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Mong-Ming Lu, Pao-Shin Chu, and Yun-Ching Lin

Abstract

A Poisson generalized linear regression model cast within a Bayesian framework is applied to forecast the seasonal tropical cyclone (TC) counts in the vicinity of Taiwan. The TC season considered is June–November and the data period used for model development is 1979–2007. A stepwise regression procedure is applied for predictor selection. Three large-scale climate variables, namely, relative vorticity at 850 hPa (Vor850), vertical wind shear, and sea level pressure over the western and central North Pacific from the antecedent May, are selected as predictors. Leave-one-out cross validation is performed and forecast skill is thoroughly evaluated. The skill level of the Bayesian regression model is better than what can be achieved by climatology and persistence methods. Most importantly, the Bayesian probabilistic inference can provide an uncertainty expression in the parameter estimation. Among the three predictors, Vor850 is found to be the most important because it reflects the variation of the ridge position of the westward extension of the western Pacific subtropical high. The model shows negative bias during the years with successive TCs, which are generated by easterly waves before approaching Taiwan. Recommendations for real-time operational forecast and future development are discussed.

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Sung-Hun Kim, Il-Ju Moon, and Pao-Shin Chu

Abstract

A statistical–dynamical model for predicting tropical cyclone (TC) intensity has been developed using a track-pattern clustering (TPC) method and ocean-coupled potential predictors. Based on the fuzzy c-means clustering method, TC tracks during 2004–12 in the western North Pacific were categorized into five clusters, and their unique characteristics were investigated. The predictive model uses multiple linear regressions, where the predictand or the dependent variable is the change in maximum wind speed relative to the initial time. To consider TC-ocean coupling effects due to TC-induced vertical mixing and resultant surface cooling, new potential predictors were also developed for maximum potential intensity (MPI) and intensification potential (POT) using depth-averaged temperature (DAT) instead of sea surface temperature (SST). Altogether, 6 static, 11 synoptic, and 3 DAT-based potential predictors were used. Results from a series of experiments for the training period of 2004–12 using TPC and DAT-based predictors showed remarkably improved TC intensity predictions. The model was tested on predictions of TC intensity for 2013 and 2014, which are not used in the training samples. Relative to the nonclustering approach, the TPC and DAT-based predictors reduced prediction errors about 12%–25% between 24- and 96-h lead time. The present model is also compared with four operational dynamical forecast models. At short leads (up to 24 h) the present model has the smallest mean absolute errors. After a 24-h lead time, the present model still shows skill that is comparable with the best operational models.

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Hui-Ling Chang, Barbara G. Brown, Pao-Shin Chu, Yu-Chieng Liou, and Wen-Ho Wang

Abstract

Focusing on afternoon thunderstorms in Taiwan during the warm season (May–October) under weak synoptic forcing, this study applied the Taiwan Auto-NowCaster (TANC) to produce 1-h likelihood nowcasts of afternoon convection initiation (ACI) using a fuzzy logic approach. The primary objective is to design more useful forecast products with uncertainty regions of predicted thunderstorms to provide nowcast guidance of ACI for forecasters. Four sensitivity tests on forecast performance were conducted to improve the usefulness of nowcasts for forecasters. The optimal likelihood threshold (Lt) for ACIs, which is the likelihood value that best corresponds to the observed ACIs, was determined to be 0.6. Because of the high uncertainty on the exact location or timing of ACIs in nowcasts, location displacement and temporal shifting of ACIs should be considered in operational applications. When a spatial window of 5 km and a temporal window of 18 min are applied, the TANC displays moderate accuracy and satisfactory discrimination with an acceptable degree of overforecasting. The nonparametric Mann–Whitney test indicated that the performance of the TANC substantially surpasses the competing Space and Time Multiscale Analysis System–Weather Research and Forecasting Model, which serves as a pertinent reference for short-range (0–6 h) forecasts at the Central Weather Bureau in Taiwan.

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