Abstract: Predictions of tropical cyclone (TC) activity have been a topic of recurrent interest and research in the past. Here we utilize reanalysis datasets of sea Surface temperature (SST) and mixed layer depth (MLD) to build a statistical seasonal forecasting model that produces outlooks of expected TC counts in the region of the Southwest Pacific (SWP). Nevertheless, the model applicability can be extended to other regions and basins. A novel TC predictor index is developed at the daily scale and used to obtain an objective classification of synoptic weather patterns. This classification has been performed by clustering the daily index predictor fields, previously transformed into principal components, using a K-mean algorithm. As a result, 49 daily weather types (DWTs) are presented which inform about the mean representative features and spatial patterns of both predictor and predictand variables. Thus, statistical relationships between TC activity and nonlinear combinations of predictor variables are found to assign daily rates of expected TCs. The cluster-based model is calibrated from 1982 to 2019 and validated by recent TC season observations, demonstrating the operational application using ensembles of long-term predictions in the Southwest Pacific. Results have shown which synoptic types of SST and MLD are favourable to cyclogenesis and activity, with additional information related to concurrent sea level pressure and precipitation synoptic patterns, as well as seasonal and interannual climate variability.

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