The hybrid local ensemble transform Kalman filter

Monthly Weather Review

Volumn 142, Issue 6, 2014, Pages 2139-2149

  Penny, Stephen G ^a,b  

^a University of Maryland   (United States)

^b CLIMATE PREDICTION CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE MATRIX; VALUE ENGINEERING;

BACKGROUND-ERROR COVARIANCES; DYNAMIC ERROR; ENSEMBLE KALMAN FILTER; ENSEMBLE MEMBERS; GAIN MATRICES; HYBRID METHOD; VARIATIONAL DATA ASSIMILATION; VARIATIONAL METHODS;

KALMAN FILTERS;

ALGORITHM; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR CORRECTION; KALMAN FILTER; OPERATIONS TECHNOLOGY; THREE-DIMENSIONAL MODELING; WEATHER FORECASTING;

EID: 84901595295     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-13-00131.1     Document Type: Article

References (36)

1. Barker, D. M., 1999: The use of synoptic-dependent error structure in 3DVAR. Var Scientific Development Paper 25, Met Office Tech. Rep., 2 pp. [Available from Met Office, FitzRoy Rd., Exeter, Devon EX1 3PB, United Kingdom.]
2. Bishop, C. H., and E. A. Satterfield, 2013: Hidden error variance theory. Part I: Exposition and analytic model. Mon. Wea. Rev., 141, 1454-1468, doi: 10. 1175/MWR-D-12-00118. 1.
3. Bishop, C. H, B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436, doi: 10. 1175/1520-0493(2001)129<0420: ASWTET>2. 0. CO;2.
4. Brett, C. E. A., K. F. Lam, K. J. H. Law, D. S. McCormick, M. R. Scott, and A. M. Stuart, 2013: Accuracy and stability of filters for dissipative PDEs. PhysicaD, 245, 34-45, doi: 10. 1016/j. physd. 2012. 11. 005.
5. Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013-1043, doi: 10. 1256/qj. 04. 15.
6. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010a: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments. Mon. Wea. Rev., 138, 1550-1566, doi: 10. 1175/2009MWR3157. 1.
7. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010b: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations. Mon. Wea. Rev., 138, 1567-1586, doi: 10. 1175/2009MWR3158. 1.
8. Derber, J., and A. Rosati, 1989: A global oceanic data assimilation system. J. Phys. Oceanogr., 19, 1333-1347, doi: 10. 1175/1520-0485(1989)019<1333: AGODAS>2. 0. CO;2.
9. Fehlberg, E., 1970: Classical Runge-Kutta fourth-order and lower order formulas with step size control and their application to heat conduction problems. Computing, 6, 61-71.
10. Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. R. Hunt, 2011: Balance and ensemble Kalman filter localization techniques. Mon. Wea. Rev., 139, 511-522, doi: 10. 1175/2010MWR3328. 1.
11. Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919, doi: 10. 1175/1520-0493(2000)128<2905: AHEKFV>2. 0. CO;2.
12. Harlim, J., and A. J. Majda, 2010: Catastrophic filter divergence in filtering nonlinear dissipative systems. Commun. Math. Sci., 8, 27-43, doi: 10. 4310/CMS. 2010. v8. n1. a3.
13. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126, doi: 10. 1016/j. physd. 2006. 11. 008.
14. Kalnay, E., 2003: Data assimilation. Atmospheric Modeling, Data Assimilation and Predictability, CambridgeUniversity Press, 136-204.
15. Kalnay, E., and Z. Toth, 1994: Removing growing errors in the analysis. Preprints, 10th Conf. on Numerical Weather Prediction, Portland, OR, Amer. Meteor. Soc., 212-215.
16. Karimi, A., and M. R. Paul, 2010: Extensive chaos in the Lorenz-96 model. Chaos, 20, 043105, doi: 10. 1063/1. 3496397.
17. Kleist, D. T., 2012: An evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Ph. D. dissertation, University of Maryland, College Park, 149 pp.
18. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter forNWP-Acomparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203, doi: 10. 1256/qj. 02. 132.
19. Lorenz, E. N., 1996: Predictability-A problem partly solved. Proceedings of a Seminar Held at ECMWF on Predictability, ECMWF Seminar Proceedings, Vol. 1, ECMWF, 1-18.
20. Lorenz, E. N., 2005: Designing chaotic models. J. Atmos. Sci., 62, 1574-1587, doi: 10. 1175/JAS3430. 1.
21. Lorenz, E. N., 2006: Regimes in simple systems. J. Atmos. Sci., 63, 2056-2073, doi: 10. 1175/JAS3727. 1.
22. Messner, J., 2009: Probabilistic forecasting using analogs in the idealized Lorenz96 setting. M. S. thesis, Dept. of Meteorology and Geophysics, University of Innsbruck, 59 pp.
23. Orrell, D., 2003: Model error and predictability over different timescales in the Lorenz '96 systems. J. Atmos. Sci., 60, 2219-2228, doi: 10. 1175/1520-0469(2003)060<2219: MEAPOD>2. 0. CO;2.
24. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428, doi: 10. 1111/j. 1600-0870. 2004. 00076. x.
25. Pazo, D., I. G. Szendro, J. M. Lopez, and M. A. Rodriguez, 2008: Structure of characteristic Lyapunov vectors in spatiotemporal chaos. Phys. Rev., 78E, 016209, doi: 10. 1103/PhysRevE. 78. 016209.
26. Penny, S. G., 2011: Data assimilation of the global ocean using the 4D local ensemble transform Kalman filter (4D-LETKF) and the Modular Ocean Model. Ph. D. dissertation, University of Maryland, College Park, 141 pp.
27. Penny, S. G., E. Kalnay, J. A. Carton, B. R. Hunt, K. Ide, T. Miyoshi, and G. Chepurin, 2013: The running-in-place algorithm applied to a global ocean general circulation model. Nonlinear Processes Geophys., 20, 1031-1046, doi: 10. 5194/npg-20-1031-2013.
28. Trevisan, A., and L. Palatella, 2011: On the Kalman filter error covariance collapse into the unstable subspace. Nonlinear Processes Geophys., 18, 243-250, doi: 10. 5194/npg-18-243-2011.
29. Wang, X., 2010: Incorporating ensemble covariance in the gridpoint statistical interpolation variational minimization: A mathematical framework. Mon. Wea. Rev., 138, 2990-2995, doi: 10. 1175/2010MWR3245. 1.
30. Wang, X., T. M. Hamill, J. S. Whitaker, and C. H. Bishop, 2007a: A comparison of hybrid ensemble transform Kalman filter-optimum interpolation and ensemble square root filter analysis schemes. Mon. Wea. Rev., 135, 1055-1076, doi: 10. 1175/MWR3307. 1.
31. Wang, X., C. Snyder, and T. M. Hamill, 2007b: On the theoretical equivalence of differently proposed ensemble-3D-VARhybrid analysis schemes. Mon. Wea. Rev., 135, 222-227, doi: 10. 1175/MWR3282. 1.
32. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF Model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116-5131, doi: 10. 1175/2008MWR2444. 1.
33. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF Model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132-5147, doi: 10. 1175/2008MWR2445. 1.
34. Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVarbased ensemble-variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 4098-4117, doi: 10. 1175/MWR-D-12-00141. 1.
35. Wilks, D. S., 2005: Effects of stochastic parametrizations in the Lorenz '96 system. Quart. J. Roy. Meteor. Soc., 131, 389-407, doi: 10. 1256/qj. 04. 03.
36. Wilks, D. S., 2006: Comparison of ensemble-MOS methods in the Lorenz '96 setting. Meteor. Appl., 13, 243-256, doi: 10. 1017/S1350482706002192.