Analysis of extreme wind gusts using a high-resolution Australian Regional Reanalysis

The characterisation of extreme wind gust speeds has historically relied on data from observations, while numerically modelling these events is limited due to their rarity and localised nature. However, the ongoing increase in computational power now allows extreme events to be characterised in fine-scale numerical data. Extreme gust events are examined using 24 years of 1.5 km horizontal grid reanalysis data for eastern Australia, and compared to those based on 1-minute data from station observations. We estimate return values over long periods using the generalised Pareto distribution (GPD) peaks-over-threshold approach. An automated algorithm that selects an optimal threshold at each grid point is outlined and gives similar results to an approach based on pre-fixing the shape factor value similar to what is applied in Australian building design standards. We also present a decision tree model that skillfully distinguishes between convective (i.e., thunderstorm) and synoptic (e.g., frontal system and cyclone) gust events using only hourly reanalysis data. The reanalysis shows high synoptic gust intensities and frequencies along the coast, consistent with station observations, and that synoptic-type extreme gusts are enhanced by topography (higher occurrence frequencies in mountain areas) while convective-type events are less affected. The reanalysis systematically overestimates parameterised gust activity, while compensating for this at many locations by underestimating the resolved wind speed, thus often producing gusts close to observed. With the proviso that these issues are considered, we find that the information on wind gusts provided by reanalysis data may be quite useful especially in regions with low observational network coverage.

Automated summary

The study examines extreme wind gust events in eastern Australia using long-term reanalysis data and station observations. By utilizing the generalised Pareto distribution and a decision tree model, the study estimates return values and distinguishes between convective and synoptic gust events. The reanalysis data proves to be valuable, especially in regions with limited observational coverage.