In this study, an automated synoptic weather typing was employed to identity the weather types most likely associated with daily typhoon/typhoon-related heavy rainfall events for Chiayi, Taiwan. The synoptic weather typing was developed using principal components analysis, an average linkage clustering procedure, and discriminant function analysis. The classification results showed that the synoptic weather typing was successful at identifying typhoon-related weather types. Five synoptic weather types (Weather Types 1-5) were identified over the past 11-year period as the primary typhoon-related weather types. These five typhoon-related weather types can capture 34 out of 36 total typhoon-related heavy rainfall days (>50 mm/d) and all nine cases with typhoon-related daily rainfall >200 mm during the period March 1998-December 2008. This result suggests that synoptic weather typing can be useful to identify historical typhoon/typhoon-related heavy rainfall events. Moreover, the method has potential to assess climate change impacts on the frequency/intensity of future typhoon/typhoon-related heavy rainfall events using future downscaled GCM climate data.
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