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Journal of Tropical Oceanography >

2016 , Vol. 35 >Issue 2: 30 - 39

DOI: https://doi.org/10.11978/2015052

Marine Hydrography

Application of scale-selective data assimilation method in ocean modeling: simulation of a strong warm eddy in Xisha

  • WANG Pinqiang ,
  • LI Yineng ,
  • PENG Shiqiu
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  • 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

Received date: 2015-04-15

  Revised date: 2015-09-23

  Online published: 2016-02-29

Supported by

The Ministry of Science and Technology of the People’s Republic of China (MOST) (2011CB403505, 2014CB953904); The Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA11010304); The National Natural Science Foundation of China (41176024, 41306013); Guangdong Marine’sdisaster emergency response technology research center (2012A032100004)

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Abstract

Scale-selective data assimilation (SSDA), through considering multiscale features in both observational data and model variables during assimilation, can selectively adjust certain scales of motion while keeping other scales unchanged. To test the effectiveness of SSDA in ocean data assimilation, an exceptional anticyclonic eddy event in August 2010 over the sea area of the Xisha Islands (referred to as Xisha Warm Eddy) is chosen. In this study, we compared the effects of assimilating sea level anomaly (SLA) and its derived geostrophic currents. Geostrophic currents were assimilated in two ways, applying the SSDA or not. Results showed that assimilating SLA or geostrophic currents can both improve the simulation of Xisha Warm Eddy. On one hand, assimilating SLA improved the horizontal size of Xisha Warm Eddy since the velocity field followed the change of pressure field, permitting divergence of sea water. On the other hand, assimilating geostrophic currents improved the intensity of Xisha Warm Eddy because SSDA can adjust pressure field according to velocity field, which helped maintain the convergence of Xisha Warm Eddy. Simultaneously assimilating both geostrophic currents and SLA improved the simulation most significantly. Therefore, SSDA is necessary for assimilating geostrophic currents.

Key words： Xisha Warm Eddy; scale-selective data assimilation (SSDA); geostrophic current; satellite altimeter

Cite this article

WANG Pinqiang , LI Yineng , PENG Shiqiu . Application of scale-selective data assimilation method in ocean modeling: simulation of a strong warm eddy in Xisha[J]. Journal of Tropical Oceanography, 2016 , 35(2) : 30 -39 . DOI: 10.11978/2015052

References

[1] 肖贤俊, 何娜, 张祖强, 等, 2011. 卫星遥感海表温度资料和高度计资料的变分同化[J]. 热带海洋学报, 30(3): 1–8. XIAO XIANJUN, HE NA, ZHANG ZUQIANG et al, 2011. Variation assimilation using satellite data of sea surface temperature and altimeter [J]. J Trop Oceanogr, 30(3):1-8 (In Chinese).
[2] 朱江, 徐启春, 王赐震, 等, 1995. 海温数值预报资料同化试验Ⅰ. 客观分析的最优插值法试验[J]. 海洋学报：中文版, 17(6): 9–20. ARBIC B K, SCOTT R B, CHELTON D B, et al, 2012. Effects of stencil width on surface ocean geostrophic velocity and vorticity estimation from gridded satellite altimeter data [J]. J Geophys Res, 117(C03029): 1–18.
[3] BLAYO E, VERRON J, MOLINES J M, 1994. Assimilation of Topex/Poseidon altimeter data into a circulation model of the North-Atlantic [J]. J Geophys Res, 99(C12): 24691–24705.
[4] CARTON J A, GIESE B S, GRODSKY S A, 2005. Sea level rise and the warming of the oceans in the Simple Ocean Data Assimilation (SODA) ocean reanalysis [J]. J Geophys Res, 110(C9).
[5] CHU XIAOQING, XUE HUIJIE, QI YIQUAN, et al, 2014. An exceptional anticyclonic eddy in the South China Sea in 2010 [J]. J Geophys Res, 119(2): 881–897.
[6] EVENSEN G, 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics [J]. J Geophys Res, 99(C5): 10143– 10162.
[7] EVENSEN G, VANLEEUWEN P J, 1996. Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model [J]. Mon Wea Rev, 124(1): 85–96.
[8] EVENSEN G, 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation [J]. Ocean Dynam, 53(4): 343–367.
[9] EVENSEN G, 2004. Sampling strategies and square root analysis schemes for the EnKF [J]. Ocean Dynam, 54(6): 539–560.
[10] EZER T, MELLOR G L, 1994. Continuous assimilation of Geosat altimeter data into a 3-dimensional primitive equation Gulf- Stream model [J]. J Phys Oceanogr, 24(4): 832–847.
[11] EZER T, MELLOR G L, 1997. Data assimilation experiments in the Gulf Stream region: How useful are satellite-derived surface data for nowcasting the subsurface fields?[J]. J Atmos Ocean Tech, 14(6): 1379–1391.
[12] HOLLAND W R, MALANOTTERIZZOLI P, 1989. Assimilation of altimeter data into an ocean circulation model-space versus time resolution studies [J]. J Phys Oceanogr, 19(10): 1507–1534.
[13] HOTEIT I, HOAR T, GOPALAKRISHNAN G, et al, 2013. A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico [J]. Dynam Atmos Oceans, 63: 1–23.
[14] KANAMITSU M, EBISUZAKI W, WOOLLEN J, et al, 2002. NCEP-DOE AMIP-II reanalysis (R-2) [J]. B Am Meteorol Soc. 83(11): 1631–1643.
[15] KOHL A, MARTINS M S, STAMMER D, 2014. Impact of assimilating surface salinity from SMOS on ocean circulation estimates [J]. J Geophy Res, 119(8): 5449–5464.
[16] LAI ZHIJUAN, SAI HAO, PENG SHIQIU, et al, 2014. On improving tropical cyclone track forecasts using a scale- selective data assimilation approach: a case study [J]. Nat Hazards, 73(3): 1353–1368.
[17] LEEUWENBURGH O, EVENSEN G, BERTINO L, 2005. The impact of ensemble filter definition on the assimilation of temperature profiles in the tropical Pacific [J]. Q J Roy Meteor Soc, 131(613): 3291–3300.
[18] LIU BIN, XIE LIAN, 2012. A scale-selective data assimilation approach to improving tropical cyclone track and intensity forecasts in a limited-area model: A case study of hurricane Felix (2007) [J]. Weather Forecast, 27(1): 124–140.
[19] MANKAD B M, SHARMA R, BASU S, et al, 2012. Altimeter data assimilation in the tropical Indian Ocean using water property conserving scheme [J]. J Earth Syst Sci, 121(1): 251–262.
[20] MELLOR G L, EZER T, 1991. A Gulf-Stream model and an altimetry assimilation scheme [J]. J Geophys Res, 96(C5): 8779–8795.
[21] OKE P R, ALLEN J S, MILLER R N, et al, 2002. Assimilation of surface velocity data into a primitive equation coastal ocean model [J]. J Geophys Res, 107(C9): 5–1–5–25.
[22] OKE P R, SAKOV P, CORNEY S P, 2007a. Impacts of localisation in the EnKF and EnOI: Experiments with a small model [J]. Ocean Dynam, 57(1): 32–45.
[23] OKE P R, SCHILLER A, 2007b. Impact of Argo, SST, and altimeter data on an eddy-resolving ocean reanalysis [J]. Geophys Res Lett, 34(L19601): 1–7.
[24] PADUAN J D, SHULMAN I, 2004. HF radar data assimilation in the Monterey Bay area [J]. J Geophys Res, 109(C7S09): 1–17.
[25] PENG SHIQIU, XIE LIAN, 2006. Effect of determining initial conditions by four-dimensional variational data assimilation on storm surge forecasting [J]. Ocean Model, 14(1/2): 1–18.
[26] PENG SHIQIU, XIE LIAN, LIU BIN, et al. 2010. Application of scale- selective data assimilation to regional climate modeling and prediction [J]. Mon Wea Rev, 138(4): 1307–1318.
[27] PENG YU, KURAPOV A L, EGBERT G D, et al, 2012. Variational assimilation of HF radar surface currents in a coastal ocean model off Oregon [J]. Ocean Model, 49–50: 86–104.
[28] SAKOV P, OKE P R, 2008. Implications of the form of the ensemble transformation in the ensemble square root filters [J]. Mon Wea Rev, 136(3): 1042–1053.
[29] SCOTT R B, FURNIVAL D G, 2012. Assessment of traditional and new eigenfunction bases applied to extrapolation of surface geostrophic current time series to below the surface in an idealized primitive equation simulation [J]. J Phys Oceanogr, 42(1): 165–178.
[30] SHU YEQIANG, ZHU JIANG, WANG DONDXIAO, et al, 2009. Performance of four sea surface temperature assimilation schemes in the South China Sea [J]. Cont Shelf Res, 29(11/12): 1489–1501.
[31] SHULMAN I, PADUAN J D, 2009. Assimilation of HF radar-derived radials and total currents in the Monterey Bay area [J]. Deep-Sea Res Pt II, 56(3–5): 149–160.
[32] SZUTS Z B, BLUNDELL J R, CHIDICHIMO M P, et al, 2012. A vertical-mode decomposition to investigate low-frequency internal motion across the Atlantic at 26° N [J]. Ocean Sci, 8(3): 345–367.
[33] WU C R, SHAW P T, CHAO S Y, 1999. Assimilating altimetric data into a South China Sea model [J]. J Geophys Res, 104(C12): 29987–30005.
[34] WUNSCH C, 2013. Baroclinic motions and energetics as measured by altimeters[J]. J Atmos Ocean Tech, 30(1): 140–150.
[35] XIAO XIANJUN, WANG DONGXIAO, YAN CHANGXIANG, et al, 2008. Evaluation of a 3dVAR system for the South China Sea [J]. Prog Nat Sci, 18(5): 547–554.
[36] XIE LIAN, LIU BIN, PENG SHIQIU, 2010. Application of scale- selective data assimilation to tropical cyclone track simulation [J]. J Geophys Res, 115(D17). doi:10.1029/2009JD013471.
[37] ZHU JIANG, ZHOU GUANGQING, YAN CHANGXIANG, et al, 2006. A three-dimensional variational ocean data assimilation system: scheme and preliminary results [J]. Sci China Ser D-Earth Sci, 49(11): 1212–1222.
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