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Korean Meteorological Society (한국기상학회)
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Development of the Selected Multi-model Consensus Technique for the Tropical Cyclone Track Forecast in the Western North Pacific

태풍 진로예측을 위한 다중모델 선택 컨센서스 기법 개발

  • Jun, Sanghee (National Typhoon Center, Korea Meteorological Administration) ;
  • Lee, Woojeong (National Typhoon Center, Korea Meteorological Administration) ;
  • Kang, KiRyong (National Typhoon Center, Korea Meteorological Administration) ;
  • Yun, Won-Tae (National Typhoon Center, Korea Meteorological Administration)
  • Received : 2015.02.10
  • Accepted : 2015.06.15
  • Published : 2015.06.30
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Abstract

A Selected Multi-model CONsensus (SMCON) technique was developed and verified for the tropical cyclone track forecast in the western North Pacific. The SMCON forecasts were produced by averaging numerical model forecasts showing low 70% latest 6 h prediction errors among 21 models. In the homogeneous comparison for 54 tropical cyclones in 2013 and 2014, the SMCON improvement rate was higher than the other forecasts such as the Non-Selected Multi-model CONsensus (NSMCON) and other numerical models (i.e., GDAPS, GEPS, GFS, HWRF, ECMWF, ECMWF_H, ECMWF_EPS, JGSM, TEPS). However, the SMCON showed lower or similar improvement rate than a few forecasts including ECMWF_EPS forecasts at 96 h in 2013 and at 72 h in 2014 and the TEPS forecast at 120 h in 2013. Mean track errors of the SMCON for two year were smaller than the NSMCON and these differences were 0.4, 1.2, 5.9, 12.9, 8.2 km at 24-, 48-, 72-, 96-, 120-h respectively. The SMCON error distributions showed smaller central tendency than the NSMCON's except 72-, 96-h forecasts in 2013. Similarly, the density for smaller track errors of the SMCON was higher than the NSMCON's except at 72-, 96-h forecast in 2013 in the kernel density estimation analysis. In addition, the NSMCON has lager range of errors above the third quantile and larger standard deviation than the SMCON's at 72-, 96-h forecasts in 2013. Also, the SMCON showed smaller bias than ECMWF_H for the cross track bias. Thus, we concluded that the SMCON could provide more reliable information on the tropical cyclone track forecast by reflecting the real-time performance of the numerical models.

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References

  1. Aemisegger, F., 2009: Tropical cyclone forecast verification: three approaches to the assessment of the ECMWF model. ETH, 89 pp.
  2. Burton, A., P. Caroff, J. Franklin, E. Fukada, T. C. Lee, C. Sampson, and T. Smith, 2006: Sharing experiences in operational consensus forecasting. Proc. Sixth WMO Int. Workshop on Tropical Cyclones, Topic 3a, San Jose, Costa Rica, WMO, 424-441.
  3. Cangialosi, J. P., and J. L. Franklin, 2015: 2012 National Hurricane Center forecast verification report. NHC, 82 pp [Available online at http://www.nhc.noaa.gov/verification/pdfs/Verification_2014.pdf.].
  4. Carr III, L. E., and R. L. Elsberry, 2000a: Dynamical tropical cyclone track forecast errors. Part I: tropical region error sources. Wea. Forecasting, 15, 647-661.
  5. Carr III, L. E., and R. L. Elsberry, 2000b: Dynamical tropical cyclone track forecast errors. Part II: midlatitude circulation influences. Wea. Forecasting, 15, 662-681. https://doi.org/10.1175/1520-0434(2000)015<0662:DTCTFE>2.0.CO;2
  6. Carr III, L. E., and R. L. Elsberry, 2001: Beta test of the systematic approach expert system prototype as a tropical cyclone track forecasting aid. Wea. Forecasting, 16, 355-368. https://doi.org/10.1175/1520-0434(2001)016<0355:BTOTSA>2.0.CO;2
  7. 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
  8. Elsberry, R. L., and L. E. Carr III, 2000: Consensus of dynamical tropical cyclone track forecasts-errors versus spread. Mon. Wea. Rev., 128, 4131-4138. https://doi.org/10.1175/1520-0493(2000)129<4131:CODTCT>2.0.CO;2
  9. Goerss, J. S., 2000: Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Wea. Rev., 128, 1187-1193. https://doi.org/10.1175/1520-0493(2000)128<1187:TCTFUA>2.0.CO;2
  10. Goerss, J. S., and C. R. Sampson, 2004: A history of western North Pacific tropical cyclone track forecast skill. Wea. Forecasting, 19, 633-638. https://doi.org/10.1175/1520-0434(2004)019<0633:AHOWNP>2.0.CO;2
  11. Heming, J., and J. Goerss, 2010: Track and structure forecasts of tropical cyclones. Global perspectives on tropical cyclones, Chap.10, Chan, J. C. L., and J. D. Kepert (Ed), World Scientific Publishing, Singapore, 287-323.
  12. Jun, S., W. Lee, K. Kang, K. Y. Byun, J. Kim, and W. T. Yun, 2015: Applicability of the superensemble to the tropical cyclone track forecasts in the western North Pacific. Asia-Pac. J. Atmos. Sci., 51, 39-48. https://doi.org/10.1007/s13143-014-0058-x
  13. Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, E. Williford, S. Gadgil, and S. Surendran, 2000: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13, 4196-4216. https://doi.org/10.1175/1520-0442(2000)013<4196:MEFFWA>2.0.CO;2
  14. Lee, Y., H. J. Kwon, and D.-C. Joo, 2011: Dynamic database Typhoon Track Prediction (DYTRAP). Atmosphere, 21, 209-220 (in Korean with English abstract).
  15. Neumann, C. J., and J. M. Pelissier, 1981: Models for the prediction of tropical cyclone motions over the North Atlantic: An operational evaluation. Mon. Wea. Rev., 109, 522-538. https://doi.org/10.1175/1520-0493(1981)109<0522:MFTPOT>2.0.CO;2
  16. NHC, 2009: Technical Summary of the National Hurricane Center Track and Intensity Models. NHC, 18 pp [Available online at http://www.nhc.noaa.gov/pdf/model_summary_20090724.pdf.].
  17. Qi, L., H. Yu, and P. Chen, 2013: Selective ensemble-mean technique for tropical cyclone track forecast by using ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 140, 805-813.
  18. Sampson, C. R., J. S. Goerss, and H. C. Weber, 2006: Operational performance of a new barotropic model (WBAR) in the western Pacific basin. Wea. Forecasting, 21, 656-662. https://doi.org/10.1175/WAF939.1
  19. Sampson, C. R., J. A. Knaff, and E. M. Fukada, 2007: Operational evaluation of a selective consensus in the western North Pacific baison. Wea. Forecasting, 22, 671-675. https://doi.org/10.1175/WAF991.1
  20. Sinnott, R. W., 1984: Virtues of the Harversine. Sky Telescpe, 68, 159-161.
  21. Wilks, D. S., 2006: Statistical methods in the atmospheric sciences. Academic press, 627 pp.
  22. Williford, C. E., T. N. Krishnamurti, R. Correa Torres, S. Cocke, Z. Christidis, and T. S. Vijaya Kumar, 2003: Real-time multimodel superensemble forecasts of Atlantic tropical systems of 1999. Mon. Wea. Rev., 131, 1878-1894. https://doi.org/10.1175//2571.1
  23. WMO, 2007: Sixth WMO International Workshop on Tropical Cyclone (IWTC-VI). WMO, 92 pp [Available online at http://www.aoml.noaa.gov/hrd/Landsea/WWRP2007_1_IWTC_VI.pdf.].
  24. Yu, H., P. Chen, Q. Li, and B. Tang, 2013: Current capability of operational numerical models in predicting tropical cyclone intensity in the western North Pacific. Wea. Forecasting, 28, 353-367. https://doi.org/10.1175/WAF-D-11-00100.1

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