In the paper, we statistically analysed data on the average hourly wind speed obtained from the meteorological station Poprad (located at the Poprad-Tatry airport, the Prešov region, Northern Slovakia) for the period 2005–2021. High altitude and rough mountainous terrain influence the weather conditions considerably and are a source of occasional weather risks. Finding an appropriate wind speed distribution for modelling the wind speed data is therefore important to determine the wind profile at this particular location. In addition to the commonly used two- and three-parameter Weibull distribution, a more flexible exponentiated Weibull (EW) distribution was applied to model the wind speed. Based on the results of the goodness-of-fit criteria (the Kolmogorov–Smirnov test, the Anderson–Darling test, Akaike’s and Bayesian information criteria, the root mean square error, and the coefficient of determination), the EW distribution obtained a significantly better fit to seasonal and monthly wind speed data, especially around the peaks of the data. The EW distribution also proved to be a good model for data with high positive skewness. Therefore, we can recommend the EW distribution as a flexible distribution for modelling a dataset with extremely strong winds or outliers in the direction of the right tail. Alongside the wind speed analysis, we also provided the wind direction analysis, finding out that the most prevailing direction was west (W)—with an occurrence rate of 34.99%, and a mean wind speed of 3.91 m/s, whereas the northern (N) direction featured the lowest occurrence rate of only 4.45% and the mean wind speed of 1.99 m/s.

This present study aims to evaluate the stratospheric ozone that was continuously measured during 2016 over Campo Grande, the capital of South Mato Grosso state, Brazil. To determine the best-adjusted distribution describing the ozone (O₃) co-generation data in Campo Grande, 15 functions were used while modeling the numerical results. Five sets of data were used: the entire year, spring (September to December), summer (December to March, high solar radiation data), autumn (March to June), and winter (June to September, low solar radiation data) to study the seasonal variation in the statistical behavior of the probability distribution functions. The distribution performances are evaluated using three tests of quality, namely Kolmogorov–Smirnov (K-S), Anderson–Darling (A-D), and Chi-square tests. Finally, all the results of the fitted quality tests have been compared. It has been observed that the generalized extreme value distribution provides a good fit all along the year, while for specific seasons the best distributions vary. The best distributions, according to the seasons, are Gamma 3P for the winter, lognormal 3P for spring, Weibull for summer and Gamma 3P for autumn, respectively. There was a coincidence in the probability distribution function adjustment in winter and autumn, period with lower O₃ concentrations, kurtosis, and skewness. In the summer and spring, it was observed higher concentrations of O₃, kurtosis, and asymmetry and different probability distribution functions.