Climate Prediction of Tropical Cyclone Activity in the Vicinity of Taiwan Using the Multivariate Least Absolute Deviation Regression Method

  • Author(s): Pao-Shin Chu, Xin Zhao, Ching-Teng Lee, and Mong-Ming Lu
  • DOI:


  • Keywords: LAD regression, Seasonal tropical cyclone forecasting, Taiwan, Cross-validation prediction skills, Climate forcings



In this study, a multivariate linear regression model is applied to predict the seasonal tropical cyclone (TC) count in the vicinity of Taiwan using large-scale climate variables available from the preceding May. Here the season encompasses the five-month period from June through October, when typhoons are most active in the study domain. The model is based on the least absolute deviation so that regression estimates are more resistant (i.e., not unduly influenced by outliers) than those derived from the ordinary least square method. Through lagged correlation analysis, five parameters (sea surface temperature, sea level pressure, precipitable water, low-level relative vorticity, and vertical wind shear) in key locations of the tropical western North Pacific are identified as predictor datasets. Results from crossvalidation suggest that the statistical model is skillful in predicting TC activity, with a correlation coefficient of 0.63 for 1970 - 2003. If more recent data are included, the correlation coefficient reaches 0.69 for 1970 - 2006. Relative importance of each predictor variable is evaluated. For predicting higher than normal seasonal TC activity, warmer sea surface temperatures, a moist troposphere, and the presence of a low-level cyclonic circulation coupled with low-latitude westerlies in the Philippine Sea in the antecedent May appear to be important.

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