A space-time typhoon trajectories analysis in the vicinity of Taiwan
- 362 Downloads
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.
KeywordsTyphoon 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.
- Bilmes JA (1998) A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Int Comput Sci Inst 4(510):126Google Scholar
- Central Weather Bureau T (2014) Categorized paths of typhoons invading Taiwan? http://www.cwb.gov.tw/V7e/knowledge/encyclopedia/ty017.htm. Accessed 20 Jan 2014
- Dobson AJ (1999) An introduction to generalized linear models. Chapman & Hall, Boca RatonGoogle Scholar
- Filho I G C (2008). Mixture models for the analysis of gene expression: integration of multiple experiments and cluster validation. Doctoral dissertation, Freie Universität BerlinGoogle Scholar
- Fox J (2008) Applied regression analysis and generalized linear models. SAGE, Los AngelesGoogle Scholar
- Gaffney SJ (2004) Probabilistic curve-aligned clustering and prediction with regression mixture models. University of California, IrvineGoogle Scholar
- Huang J-C, Yu C-K, Lee J-Y, Cheng L-W, Lee T-Y, Kao S-J (2012) Linking typhoon tracks and spatial rainfall patterns for improving flood lead time predictions over a mesoscale mountainous watershed. Water Resour Res 48(9):W09540Google Scholar
- Japan Meteorological Agency J (2014). International Centers Operated by JMA. http://www.jma.go.jp/jma/en/Activities/intcorp.html. Accessed 20 Jan 2014
- Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471CrossRefGoogle Scholar