Journal of Meteorological Research

, Volume 33, Issue 5, pp 837–850 | Cite as

Seasonal Climate Prediction Models for the Number of Landfalling Tropical Cyclones in China

  • Baoqiang Tian
  • Ke FanEmail author
Regular Article


Two prediction models are developed to predict the number of landfalling tropical cyclones (LTCs) in China during June-August (JJA). One is a statistical model using preceding predictors from the observation, and the other is a hybrid model using both the aforementioned preceding predictors and concurrent summer large-scale environmental conditions from the NCEP Climate Forecast System version 2 (CFSv2). (1) For the statistical model, the year-to-year increment method is adopted to analyze the predictors and their physical processes, and the JJA number of LTCs in China is then predicted by using the previous boreal summer sea surface temperature (SST) in Southwest Indonesia, preceding October South Australia sea level pressure, and winter SST in the Sea of Japan. The temporal correlation coefficient between the observed and predicted number of LTCs during 1983–2017 is 0.63. (2) For the hybrid prediction model, the prediction skill of CFSv2 initiated each month from February to May in capturing the relationships between summer environmental conditions (denoted by seven potential factors: three steering factors and four genesis factors) and the JJA number of LTCs is firstly evaluated. For the 2- and 1-month leads, CFSv2 has successfully reproduced these relationships. For the 4-, 3-, and 2-month leads, the predictor of geopotential height at 500 hPa over the western North Pacific (WNP) shows the worst forecasting skill among these factors. In general, the summer relative vorticity at 850 hPa over the WNP is a modest predictor, with stable and good forecasting skills at all lead times.

Key words

tropical cyclone Climate Forecast System version 2 (CFSv2) year-to-year increment prediction 


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  1. 1.Nansen-Zhu International Research Centre, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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