Applications of the Mean Recentering Scheme to Improve Typhoon Track Prediction: A Case Study of Typhoon Nanmadol (2011)

• Published in: Journal of the Meteorological Society of Japan Vol. 92; no. 6; pp. 559 - 584
• Main Authors: CHANG, Chih-Chien, YANG, Shu-Chih, KEPPENNE, Christian
• Format: Journal Article
• Language: English
• Published: Meteorological Society of Japan 01.01.2014
• Subjects: EnKF; ensemble forecast; typhoon prediction
• ISSN: 0026-1165; 2186-9057
• DOI: 10.2151/jmsj.2014-604

Under strong nonlinear dynamics, the assumption of a Gaussian distribution for an ensemble may be strongly violated, and thus the mean of the ensemble cannot be the best estimate for the atmosphere. A mean recentering (MRC) scheme is proposed to handle a track ensemble that has a strong non-Gaussian distribution when the track prediction is conducted under a highly uncertain condition. The validity of the MRC scheme is confirmed using a case study of Typhoon Nanmadol in 2011, which moves northward initially but turns westward sharply at 0000UTC 24 August. Factors contributing to Nanmadol’s movement prediction include the saddle field between typhoons Nanmadol and Talas, the development of the subtropical high on the north side of both typhoons, and Nanmadol’s own circulation.  The MRC scheme successfully improves the typhoon track prediction with a regional ensemble prediction system based on the Weather and Research Forecasting (WRF) model. The corrections from the MRC scheme allow the ensemble to capture the realistic behavior when the original ensemble track prediction is poor. Such ensemble adjustment can provide positive feedback to the background error covariance used in the ensemble-based data assimilation system. Results from the WRF-local ensemble transform Kalman filter (WRF-LETKF) system incorporated with the MRC scheme show that the ensemble track prediction can be significantly improved during the WRF-LETKF’s spin-up period when Nanmadol movement is highly uncertain. By dynamically adjusting the MRC scheme, the ensemble avoids suffering from the non-Gaussian and over-dispersive issues observed in the original ensemble prediction.