Estimation of global tropical cyclone wind speed probabilities using the STORM dataset

Nadia Bloemendaal,Jeroen C J H Aerts,Hans De Moel,Sanne Muis,Ivan D Haigh

doi:10.1038/s41597-020-00720-x

Nadia Bloemendaal, Jeroen C J H Aerts + Show 3 more

Open Access

https://doi.org/10.1038/s41597-020-00720-x

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Abstract

Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.

Highlights

Tropical cyclones (TCs) are amongst the deadliest and costliest natural disasters, affecting people, economies and the environment in coastal areas around the globe
A benefit of this approach is that no specific shape of the return periods (RPs)-curve is assumed; RPs are calculated per given wind speed without interpolation or smoothening of the data
The highest RP is limited by the temporal length of the data as this method does not allow for extrapolation beyond this timespan

Summary

Introduction

Tropical cyclones (TCs) are amongst the deadliest and costliest natural disasters, affecting people, economies and the environment in coastal areas around the globe. RPs could not be calculated for those coastal segments that were not hit by a TC in the 85 years of observations This shows that, due to the short length of the observational record, data often needs to be aggregated over larger spatial regions to perform a RP analysis, hereby omitting the spatial heterogeneity. Estimating RPs comes with large uncertainties, especially for RPs exceeding the length of the observational record. To overcome these limitations, the methodology of synthetic TC track generation has been developed over the past few decades[9,10,11,12]. Using a Monte Carlo approach, this procedure is repeated recurrently to construct a TC dataset having the same statistical characteristics as the input dataset, but spanning hundreds to thousands of years

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