Four-dimensional assimilation of multitime wind profiles over a single station and numerical simulation of a mesoscale convective system observed during IHOP_2002

Monthly Weather Review

Volumn 139, Issue 11, 2011, Pages 3369-3388

  Zhang, Lei ^a   Pu, Zhaoxia ^a  

^a UNIVERSITY OF UTAH   (United States)

Author keywords

Data assimilation; Lidars Lidar observations; Mesoscale systems; Numerical weather prediction forecasting; Surface observations

Indexed keywords

DATA ASSIMILATION; LIDARS/LIDAR OBSERVATIONS; MESO-SCALE SYSTEMS; NUMERICAL WEATHER PREDICTION; SURFACE OBSERVATIONS;

BOUNDARY LAYER FLOW; COMPUTER SIMULATION; DATA PROCESSING; OPTICAL RADAR; STORMS;

WEATHER FORECASTING;

BORDER REGION; COMPUTER SIMULATION; CONVECTIVE SYSTEM; DATA ACQUISITION; FOUR-DIMENSIONAL MODELING; LIDAR; MESOSCALE METEOROLOGY; OBSERVATIONAL METHOD; WEATHER FORECASTING; WIND PROFILE;

KANSAS; OKLAHOMA [UNITED STATES]; TEXAS COUNTY [OKLAHOMA]; UNITED STATES;

EID: 84862967528     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2011MWR3569.1     Document Type: Article

References (34)

1. Barker, D. M., W. Huang, Y. R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional data assimilation system for use with MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897-914.
2. Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/ hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569-585.
3. Chen, S.-H., and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model. J. Meteor. Soc. Japan, 80, 99-118.
4. Childs, P. P., A. L. Qureshi, S. Raman, K. Alapaty, R. Ellis, R. Boyles, and D. Niyogi, 2006: Simulation of convective initiation during IHOP_2002 using the Flux-Adjusting SurfaceDataAssimilation System (FASDAS). Mon. Wea. Rev., 134, 134-148.
5. Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387.
6. Crook, N. A., and J. Sun, 2002: Assimilating radar, surface and profiler data for the Sydney 2000 forecast demonstration project. J. Atmos. Oceanic Technol., 19, 888-898.
7. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale twodimensional model. J. Atmos. Sci., 46, 3077-3107.
8. Fritsch, J. M., and R. E. Carbone, 2004: Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy. Bull.Amer. Meteor. Soc., 85, 955-965.
9. Fujita, T., 1963: Analytical mesometeorology:Areview. SevereLocal Storms, Meteor. Monogr., No. 27, Amer. Meteor. Soc., 77-128.
10. Gentry, B. M., and H.-L. Chen, 2003: Tropospheric wind measurements obtained with the Goddard Lidar Observatory for Winds (GLOW): Validation and performance. Lidar Remote Sensing for Industry and Environment Monitoring II, U. N. Singh, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 4484), 74-81.
11. Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322-2339.
12. Huang, X.-Y. and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299-314.
13. Kain, J. S., and J. M. Fritsch, 1993: The role of the convective "trigger function" in numerical prediction of mesoscale convective systems. Meteor. Atmos. Phys., 49, 93-106.
14. Lee, B. D., R. D. Farley, and M. R. Hjelmfelt, 1991: A numerical case study of convection initiation along colliding convergence boundaries in northeast Colorado. J. Atmos. Sci., 48, 2350-2366.
15. LeMone, M. A., E. Zipser, and S. B. Trier, 1998: The role of environmental shear and thermodynamic conditions in determining the structure and evolution of mesoscale convective systems during TOGA COARE. J. Atmos. Sci., 55, 3493-3518.
16. Liu, H., and M. Xue, 2008: Prediction of convective initiation and storm evolution on 12 June 2002 during IHOP_2002. Part I: Control simulation and sensitivity experiments. Mon. Wea. Rev., 136, 2261-2282.
17. Lorenc, A. C., 2003: Modelling of error covariances by fourdimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 129, 3167-3182.
18. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 663-16 682.
19. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
20. Pu, Z., L. Zhang, and G. D. Emmitt, 2010: Impact of airborne Doppler wind lidar data on numerical simulation of a tropical cyclone. Geophys. Res. Lett., 37, L05801, doi:10.1029/ 2009GL041765.
21. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005:Adescription of the advanced research WRF Version 2. NCAR Tech. Note NCAR/TN-4681STR, 88 pp.
22. Stauffer, D., and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale. Mon. Wea. Rev., 118, 1250-1277.
23. Sun, J., and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16, 117-132.
24. Veerse, F., and J. N. Thépaut, 1998: Multiple-truncation incremental approach for four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 124, 1889-1908.
25. Wakimoto, R. M., H. V. Murphey, and R. G. Fovell, 2004: Mantle echoes associated with deep convection: Observations and numerical simulations. Mon. Wea. Rev., 132, 1701-1720.
26. Weckwerth, T. M. and Coauthors, 2004: An overview of the International H2O Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253-277.
27. Weckwerth, T. M., H. V. Murphey, C. Flamant, J. Goldstein, and C. R. Pettet, 2008: An observational study of convective initiation on 12 June 2002 during IHOP_2002. Mon. Wea. Rev., 136, 2283-2304.
28. Wilks, D. S., 1995: Statistical Methods in Atmospheric Sciences. Academic Press, 467 pp.
29. Wulfmeyer, V., H.-S. Bauer, M. Grzeschik, A. Behrendt, F. Vandenberghe, E. V. Browell, S. Ismail, and R. A. Ferrare, 2006: Four-dimensional variational assimilation of water vapor differential absorption lidar data: The first cast study within IHOP_2002. Mon. Wea. Rev., 134, 209-230.
30. Xiao, Q.-N., and J. Sun, 2007: Multi-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 3381-3404.
31. Xue, M., and W. J. Martin, 2006: A high-resolution modeling study of the 24 May 2002 case during IHOP. Part I: Numerical simulation and general evolution of the dryline and convection. Mon. Wea. Rev., 134, 149-171.
32. Zängl, G., 2004: The sensitivity of simulated orographic precipitation to model components other than cloud microphysics. Quart. J. Roy. Meteor. Soc., 130, 1857-1875.
33. Zhang, F., A. Odins, and J. W. Nielsen-Gammon, 2006: Mesoscale predictability of an extreme warm-season rainfall event. Wea. Forecasting, 21, 149-166.
34. Zhang, M., F. Zhang, X.-Y. Huang, and X. Zhang, 2011: Intercomparison of an ensemble Kalman filter with three- and four dimensional variational data assimilation methods in a limited-area model during the month of June 2003. Mon. Wea. Rev., 139, 566-572.