The Annual Rate of Independent Events for the analysis of the extreme wind speed. Issue 156 (September 2016)

• Record Type: Journal Article
• Authors: Torrielli, Alessio; Repetto, Maria Pia; Solari, Giovanni
• Is Part Of: Journal of wind engineering and industrial aerodynamics. Issue 156(2016)
• Page Start: 104
• Page End: 114
• Publication Date: 2016-09
• Subjects: Annual Rate of Independent Events -- Extreme value distribution -- Large datasets -- Long-term Monte Carlo simulation -- Mean wind speed; Wind-pressure -- Periodicals; Buildings -- Aerodynamics -- Periodicals; Pression du vent -- Périodiques; Constructions -- Aérodynamique -- Périodiques; Buildings -- Aerodynamics; Wind-pressure; Periodicals
• DOI: 10.1016/j.jweia.2016.07.010
• Languages: English
• ISSNs: 0167-6105

Abstract

Abstract: The statistical modeling of the extreme mean wind speed is a very controversial subject. Several models are currently being applied in the literature, based on different assumptions and supplying quite different results. This paper takes a step back towards the common root of many extreme value models: the Annual Rate of Independent Events (ARIE). Starting from an analysis of large datasets of synthetic wind observations, the ARIE is inspected from a new point of view and some features so far hidden are brought to light. Based on that, a new effective approach to model the distribution of the extreme mean wind speed is proposed. The reliability of the proposed model is discussed processing both large datasets of synthetic wind observations, and small-sized datasets representative of real conditions. ARIE sensitivity to uncertainties in the parent distribution is also evaluated and critically discussed. Highlights: Two large-sized wind speed datasets are generated, reproducing different wind climates. The Annual Rate of Independent Events (ARIE) is analyzed on the basis of the synthetic datasets. A simple analytic model of ARIE is proposed on varying the wind velocity. A new approach to the EV distribution based on the ARIE model is described. The accuracy of the EV model is discussed with reference to synthetic datasets of realistic length.