An ensemble recentering Kalman filter with an application to Argo temperature data assimilation into the NASA GEOS-5 coupled model

Ocean Modelling

Volumn 77, Issue , 2014, Pages 50-55

  Keppenne, Christian L ^a,b  

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b SCIENCE SYSTEMS AND APPLICATIONS INC   (United States)

Author keywords

Coupled data assimilation; Data assimilation; Ensemble Kalman filter; Kalman filter; Particle filter

Indexed keywords

ALGORITHMS; DATA PROCESSING; ESTIMATION; NASA;

COMPUTATIONAL COSTS; DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; ENSEMBLE OF MODELS; PARTICLE FILTER; PRIOR DISTRIBUTION; TEMPERATURE PROFILES; TWO-STEP ALGORITHMS;

KALMAN FILTERS;

ARGO; ATMOSPHERE-OCEAN COUPLING; DATA ASSIMILATION; ENSEMBLE FORECASTING; EOS; GENERAL CIRCULATION MODEL; KALMAN FILTER; TEMPERATURE PROFILE;

EID: 84899118471     PISSN: 14635003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ocemod.2014.03.001     Document Type: Article

References (14)

1. Bloom S., Takacs L., DaSilva A., Ledvina D. Data assimilation using incremental analysis updates. Mon. Weather Rev. 1996, 124:1256-1271.
2. Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 1994, C99:10143-10162.
3. Evensen G., van Leeuwen P.J. Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasi-geostrophic model. Mon. Weather Rev. 1996, 124:85-96.
4. Gordon N.J., Salmond D.J., Smith A.F.M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proc. Radar Signal Process. 1993, 140(2):107-113.
5. Hill C., Deluca C., Balaji V., Suarez M., Da Silva A. The architecture of the earth system modeling framework. Comput. Sci. Eng. 2004, 6(1):18-28.
6. Hunke, E.C., Lipscomb, W.H., Turner, A.K., Jeffery, N., Elliott S., 2013. CICE: the Los Alamos Sea Ice Model Documentation and Software User's Manual Version 5.0, LA-CC-06-012, Los Alamos National Laboratory, Los Alamos NM.
7. Kalman R. A new approach to linear filtering and prediction problems. J. Basic Eng. 1960, D82:35-45.
8. Keppenne C.L., Rienecker M.M. Assimilation of temperature into an isopycnal ocean general circulation model using a parallel ensemble Kalman filter. J. Mar. Syst. 2003, 40-41:363-380.
9. Keppenne C.L., Rienecker M.M., Kurkowski N.P., Adamec D.A. Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction. Nonlinear Process. Geophys. 2005, 12:491-503.
10. Keppenne C.L., Rienecker M.M., Jacob J.P., Kovach R.M. Error covariance modeling in the GMAO ocean ensemble Kalman filter. Mon. Weather Rev. 2008, 136:2964-2982.
11. Oke P.R., Brassington G.B., Griffin D.A., Schiller A. Ocean data assimilation: a case for ensemble optimal interpolation. Aust. Meteorol. Oceanogr. J. 2010, 59:67-76.
12. Rienecker, M.M., Suarez,M.J., Todling, R., Bacmeister, J., Takacs, L., Liu, H.-C., Gu, W., Sienkiewicz, M., Koster, R.D., Gelaro, R., Stajner, I., Nielsen, J.E., (2008). The GEOS-5 data assimilation system. Documentation Versions 5.0.1, 5.1.0, and 5.20, NASA Tech. Rep. Series on Global Modeling and Data Assimilation, 27, NASA/TM-2008-104606.
13. Vernieres, G., Rienecker, M.M., Kovach, R.N., Keppenne C.L., 2012. The GEOS-iODAS description and evaluation. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, 30, NASA/TM-2012-104606.
14. Weerts A.H., El Serafy G.Y.H. Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models. Water Resour. Res. 2006, 42(9):17. W09043.