On improving tropical cyclone track forecasts using a scale-selective data assimilation approach: a case study
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- 作者：Zhijuan Lai (1) (2)
Sai Hao (2) (3)
Shiqiu Peng (1)
Bei Liu (4)
Xiangqian Gu (5)
Yu-Kun Qian (1) - 关键词：Tropical cyclone (TC) track forecast ; Scale ; selective data assimilation (SSDA) ; Improvement ; Sensitivity
- 刊名：Natural Hazards
- 出版年：2014
- 出版时间：September 2014
- 年：2014
- 卷：73
- 期：3
- 页码：1353-1368
- 全文大小：993 KB
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Sai Hao (2) (3)
Shiqiu Peng (1)
Bei Liu (4)
Xiangqian Gu (5)
Yu-Kun Qian (1)
1. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
2. University of Chinese Academy of Sciences, Beijing, 100049, China
3. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
4. Institute of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, 524088, China
5. State Key Laboratory of Sever Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China - ISSN：1573-0840
文摘
A dynamical downscaling approach based on scale-selective data assimilation (SSDA) is applied to tropical cyclone (TC) track forecasts. The results from a case study of super Typhoon Megi (2010) show that the SSDA approach is very effective in improving the TC track forecasts by fitting the large-scale wind field from the regional model to that from the global forecast system?(GFS) forecasts while allowing the small-scale circulation to develop freely in the regional model. A comparison to the conventional spectral-nudging four-dimensional data assimilation (FDDA) indicates that the SSDA approach outperforms the FDDA in TC track forecasts because the former allows the small-scale features in a regional model to develop more freely than the latter due to different techniques used. In addition, a number of numerical experiments are performed to investigate the sensitivity of SSDA’s effect in TC track forecasts to some parameters in SSDA, including the cutoff wave number, the vertical layers of the atmosphere being adjusted, and the interval of SSDA implementation. The results show that the improvements are sensitive in different extent to the above three parameters.