As such, PECA offers a more appropriate tool for the comparison of ensembles generated by using different analysis schemes. Ensemble perturbations from both the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated and found to perform similarly. The error variance explained by either ensemble increases with the number of members and the lead time. The dynamically conditioned NCEP and ECMWF perturbations outpe...

Analysis differences and error variance estimates from multi-centre analysis data

NWP forecast performance has made great progress during the past decade due to a few important factors. First, the numerical forecast models at major numerical weather prediction (NWP) centres have improved tremendously due to more accurate physics parametrisation schemes and increased computing power, which permits the use of higher resolution forecast models. Second, more observations, more accurate observing systems and improved data assimilation (DA) methods have been developed, such as 4D-Var (Rabier et al. 2000) and ensemble Kalman filters (Whitaker and Hamill 2002; Tippett et al. 2003; Whitaker et al. 2007). More accurate DA systems have played a key role in providing more accurate initial conditions for the NWP models, which have improved weather forecasts, particularly over the short and medium ranges. Analysis differences and error variance estimates from multi-centre analysis data

Controlling Noise in Ensemble Data Assimilation Schemes

Abstract A variety of ad hoc procedures have been developed to prevent filter divergence in ensemble-based data assimilation schemes. These procedures are necessary to reduce the impacts of sampling errors in the background error covariance matrix derived from a limited-size ensemble. The procedures amount to the introduction of additional noise into the assimilation process, possibly reducing the accuracy of the resulting analyses. The effects of this noise on analysis and forecast performance are investigated in a perfect model scenario. Alternative schemes aimed at controlling the unintended injection of noise are proposed and compared. Improved analysis and forecast accuracy is observed in schemes with minimal alteration to the evolving ensemble-based covariance structure.