A space-time typhoon trajectories analysis in the vicinity of Taiwan

  • Yuan-Chien Lin
  • Tsang-Jung Chang
  • Mong-Ming Lu
  • Hwa-Lung Yu
Original Paper


Tropical cyclones are one of the most serious natural disasters in northwestern Pacific Ocean. In general, an average of three to four typhoons invades the vicinity of Taiwan annually, which brings heavy rainfalls and strong winds resulting in disasters including flooding, mudflows, and landslides, leading to severe damage to economies and casualties. Studies show that different tracks of typhoon can cause distinct spatio-temporal patterns of rainfall events at different regions of Taiwan. As a result, understanding the trajectories of tropical cyclones and their relationship to climatic variables at global scale is crucial for hydrological modeling and disaster migration in Taiwan, especially under the conditions of climate change. This study applied a probabilistic curve clustering technique, which is based on a regression mixture model, to classify the best tracks of typhoons across the area within 6° around Taiwan during the period of 1951–2009. For the purposes of modeling and forecasting the typhoon trajectories, the track cluster is performed separately in different seasons due to their distinct driving forces to typhoon movements. A generalized linear model (GLM) is used to characterize the relationship between the identified typhoon tracks and the dominant climate features derived from NCEP reanalysis data. Results showed the six major typhoon tracks in the vicinity of Taiwan for different seasons respectively. The result of GLM cross validation showed that the frequency of typhoon tracks passing cross Taiwan in summer can significantly depend upon with two empirical orthogonal functions (EOFs) of sea level pressure, and the third EOF of sea surface temperature.


Typhoon Generalized linear model Trajectory modeling 



Data in this study were provided by NOAA/OAR/ESRL PSD, National Centers for Environmental Prediction (NCEP), and Japan Meteorological Agency (JMA). The MATLAB Curve Clustering Toolbox (CCtoolbox) was provided by Scott Gaffney. This study was supported by funds from Taiwan National Science Council (NSC 101-2628-E-002-017-MY3 and NSC 102-2221-E-002-140-MY3), and Central Weather Bureau in Taiwan.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yuan-Chien Lin
    • 1
  • Tsang-Jung Chang
    • 1
  • Mong-Ming Lu
    • 2
  • Hwa-Lung Yu
    • 1
  1. 1.Bioenvironmental Systems EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Central Weather BureauTaipeiTaiwan

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