Catastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°S-40°S, 90°E-160°E) and its four sub-regions. We test ten unique covariate models, each using different representations of the influence of El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM), and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of pre-season TC count outlooks generated between April-October for the AR TC season (November-April) and in-season TC count outlooks generated between November-January for the remaining AR TC season are tested. Results demonstrate skilful TC count outlooks can be generated in April (i.e. 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values between r= 0.59-0.78 and covariates explaining between 35-60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature (SST) highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly pre-season and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.
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