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How well can global ensemble forecasts predict tropical cyclones in the southwest Indian Ocean?

Emerton, E., Hodges, K., Stephens, L., Amelie, V., Mustafa, M., Rakotomavo, Z., Coughlan de Perez, E., Magnusson, L. and Vidale, P. L. ORCID: https://orcid.org/0000-0002-1800-8460 (2024) How well can global ensemble forecasts predict tropical cyclones in the southwest Indian Ocean? Meteorological Applications. ISSN 1469-8080 (In Press)

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Abstract/Summary

The southwest Indian Ocean (SWIO) recently experienced its most active, costliest, and deadliest, cyclone season on record (2018-2019). The anticipation and forecasting of natural hazards such as tropical cyclones, are crucial to preparing for their impacts, but it is important to understand how well forecasts can predict them. Despite the vulnerability of the SWIO to tropical cyclones, comparatively little research has focussed on this region, including understanding the ability of numerical weather prediction systems to predict cyclones and their impacts in south-east Africa. In this study, we evaluate ensemble probabilistic and high-resolution deterministic forecasts of tropical cyclones in the SWIO from 2010-2020, using two state-of-the-art global forecasting systems, from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office. We evaluate predictions of the track, assessing both the location of the centre of the storm and its speed of movement, and the intensity, looking at maximum wind speeds and minimum central pressure, and discuss how the forecasts have changed and improved over the 10-year period. We also investigate the impact of the Madden-Julian Oscillation (MJO) on tropical cyclones and their forecasts. Overall, ECMWF typically provides more accurate forecasts, but both tend to underestimate the translation speed and intensity. The MJO impacts where and when tropical cyclones form, their tracks and intensities, and forecast skill. The results presented here are intended to provide an increased understanding of the ability of global forecasting systems to predict tropical cyclones in the SWIO, for the purpose of decision-making and anticipatory action.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:112621
Publisher:Wiley
Publisher Statement:.

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