| dc.description.abstract | AbstractSubseasonal probabilistic prediction of tropical cyclone (TC) genesis is investigated here using models from the Seasonal to Subseasonal (S2S) Prediction dataset. Forecasts are produced for basin-wide TC occurrence at weekly temporal resolution. Forecast skill is measured using the Brier skill score relative to a seasonal climatology that varies monthly through the TC season. Skill depends on models? characteristics, lead time, and ensemble prediction design. Most models show skill for week 1 (days 1?7), the period when initialization is important. Among the six S2S models examined here, the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the best performance, with skill in the Atlantic, western North Pacific, eastern North Pacific, and South Pacific at week 2. Similarly, the Australian Bureau of Meteorology (BoM) model is skillful in the western North Pacific, South Pacific, and across northern Australia at week 2. The Madden?Julian oscillation (MJO) modulates observed TC genesis, and there is a relationship, across models and lead times, between models? skill scores and their ability to accurately represent the MJO and the MJO?TC relation. Additionally, a model?s TC climatology also influences its performance in subseasonal prediction. The dependence of the skill score on the simulated climatology, MJO, and MJO?TC relationship, however, varies from one basin to another. Skill scores increase with the ensemble size, as found in previous weather and seasonal prediction studies. | |
