Abstract
Based on the operational regional ensemble prediction system (REPS) in China Meteorological Administration (CMA), this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF) and a dynamical downscaling of global ensemble perturbations. One month consecutive tests are implemented to evaluate the performance of both methods in the operational REPS environment. The perturbation characteristics are analyzed and ensemble forecast verifications are conducted; furthermore, a TC case is investigated. The main conclusions are as follows: the ETKF perturbations contain more power at small scales while the ones derived from downscaling contain more power at large scales, and the relative difference of the two types of perturbations on scales become smaller with forecast lead time. The growth of downscaling perturbations is more remarkable, and the downscaling perturbations have larger magnitude than ETKF perturbations at all forecast lead times. However, the ETKF perturbation variance can represent the forecast error variance better than downscaling. Ensemble forecast verification shows slightly higher skill of downscaling ensemble over ETKF ensemble. A TC case study indicates that the overall performance of the two systems are quite similar despite the slightly smaller error of DOWN ensemble than ETKF ensemble at long range forecast lead times.
Highlights
It has long been known that numerical weather prediction (NWP) is sensitive to the initial condition (IC) error, model error and the chaotic nature of atmosphere, an ensemble prediction method [1] has emerged as a practical way for providing probabilistic forecasts
We evaluate the quality of IC perturbation states and ensemble forecasts from each methodology
The results presented above indicate, when applied to the self cycling of GRAPES-regional ensemble prediction system (REPS), the ensemble transform Kalman filter (ETKF) technique can create analysis perturbations from forecast perturbations which are completely produced by regional model and in principle, provide IC perturbations at all scales resolved by regional model
Summary
It has long been known that numerical weather prediction (NWP) is sensitive to the initial condition (IC) error, model error and the chaotic nature of atmosphere, an ensemble prediction method [1] has emerged as a practical way for providing probabilistic forecasts. One of the possible choices is dynamical downscaling of a global EPS, which interpolates forecast fields from a set of representative members of the global EPS to obtain different ICs for the regional domain with higher resolution This method has been successfully applied in some current operational REPS [7,8,9]. Tends to perturb synoptic-scale disturbances and showed the best ratio of the ensemble spread to the RMSE, while the regional version of BGM, ET and SVs tend to perturb meso-scale disturbances, affecting local intense rains more Whether these regional IC perturbation generators can yield advantage over dynamical downscaling is still obscure, there is no doubt that these methods can produce more information of small/meso-scale uncertainties than dynamical downscaling, and this information is useful for the forecasting of local severe convective weather [19,20].
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