A Hybrid Dynamical-Statistical Model for Advancing Subseasonal Tropical Cyclone Prediction Over the Western North Pacific

Tropical cyclone (TC) genesis prediction at the extended-range to subseasonal timescale (a week to several weeks) is a gap between weather and climate predictions. The current dynamical prediction systems and statistical models show limited skills in TC genesis forecasting at the lead time of 1–3 weeks. A hybrid dynamical-statistical model is developed that reveals capability in predicting basin-wide TC frequency in every 10-day period over the western North Pacific at a 25-day forecast lead, which is superior to the statistical and dynamical model-based predictions examined in this study. In this hybrid model, the cyclogenesis counts for different TC clusters are predicted, respectively, using the statistical models in which the large-scale predictors associated with intraseasonal oscillation evolutions are provided by a dynamical model. A probabilistic map of TC tracks at the subseasonal timescale is further predicted by incorporating the climatological probability of track distributions of these TC clusters.