Browse > Article

A Study of Global Ocean Data Assimilation using VAF  

Ahn, Joong-Bae (Department of Atmospheric Sciences, Pusan National University,)
Yoon, Yong-Hoon (Meteorological Research Institute)
Cho, Eek-Hyun (Korea Meteorological Administration)
Oh, He-Ram (Department of Atmospheric Sciences, Pusan National University,)
Publication Information
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY / v.10, no.1, 2005 , pp. 69-78 More about this Journal
Abstract
ARCO and TAO data which supply three dimensional global ocean information are assimilated to the background field from a general circulation model, MOM3. Using a variational Analysis using Filter (VAF), which is a spatial variational filter designed to reduce computational time and space efficiently and economically, observed ARGO and TAO data are assimilated to the OGCM-generated background sea temperature for the generation of initial condition of the model. For the assessment of the assimilation impact, a comparative experiment has been done by integrating the model from different intial conditions: one from ARGO-, TAO-data assimilated initial condition and the other from background state without assimilation. The assimilated analysis field not only depicts major oceanic features more realistically but also reduces several systematic model bias that appear in every current OGCMs experiments. From the 10-month of model integrations with and without assimilated initial conditions, it is found that the major assimilated characteristics in sea temperature appeared in the initial field remain persistently throughout the integration. Such implies that the assimilated characteristics of the reduced sea temperature bias is to last in the integration without rapid restoration to the non-assimilated OGCM integration state by dispersing mass field in the form of internal gravity waves. From our analysis, it is concluded that the data assimilation method adapted in this study to MOM3 is reasonable and applicable with dynamical consistency. The success in generating initial condition with ARGO and TAO data assimilation has significant implication upon the prediction of the long-term climate and weather using ocean-atmosphere coupled model.
Keywords
ARGO; Ocean Data Assimilation; VAF; OGCM; TAO;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Lorenc, AC., 1992. Interactive analysis using covariance functions and filters. Quart. J. Roy. Meteor. Soc., 118: 569-591
2 Semtner, A.J., 1976. A model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate. J. Phys. Oceanogr., 6: 379-389   DOI
3 안중배, 이진아, 2001. 해양대순환모형을 이용한 해빙의 역할에 관한 수치실험 연구. 한국해양학회지, 6: 225-233
4 Lorenc, A.C., 1986. Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112: 1177-1194   DOI
5 NOAA, cited 2004. Tropical Atmosphere Ocean Project: Data delivery. Available online from http://www.pmel.noaa.gov/tao/data_deliv/
6 Carval, T., 2002. Argo data management: User's manual. Ifremer, pp.6-7
7 Storch, J.S., V.V. Kharin, U. Cubasch, G.C. Hegerl, D. Schriever, H. Storch, and E. Zorita, 1997. A Description of a 1260-Year Control Integration with the Coupled ECHAM1/LSG General Circulation Model. J. Climate, 10: 1525-1543   DOI   ScienceOn
8 ARGO, cited 2004. Part of the integrated global observation strategy. Available online from http://www-argo.ucsd.edul
9 Busalacchi, A.J., 1997. Oceanic Observations. J. Meteor. Soc. Japan, 75: 131-154
10 Kalnay E., 2003. Atmospheric modelling, data assimilation and predictability. Cambridge University Press, pp. 136-204
11 Arfken, G., 1985. Mathematical Methods for Physicists, 3rd ed. Orlando, FL. Academic Press: pp. 428-436
12 Coriolis, cited 2004. Global ocean profiles. Available online from http:// www.coriolis.eu.org/cdc/DataSelection/cdcDataSelections.asp
13 Courtier, P., 1997. Variational methods. J. Meteor. Soc. Japan, 75: 211-218
14 Huang, X.Y., 2000. Variational analysis using spatial filters. Mon. Wea. Rev., 128: 2588-2600   DOI   ScienceOn
15 Lorenc, A.C., 1988. Optimal nonlinear objective analysis. Quart. J. Roy. Meteor. Soc., 114: 205-240   DOI
16 박혜선, 2003. 대기-해양-해빙 접합 모형 개발과 ENSO 예측성 연구. 부산대학교, 이학박사 학위논문, pp. 42-45
17 Derber, J. and A. Rosati, 1989. A global oceanic data assimilation system. J. Phys. Oceanogr., 19: 1333-1347   DOI
18 Levitus, S., 1982. Climatological Atlas of the World Ocean. NOAA Prof. Paper 13, U.S. Government Printing Office, Washington D.C., 173 pp
19 Service Argos, 2001. Basic description of the Argos system. Available online from http://www.argosinc.com/
20 Bouttier, E, and P. Coutier, 1999. Data assimilation concepts and methods: Meteorological training course lecture series. ECMWF, pp.3-33
21 Han, Y.-J., 1984. A Numerical World Ocean General Circulation Model. Part II. Baroclinic Experiment. Dyn. Atmos. Oceans, 8: 141-172   DOI   ScienceOn
22 Pacanowski, R.C. and S.M. Griffies, 1998. The GFDL modular ocean model user guide. The GFDL Ocean Group Technical Report NO. 2, Geophysical Fluid Dynamics Laboratory, Princeton, USA