Theoretical and Applied Climatology

, Volume 133, Issue 3–4, pp 973–983 | Cite as

A prediction scheme of tropical cyclone frequency based on lasso and random forest

  • Jinkai Tan
  • Hexiang Liu
  • Mengya Li
  • Jun WangEmail author
Original Paper


This study aims to propose a novel prediction scheme of tropical cyclone frequency (TCF) over the Western North Pacific (WNP). We concerned the large-scale meteorological factors inclusive of the sea surface temperature, sea level pressure, the Niño-3.4 index, the wind shear, the vorticity, the subtropical high, and the sea ice cover, since the chronic change of these factors in the context of climate change would cause a gradual variation of the annual TCF. Specifically, we focus on the correlation between the year-to-year increment of these factors and TCF. The least absolute shrinkage and selection operator (Lasso) method was used for variable selection and dimension reduction from 11 initial predictors. Then, a prediction model based on random forest (RF) was established by using the training samples (1978–2011) for calibration and the testing samples (2012–2016) for validation. The RF model presents a major variation and trend of TCF in the period of calibration, and also fitted well with the observed TCF in the period of validation though there were some deviations. The leave-one-out cross validation of the model exhibited most of the predicted TCF are in consistence with the observed TCF with a high correlation coefficient. A comparison between results of the RF model and the multiple linear regression (MLR) model suggested the RF is more practical and capable of giving reliable results of TCF prediction over the WNP.


Tropical cyclone Year-to-year increment Lasso Random Forest Prediction 



The authors are grateful to three anonymous reviewers for their constructive comments, which helped improve the representation and quality of this work. This research is jointly supported by the National Natural Science Foundation of China (Grant No. 41671095, 41665006, 41465003, 71373084, 5161101688) and the Shanghai Science and Technology Support Program (Grant No. 15DZ1207805).


  1. Chan JCL (2000) Tropical cyclone activity over the western North Pacific associated with El Niño and La Niña events. J Clim 13(16):2960–2972CrossRefGoogle Scholar
  2. Choi KS, Kim DW, Byun HR (2010) Statistical model for seasonal prediction of tropical cyclone frequency in the mid-latitudes of East Asia. Theor Appl Climatol 102(1):105–114CrossRefGoogle Scholar
  3. Demaria M (1996) The effect of vertical shear on tropical cyclone intensity change. J Atmos Sci 53(14):2076–2088CrossRefGoogle Scholar
  4. Efron B, Hastie T, Johnstone I et al (2004) Least angle regression. Ann Stat 32(2):407–499CrossRefGoogle Scholar
  5. Emanuel KA (1999) Thermodynamic control of hurricane intensity. Nature 401(6754):665–669CrossRefGoogle Scholar
  6. Emanuel K, Sobel A (2013) Response of tropical sea surface temperature, precipitation, and tropical cyclone-related variables to changes in global and local forcing. J Adv Model Earth Syst 5(2):447–458CrossRefGoogle Scholar
  7. Fan K (2007) New predictors and a new prediction model for the typhoon frequency over western North Pacific. Science China. Earth Sci 50(9):1417. doi: 10.1007/s11430-007-0105-x
  8. Fan K (2010) A prediction model for Atlantic named storm frequency using a year-by-year increment approach. Weather Forecast 25(6):1842–1851. doi: 10.1175/2010WAF2222406.1
  9. Fan K, Wang H (2009) A new approach to forecasting typhoon frequency over the western north pacific weather & forecasting. 24(4):974–986. doi: 10.1175/2009WAF2222194.1
  10. Frank W M (1982) Large-scale characteristics of tropical cyclones[J]. Mon Weather Rev 110(6):572–586. doi: 10.1175/1520-0493(1982)110<0572:LSCOTC>2.0.CO;2
  11. Fuentes MMPB, Abbs D (2010) Effects of projected changes in tropical cyclone frequency on sea turtles. Mar Ecol Prog 412(6):283–292CrossRefGoogle Scholar
  12. Geng H, Shi D, Zhang W et al (2016) A prediction scheme for the frequency of summer tropical cyclone landfalling over China based on data mining methods. Meteorol Appl 23(4):587–593CrossRefGoogle Scholar
  13. Gray W M (1968) Global view of the origin of tropical disturbances and storms. Mon Weather Rev 96(10):87. doi: 10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2
  14. Huang Y, Jin L (2013) A prediction scheme with genetic neural network and Isomap algorithm for tropical cyclone intensity change over western North Pacific. Meteorog Atmos Phys 121(3):143–152CrossRefGoogle Scholar
  15. Jaimes B, Shay LK, Uhlhorn EW (2015) Enthalpy and momentum fluxes during hurricane earl relative to underlying ocean features. Mon Weather Rev 143(1):111–131CrossRefGoogle Scholar
  16. Jin L, Yao C, Huang X (2006) An improved method on meteorological prediction modeling using genetic algorithm and artificial neural network. World congress on intelligent control and automation. IEEE 31–35Google Scholar
  17. Kurihara Y, Tuleya RE (1981) A numerical simulation study on the genesis of a tropical storm. Mon Weather Rev 109(8):1629CrossRefGoogle Scholar
  18. Kwon HJ, Lee W, Won S et al (2007) Statistical ensemble prediction of the tropical cyclone activity over the western North Pacific. Geophys Res Lett 34(24):497–507CrossRefGoogle Scholar
  19. Li RCY, Zhou W (2014) Interdecadal change in South China Sea tropical cyclone frequency in association with zonal sea surface temperature gradient. J Clim 27(14):5468–5480CrossRefGoogle Scholar
  20. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
  21. Liu H, Zhang DL, Chen J et al (2013) Prediction of tropical cyclone frequency with a wavelet neural network model incorporating natural orthogonal expansion and combined weights. Nat Hazards 65(1):63–78CrossRefGoogle Scholar
  22. Lu XY, Duan YH (2011) Characteristics of the tropical cyclogenesis in the summer monsoon trough. Acta Meteor Sin 69(6):990–1000Google Scholar
  23. Mccreary JP, Anderson DLT (2009) A simple model of El Niño and the southern oscillation. Mon Weather Rev 112(5):934CrossRefGoogle Scholar
  24. Park JY, Kug JS, Park J et al (2012) Relationship between interannual variability of phytoplankton and tropical cyclones in the western North Pacific. Ocean Polar Res 34(1):29–35CrossRefGoogle Scholar
  25. Peng M, Xie L, Pietrafesa LJ (2006) Tropical cyclone induced asymmetry of sea level surge and fall and its presentation in a storm surge model with parametric wind fields. Ocean Model 14(1–2):81–101CrossRefGoogle Scholar
  26. Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2):181–199CrossRefGoogle Scholar
  27. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
  28. Schade LR, Emanuel KA (2001) The ocean’s effect on the intensity of tropical cyclones: results from a simple coupled atmosphere-ocean model. J Atmos Sci 56(4):642–651CrossRefGoogle Scholar
  29. Schopf PS, Suarez MJ (1988) Vacillations in a coupled ocean–atmosphere model. J Atmos Sci 45(3):549–566CrossRefGoogle Scholar
  30. Shapiro LJ, Goldenberg SB (2010) Atlantic sea surface temperatures and tropical cyclone formation. J Clim 11(4):578–590CrossRefGoogle Scholar
  31. Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc 73(3):273–282CrossRefGoogle Scholar
  32. Vecchi GA, Soden BJ (2007) Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature 450(7172):1066–1070CrossRefGoogle Scholar
  33. Wu L, Duan J (2015) Extended simulation of tropical cyclone formation in the western North Pacific monsoon trough. J Atmos Sci 72(12):150918123147005CrossRefGoogle Scholar
  34. Zhou B, Cui X (2009) Modeling the influence of spring Hadley circulation on the summer tropical cyclone frequency in the western North Pacific. Chin J Geophys 52(6):1231–1236CrossRefGoogle Scholar
  35. Zong H, Wu L (2015) Synoptic-scale influences on tropical cyclone formation within the western North Pacific monsoon trough. Mon Weather Rev 143(9):150506105915009CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  • Jinkai Tan
    • 1
    • 2
  • Hexiang Liu
    • 3
    • 4
  • Mengya Li
    • 1
    • 2
  • Jun Wang
    • 1
    • 2
    Email author
  1. 1.Key Laboratory of Geographic Information Science (Ministry of Education)East China Normal UniversityShanghaiChina
  2. 2.School of Geographic SciencesEast China Normal UniversityShanghaiChina
  3. 3.Guangxi Key Laboratory of Marine Disaster in the Beibu GulfQinzhou UniversityQinzhouChina
  4. 4.College of Mathematics and Statistics SciencesGuangxi Teachers Education UniversityNanningChina

Personalised recommendations