Error covariance estimation for coupled data assimilation using a lorenz atmosphere and a simple pycnocline ocean model

Journal of Climate

Volumn 26, Issue 24, 2013, Pages 10218-10231

  Han, Guijun ^a   Wu, Xinrong ^a   Zhang, Shaoqing ^b   Liu, Zhengyu ^c,d   Li, Wei ^a  

^a Ministry of Natural Resources of the People's Republic of China   (China)

^b PRINCETON UNIVERSITY   (United States)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

^d PEKING UNIVERSITY   (China)

Author keywords

Coupled models; Data assimilation

Indexed keywords

COUPLED MODELING; COUPLED MODELS; DATA ASSIMILATION; DATA ASSIMILATION SYSTEMS; DIFFERENT TIME SCALE; ERROR COVARIANCES; EXTRACT INFORMATIONS; HIGH-FREQUENCY INFORMATIONS;

OCEANOGRAPHY;

CLIMATE MODELS;

AIR-SEA INTERACTION; ATMOSPHERE-OCEAN COUPLING; COVARIANCE ANALYSIS; DATA ASSIMILATION; GENERAL CIRCULATION MODEL;

EID: 84890236690     PISSN: 08948755     EISSN: None     Source Type: Journal    
DOI: 10.1175/JCLI-D-13-00236.1     Document Type: Article

References (25)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
2. Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634-642.
3. Anderson, J. L., 2007: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, 59A, 210-224.
4. Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72-83.
5. Asselin, R., 1972: Frequency filter for time integrations. Mon. Wea. Rev., 100, 487-490.
6. Chen, D., 2010: Coupled data assimilation for ENSO prediction. Adv. Geosci., 18, 45-62.
7. Chen, D., S. E. Zebiak, A. J. Busalacchi, and M. A. Cane, 1995: An improved procedure for El Nĩno forecasting: Implications for predictability. Science, 269, 1699-1702.
8. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143-10 162.
9. Gnanadesikan, A., 1999: A simple predictive model for the structure of the oceanic pycnocline. Science, 283, 2077-2079.
10. Hollingsworth, A., K. Arpe, M. Tiedtke, M. Capaldo, and H. Savijarvi, 1980: The performance of a medium-range forecast model in winter-Impact of physical parameterizations. Mon. Wea. Rev., 108, 1736-1773.
11. Li, H., E. Kalnay, and T. Miyoshi, 2009: Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 523-533.
12. Liu, Z., S. Wu, S. Zhang, Y. Liu, and X. Rong, 2013: Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction. Adv. Atmos. Sci., 30, 1235-1248, doi:10.1007/s00376-013-2268-z.
13. Lorenz, E. N., 1963: Deterministic non-periodic flow. J. Atmos. Sci., 20, 130-141.
14. Miyoshi, T., 2011: The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon. Wea. Rev., 139, 1519-1535.
15. Robert, A., 1969: The integration of a spectral model of the atmosphere by the implicit method. Proc. WMO/IUGG Symp. on NWP, Tokyo, Japan, Japan Meteorological Society, 19-24.
16. Singleton, T., 2011: Data assimilation experiments with a simple coupled ocean-atmosphere model. Ph. D. thesis, University of Maryland, College Park, 116 pp.
17. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
18. Zhang, S., 2011a: Impact of observation-optimized model parameters on decadal predictions: Simulation with a simple pycnocline prediction model. Geophys. Res. Lett., 38, L02702, doi:10.1029/2010GL046133.
19. Zhang, S., 2011b:Astudy of impacts of coupled model initial shocks and state-parameter optimization on climate prediction using a simple pycnocline prediction model. J. Climate, 24, 6210-6226.
20. Zhang, S., and J. L. Anderson, 2003: Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus, 55A, 126-147.
21. Zhang, S., and A. Rosati, 2010: An inflated ensemble filter for ocean data assimilation with a biased coupled GCM. Mon. Wea. Rev., 138, 3905-3931.
22. Zhang, S., J. L. Anderson, A. Rosati, M. J. Harrison, S. P. Khare, and A. Wittenberg, 2004: Multiple time level adjustment for data assimilation. Tellus, 56A, 2-15.
23. Zhang, S., M. J. Harrison, A. Rosati, and A. Wittenberg, 2007: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon. Wea. Rev., 135, 3541-3564.
24. Zhang, S., Z. Liu, A. Rosati, and T. Delworth, 2012: A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model. Tellus, 64A, 10963, doi:10.3402/tellusa.v64i0.10963.
25. Zhang, S., M. Winton, A. Rosati, T. Delworth, and B. Huang, 2013: Impact of enthalpy-based ensemble filtering sea ice data assimilation on decadal predictions: Simulation with a conceptual pycnocline prediction model. J. Climate, 26, 2368-2378.