Reanalysis without radiosondes using ensemble data assimilation

Monthly Weather Review

Volumn 132, Issue 5, 2004, Pages 1190-1200

  Whitaker, Jeffrey S ^a   Compo, Gilbert P ^a   Wei, Xue ^a   Hamill, Thomas M ^a  

^a EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COSTS; DATA REDUCTION; ERROR ANALYSIS;

DATA ASSIMILATION; SPARCE;

WEATHER FORECASTING;

COVARIANCE ANALYSIS; DATA ASSIMILATION; ERROR ANALYSIS; NORTHERN HEMISPHERE; RADIOSONDE; TROPOSPHERE;

EID: 2442668784     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/1520-0493(2004)132<1190:RWRUED>2.0.CO;2     Document Type: Article

References (31)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
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. Andrews, A., 1968: A square root formulation of the Kalman covariance equations. AIAA J., 6, 1165-1168.
4. Bishop, C. H., B. 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. Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
6. Cohn, S. E., N. S. Sivakumaran, and R. Todling, 1994: A fixed-lag Kalman smoother for retrospective data assimilation. Mon. Wea. Rev., 122, 2838-2867.
7. Dee, D. P., L. Rukhovets, R. Todling, A. M. da Silva, and J. W. Larson, 2001: An adaptive buddy check for observational quality control. Quart. J. Roy. Meteor. Soc., 127, 2451-2471.
8. Diaz, H., C. Folland, T. Manabe, D. Parker, R. Reynolds, and S. Woodruff, 2002: Workshop on advances in the use of historical marine climate data. WHO Bull., 51, 377-380.
9. Gelb, A., J. F. Kasper, R. A. Nash, C. F. Price, and A. A. Sutherland, 1974: Applied Optimal Estimation. MIT Press, 374 pp.
10. Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D-variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919.
11. Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790.
12. Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble reforecasting: Improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., in press.
13. Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, and D. Xiaosu, Eds., 2001: Climate Change 2001: The Scientific Basis: Contribution of Working Group I to the Third Assessment Report of the International Panel on Climate Change. Cambridge University Press, 572 pp.
14. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
15. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
16. Ide, K., P. Courtier, M. Ghil, and A. C. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential, and variational. J. Meteor. Soc. Japan 75, 181-189.
17. Jones, P. D., 1987: The early twentieth century Arctic high-Fact or fiction? Climate Dyn., 1, 63-75.
18. Kistler, R., and Coauthors, 2001: The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor, Soc., 82, 247-268.
19. Liu, Z.-Q., and F. Rabier, 2002: The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study. Quart. J. Roy. Meteor. Soc., 128, 1367-1386.
20. Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 1177-1194.
21. Manabe, T., 1999: The digitized Kobe collection, phase I: Historical surface marine meteorological observations in the archive of the Japan Meteorological Agency. Bull. Amer. Meteor. Soc., 80, 2703-2715.
22. Maybeck, P. S., 1979: Square root filtering. Stochastic Models, Estimation and Control, Vol. 1. Academic Press, 368-409.
23. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1967-1988.
24. Schmith, T., H. Alexandersson, and H. Tuomenvirta, 1997: North Atlantic-European pressure observations 1868-1995 (WASA dataset version 1.0). Danish Meteorological Institute Tech. Rep. 97-3, 13 pp.
25. Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 1485-1490.
26. Trenberth, K. E., and D. A. Paolino, 1980: The Northern Hemisphere sea-level pressure dataset: Trends, errors and discontinuties. Mon. Wea. Rev., 108, 855-872.
27. United States Weather Bureau, 1944: Daily Synoptic Series, Historical Weather Maps, Northern Hemisphere Sea-Level, January 1899 to June 1939. Cooperative project of U.S. Army, Air Force, and Weather Bureau Statistics Division.
28. Whitaker, J. S., and G. P. Compo, 2002: An ensemble Kalman smoother for reanalysis. Proc. Symp. on Observations, Data Assimilation and Probabilistic Prediction, Orlando, FL, Amer. Meteor. Soc., 144-147.
29. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
30. Woodruff, S., H. Diaz, J. Elms, and S. Worley, 1998: COADS release 2 data and metadata enhancements for improvements of marine surface flux fields. Phys. Chem. Earth, 23, 517-527.
31. Wu, W., M. Iredell, S. Saha, and P. Caplan, cited 1997: Changes to the 1997 NCEP operational MRF model analysis/forecast system. NCEP Technical Procedures Bulletin 443, National Weather Service, Office of Meteorology, Programs and Plans Division, Silver Spring, MD. [Available online at http://www.nws.noaa.gov/om/tpb/indexb.htm.]