Mixed-Resolution Ensemble Data Assimilation

Monthly Weather Review

Volumn 141, Issue 9, 2013, Pages 3007-3021

  Rainwater, Sabrina ^a   Hunt, Brian ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND COVARIANCES; COMPUTATION TIME; COMPUTATIONAL TIME; DATA ASSIMILATION; ENSEMBLE DATA ASSIMILATION; ENSEMBLE FORECASTS; ENSEMBLE KALMAN FILTER; ENSEMBLE MEMBERS;

COVARIANCE MATRIX; KALMAN FILTERS;

COMPUTER SIMULATION;

ACCURACY ASSESSMENT; COVARIANCE ANALYSIS; DATA ASSIMILATION; ENSEMBLE FORECASTING; KALMAN FILTER;

EID: 84883780658     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-12-00234.1     Document Type: Article

References (34)

1. Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99-111.
2. Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741-2758.
3. Ballabrera-Poy, J., E. Kalnay, and S.-C. Yang, 2009: Data assimilation in a system with two scales-Combining two initialization techniques. Tellus, 61A, 539-549.
4. 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.
5. Buizza, R., P. L. Houtekamer, Z. Toth, G. Pellerin, M. Wei, and Y. Zhu, 2005:Acomparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 1076-1097.
6. Buizza, R., J.-R. Bidlot, N. Wedi, M. Fuentes, M. Hamrud, G. Holt, and F. Vitart, 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Quart. J. Roy. Meteor. Soc., 133, 681-695.
7. Du, J., 2004: Hybrid ensemble prediction system: A new ensembling approach. Preprints, Symp. 50th Anniversary of Operational Numerical Weather Prediction, College Park, MD, NCEP, 4.2. [Available online at http://www.emc.ncep.noaa. gov/mmb/SREF/hybrid_50thNWP.pdf.]
8. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143- 10 162.
9. Gao, J., and M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with assimilated Doppler radar data. Mon. Wea. Rev., 136, 945-963.
10. Gao, J., M., and D. J. Stensrud, 2010: The development of a hybrid EnKF-3DVar algorithm for storm-scale data assimilation. Preprints, 25th Conf. Several Local Storms, Denver, CO, Amer. Meteor. Soc., 7.4. [Available online at https://ams. confex.com/ams/25SLS/webprogram/Paper176004.html.]
11. 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.
12. Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919.
13. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
14. Hunt, B. R., E. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126.
15. Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409-418.
16. 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.
17. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP-A comparison with 4D-VAR. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.
18. Lorenz, E. N., 1996: Predictability-Aproblem partly solved. Proc. Seminar on Predictability, Vol. 1, Reading, United Kingdom, ECMWF, 1-19.
19. Lorenz, E. N.,2005: Designing chaotic models. J. Atmos. Sci., 62, 1574-1587.
20. Lorenz, E. N., and K. A. Emanuel, 1998: Optimal sites for supplementary weather observations: Simulation with a smallmodel. J. Atmos. Sci., 55, 399-414.
21. 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.
22. Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73-119.
23. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
24. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
25. Rabier, F., H. Jarvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation-Part I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143-1170.
26. Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317-2330.
27. Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297-3319.
28. Wang, X., C. H. Bishop, and S. J. Julier, 2004: Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble? Mon. Wea. Rev., 132, 1590- 1605.
29. Wang, X., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble/3D-Var hybrid analysis schemes. Mon. Wea. Rev., 135, 222-227.
30. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
31. 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. Quart. J. Roy. Meteor. Soc., 135, 251-262.
32. Yoon, Y.-N., E. Ott, and I. Szunyogh, 2010: On the propagation of information and the use of localization in ensemble Kalman filtering. J. Atmos. Sci., 67, 3823-3834.
33. Zhang, M., and F. Zhang, 2012: E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model. Mon. Wea. Rev., 140, 587-600.
34. Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133, 1710-1726.