DOI QR코드

DOI QR Code

Improvement in the Simulation of Wind Fields Over the Complex Coastal Area, Korea

한반도 복잡 해안지역의 바람장 모의 개선

  • Kim, Yoo-Keun (Department of Atmospheric Sciences, Pusan National University) ;
  • Bae, Joo-Hyun (Department of Atmospheric Sciences, Pusan National University) ;
  • Jeong, Ju-Hee (Department of Atmospheric Sciences, Pusan National University) ;
  • Kweon, Ji-Hye (Department of Atmospheric Sciences, Pusan National University) ;
  • Seo, Jang-Won (Marine Meteorology & Earthquake Research Laboratory, Meteorological Research Institute) ;
  • Kim, Yong-Sang (Information Management Division, Korea Meteorological Administration)
  • Published : 2006.05.01

Abstract

We focused on improvement in simulation of wind fields for the complex coastal area. Local Analysis and Prediction System(LAPS) was used as a data assimilation method to improve initial conditions. Case studies of different LAPS inputs were performed to compare improvement of wind fields. Five cases have been employed : I) non data assimilation, II) all available data, III) AWS, buoy, QuikSCAT, IV) AWS, buoy, wind profiler, V) AWS, buoy, AMEDAS. Data assimilation can supplement insufficiency of the mesoscale model which does not represent detailed terrain effect and small scale atmospheric flow fields. Result assimilated all available data showed a good agreement to the observations rather than other cases and estimated veil the local meteorological characteristics including sea breeze and up-slope winds. Result using wind profiler data was the next best thing. This implies that data assimilation with many high-resolution sounding data could contribute to the improvements of good initial condition in the complex coastal area. As a result, these indicated that effective data assimilation process and application of the selective LAPS inputs played an important role in simulating wind fields accurately in a complex area.

Keywords

References

  1. Estoque, M. A., 1961, A Theoretical Investigation of the Sea Breeze, Quart. J. Roy. Meteorol. Soc., 19, 244-250
  2. Steyn, D. G., 1997, Interactions between the Thermal Internal Boundary Layer and Sea Breezes, in Proceedings of the EURASAP Workshop on the Determination of the Mixing Height: Current Progress and Problems, Riso, Denmark, 137-140
  3. 김용상, 오재호, 차주완, 서애숙, 1999, 국지규모 기상 분석 시스템(LAPS)의 한반도 적용 및 시험, 기상연구논문집, 52-62
  4. Andersson, E., J. Pailleux, J. N. Thepaut, J. R. Eyre, A P. McNally, G. A. Kelly and P. Courtier, 1994, Use of cloud-clear radiances in three/four-dimension variational data assimilation, Quart. J. Roy. Meteor. Soc., 120, 627-653 https://doi.org/10.1002/qj.49712051707
  5. Mo, K. C., X. L. Wang, R. Kistler, M. Kanamitsu and E. Kalnay, 1995, Impact of satellite data on the CDAS-reanalysis system, Mon. Wea. Rev., 123, 124-139 https://doi.org/10.1175/1520-0493(1995)123<0124:IOSDAT>2.0.CO;2
  6. Zupanski, D., 1997, A general weak constraint applicable to operational 4DVAR data assimilation systems, Mon. Wea. Rev., 125, 2274-2292 https://doi.org/10.1175/1520-0493(1997)125<2274:AGWCAT>2.0.CO;2
  7. Leslie, L. M., J. F. Marshall, R. P. Morision, C. Spinose, R. J. Purser, N. Pescod and R. Seecamp, 1998, Improved hurricane track forecasting from the continuous assimilation of high quality satellite wind data, Mon. Wea. Rev., 126, 1248-1257 https://doi.org/10.1175/1520-0493(1998)126<1248:IHTFFT>2.0.CO;2
  8. 박세영, 이태영, 신현철, 주상원, 2004, 전지구 3차원 변분자료동화에 있어 QuicSCAT 해상풍 자료의 효과, 한국기상학회 봄철 학술발표회, 244-245
  9. 과학기술부, 2000, 한반도 국지기상 자료동화 기법 개발, 89pp
  10. McGinley, J. A., 1995, Opportunities for high resolution data analysis, prediction and product dissemination within the local weather office. Preprints. 14th Conf. on Weather Analysis and Forecasting, Dallas. TX, Amer. Meteor. Soc., 478-485
  11. Albers, S. C., 1995, The LAPS wind analysis, Wea. Forecasting, 10, 342-352 https://doi.org/10.1175/1520-0434(1995)010<0342:TLWA>2.0.CO;2
  12. Albers, S. C., J. A. McGinley, D. L. Birkenheour and J. R. Smart, 1996, The Local Analysis and Prediction System; Analysis of clouds, precipitation and temperature, Wea. Forecasting, 11, 273-287 https://doi.org/10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;2
  13. 김용상, 최준태, 이용회, 오재호, 2001, 제주 지역에 접합한 중규모 단시간 예측 시스템의 개발, 한국지구과학회지, 22(3), 186-194
  14. Barnes, S. L., 1964, A technique for maximizing details in numerical weather map analysis, J. Appli. Meteor., 3, 369-409
  15. Lewis, J. M., 1971, Variational subsynoptic analysis with applications to severe local storms, Mon. Wea. Rev., 99, 786-795 https://doi.org/10.1175/1520-0493(1971)099<0786:VSAWAT>2.3.CO;2
  16. Richard, E., P. Mascart and E. Nickerson, 1989, The role of surface friction in downslope wind- storms, J. Appli. Meteo., 28, 241-251 https://doi.org/10.1175/1520-0450(1989)028<0241:TROSFI>2.0.CO;2
  17. 김용상, 오재호, 2000, 위성휘도온도 자료를 이용한 지상온도 분석장의 개선, The Journal of KOREA Analysis Society, 2(4), 455-463
  18. Groves, J. R., 1989, A practical soil moisture profile model, Water Resources Bulletin, 25, 875-880 https://doi.org/10.1111/j.1752-1688.1989.tb05403.x
  19. 김용상, 장태규, 박옥란, 2001, 3차원 구름 분석 알고리즘의 개발 및 응용, 한국자료분석학회지, 3(1), 97-106
  20. Dudhia, J., 1993, A nonhydrostatic version of the penn state/NCAR mesoscale model: validation tests and simulation of an Atlantic cyclone and cold front, Mon. Wea. Rev., 121, 1493-1513 https://doi.org/10.1175/1520-0493(1993)121<1493:ANVOTP>2.0.CO;2
  21. Stauffer, D. R. and N. L. Seaman, 1994, Multiscale four-dimensional data assimilation, J. Appl. Meteor., 33, 416-434 https://doi.org/10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2