Automated extraction and classification of thunderstorm and non-thunderstorm wind data for extreme-value analysis

Design wind loads are partly based on extreme value analyses of historical wind data, and limitations on the quantity and spatial resolution of wind data pose a significant challenge in such analyses. A promising source of recent wind speed and direction data is the automated surface observing system (ASOS), a network of about 1000 standardized US weather stations. To facilitate the use of ASOS data for structural engineering purposes, procedures and software are presented for (a) extraction of peak gust wind data and thunderstorm observations from archived ASOS reports, (b) classification of wind data as thunderstorm or non-thunderstorm to enable separate analyses, and (c) construction of data sets separated by specified minimum time intervals to ensure statistical independence. The procedures are illustrated using approximately 20-year datasets from three ASOS stations near New York City. It is shown that for these stations thunderstorm wind speeds dominate the extreme wind climate at long return periods. Also presented are estimates based on commingled data sets (i.e., sets containing, indiscriminately, both non-thunderstorm and thunderstorm wind speeds), which until now have been used almost exclusively for extreme wind speed estimates in the US. Analyses at additional stations will be needed to check whether these results are typical for locations with both thunderstorm and nonthunderstorm winds. & 2009 Elsevier Ltd. All rights reserved.