{'fraction_non_alphanumeric': 0.02129471890971039, 'fraction_numerical': 0.011073253833049404, 'mean_word_length': 5.25, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Extreme wind estimates by the conditional mean exceedance procedure

We describe work aimed at improving procedures for the estimation of non-tomadic extreme wind speeds, regardless of’their direction, in regions not subjected to hurricanes. Using the Generalized Pareto Distribution (GPD) approach and the Conditional Mean Exceedance (CME) estimation method, we analyze 115 17-year to 52-year sets of largest annual speeds and sets drawn from 48 15-year to 26-year records of maximum daily wind speeds. Based on this analysis we attempt an assessment of the widely held belief that the Gumbel distribution with site-dependent location and scale parameters is a universal model of extreme wind speeds. Some of our results suggest that the reverse Weibull distribution is a more appropriate model. This would result in more reasonable estimates of wind-induced failure probabilities and wind load factors than the corresponding estimates based on the Gumbel distribution. However, our assessment is so far only tentative owing to uncertainties inherent in our results. Future work based on lower thresholds (larger data samples) and alternative estimation methods is planned.