Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system
By Mozheng Wei, Z. Toth, R. Wobus, Yuejian Zhu. 2008
296 citations recorded by the source corpus.
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.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/fa480113493c1ebc04826ffc26f9d4202cf5b9c4
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Citations in the corpus
- Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment (2017, methodology): “The forecast skill of the GEFS has been improved sig-nificantly since then, benefiting from upgrades in ensemble initial perturbation generation (Wei et al. 2006, 2008), the inclusion of stochastic model perturbations (Hou et al. 2006), higher model resolution, and larger ensemble size, as well as…”
- The Multiensemble Approach: The NAEFS Example (2009, methodology): “The operational NCEP system configuration is described in (Wei et al. 2008).”
- A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending (2014, methodology): “2003) for IC perturbations, while the ensemble of LBCs required for SREP is obtained from the global ET ensemble (Wei et al. 2008).”
- On the Ability of Global Ensemble Prediction Systems to Predict Tropical Cyclone Track Probabilities (2010, methodology): “An ensemble transform method is employed to create initial perturbations (Wei et al. 2008).”