The Effects of the Changed Initial Conditions on the Wind Fields Simulation According to the Objective Analysis Methods

객관분석기법에 의한 바람장 모의의 초기입력장 변화 효과 분석

  • Kim, Yoo-Keun (Department of Atmospheric Sciences, Pusan National University) ;
  • Jeong, Ju-Hee (Department of Atmospheric Sciences, Pusan National University) ;
  • Bae, Joo-Hyun (Department of Atmospheric Sciences, Pusan National University) ;
  • Kwun, Ji-Hye (Department of Atmospheric Sciences, Pusan National University) ;
  • Seo, Jang-Won (Marine Meteorology & Earthquake Research Laboratory, Meteorological Research Institute)
  • 김유근 (부산대학교 대기과학과) ;
  • 정주희 (부산대학교 대기과학과) ;
  • 배주현 (부산대학교 대기과학과) ;
  • 권지혜 (부산대학교 대기과학과) ;
  • 서장원 (기상연구소 해양기상지진연구실)
  • Published : 2006.08.01


We employed two data assimilation techniques including MM5 Four Dimensional Data Asssimilation (FDDA) and Local Analysis and Prediction System (LAPS) to find out the effects of the changed inetial conditions on the wind fields simulation according to the objective analysis methods. We designed 5 different modeling cases. EXP B used no data assimilation system. Both EXP Fl using surface observations and EXP F2 with surface and upper-air observations employed MM5 FDDA. EXP Ll using surface observations and EXP L2 with surface and upper-air observations used LAPS. As results of, simulated wind fields using MM5 FDDA showed locally characterized wind features due to objective analysis techniques in FDDA which is forcefully interpolating simulated results into observations. EXP Fl represented a large difference in comparison of wind speed with EXP B. In case of LAPS, simulated horizontal distribution of wind fields showed a good agreement with the patterns of initial condition and EXP Ll showed comparably lesser effects of data assimilation of surface observations than EXP Fl. When upper-air observations are applied to the simulations, while MM5 FDDA could hardly have important effects on the wind fields simulation and showed little differences with simulations with merely surface observations (EXP Fl), LAPS played a key role in simulating wind fields accurately and it could contribute to alleviate the over-estimated winds in EXP Ll simulations.


  1. McKendry, I. G., 1993, Ground-level ozone in Montreal, Canada, Atmospheric Environment, 27B(1), 93-103
  2. Lalas, D. P., V. R. Veirs, G. Karras and G. Kallos, 1982, An analysis of the $SO_{2}$ concentration levels in Athens Greece, Atmospheric Environment, 16(3), 531-544
  3. Liu, S. C, M. Trainer, F. C. Fehsenfeld, D. D. Parrish, E. J. Williams, D. W. Fahey, G. Huber and P. C. Murphy, 1987, Ozone production in the rural troposphere and implications for regional ozone distributions, Journal of Geophysical Research, 92, 4191-4207
  4. Fast, J. D., 1995, Mesoscale Modeling and Four-Dimensional Data Assimilation in Areas of Highly Complex Terrain, J. Appl, Meteor, 34, 2762-2782<2762:MMAFDD>2.0.CO;2
  5. Stauffer, D. R. and N. L. Seaman, 1994, Multiscale Four-Dimensional Data Assimilation, J. Appl. Meteor., 33, 416-426<0416:MFDDA>2.0.CO;2
  6. 이화운, 최현정, 이강열, 2005, 객관분석에 의한 복잡지형의 대기유동장 수치모의와 모델에 의한 자료질 조절효과, 한국대기환경과학회지, 21(1), 97-105
  7. 김용상, 박옥란, 황승언, 2002, 기상연구소의 국지규모 기상분석 및 예측 시스템(KLAPS)의 실시간 운영, 한국기상학회지, 38(1), 1-10
  8. Albers, S. C., 1995, The LAPS Wind Analysis, Weather and Forecasting, 10, 342-352<0342:TLWA>2.0.CO;2
  9. Albers, S. C. A. M. John, L. B. Daniel and R. S. John, 1996, The Local Analysis and Prediction System (LAPS) : Analyses of Clouds, Precipitation, and Temperature, Weather and Forecasting, 11, 273-287<0273:TLAAPS>2.0.CO;2
  10. Dudhia, J., 1993, A nonhydrostatic version of the penn statlNCAR mesoscale model: validation tests and simulation of an Atlantic cyclone and cold front, Mon. Wea. Rev, 121, 1493-1513<1493:ANVOTP>2.0.CO;2
  11. Hong, S. Y. and H. L. Pan, 1996, Comparison of NCEP-NCAR Reanalysis with 1987 FIFE Data, Mon. Wea. Rea., 124, 1480-1498<1480:CONNRW>2.0.CO;2
  12. Kain, H. S. andJ. M. Fritsch, 1993, Convective parameterization for mesoscale models; The Kain-Fritsch scheme, The representation of cumulus convection in numerical models, K. A. Emanuel and D. J. Raymond, Eds., Amer. Meteor. Soc., 246
  13. Reisner, J., R. J. Rasmussen and R. T. Bruintjes, 1998, Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model, Quart. J. Roy. Meteor. Soc., 124B, 1071-1107
  14. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Jacono and S. A Clough, 1997, Racliative transfer for inhomogeneous atmosphere : RRTM, a validated correlated-k model for the long wave, J. Geophy. Res., 102(D14), 16663-16682
  15. National Center for Atmospheric Research, 1994, A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model(MM5), NCAR/TN-398+STR
  16. McGinley, J., S. Albers and P. Stamus, 1991, Validation of a composite convective index as defined by a real time local analysis system, Wea. Forecasting, 6, 337-356<0337:VOACCI>2.0.CO;2