An extreme wind speed climatology - Atmospheric driver identification using neural networks

George Blougouras; Kostas Philippopoulos; Chris G Tzanis

doi:10.1016/j.scitotenv.2023.162590

An extreme wind speed climatology - Atmospheric driver identification using neural networks

Sci Total Environ. 2023 Jun 1:875:162590. doi: 10.1016/j.scitotenv.2023.162590. Epub 2023 Mar 5.

Authors

George Blougouras¹, Kostas Philippopoulos², Chris G Tzanis³

Affiliations

¹ Climate and Climatic Change Group, Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece. Electronic address: georgeblou@phys.uoa.gr.
² Climate and Climatic Change Group, Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece. Electronic address: kphilip@phys.uoa.gr.
³ Climate and Climatic Change Group, Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece. Electronic address: chtzanis@phys.uoa.gr.

PMID: 36871729
DOI: 10.1016/j.scitotenv.2023.162590

Abstract

Extreme wind speeds are a significant climate risk, potentially endangering human lives, causing damage to infrastructure, affecting maritime and aviation activity, along with the optimal operation of wind energy conversion systems. In this context, accurate knowledge of return levels for various return periods of extreme wind speeds and their atmospheric circulation drivers is essential for effective risk management. In this paper, location-specific extreme wind speed thresholds are identified and return levels of extremes are estimated using the Peaks-Over-Threshold method of the Extreme Value Analysis framework. Furthermore, using an environment-to-circulation approach, the key atmospheric circulation patterns that cause extreme wind speeds are identified. The data used for this analysis are hourly wind speed data, mean sea level pressure and geopotential at 500 hPa from the ERA5 reanalysis dataset, at a horizontal resolution of 0.25° × 0.25°. The thresholds are selected utilizing the Mean Residual Life plots, while the exceedances are modeled with the General Pareto Distribution. The diagnostic metrics exhibit satisfactory goodness-of-fit and the maxima of extreme wind speed return levels are located over marine and coastal areas. The optimal Self-Organizing-Map (2 × 2) is selected using the Davies-Bouldin criterion, and the atmospheric circulation patterns are related to the cyclonic activity in the area. The proposed methodological framework can be applied to other areas, that are endangered by extreme phenomena or in need of accurately assessing the principal drivers of extremes.

Keywords: Extreme events; Extreme value analysis; Risk assessment; Self-organizing map; Synoptic climatology; Wind speed.