Statistical‐dynamical seasonal forecast of western North Pacific and East Asia landfalling tropical cyclones using the high‐resolution GFDL FLOR coupled model. (15th April 2016) - Zhang, Wei; Villarini, Gabriele; Vecchi, Gabriel A.; Murakami, Hiroyuki; Gudgel, Richard
Abstract: This study examines the seasonal prediction of western North Pacific [WNP) and East Asia landfalling tropical cyclones (TCs) using the Geophysical Fluid Dynamics Laboratory(GFDL) Forecast‐oriented Low Ocean Resolution version of CM2.5 with Flux Adjustment (FLOR‐FA) and finite‐mixture‐model (FMM)‐based statistical cluster analysis. Using the FMM‐based cluster analysis, seven clusters are identified from the historical and FLOR‐FA‐predicted TC tracks for the period 1980–2013. FLOR‐FA has significant skill in predicting year‐to‐year variations in the frequency of TCs within clusters 1 (recurving TCs) and 5 (straight‐moving TCs). By building Poisson regression models for each cluster using key predictors (i.e., sea surface temperature, 500 hPa geopotential height, and zonal vertical wind shear), the predictive skill for almost all the clusters at all initialization months improves with respect to the dynamic prediction. The prediction of total WNP TC frequency made by combining hybrid predictions for each of the seven clusters in the hybrid model shows skill higher than what achieved using the TC frequency directly from FLOR‐FA initialized from March to July. However, the hybrid predictions for total WNP TC frequency initialized from January to February exhibit lower skill than FLOR‐FA. The prediction of TC landfall over East Asia made by combining the hybrid models of TC frequency in each cluster and its landfall rate over East Asia also outperforms FLOR‐FA for all initialization months January through July. Key Points: Seven clusters are identified from the historical and predicted TC tracks for the period 1980–2013 The prediction of WNP TC frequency shows skill higher than FLOR‐FA in most of initialization months The prediction of TC landfall with hybrid models outperforms FLOR‐FA for all initialization months