Four-dimensional variational data assimilation of heterogeneous mesoscale observations for a strong convective case

Monthly Weather Review

Volumn 128, Issue 3, 2000, Pages 619-643

  Guo, Y R ^a   Kuo, Y H ^a   Dudhia, J ^a   Parsons, D ^a   Rocken, C ^b  

^a NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^b UNIVERSITY CORPORATION FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVECTION; DATA ASSIMILATION; SQUALL LINE;

CONVECTION; DATA ASSIMILATION; MESOSCALE METEOROLOGY; SQUALL LINE;

EID: 0034059532     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/1520-0493(2000)128<0619:FDVDAO>2.0.CO;2     Document Type: Article

References (40)

1. Barth, M. F., R. B. Chadwick, and D. W. van de Kamp, 1994: Data processing algorithms used by NOAA's wind profiler demonstration network. Ann. Geophys., 12, 518-528.
2. Businger, S., and Coauthors, 1996: The promise of GPS in atmospheric monitoring. Bull. Amer. Meteor. Soc., 77, 5-18.
3. Courtier, P., and O. Talagrand, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. Part II. Numerical results. Quart. J. Roy. Meteor. Soc., 113, 1329-1368.
4. Dabberdt, W. F., and T. W. Schlatter, 1996: Research opportunities from emerging atmospheric observing and modeling capabilities. Bull. Amer. Meteor. Soc., 77, 305-323.
5. Deardorff, J. W., 1972: Parameterization of the planetary boundary layer for use in general circulation models. Mon. Wea. Rev., 100, 93-106.
6. Derber, J. C., 1989: A variational continuous assimilation technique. Mon. Wea. Rev., 117, 2437-2446.
7. Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077-3107.
8. Emanuel, K., and Coauthors, 1995: Report of the first prospectus development team of the U.S. Weather Research Program to NOAA and the NSF. Bull. Amer. Meteor. Soc., 76, 1194-1208.
9. Errico, R. M., 1997: What is an adjoint model? Bull. Amer. Meteor. Soc., 78, 2577-2591.
10. Fillion, L., and R. Errico, 1997: Variational assimilation of precipitation data using moist convective parameterization schemes: A ID-Var study. Mon. Wea. Rev., 125, 2917-2942.
11. Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 138 pp. [Available from National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.]
12. Guo, Y.-R., and S. Chen, 1994: Terrain and land use for the Fifth-Generation Penn State/NCAR Mesoscale Modeling System (MM5): Program TERRAIN. NCAR Tech. Note TN-397+1A, 114 pp. [Available from National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.]
13. Kuo, Y.-H., Y.-R. Guo, and E. R. Westwater, 1993: Assimilation of precipitable water measurements into a mesoscale numerical model. Mon. Wea. Rev., 121, 1215-1238.
14. _, X. Zou, and Y.-R. Guo, 1996: Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model. Part I: Moisture retrieval and sensitivity experiments. Mon. Wea. Rev., 124, 122-147.
15. Lewis, J. M., and J. C. Derber, 1985: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus, 37A, 309-322.
16. Lu, C., and G. L. Browning, 1998: The Impact of observational and model errors on four-dimensional data assimilation. J. Atmos. Sci., 55, 995-1010.
17. Navon, I. M., X. Zou, J. Derber, and J. Sela, 1992: Variational data assimilation with an adiabatic version of the NMC spectral model. Mon. Wea. Rev., 120, 1433-1446.
18. Parrish, D. E. and J. C. Derber, 1992: The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
19. Rocken, C., T. Van Hove, T. Johson, F. Solheim, R. H. Ware, M. Bevis, S. Businger, and S. R. Chiswell, 1995: GPS/STORM-GPS sensing of atmospheric water vapor for meteorology. J. Atmos. Oceanic Technol., 12, 468-478.
20. Stensrud, D. J., and J.-W. Bao, 1992: Behaviors of variational and nudging assimilation techniques with a chaotic low-order model. Mon. Wea. Rev., 120, 3016-3028.
21. Stokes, G. M., and S. E. Schwartz, 1994: The Atmospheric Radiation Measurement (ARM) program: Programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc., 75, 1201-1221.
22. Thepaut, J.-N., D. Vasiljevic, P. Courtier, and J. Pailleux, 1993: Variational assimilation of conventional meteorological observations with a multilevel primitive equation model. Quart. J. Roy. Meteor. Soc., 119, 153-186.
23. _, P. Courtier, G. Belaud, and G. Lemaitre, 1996: Dynamical structure functions in a four-dimensional variational assimilation: A case study. Quart. J. Roy. Meteor. Soc., 122, 535-561.
24. Tsuyuki, T., 1997: Variational data assimilation in the Tropics using precipitation data. Part III: Assimilation of SSM/I precipitation rates. Mon. Wea. Rev., 125, 1447-1463.
25. Vukicevic, T., and R. M. Errico, 1993: Linearization and adjoint of parameterized moist diabatic processes. Tellus, 45A, 493-510.
26. _, and J.-W. Bao, 1998: The effect of linearization errors on 4DVAR data assimilation. Mon. Wea. Rev., 126, 1695-1706.
27. Warner, T. T., R. A. Peterson, and R. E. Treadon, 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 2599-2617.
28. Wilson, J. W., and E. A. Brandes, 1979: Radar measurement of rainfall - A summary. Bull. Amer. Meteor. Soc., 60, 1048-1058.
29. Xu, Q., 1996a: Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples. J. Attnos. Sci., 53, 1123-1142.
30. _, 1996b: Generalized adjoint for physical processes with parameterized discontinuities. Part II: Vector formulation and matching conditions. J. Atmos. Sci., 53, 1143-1155.
31. Zou, X., 1997: Tangent linear and adjoint of "on/off" processes and their feasibility for use in 4-dimensional variational data assimilation. Tellus, 49A, 3-31.
32. _, and Y.-H. Kuo, 1996: Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model. Mon. Wea. Rev., 124, 2859-2882.
33. _, I. M. Navon, and J. Sela, 1993: Control of gravitational oscillations in variational data assimilation. Mon. Wea. Rev., 121, 272-289.
34. _, Y.-H. Kuo, and Y.-R. Guo, 1995: Assimilation of atmospheric radio refractivity using a nonhydrostatic adjoint model. Mon. Wea. Rev., 123, 2229-2249.
35. _, F. Vandenberghe, M. Pondeca, and Y.-H. Kuo, 1997: Introduction to adjoint techniques and the MM5 adjoint modeling system. NCAR Tech. Note TN-435-STR, 110 pp. [Available from National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.]
36. Zupanski, D., 1997: A general weak constraint applicable to operational 4DVAR data assimilation system. Mon. Wea. Rev., 125, 2274-2292.
37. _, and F. Mesinger, 1995: Four-dimensional variational assimilation of precipitation data. Mon. Wea. Rev., 123, 1112-1127.
38. Zupanski, M., 1993: A preconditioning algorithm for large-scale minimization problems. Tellus, 45A, 478-492.
39. _, 1996: A preconditioning algorithm for four-dimensional variational data assimilation. Mon. Wea. Rev., 124, 2562-2573.
40. _, and D. Zupanski, 1995: Recent development of NMC's regional four-dimensional variational data assimilation system. Proc. Second Int. Symp. on Assimilation of Observation in Meteorology and Oceanography, Tokyo, Japan, World Meteorological Organization, 367-372.