Anisotropic background error covariance modelling and its application in Typhoon Fanapi

Chen Yaodeng; Chen Xiaomeng; Min Jinzhong; Xing Jianyong; Wang Hongli

doi:10.3969/j.issn.0253-4193.2016.09.004

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Chen Yaodeng, Chen Xiaomeng, Min Jinzhong, Xing Jianyong, Wang Hongli. Anisotropic background error covariance modelling and its application in Typhoon Fanapi[J]. Haiyang Xuebao, 2016, 38(9): 32-45. doi: 10.3969/j.issn.0253-4193.2016.09.004

Citation:

Chen Yaodeng, Chen Xiaomeng, Min Jinzhong, Xing Jianyong, Wang Hongli. Anisotropic background error covariance modelling and its application in Typhoon Fanapi[J]. Haiyang Xuebao, 2016, 38(9): 32-45. doi: 10.3969/j.issn.0253-4193.2016.09.004

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Anisotropic background error covariance modelling and its application in Typhoon Fanapi

doi: 10.3969/j.issn.0253-4193.2016.09.004

1.
Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Fujian Meteorological Service Center, Fuzhou 350001, China
3.
Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China
4.
Global Systems Division, NOAA/Earth System Research Laboratory, Colorado, USA

Received Date: 2015-10-16
Rev Recd Date: 2016-06-08

Abstract

Abstract

Based on ensemble-variational hybrid data assimilation system, the anisotropic and some flow-dependent background error covariance was introduced into data assimilation systems by combining historical forecast error covariance with the static background error covariance. The historical forecast error covariance was calculated from the forecasts of difference between the different forecasts respectively valid at the same time. Single observation experiments demonstrate that the background error covariance modeled by the new method has the anisotropic and some flow-dependent information. A series of assimilation and simulation experiments for typhoon Fanapi show that the track, minimum sea level pressure and wind speed using the method were better than that of 3DVar. The historical forecast error covariance not need ensemble forecasts and the anisotropic and some flow-dependent information are taken into account in the data assimilation system, then the cost of the calculation is similar to that of 3DVar, so the method would be beneficial to some operational centers and research communities with limited computational resources.
- data assimilation,
- hybrid assimilation,
- background error covariance,
- anisotropic,
- typhoon

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References(14)

References

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