Quality of wind speed fitting distributions for the urban area of Palermo, Italy

摘要

This study investigates the wind speed characteristics recorded in the urban area of Palermo, in the south of Italy, by a monitoring network composed by four weather stations. This article has two main objectives: the first one, to describe with clarity and simplicity the numerical procedures adopted to perform a preliminary statistical analysis of wind speed data, providing at the same time, the necessary mathematical tools useful to perform this analysis also without special software. The second objective is to verify if there are more suitable probability distributions able to better represent the original data respect the traditional ones. After a preliminary statistical analysis, in which the wind speed time series are split and analysed for each month and season, seven probability density functions are employed to describe wind speed frequency distributions: Weibull, Rayleigh, Lognormal, Camma, Inverse Gaussian, Pearson type V and Burr. Shape and scale parameters for each weather station, period and distribution are provided. Their estimation is performed using the maximum likelihood method and the maximum likelihood estimators for each probability density function are provided. The quality of the data-fit is assessed by the classic statistical test Kolmogorov-Smirnov. The statistical test is used to rank the selected distributions in order to identify the distribution better fitting with the wind speed data measured in the urban area of Palermo. The Burr probability density function seems to be the most reliable statistical distribution.