Climate Dynamics

, Volume 48, Issue 1–2, pp 71–88 | Cite as

Dynamical–statistical seasonal prediction for western North Pacific typhoons based on APCC multi-models

  • Ok-Yeon KimEmail author
  • Hye-Mi Kim
  • Myong-In Lee
  • Young-Mi Min


This study aims at predicting the seasonal number of typhoons (TY) over the western North Pacific with an Asia-Pacific Climate Center (APCC) multi-model ensemble (MME)-based dynamical–statistical hybrid model. The hybrid model uses the statistical relationship between the number of TY during the typhoon season (July–October) and the large-scale key predictors forecasted by APCC MME for the same season. The cross validation result from the MME hybrid model demonstrates high prediction skill, with a correlation of 0.67 between the hindcasts and observation for 1982–2008. The cross validation from the hybrid model with individual models participating in MME indicates that there is no single model which consistently outperforms the other models in predicting typhoon number. Although the forecast skill of MME is not always the highest compared to that of each individual model, the skill of MME presents rather higher averaged correlations and small variance of correlations. Given large set of ensemble members from multi-models, a relative operating characteristic score reveals an 82 % (above-) and 78 % (below-normal) improvement for the probabilistic prediction of the number of TY. It implies that there is 82 % (78 %) probability that the forecasts can successfully discriminate between above normal (below-normal) from other years. The forecast skill of the hybrid model for the past 7 years (2002–2008) is more skillful than the forecast from the Tropical Storm Risk consortium. Using large set of ensemble members from multi-models, the APCC MME could provide useful deterministic and probabilistic seasonal typhoon forecasts to the end-users in particular, the residents of tropical cyclone-prone areas in the Asia-Pacific region.


Seasonal tropical cyclones Western North Pacific Multimodel ensemble Deterministic and probabilistic forecasts 



This research was supported by the APEC Climate Center. The authors acknowledge the APCC MME Producing Centres for making their hindcast/forecast data available for analysis and the APEC Climate Center for collecting and archiving them and for organizing APCC MME prediction. The single-model and multimodel predictions used in the paper are available at the APCC Data Service System (ADSS, However, some of models cannot be redistributed through the APCC due to their security issues (e.g., JMA).


  1. Alessandri A, Borrelli A, Gualdi S, Scoccimarro E, Masina S (2011) Tropical cyclone count forecasting using a dynamical seasonal prediction system: sensitivity to improved ocean initialization. J Clim 24:2963–2982CrossRefGoogle Scholar
  2. Bell GD et al (2000) Climate assessment for 1999. Bull Am Meteorol Soc 81:S1–S50CrossRefGoogle Scholar
  3. Briegel LM, Frank WM (1997) Large-scale influences on tropical cyclogenesis in the western North Pacific. Mon Weather Rev 125:1397–1413. doi: 10.1175/1520-0493(1997)125,1397:LSIOTC.2.0.CO;2 CrossRefGoogle Scholar
  4. Camargo SJ, Barnston AG (2009) Experimental dynamical seasonal forecasts of tropical cyclone activity at IRI. Weather Forecast 24:472–491CrossRefGoogle Scholar
  5. Camargo SJ, Sobel AH (2005) Western North Pacific tropical cyclone intensity and ENSO. J Clim 18:2996–3006CrossRefGoogle Scholar
  6. Camargo SJ, Zebiak SE (2002) Improving the detection and tracking of tropical storms in atmospheric general circulation models. Weather Forecast 17:1152–1162CrossRefGoogle Scholar
  7. Camargo SJ, Barnston AG, Klotzbach PJ, Landsea CW (2007a) Seasonal tropical cyclone forecasts. WMO Bull 56:297–309Google Scholar
  8. Camargo SJ, Robertson AW, Gaffney SJ, Smyth P, Ghil M (2007b) Cluster analysis of typhoon tracks. Part I: general properties. J Clim 20:3635–3653CrossRefGoogle Scholar
  9. Camargo SJ, Robertson AW, Gaffney SJ, Smyth P, Ghil M (2007c) Cluster analysis of typhoon tracks: part II: largescale circulation and ENSO. J Clim 20:3654–3676CrossRefGoogle Scholar
  10. Chan JCL (2000) Tropical cyclone activity over the western North Pacific associated with El Niño and La Niña Events. J Clim 13:2960–2972CrossRefGoogle Scholar
  11. Chan JCL, Liu KS (2004) Global warming and western North Pacific typhoon activity from an observational perspective. J Clim 17:4590–4602CrossRefGoogle Scholar
  12. Chan JCL, Shi JE, Lam CM (2001) Improvements in the seasonal forecasting of tropical cyclone activity over the western North Pacific. Weather Forecast 16:997–1004Google Scholar
  13. Choi KS, Moon IJ (2012) Influence of the Western Pacific teleconnection pattern on Western North Pacific tropical cyclone activity. Dyn Atmos Oceans 57:1–16CrossRefGoogle Scholar
  14. Chu J-H, Sampson CR, Levine AS, Fukada E (2002) The joint typhoon warning center tropical cyclone best-tracks, 1945–2000. Naval Research Laboratory.
  15. Chu P-S, Zhao X, Lee CT, Lu MM (2007) Climate prediction of tropical cyclone activity in the vicinity of Taiwan using the multivariate least absolute deviation regression method. Terr Atmos Ocean Sci 18:805–825CrossRefGoogle Scholar
  16. Davis CE, Hyde JE, Bangdiwala SI, Nelson JJ (1986) An example of dependencies among variables in a conditional logistic regression. In: Moolgavkar SH, Prentice RL (eds) Modern statistical methods in chronic disease epidemiology. Wiley, New York, pp 140–147Google Scholar
  17. DelSole T, Nattala J, Tippett MK (2014) Skill improvement from increased ensemble size and model diversity. Res Lett, Geophys. doi: 10.1002/2014GL060133 Google Scholar
  18. Goh AZC, Chan JCL (2010) An improved statistical scheme for the prediction of tropical cyclones making landfall in South China. Weather Forecast 25:587–593CrossRefGoogle Scholar
  19. Goh AZC, Chan JCL (2012) Variations and prediction of the annual number of tropical cyclone affecting Korea and Japan. Int J Climatol 32:178–189CrossRefGoogle Scholar
  20. Gray WM (1977) Tropical cyclone genesis in the western North Pacific. J Meteorol Soc Jpn 55:465–482Google Scholar
  21. Gray WM, Landsea CW, Mielke PW, Berry KJ (1992) Predicting Atlantic seasonal hurricane activity 6–11 months in advance. Weather Forecast 7:440–455CrossRefGoogle Scholar
  22. Gray WM, Landsea CW, Mielke PW, Berry KJ (1993) Predicting Atlantic basin seasonal tropical cyclone activity by 1 August. Weather Forecast 8:73–86CrossRefGoogle Scholar
  23. Gray WM, Landsea CW, Mielke PW, Berry KJ (1994) Predicting Atlantic basin seasonal tropical cyclone activity by 1 June. Weather Forecast 9(103):115Google Scholar
  24. Hagedorn R, Doblas-Reyes F, Palmer RN (2005) The rationale behind the success of multimodel ensembles in seasonal forecasting—I. Basic concept. Tellus 57A:219–233CrossRefGoogle Scholar
  25. Hanley D, Molinari J, Keyser D (2001) A composite study of the interaction between tropical cyclones and uppertropospheric troughs. Mon Weather Rev 129:2570–2584CrossRefGoogle Scholar
  26. Holland GJ (1995) Scale interactions in the western Pacific monsoon. Meteorol Atmos Phys 56:57–79CrossRefGoogle Scholar
  27. Jeong HI, Lee DY, Ashok K, Ahn J-B, Lee J-Y, Luo J-J, Schemm J-KE, Hendon HH, Braganza K, Ham Y-G (2012) Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter. Clim Dyn 39:475–493CrossRefGoogle Scholar
  28. Jin EK et al (2008) Current status of ENSO prediction skill in coupled ocean-atmosphere models. Clim Dyn 31:647–664. doi: 10.1007/s00382-008-0397-3 CrossRefGoogle Scholar
  29. Kanamitsu M, Ebisuzaki W, Woolen J, Yang SK, Hnilo JJ, Fiorni M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1643CrossRefGoogle Scholar
  30. Kharin VV, Zwiers FW (2001) Skill as function of time scale in ensemble of seasonal hindcast. Clim Dyn 17:127–141CrossRefGoogle Scholar
  31. Kim H-M, Webster PJ (2010) Extended-range seasonal hurricane forecasts for the North Atlantic with a hybrid dynamical–statistical model. Geophys Res Lett 37:L21705. doi: 10.1029/2010GL044792 Google Scholar
  32. Kim H-S, Ho C-H, Chu P-S, Kim J-H (2010) Seasonal prediction of summertime tropical cyclone activity over the East China Sea using the least absolute deviation regression and the Poisson regression. Int J Climatol 30(2):210–219Google Scholar
  33. Kim H-M, Webster PJ, Curry JA (2011) Modulation of North Pacific tropical cyclone activity by three phases of ENSO. J Clim 24:1839–1849. doi: 10.1175/2010JCLI3939.1 CrossRefGoogle Scholar
  34. Kim H-S, Ho C-H, Kim J-H, Chu P-S (2012) Track-pattern-based model for seasonal prediction of tropical cyclone activity in the Western North Pacific. J Clim 25:4660–4678CrossRefGoogle Scholar
  35. Kim H-M, Lee MI, Webster PJ, Kim D, Yoo J (2013) A physical basis for the probabilistic prediction of the accumulated tropical cyclone kinetic energy in the Western North Pacific. J Clim 26:7981–7991CrossRefGoogle Scholar
  36. Kim HM, Chang EK, Zhang M (2015) Statistical-dynamical seasonal forecast for tropical cyclones affecting New York State. Weather Forecast. doi: 10.1175/WAF-D-14-00089.1 Google Scholar
  37. Klotzbach PJ, Barnston AG, Bell G, Camargo SJ, Chan JCL, Lea A, Saunders M, Vitart F (2012) Seasonal forecasting of tropical cyclones. In: Guard C (ed) Global guide to tropical cyclone forecasting, 2nd edn. World Meteorological OrganizationGoogle Scholar
  38. Kug J-S, Kang I-S, Choi D-H (2008) Seasonal climate predictability with tier-one and tier-two prediction systems. Clim Dyn 31:403–416CrossRefGoogle Scholar
  39. Li X, Yang S, Wang H, Jia X, Kumar A (2013) A dynamical–statistical forecast model for the annual frequency of western Pacific tropical cyclones based on the NCEP climate forecast system version 2. J Geophys Res Atmos 118:12061–12074. doi: 10.1002/2013JD020708 CrossRefGoogle Scholar
  40. Lu M-M, Chu P-S, Lin Y-C (2010) Seasonal prediction of tropical cyclone activity near Taiwan using the Bayesian multivariate regression method. Weather Forecast 25:1780–1795CrossRefGoogle Scholar
  41. Min YM, Kryjov VN, Park C-K (2009) A probabilistic multimodel ensemble approach to seasonal prediction. Weather Forecast 24:812–828CrossRefGoogle Scholar
  42. Min YM, Kryjov VN, Oh SM (2014) Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: retrospective (1983–2003) and real-time forecasts (2008–2013). J Geophys Res 119:12132–12150CrossRefGoogle Scholar
  43. Molinari J, Vollaro D (2013) What percentage of western North Pacific tropical cyclones form within the monsoon trough? Mon Weather Rev 141:499–505CrossRefGoogle Scholar
  44. O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690CrossRefGoogle Scholar
  45. Palmer TN, Doblas-Reyes FJ, Hagedorn R, Alessandri S, Gualdi S, Andersen U, Feddersen H, Cantelaube P, Terres J-M, Davey M, Graham R, Délécluse P, Lazar A, Déqué M, Guérémy J-F, Díez E, Orfila B, Hoshen M, Morse AP, Keenlyside N, Latif M, Maisonnave E, Rogel P, Marletto V, Thomson MC (2004) Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull Am Meteorol Soc 85:853–872CrossRefGoogle Scholar
  46. Park S, Choi K, Hyun Y, Kim T (2011) Seasonal prediction system for typhoon genesis frequency and track patterns at national typhoon center/KMA. In: The 4th China-Korea joint tropical cyclone workshop, Shanghai, China, 18–24 December, 2011, Abstract 7–14, Shanghai typhoon institute, Shanghai, ChinaGoogle Scholar
  47. Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625CrossRefGoogle Scholar
  48. Ritchie EA, Holland GJ (1999) Large-scale patterns associated with tropical cyclogenesis in the western Pacific. Mon Weather Rev 127:2027–2043CrossRefGoogle Scholar
  49. Sadler JC (1976) A role of the tropical upper tropospheric trough in early season typhoon development. Mon Weather Rev 104:1266–1278CrossRefGoogle Scholar
  50. Sadler JC (1978) Mid-season typhoon development and intensity changes and the tropical upper tropospheric trough. Mon Weather Rev 106:1137–1152CrossRefGoogle Scholar
  51. Saha S et al (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208CrossRefGoogle Scholar
  52. Stowasser M, Wang Y, Hamilton K (2007) Tropical cyclone changes in the western North Pacific in a global warming scenario. J Clim 20:2378–2396CrossRefGoogle Scholar
  53. Sun JQ, Ahn JB (2011) A GCM-based forecasting model for the landfall of tropical cyclones in China. Adv Atmos Sci 28(5):1049–1055. doi: 10.1007/s00376-011-0122-8 CrossRefGoogle Scholar
  54. Titley DW, Elsberry RL (2000) Large intensity changes in tropical cyclones: a case study of supertyphoon Flo during TCM-90. Mon Weather Rev 128:3556–3573CrossRefGoogle Scholar
  55. Vecchi GA et al (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27:7994–8016CrossRefGoogle Scholar
  56. Villarini G, Vecchi GA, Knutson TR, Smith JA (2011) Is the recorded increase in short-duration North Atlantic tropical storms spurious? J Geophys Res 116:D10114. doi: 10.1029/2010JD015483 CrossRefGoogle Scholar
  57. Vitart F (2006) Seasonal forecasting of tropical storm frequency using a multi-model ensemble. Q J R Meteorol Soc 132:647–666CrossRefGoogle Scholar
  58. Wang B, Chan JCL (2002) How strong ENSO events affect tropical storm activity over the western North Pacific. J Clim 15:1643–1658CrossRefGoogle Scholar
  59. Wang B, Lee JY, Kang I-S, Shukla J, Park C-K, Kumar A, Schemm J, Cocke S, Kug J-S, Luo J-J, Zhou T, Wang B, Fu X, Yun W-T, Alves O, Jin EK, Kinter J, Kirtman B, Krishnamurti T, Lau NC, Lau W, Liu P, Pegion P, Rosati T, Schubert S, Stern W, Suarez M, Yamagata T (2009a) Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1984–2004). Clim Dyn 33:93–117CrossRefGoogle Scholar
  60. Wang H, Scheme JKE, Kumar A, Wang W, Long L, Chelliah M, Bell GD, Peng P (2009b) A statistical forecast model for Atlantic seasonal hurricane activity based on the NCEP dynamical seasonal forecast. J Clim 22:4481–4500CrossRefGoogle Scholar
  61. Weigel AP, Liniger MA, Appenzeller C (2008) Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q J R Meteorol Soc 134:241–260CrossRefGoogle Scholar
  62. Wilks DS (2006) Statistical methods in the atmospheric sciences. International Geophysics Series, 2nd edn, vol 91. Academic Press, pp 627Google Scholar
  63. Wu L, Wen Z, Huang R, Wu R (2012) Possible linkage between the monsoon trough variability and the tropical cyclone activity over the western North Pacific. Mon Weather Rev 140:140–150. doi: 10.1175/MWR-D-11-00078.1 CrossRefGoogle Scholar
  64. Yoshida R, Ishikawa H (2013) Environmental factors contributing to tropical cyclone genesis over the western North Pacific. Mon Weather Rev 141:451–467CrossRefGoogle Scholar
  65. Zhao H, Wu L, Zhou W (2011) Interannual changes of tropical cyclone intensity in the western North Pacific. J Meteorol Soc Jpn 89:243–253. doi: 10.2151/jmsj.2011-305 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ok-Yeon Kim
    • 1
    Email author
  • Hye-Mi Kim
    • 2
  • Myong-In Lee
    • 3
  • Young-Mi Min
    • 1
  1. 1.APEC Climate Center (APCC)BusanKorea
  2. 2.School of Marine and Atmospheric SciencesStony Brook UniversityNYUSA
  3. 3.School of Urban and Environmental EngineeringUNISTUlsanKorea

Personalised recommendations