Observing system simulation experiments at the National Centers for Environmental Prediction

Journal of Geophysical Research Atmospheres

Volumn 115, Issue 7, 2010, Pages

  Masutani, Michiko ^a,b   Woollen, John S ^a,c   Lord, Stephen J ^a   Emmitt, G David ^d   Kleespies, Thomas J ^e   Wood, Sidney A ^d   Greco, Steven ^d   Sun, Haibing ^e,f   Terry, Joseph ^c,g   Kapoor, Vaishali ^e,h   Treadon, Russ ^a   Campana, Kenneth A ^a,d  

^a CLIMATE PREDICTION CENTER   (United States)

^b Wyle Information Systems Group   (United States)

^c SAIC FREDERICK INC   (United States)

^d National Environmental Satellite Data and Information Service NOAA ^*  (United States)

^e NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

^f Perot Systems   (United States)

^g NASA GODDARD SPACE FLIGHT CENTER   (United States)

^h SCIENCE AND TECHNOLOGY CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; EXPERIMENTS; OPTICAL RADAR;

ATMOSPHERIC SIMULATIONS; DATA ASSIMILATION SYSTEMS; DATA IMPACT; DOPPLER WIND LIDAR; NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTIONS; OBSERVING SYSTEM SIMULATION EXPERIMENTS; OBSERVING SYSTEMS; QUANTITATIVE EVALUATION; RADIANCE DATA; SIMULATED DATA; SIMULATION EXPERIMENTS; WEATHER FORECASTS;

WEATHER FORECASTING;

ATMOSPHERIC MODELING; DATA ASSIMILATION; DOPPLER LIDAR; INSTRUMENTATION; NUMERICAL MODEL; OBSERVATORY; PROXY CLIMATE RECORD; QUANTITATIVE ANALYSIS; RADIANCE; SATELLITE IMAGERY; WEATHER FORECASTING;

EID: 77951059451     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009JD012528     Document Type: Article

References (40)

1. Arnold, C. P., Jr., and C. H. Dey (1986), Observing-systems simulation experiments: Past, present, and future, Bull. Am. Meteorol. Soc., 67, 687-695.
2. Atlas, R. (1997), Atmospheric observation and experiments to assess their usefulness in data assimilation, J. Meteorol. Soc. Jpn., 75, 111-130.
3. Becker, B. D., H. Roquet, and A. Stoffelen (1996), A simulated future atmospheric observation database including ATOVS, ASCAT, and DWL, Bull. Am. Meteorol. Soc., 77, 2279-2294.
4. Cardinali, C., J. Pailleux, and J.-N. Thépaut (1998), Use of simulated Doppler wind lidar data in NWP: An impact study, Note de travail du Groupe de Modelisation pour L'Assimilation et le Prevision, 6, Cent. Natl. de la Rech. Sci., Météo, France.
5. Cress, A., and W. Wergen (2001), Impact of profile observations on the German Weather Service's NWP system, Meteorol. Z., 10, 91-101.
6. Derber, J. C., and W.-S. Wu (1998), The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system, Mon. Weather Rev., 126, 2287-2299.
7. Errico, R. M., R. Yang, M. Masutani, and J. Woollen (2007), Estimation of some characteristics of analysis error inferred from an observation system simulation experiment, Meteorol. Z., 16, 695-708.
8. Fisher, M. (2003), Estimation of entropy reduction and degrees of freedom for signal for large variational analysis systems, Tech. Memo., 397, 18 pp., Eur. Cent. for Med. Range Weather Forecasts, Reading, U. K.
9. Global Climate and Weather Modeling Branch (2003), The GFS atmospheric model, NCEP Off. Note 442, Natl. Cent. for Environ. Predict., Washington, D. C.
10. Global Climate and Weather Modeling Branch (2004), SSI analysis system 2004, NCEP Off. Note 443, Natl. Cent. for Environ. Predict., Washington, D. C.
11. Hahn, C. J., S. G. Warren, and J. London (1996), Edited synoptic cloud reports from ships and land stations over the globe, 1982-1991, Publ. 4367, NDP-026B, Environ. Sci. Div., U.S. Dep. of Energy, Washington, D. C.
12. Halem, M., and R. Dlouhy (1984), Observing system simulation experiments related to space-borne lidar wind profiling: Part 1. Forecast impact of highly idealized observing systems, in Conference on Satellite Meteorology/ Remote Sensing and Applications, pp. 272-279, Am. Meteorol. Soc., Boston, Mass.
13. Hamill, T. M., R. P. d'Entrement, and J. T. Bunting (1992), A description of the Air Force real-time, nephanalysis model, Weather Forecast., 7, 288-306.
14. Henderson-Sellers, A. (1986), Layer cloud amount for January and July 1979 from 3D-Nephanalysis, J. Clim. Appl. Meteorol., 24, 118-132.
15. Hoskins, B. J., and T. Ambrizzi (1993), Rossby wave propagation on a realistic longitudinally varying flow, J. Atmos. Sci., 50, 1661-1671.
16. Kalnay, E., J. C. Jusem, and J. Pfaendtner (1985), The relative importance of mass and wind data in the FGGE observing system, in Proceedings of the NASA Symposium on Global Wind Measurements, Columbia, pp. 1-5, NASA, Washington, D. C.
17. Keil, M. (2004), Assimilating data from a simulated global constellation of stratospheric balloons, Q. J. R. Meteorol. Soc., 130, 2475-2493.
18. Kleespies, T. J., and D. Crosby (2001), Correlated noise modeling for satellite radiance simulation, in American Meteorology Society Preprint Volume for the 11th Conference on Satellite Meteorology and Oceanography, October 2001, Madison, Wisconsin, pp. 604-605, Boston, Mass.
19. Kleespies, T. J., P. van Delst, L. M. McMillin, and J. Derber (2004), Atmospheric transmittance of an absorbing Gas: 6. An OPTRAN status report and introduction to the NESDIS/NCEP Community Radiative Transfer Model, Appl. Opt., 43(15), 3103-3109.
20. Lahoz, W. A., R. Brugge, D. R. Jackson, S. Migliorini, R. Swinbank, R. Lary, and A. Lee (2005), An observing system simulation experiment to evaluate the scientific merit of wind and ozone measurements from the future SWIFT instrument, Q. J. R. Meteorol. Soc., 131, 503-523.
21. Liu, J., and E. Kalnay (2007), Simple Doppler wind lidar adaptive observation experiments with 3D-Var and an ensemble Kalman filter in a global primitive equations model, Geophys. Res. Lett., 34, L19808, doi:10.1029/ 2007GL030707.
22. Lord, S. J., E. Kalnay, R. Daley, G. D. Emmitt, and R. Atlas (1997), Using OSSEs in the design of the future generation of integrated observing systems, in AMS Preprint Volume, 1st Symposium on Integrated Observation Systems, Long Beach, Calif., 2-7 Feb.
23. Marseille, G. J., A. Stoffelen, and J. Barkmeijer (2008a), Sensitivity observing system experiment (SOSE): A new effective NWP-based tool in designing the global observing system, Tellus, Ser. A, 60, 216-233.
24. Marseille, G. J., A. Stoffelen, and J. Barkmeijer (2008b), Impact assessment of prospective space-borne Doppler wind lidar observation scenarios, Tellus, Ser. A, 60, 234-248.
25. Marseille, G. J., A. Stoffelen, and J. Barkmeijer (2008c), A cycled sensitivity observing system experiment on simulated Doppler wind lidar data during the 1999 christmas storm "Martin," Tellus, Ser. A, 60, 249-260.
26. Masutani, M., K. Campana, S. Lord, and S.-K. Yang (1999), Note on cloud cover of the ECMWF Nature Run used for OSSE/NPOESS project, Off. Note 427, Natl. Cent. for Environ. Predict., Camp Springs, Md.
27. Masutani, M., J. S. Woollen, S. J. Lord, T. J. Kleespies, G. D. Emmitt, H. Sun, S. A. Wood, S. Greco, J. Terry, and K. Campana (2006), Observing system simulation experiments at NCEP, Off. Note 451, Natl. Cent. for Environ. Predict., Camp Springs, Md.
28. Masutani, M., et al. (2007), Progress in joint OSSEs, in AMS Preprint Volume for 18th Conference on Numerical Weather Prediction, Parkcity, UT, 25-29 June.
29. McNally, A. P., J. C. Derber, W.-S. Wu, and B. B. Katz (2000), The use of TOVS level-1 radiances in the NCEP SSI analysis system, Q. J. R. Meteorol. Soc., 129, 689-724.
30. Parrish, D. F., and J. C. Derber (1992), The National Meteorological Center's spectral statistical interpolation analysis system, Mon. Weather Rev., 120, 1747-1763.
31. Rohaly, G. D., and T. N. Krishnamurti (1993), An observing system simulation experiment for the laser atmospheric wind sounder (LAWS), J. Appl. Meteorol., 32, 1453-1471.
32. Sato, Y., M. S. F. V. De Pondeca, R. J. Purser, and D. F. Parrish (2009), Ensemble-based background error covariance implementations using spatial recursive filters in NCEP's grid-point statistical interpolation system, NCEP Off. Note 459, 29 pp., Natl. Cent. for Environ. Predict., Camp Springs, Md.
33. Saunders, R. W., M. Matricardi, and P. Brunel (1999), An improved fast radiative transfer model for assimilation of satellite radiance observations, Q. J. R. Meteorol. Soc., 125, 1407-1425.
34. Stoffelen, A., et al. (2005), The atmospheric dynamics mission for global wind field measurement, Bull. Am. Meteorol. Soc. Am., 86, 73-87.
35. Stoffelen, A., G. J. Marseille, F. Bouttier, D. Vasiljevic, S. De Haan, and C. Cardinali (2006), ADM-Aeolus Doppler wind lidar observing system simulation experiment, Q. J. R. Meteorol. Soc., 619, 1927-1948.
36. Tan, D. G. H., E. Andersson, M. Fisher, and L. Isaksen (2007), Observing system impact assessment using a data assimilation ensemble technique: Application to theADM-Aeolus wind profiling mission, Q. J. R. Meteorol. Soc., 133, 381-390.
37. Tiedtke, M. (1989), A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117(8), 1779-1800.
38. Tiedtke, M. (1993), Representation of clouds in large-scale models, Mon. Weather Rev., 121, 3040-3061.
39. Warren, S. G., C. J. Hahn, J. London, R. M. Chervin, and R. L. Jenne (1988), Global distribution of total cloud cover and cloud type amounts over the ocean, Tech. Note, NCAR/TN-317+STR, 41 pp.+170 maps, Natl. Cent. for Atmos. Res., Boulder, Colo.
40. World Meteorological Organization (WMO) (2002), Guide to WMO table driven code forms FM94 BUFR and FM95 CREX, Geneva, Switzerland.