Estimating a joint probability distribution model of fluctuating wind speeds of monsoons from field-measured wind speed data

Modelling the complete probabilistic information of wind speed processes, which can describe the joint probability distributions of wind speeds in the time domain, is significant and challenging. In this study, a joint probability distribution model of fluctuating wind speeds of monsoons is directly estimated from field-measured wind speed samples. The fluctuating wind speeds are modelled by a harmonisable process in the frequency domain. The marginal probability distribution of the wind speed frequency components is represented by a derived mixture distribution model consisting of the Gaussian distribution and generalised Pareto distribution. The probabilistic dependence among the frequency components is modelled by the D-vine copula distribution. The results show that the measured samples are nonstationary in the time domain and non-Gaussian in both time and frequency domains. The probabilistic dependence among the frequency components is moderate but cannot be ignored. The estimated joint probability distribution model can characterise the mean value, standard deviation, kurtosis coefficient, probability density functions (PDFs) of the measured samples and the PDF of the measured maximum wind speed in the time domain. The wind skewness coefficient and the PDF of the minimum wind speed from the obtained joint probability distribution model have certain differences from the measured results.