Intercomparison of an ensemble Kalman filter with three- and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003

Monthly Weather Review

Volumn 139, Issue 2, 2011, Pages 566-572

  Zhang, Meng ^a   Zhang, Fuqing ^a   Huang, Xiang Yu ^b   Zhang, Xin ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

Data assimilation; In situ observations; Kalman filters; Model comparison; Surface observations

Indexed keywords

ANALYSIS TIME; DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; FORECAST ERRORS; FOUR-DIMENSIONAL VARIATIONAL DATA ASSIMILATION; IN SITU OBSERVATIONS; INTERCOMPARISONS; LEAD TIME; LIMITED AREA MODELS; MODEL COMPARISON; SURFACE OBSERVATION; SURFACE OBSERVATIONS; WIND PROFILER;

FORECASTING; KALMAN FILTERS; SHIPS; THREE DIMENSIONAL;

DATA PROCESSING;

AIR TEMPERATURE; COMPARATIVE STUDY; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; KALMAN FILTER; OBSERVATIONAL METHOD; PERFORMANCE ASSESSMENT; VARIANCE ANALYSIS; WEATHER FORECASTING; WIND;

UNITED STATES;

EID: 79953213364     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010MWR3610.1     Document Type: Article

References (21)

1. Barker, D.M., W. Huang, Y.-R. Guo, A. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897-914.
2. 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.
3. 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.
4. Caya, A., J. Sun, and C. Snyder, 2005: A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea. Rev., 133, 3081-3094.
5. Davis, C., and Coauthors, 2004: The Bow-Echo and MCV Experiment: Observations and opportunities. Bull. Amer. Meteor. Soc., 85, 1075-1093.
6. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143-10 162.
7. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
8. Hawblitzel, D. P., F. Zhang, Z. Meng, and C. A. Davis, 2007: Probabilistic evaluation of the dynamics and predictability of a mesoscale convective vortex of 10-13 June 2003. Mon. Wea. Rev., 135, 1544-1563.
9. Houtekamer, P.L., H. L. Mitchell, G.Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620.
10. Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299-314.
11. Meng, Z., and F. Zhang, 2008a: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 522-540.
12. Meng, Z., and F. Zhang, 2008b: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparisonwith3DVARin amonth-long experiment. Mon.Wea. Rev., 136, 3671-3682.
13. Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846-2866.
14. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
15. Skamarock,W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: Adescription of theAdvanced Research WRF Version 2. NCAR Tech. Note 4681STR, 88 pp.
16. Torn, R. D., and G. J. Hakim, 2008: Performance characteristics of a pseudo-operational ensemble Kalman filter. Mon. Wea. Rev., 136, 3947-3963.
17. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132-5147.
18. Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Rev., 136, 463-482.
19. Yang, S.-C., M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2009: Comparison of local ensemble transform Kalman filter, 3DVAR, and 4DVAR in a quasigeostrophic model. Mon. Wea. Rev., 137, 693-709.
20. 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. Wea. Rev., 132, 1238-1253.
21. Zhang, F.,M. Zhang, and J. A. Hansen, 2009:Coupling ensemble Kalman filter with four-dimensional variational data assimilation. Adv. Atmos. Sci., 26, 1-8.