Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Validations of Typhoon Intensity Guidance Models in the Western North Pacific

북서태평양 태풍 강도 가이던스 모델 성능평가

  • Oh, You-Jung (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University) ;
  • Moon, Il-Ju (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University) ;
  • Kim, Sung-Hun (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University) ;
  • Lee, Woojeong (National Typhoon Center, Korea Meteorological Administration) ;
  • Kang, KiRyong (National Typhoon Center, Korea Meteorological Administration)
  • 오유정 (제주대학교 태풍연구센터/해양기상학협동과정) ;
  • 문일주 (제주대학교 태풍연구센터/해양기상학협동과정) ;
  • 김성훈 (제주대학교 태풍연구센터/해양기상학협동과정) ;
  • 이우정 (기상청 국가태풍센터) ;
  • 강기룡 (기상청 국가태풍센터)
  • Received : 2015.08.03
  • Accepted : 2016.01.25
  • Published : 2016.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

Keywords

References

  1. Choi, E. S., and I. J. Moon, 2008: The variation of extreme values in the precipitation and wind speed during 56 years in korea. Atmosphere, 18, 397-416.
  2. DeMaria, M., M. Mainelli, L. K. Shay, J. A. Knaff, and J. Kaplan, 2005: Further improvements to the statistical hurricane intensity prediction scheme (SHIPS). Wea. Forecasting, 20, 531-543. https://doi.org/10.1175/WAF862.1
  3. Elsberry, R. L., 2014: Advances in research and forecasting of tropical cyclones from 1963-2013. Asia-Pac. J. Atmos. Sci., 50, 3-16. https://doi.org/10.1007/s13143-014-0001-1
  4. Kim, H. S., J. H. Kim, C. H. Ho, and P. S. Chu, 2011: Pattern classification of typhoon tracks using the fuzzy c-means clustering method. J. Climate, 24, 488-508. https://doi.org/10.1175/2010JCLI3751.1
  5. Knaff, J., C. R. Sampson, and M. DeMaria, 2005: An operational statistical typhoon intensity prediction scheme for the western north pacific. Wea. Forecasting, 20, 688-699. https://doi.org/10.1175/WAF863.1
  6. Lee, D. K., D. E. Jang, and T. K. Wee, 1992: Typhoons approaching korea, 1960-1989. Part I: Statistics and synoptic overview. Atmosphere, 28, 133-147.
  7. Lee, J. W., S. W. Lee, S. O. Han, S. J. Lee, and D. E. Jang, 2011: The impact of satellite observations on the UM-4DVar analysis and prediction system at KMA. Atmosphere, 21, 85-93.
  8. Lee, Y. J., H. J. Kwon, and D. C. Joo, 2011: Dynamic database typhoon track prediction (DYTRAP). Atmosphere, 21, 209-220.
  9. Nakagawa, M., 2009: Outline of the high resolution global model at the japan meteorological agency. RSMC Tokyo-Typhoon Center Technical Rev., 11, 1-13.
  10. National Centers for Environmental Prediction (NCEP), 2003: The GFS atmospheric model. NCEP Office Note 442, NCEP Modeling Center, 14 pp.
  11. National Emergency Management Agency (NEMA), 2011: Annual Disaster Report, NEMA, 986 pp.
  12. National Typhoon Center-Korea Meteorological Administration (NTC-KMA), 2011: Typhoon white book. NTC-KMA, 342 pp.
  13. National Typhoon Center-Korea Meteorological Administration (NTC-KMA), 2013: Analysis report for the affecting typhoons to korea peninsula (2013). NTC-KMA, 120 pp.
  14. National Typhoon Center-Korea Meteorological Administration (NTC-KMA), 2014: Analysis report for the affecting typhoons to korea peninsula (2014). NTC-KMA, 83 pp.
  15. Park, S. Y., S.-W. Joo, H. C. Shin, and J. Y. Cho, 2008: Coupling of typhoon bogusing for KMA WRF. Proc. Spring Meeting of KMS, 2008, 260-261.
  16. Rogers, R., and Coauthors, 2006: The intensity forecasting experiment : A NOAA mutiyear field program for improving tropical cyclone intensity forecasts. Bull. Amer. Meteor. Soc., 87, 1523-1537. https://doi.org/10.1175/BAMS-87-11-1523
  17. Simiu, E., and R. H. Scanlan, 1978: Wind effects on structures: an introduction to wind engineering. Wiley-Interscience, 458 pp.
  18. Vijay, T., and HWRF Team, 2014: Overview of the NCEP Operational HWRF modeling system. Joint EMC/DTC HWRF Tutorial, 47 pp.
  19. Yamaguchi, M., and T. Komori, 2009: Outline of the typhoon ensemble prediction system at the japan meteorological agency. RSMC Tokyo-Typhoon Center Technical Rev., 11, 14-24.
  20. Yamaguchi, M., T. Nakazawa, and K. Aonashi, 2012: Tropical cyclone track forecasts using JMA model with ECMWF and JMA initial conditions. Geophys. Res. Lett., 39, doi:10.1029/2012GL051473.