Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region

doi:10.1016/j.apor.2022.103375

IOCAS-IR > 海洋环流与波动重点实验室

	Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region
	Xu, Minjie 1; Wang, Yuzhe 2,3,9; Zhang, Jicai 4; Yang, Dezhou 5; Yin, Xunqiang 6; Gao, Yanqiu 7; Wang, Guansuo 8; Lv, Xianqing 2,3,9
	2022-12-01
发表期刊	APPLIED OCEAN RESEARCH
ISSN	0141-1187
卷号	129 页码:17
通讯作者	Zhang, Jicai([email protected]) ; Yin, Xunqiang([email protected])
摘要	Cold spell events can bring strong wind and low-temperature freezing and induce a significant rise of the water level, which have negative impacts on the economy. Based on a regional high-resolution ocean model, the satellite observations, including sea surface temperature (SST) and absolute dynamic topography (ADT), and the Argo data are assimilated to improve the modeling results over the Asia-Pacific area during a strong cold spell event of 2020 by using Ensemble Adjustment Kalman Filter (EAKF). To reduce the calculation cost, the ensemble for data assimilation is obtained by a dynamic sampling method which is based on the model forecasting biases and the EAKF is implemented by using an efficient parallelization scheme. The root mean square (RMS) error of SST decreased from 0.72 degrees C to 0.07 degrees C after assimilation, which was reduced by 90.28%. Besides, the RMS error of ADT was decreased from 0.28 m to 0.12 m, which was reduced by 57.14%. Responses of the ocean during the 2020 cold spell event were better reproduced using optimal EAKF configurations. As the speed of the wind increased and the air temperature dropped, the heat transported from the ocean to the atmosphere in the ocean model increased. Furthermore, the temporal evolutions of the ocean state were captured by data assimilation, which was the decrease of temperature and the increase of salinity and density. The mixed layers in the Kuroshio Extension were thicker due to excessive surface cooling. The seawater first increased and then overflowed in the Bohai Bay and Laizhou Bay. In conclusion, the EAKF data assimilation in this regional high-resolution ocean model has successfully reduced the model biases and the physical processes in reality can be well produced.
关键词	Data assimilation High-resolution ocean model Ensemble adjustment kalman filter Cold spell event
DOI	10.1016/j.apor.2022.103375
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China; Southern Marine Science and Engineering Guang-dong Laboratory (Zhuhai); [41876086]; [41976201]; [SML2021SP314]
WOS研究方向	Engineering ; Oceanography
WOS类目	Engineering, Ocean ; Oceanography
WOS记录号	WOS:000883862100003
出版者	ELSEVIER SCI LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/180336
专题	海洋环流与波动重点实验室
通讯作者	Zhang, Jicai; Yin, Xunqiang
作者单位	1.Yantai Univ, Sch Ocean, Yantai, Peoples R China 2.Ocean Univ China, Frontier Sci Ctr Deep Ocean Multispheres & Earth S, Qingdao, Peoples R China 3.Ocean Univ China, Phys Oceanog Lab, Qingdao, Peoples R China 4.East China Normal Univ, State Key Lab Estuarine & Coastal Res, Shanghai, Peoples R China 5.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 6.Minist Nat Resources, Inst Oceanog 1, Qingdao, Peoples R China 7.Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou, Peoples R China 8.Minist Nat Resources, East China Sea Forecasting, Hazard Mitigat Ctr, Shanghai, Peoples R China 9.Qingdao Natl Lab Marine Sci & Technol, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Xu, Minjie,Wang, Yuzhe,Zhang, Jicai,et al. Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region[J]. APPLIED OCEAN RESEARCH,2022,129:17.
APA	Xu, Minjie.,Wang, Yuzhe.,Zhang, Jicai.,Yang, Dezhou.,Yin, Xunqiang.,...&Lv, Xianqing.(2022).Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region.APPLIED OCEAN RESEARCH,129,17.
MLA	Xu, Minjie,et al."Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region".APPLIED OCEAN RESEARCH 129(2022):17.

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