Chu, P.-S.P.-S.ChuZhao, X.X.ZhaoHo, C.-H.C.-H.HoKim, H.-S.H.-S.KimLU MONG-MINGKim, Joo-HongJoo-HongKim2020-02-252020-02-252010https://scholars.lib.ntu.edu.tw/handle/123456789/463552A new approach to forecasting regional and seasonal tropical cyclone (TC) frequency in the western North Pacific using the antecedent large-scale environmental conditions is proposed. This approach, based on TC track types, yields probabilistic forecasts and its utility to a smaller region in the western Pacific is demonstrated. Environmental variables used include the monthly mean of sea surface temperatures, sea level pressures, low-level relative vorticity, vertical wind shear, and precipitable water of the preceding May. The region considered is the vicinity of Taiwan, and typhoon season runs from June through October. Specifically, historical TC tracks are categorized through a fuzzy clustering method into seven distinct types. For each cluster, a Poissonor probit regression model cast in the Bayesian framework is applied individually to forecast the seasonal TC activity. With a noninformative prior assumption for the model parameters, and following Chu and Zhao for the Poisson regression model, a Bayesian inference for the probit regression model is derived. A Gibbs sampler based on the Markov chain Monte Carlo method is designed to integrate the posterior predictive distribution. Because cluster 5 is the most dominant type affecting Taiwan, a leave-one-out cross-validation procedure is applied to predict seasonal TC frequency for this type for the period of 1979-2006, and the correlation skill is found to be 0.76. © 2010 American Meteorological Society.[SDGs]SDG13[SDGs]SDG14Bayesian forecasting; Bayesian frameworks; Bayesian inference; Cross validation; Environmental conditions; Environmental variables; Fuzzy clustering method; Gibbs samplers; Leave-one-out; Markov chain Monte Carlo method; Model parameters; New approaches; Non-informative prior; Poisson regression models; Precipitable water; Predictive distributions; Probabilistic forecasts; Regression model; Relative vorticity; Sea level pressure; Sea surface temperatures; Tropical cyclone; Typhoon activity; Vertical wind shear; Western North Pacific; Western Pacific; Atmospheric temperature; Bayesian networks; Forecasting; Fuzzy clustering; Fuzzy systems; Hurricanes; Inference engines; Markov processes; Monte Carlo methods; Poisson distribution; Sea level; Storms; Regression analysis; Bayesian analysis; climate prediction; forecasting method; fuzzy mathematics; Markov chain; Monte Carlo analysis; probability; regression analysis; sea level pressure; sea surface temperature; tropical cyclone; vorticity; weather forecasting; wind shear; Pacific Ocean; Pacific Ocean (North); Taiwanjournal article10.1175/2010JCLI3710.12-s2.0-79251577391https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251577391&doi=10.1175%2f2010JCLI3710.1&partnerID=40&md5=d5a48fc9514ca177142218c351d0a29b