Generation of a global synthetic tropical cyclone hazard dataset using STORM

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

doi:10.1038/s41597-020-0381-2

Abstract

Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions.

Highlights

Background & SummaryTropical cyclones (TCs), referred to as hurricanes or typhoons, are one of the deadliest natural disasters, significantly impacting people, economies and the environment in coastal areas when they make landfall
We present the synthetic algorithm Synthetic Tropical cyclOne geneRation Model (STORM), and apply it to develop a global dataset representative of 10,000 years of TC activity under present climate conditions
The dataset is useful for TC risk assessments as it can serve as input for storm surge and wave impact modeling, and has characteristics important for wind damage assessments

Summary

Background & Summary

Tropical cyclones (TCs), referred to as hurricanes or typhoons, are one of the deadliest natural disasters, significantly impacting people, economies and the environment in coastal areas when they make landfall. Another method that has been widely explored in the past decades is the generation of synthetic TC tracks[7,8,9] In such an approach, TC tracks and intensities are statistically resampled and modeled from an underlying dataset, which can be either historical TC tracks[8,10,11] or meteorological datasets from climate models[12]. The length of the resulting dataset (i.e. 10,000 years under the same climate conditions) enables proper statistical analysis of return periods of various landfalling TCs. The dataset is useful for TC risk assessments as it can serve as input for storm surge and wave impact modeling, and has characteristics important for wind damage assessments (maximum 10-meter wind speed)

Creating the synthetic TCs

May–30 November