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- Author or Editor: Andrew D. Magee x
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Abstract
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°–40°S, 90°–160°E) and its four subregions. We test 10 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 preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful 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 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.
Abstract
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°–40°S, 90°–160°E) and its four subregions. We test 10 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 preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful 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 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.
Abstract
Tropical cyclones (TCs) produce extreme winds, large waves, storm surges, intense rainfall, and flooding and account for almost 75% of natural disasters across the southwest Pacific (SWP) region. The island nations and territories across the SWP rely on seasonal TC outlooks for insights into possible risks for the coming TC season. Launched in July 2020, the Long-Range Tropical Cyclone Outlook for the Southwest Pacific (TCO-SP) provides deterministic (frequency) and probabilistic (likelihood) TC outlooks for 12 subregional and island-scale locations up to 4 months (July) before the start of the SWP TC season (November–April). Following TCO-SP’s first season of operation, this study (i) outlines the process of generating and communicating TCO-SP outlooks, (ii) provides a postseason validation of TCO-SP performance on the 2020/21 SWP TC season, and (iii) reports on the results of a questionnaire used to determine end-user needs and user-perceived usefulness of TCO-SP. Postseason validation indicates that TCO-SP successfully predicted a near-normal SWP TC season. Island- and regional-scale guidance also performed well, with an average skill score of 54% across all regions. Analysis of responses to a TCO-SP questionnaire revealed a diverse and global user base that indicate the core features of TCO-SP (island-scale/regional-scale outlooks, regular monthly updates, and an outlook lead time up to 4 months before the start of the TC season) are particularly useful. TCO-SP will continue to innovate to deliver reliable and trusted TC outlooks with a goal to reduce disaster risk and increase resilience across the SWP region.
Abstract
Tropical cyclones (TCs) produce extreme winds, large waves, storm surges, intense rainfall, and flooding and account for almost 75% of natural disasters across the southwest Pacific (SWP) region. The island nations and territories across the SWP rely on seasonal TC outlooks for insights into possible risks for the coming TC season. Launched in July 2020, the Long-Range Tropical Cyclone Outlook for the Southwest Pacific (TCO-SP) provides deterministic (frequency) and probabilistic (likelihood) TC outlooks for 12 subregional and island-scale locations up to 4 months (July) before the start of the SWP TC season (November–April). Following TCO-SP’s first season of operation, this study (i) outlines the process of generating and communicating TCO-SP outlooks, (ii) provides a postseason validation of TCO-SP performance on the 2020/21 SWP TC season, and (iii) reports on the results of a questionnaire used to determine end-user needs and user-perceived usefulness of TCO-SP. Postseason validation indicates that TCO-SP successfully predicted a near-normal SWP TC season. Island- and regional-scale guidance also performed well, with an average skill score of 54% across all regions. Analysis of responses to a TCO-SP questionnaire revealed a diverse and global user base that indicate the core features of TCO-SP (island-scale/regional-scale outlooks, regular monthly updates, and an outlook lead time up to 4 months before the start of the TC season) are particularly useful. TCO-SP will continue to innovate to deliver reliable and trusted TC outlooks with a goal to reduce disaster risk and increase resilience across the SWP region.
