Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation

Geophysical Research Letters

Volumn 43, Issue 2, 2016, Pages 752-759

  Sluka, Travis C ^a   Penny, Stephen G ^a,b   Kalnay, Eugenia ^a   Miyoshi, Takemasa ^a,c  

^a University of Maryland   (United States)

^b CLIMATE PREDICTION CENTER   (United States)

^c RIKEN   (Japan)

Author keywords

coupled models; data assimilation; ensemble Kalman filter

Indexed keywords

ERRORS; KALMAN FILTERS;

ATMOSPHERIC OBSERVATIONS; COUPLED MODELS; DATA ASSIMILATION; ENSEMBLE DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; INTERMEDIATE COMPLEXITY; OBSERVING SYSTEM SIMULATION EXPERIMENTS; OCEAN-ATMOSPHERE COUPLED MODEL;

OCEANOGRAPHY;

ATMOSPHERE-OCEAN COUPLING; ATMOSPHERE-OCEAN SYSTEM; CLIMATE MODELING; COVARIANCE ANALYSIS; DATA ASSIMILATION; ERROR ANALYSIS; KALMAN FILTER; NUMERICAL MODEL; OBSERVATIONAL METHOD;

EID: 84958064179     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2015GL067238     Document Type: Article

References (37)

1. Anderson, J. L., (2001), An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129 (12), 2884-2903, doi: 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2
2. Berner, J., G. J. Shutts, M. Leutbecher, and, T. N. Palmer, (2009), A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system, J. Atmos. Sci., 66 (3), 603-626, doi: 10.1175/2008JAS2677.1
3. Bonavita, M., L. Isaksen, and, E. Hõlm, (2012), On the use of EDA background error variances in the ECMWF 4D-Var, Q. J. R. Meteorol. Soc., 138 (667), 1540-1559, doi: 10.1002/qj.1899
4. Gaspari, G., and, S. E. Cohn, (1999), Construction of correlation functions in two and three dimensions, Q. J. R. Meteorol. Soc., 125, 723-757, doi: 10.1002/qj.49712555417
5. Gibson, J., P. Kallberg, S. Uppala, A. Hernandez, A. Nomura, and, E. Serrano, (1999), ECMWF re-analysis project report series, 1. ERA-15 description, version 2, Tech. Rep., European Centre for Medium-Range Weather Forecasts, Reading, U. K
6. Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and, B. R. Hunt, (2011), Balance and ensemble Kalman filter localization techniques, Mon. Weather Rev., 139 (2), 511-522, doi: 10.1175/2010MWR3328.1
7. Han, G., X. Wu, S. Zhang, Z. Liu, and, W. Li, (2013), Error covariance estimation for coupled data assimilation using a Lorenz atmosphere and a simple pycnocline ocean model, J. Clim., 26 (24), 10,218-10,231, doi: 10.1175/JCLI-D-13-00236.1
8. Houtekamer, P. L., and, H. L. Mitchell, (2005), Ensemble Kalman filtering, Q. J. R. Meteorol. Soc., 131 (613), 3269-3289, doi: 10.1256/qj.05.135
9. Hunt, B. R., E. J. Kostelich, and, I. Szunyogh, (2007), Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Phys. D Nonlinear Phenom., 230 (1-2), 112-126, doi: 10.1016/j.physd.2006.11.008
10. Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang, and, J. Ballabrera-Poy, (2007), 4-D-Var or ensemble Kalman filter?, Tellus A, 59 (5), 758-773, doi: 10.3402/tellusa.v59i5.15162
11. Kleist, D. T., and, K. Ide, (2015), An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results, Mon. Weather Rev., 143 (2), 433-451, doi: 10.1175/MWR-D-13-00351.1
12. Kröger, J., and, F. Kucharski, (2011), Sensitivity of ENSO characteristics to a new interactive flux correction scheme in a coupled GCM, Clim. Dyn., 36 (1-2), 119-137, doi: 10.1007/s00382-010-0759-5
13. Kucharski, F., F. Molteni, and, A. Bracco, (2006), Decadal interactions between the western tropical Pacific and the North Atlantic Oscillation, Clim. Dyn., 26 (1), 79-91, doi: 10.1007/s00382-005-0085-5
14. Kucharski, F., F. Ikram, F. Molteni, R. Farneti, I.-S. Kang, H.-N. No, M. P. King, G. Giuliani, and, K. Mogensen, (2015), Atlantic forcing of Pacific decadal variability, Clim. Dyn., 1-15, doi: 10.1007/s00382-015-2705-z
15. Laloyaux, P., M. Balmaseda, D. Dee, K. Mogensen, and, P. Janssen, (2015), A coupled data assimilation system for climate reanalysis, Q. J. R. Meteorol. Soc., doi: 10.1002/qj.2629, in press
16. Lea, D., I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and, M. Thurlow, (2015), Assessing a new coupled data assimilation system based on the Met Office coupled atmosphere, land, ocean, sea ice model, Mon. Weather Rev., 143 (11), 4678-4694, doi: 10.1175/MWR-D-15-0174.1
17. Lawless, A., (2012), Coupled model data assimilation, Tech. Rep., p. 25, Int. Work. Coupled Data Assim. Univ. Reading, Reading, England
18. 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 (5), 1235-1248, doi: 10.1007/s00376-013-2268-z
19. Lu, F., Z. Liu, S. Zhang, and, Y. Liu, (2015), Strongly coupled data assimilation using Leading Averaged Coupled Covariance (LACC). Part I: Simple model study, Mon. Weather Rev., 143 (9), 3823-3837, doi: 10.1175/MWR-D-14-00322.1
20. Lu, F., Z. Liu, S. Zhang, and, Y. Liu, (2015), Strongly coupled data assimilation using Leading Averaged Coupled Covariance (LACC). Part II: CGCM applications, Mon. Weather Rev., 143 (11), 4645-4659, doi: 10.1175/MWR-D-15-0088.1
21. Luo, X., and, I. Hoteit, (2014), Ensemble Kalman filtering with a divided state-space strategy for coupled data assimilation problems, Mon. Weather Rev., 142 (12), 4542-4558, doi: 10.1175/MWR-D-13-00402.1
22. Madec, G., (2008), NEMO Ocean Engine, Institut Pierre-Simon Laplace (IPSL), 300 pp., France. Note du Pole de modilisation
23. Miyoshi, T., (2005), Ensemble Kalman filter experiments with a primitive-equation global model, PhD thesis, Dept. of Atmos. and Oceanic Sci., Univ. of Maryland, College Park, Md
24. Miyoshi, T., (2011), The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter, Mon. Weather Rev., 139 (5), 1519-1535, doi: 10.1175/2010MWR3570.1
25. Mogensen, K., M. Alonso, and, A. Weaver, (2012), The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for System 4, Tech. Rep., CERFACS, Toulouse, France
26. Molteni, F., (2003), Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments, Clim. Dyn., 20 (1), 175-191, doi: 10.1007/s00382-002-0268-2
27. Penny, S. G., D. W. Behringer, J. A. Carton, and, E. Kalnay, (2015), A hybrid global ocean data assimilation system at NCEP, Mon. Weather Rev., 143 (11), 4660-4677, doi: 10.1175/MWR-D-14-00376.1
28. Roemmich, D., G. Johnson, S. Riser, R. Davis, J. Gilson, W. B. Owens, S. Garzoli, C. Schmid, and, M. Ignaszewski, (2009), The Argo Program: Observing the global oceans with profiling floats, Oceanography, 22 (2), 34-43, doi: 10.5670/oceanog.2009.36
29. Saha, S., et al., (2014), The NCEP climate forecast system version 2, J. Clim., 27 (6), 2185-2208, doi: 10.1175/JCLI-D-12-00823.1
30. Shutts, G., (2005), A kinetic energy backscatter algorithm for use in ensemble prediction systems, Q. J. R. Meteorol. Soc., 131 (612), 3079-3102, doi: 10.1256/qj.04.106
31. Singleton, T., (2011), Data assimilation experiments with a simple coupled ocean-atmosphere model, PhD thesis, Dep. of Atmos. and Oceanic Sci., Univ. of Maryland, College Park, Md
32. Smith, P. J., A. M. Fowler, and, A. S. Lawless, (2015), Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model, Tellus A, 67, 1-25, doi: 10.3402/tellusa.v67.27025
33. Tardif, R., G. J. Hakim, and, C. Snyder, (2013), Coupled atmosphere-ocean data assimilation experiments with a low-order climate model, Clim. Dyn., 43 (5-6), 1631-1643, doi: 10.1007/s00382-013-1989-0
34. Whitaker, J. S., and, T. M. Hamill, (2012), Evaluating methods to account for system errors in ensemble data assimilation, Mon. Weather Rev., 140 (9), 3078-3089, doi: 10.1175/MWR-D-11-00276.1
35. Yang, S.-C., E. Kalnay, B. Hunt, and, N. E. Bowler, (2009), Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter, Q. J. R. Meteorol. Soc., 135 (638), 251-262, doi: 10.1002/qj.353
36. Zhang, F., C. Snyder, and, J. Sun, (2004), Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter, Mon. Weather Rev., 132 (5), 1238-1253, doi: 10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2
37. 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. Weather Rev., 135 (10), 3541-3564, doi: 10.1175/MWR3466.1.