Statistical estimation of spatial wave extremes for tropical cyclones from small data samples: validation of the STM-E approach using long-term synthetic cyclone data for the Caribbean Sea

TL;DR

This paper evaluates the STM-E model for estimating rare tropical cyclone wave heights using synthetic data, demonstrating its accuracy and reduced bias compared to traditional methods in the Caribbean Sea.

Contribution

The study validates the STM-E approach with synthetic cyclone data, showing improved accuracy and realistic uncertainty estimates for rare event return values.

Findings

STM-E provides consistent 500-year return value estimates from 200-year samples.

STM-E yields more accurate and less biased estimates than single location methods.

The approach offers realistic uncertainty quantification for extreme wave height estimates.

Abstract

Occurrences of tropical cyclones at a location are rare, and for many locations, only short periods of observations or hindcasts are available. Hence, estimation of return values (corresponding to a period considerably longer than that for which data is available) for cyclone-induced significant wave height (SWH) from small samples is challenging. The STM-E (space-time maximum and exposure) model was developed to provide reduced bias in estimates of return values compared to competitor approaches in such situations, and realistic estimates of return value uncertainty. STM-E exploits data from a spatial neighbourhood satisfying certain conditions, rather than data from a single location, for return value estimation. This article provides critical assessment of the STM-E model for tropical cyclones in the Caribbean Sea near Guadeloupe for which a large database of synthetic cyclones is available, corresponding to more than 3,000 years of observation. Results indicate that STM-E yields values for the 500-year return value of SWH and its variability, estimated from 200 years of cyclone data, consistent with direct empirical estimates obtained by sampling 500 years of data from the full synthetic cyclone database; similar results were found for estimation of the 100-year return value from samples corresponding to approximately 50 years of data. In general, STM-E also provides reduced bias and more realistic uncertainty estimates for return values relative to single location analysis.