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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 Wang
Original Paper

Abstract

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.

Keywords

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

Notes

Acknowledgments

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).

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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
  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

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