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Open AccessArticle

Study on Scale-Selective Initial Perturbation for Regional Ensemble Forecast

Nanjing University of Information Science & Technology, Nanjing 210044, China
Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
Numerical Weather Prediction Centre, China Meteorological Administration, Beijing 100081, China
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(5), 285;
Received: 25 April 2019 / Revised: 16 May 2019 / Accepted: 16 May 2019 / Published: 21 May 2019
(This article belongs to the Special Issue Advancements in Mesoscale Weather Analysis and Prediction)
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. View Full-Text
Keywords: regional ensemble forecast; initial condition perturbation; scale-selective; ensemble transform Kalman filter regional ensemble forecast; initial condition perturbation; scale-selective; ensemble transform Kalman filter
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Xia, Y.; Zhang, H.; Chen, J. Study on Scale-Selective Initial Perturbation for Regional Ensemble Forecast. Atmosphere 2019, 10, 285.

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