CALCULATION OF CLIMATE LOADS DESIGN VALUES ACCORDING TO THE PROBABILITY MODEL OF ANNUAL MAXIMUM SERIES

The aim of this work is to improve a method for determining the characteristic values of climatic loads according to a probabilistic model of the annual maxima sequence, by choosing a rational type of generalized extreme value distribution law. An analysis is provided regarding the suitability of using four types of distributions for describing a data collection of maximum values of climatic loads. Using example data from the meteorological stations of Ukraine, it is found that for coefficients of variation smaller than 0.85–1.0, it is advisable to use the double exponential Gumbel distribution (generalized extreme value distribution type-I), and at higher values of the coefficient of variation, it is advisable to use the Weibull distribution (generalized extreme value distribution type-III). Recommendations are provided for considering the accuracy in the estimations of the characteristic values of loads according to the probabilistic model for the annual maximum value series.

Modeling the wind speed in North Morocco

The change of the wind speed is strictly related to several natural factors such as local topographical and the ground cover variations, then any adjustment has to take into account the statistical variation for each specific region under study. Unlike the Weibull distribution, which is most used in wind speed modeling, we investigate two alternative distribution functions for wind speed by using the extreme value theory. The generalized Champernowne distribution function and the mixture Log-normal-Pareto distribution function are considered. We demonstrate that the proper generalized extreme value distribution gives a good fit for wind speed in the North Moroccan. In order to validate the models, a comparison of the produced aggregate wind energy in the aeolian wind turbine was being established. The empirical study shows that the generalized extreme value distribution reflects better the intensity of the wind power energy.

Regional analysis of maximum rainfall using L-moment and LH-moment: A comparative case study for the northeast India

Rainfall data of the northeast region of India has been considered for selecting best fit model for rainfall frequency analysis. The methods of L-moment has been employed for estimation of parameters five probability distributions, namely Generalized extreme value (GEV), Generalized Logistic(GLO), Pearson type 3 (PE3), 3 parameter Log normal (LN3) and Generalized Pareto (GPA) distributions. The methods of LH-moment of four orders (L1 L2, L3 & L4-moments) have also been used for estimating the parameters of three probability distributions namely Generalized extreme value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions. PE3 distribution has been selected as the best fitting distribution using L-moment, GPA distribution using L1-moment and GLO distribution using L2, L3 & L4-moments. Relative root mean square error (RRMSE) and RBIAS are employed to compare between the results found from L-moment and LH-moment analysis. It is found that GPA distribution designated by L1-moment method is the most suitable and the best fitting distribution for rainfall frequency analysis of the northeast India. Also the L1-moment method is significantly more efficient than L-moment and other orders of LH-moment for rainfall frequency analysis of the northeast India.

Seasonal and continual wind speed modelling for the coastal urban city, Karachi

ABSTRACT. Variation in wind speed not only indicates the strengthening or weakening of pressure systems but its role in wind farm in the vicinity of coastal area is also crucial. Probability distributions through time series of wind speed data serves foremost basic need for the said parameters. Exploratory data analysis revealed that for coastal city Karachi, maximum wind speed (~23 m/s) occurred during monsoon with its peak during postmonsoon with maximum deviation (~3.5 m/s). Mean / trimmed mean during spring and postmonsoon (~11.5 m/s) as well as in premonsoon and monsoon (~18.5 m/s) remain almost identical while minimum wind blowing during winter and postmonsoon are also identical (~6 m/s). Autumn and winter exhibits least standard deviations. Critical and statistical values have been compared for distribution modelling, while parametric values of different seasonal and continual distributions are also estimated. The study is supported by cumulative distribution functions and probability-probability plots. It is not uncommon to use Weibull distribution for wind speed modelling. By using daily data time series of wind speed for the coastal station Karachi, it has been explored that widely accepted Weibull distribution provides comparatively poor distribution results when compared to other more complicated models (i.e., Wakeby and generalized extreme value distributions]. It is found that annual and seasonal wind comes after the Wakeby distribution except premonsoon summer which follows the generalized extreme value distribution (GEV) for the city. No continual and / or seasonal wind speed follows the Weibull distribution, ultimately and / or more appropriately. The study may give some new insights for aviation and wind engineering purposes.

A bayesian analysis of the annual maximum temperature using generalized extreme value distribution

The annual maximum temperature was modeled using the Generalized Extreme Value (GEV) distribution to Jijel weather station. The Mann-Kendall (MK) and Kwiatkowski Phillips, Schmidt and Shin (KPSS) tests suggest a stationary model without linear trend in the location parameter. The Kurtosis and the Skewness statistics indicated that the normality assumption was rejected. The Likelihood Ratio test was used to determine the best model and the goodness-of-fit tests showed that our data is suitable with a stationary Gumbel distribution. The Maximum Likelihood estimation method and the Bayesian approach using the Monte Carlo method by Markov Chains (MCMC) were used to find the parameters of the Gumbel distribution and the return levels were obtained for different periods. JEL Classification: C1, C13, C46, C490.