Abstract
To improve the skills of the regional ensemble forecast system (REFS), a modified ensemble transform Kalman filter (ETKF) initial perturbation strategy was developed. First, sensitivity tests were conducted to investigate the influence of the perturbation scale on the ensemble spread growth and forecast skill. In addition, the scale characteristic of the forecast error was analyzed based on the results of these tests, and a new initial condition perturbation method was developed through scale-selection of the ETKF perturbations, namely, ETKF-SS (scale-selective ETKF). The performances of the ETKF-SS scheme and the original ETKF (hereinafter referred to as ETKF) scheme were tested and compared. The results showed that the large-scale perturbations were much easier to grow than the original ETKF perturbations. In addition, scale analysis of the forecast error showed that the large-scale errors showed significant growth at the upper levels, while the small and meso-scale errors grew fast at the lower levels. The comparison results of the ETKF-SS and the ETKF showed that the ETKF-SS perturbations had more obvious growth than the ETKF perturbations, and the ETKF-SS perturbations in the short-term forecast lead times were more precise than the ETKF perturbations. The ensemble forecast verification results showed that the ETKF-SS ensemble had a larger spread and smaller root mean square error than the ETKF at short forecast lead times, while the probabilistic scores of the ETKF-SS also outperformed those of the ETKF method. In addition, the ETKF-SS ensemble can provide a better precipitation forecast than the ETKF.
Highlights
The non-linear and unstable characteristics of the atmosphere lead to intrinsic uncertainty in numerical weather prediction (NWP), which means slight errors in the initial state will amplify quickly and affect forecast skills [1]; in addition, the imperfections of the numerical model may cause forecast error
The ensemble forecast verification results showed that the ensemble transform Kalman filter (ETKF)-SS ensemble had a larger spread and smaller root mean square error than the ETKF at short forecast lead times, while the probabilistic scores of the ETKF-SS outperformed those of the ETKF method
To solve the problem of forecast uncertainty caused by the initial state error, model error, and chaos of the atmosphere, ensemble forecast technology was proposed by Leith et al [2], and it has become an effective tool in many NWP centers
Summary
The non-linear and unstable characteristics of the atmosphere lead to intrinsic uncertainty in numerical weather prediction (NWP), which means slight errors in the initial state will amplify quickly and affect forecast skills [1]; in addition, the imperfections of the numerical model may cause forecast error. To solve the problem of forecast uncertainty caused by the initial state error, model error, and chaos of the atmosphere, ensemble forecast technology was proposed by Leith et al [2], and it has become an effective tool in many NWP centers. Hazardous high-impact weather is related mostly to small-scale dynamical mechanisms, which contain more forecast uncertainties. More investigations are needed to deepen understanding of the uncertainty source related to high-impact weather to improve forecast skills. The regional ensemble forecast system (REFS) is an effective tool for local high-impact weather forecasting, and it has become a hot topic in present research [3,4,5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
