Regional And At-site Quantile Estimation Of Wind Speed

Estimation of quantiles of annual maximum wind speed(AMWS)is needed in different environmental fields,engineering risk analysis,designing structure,renew-able energy sources,agricultural operations,and climatology.These estimates are of immense importance for the codification of wind speed.The quantiles of AMWS can be estimated for different meteorological stations of interest by using regional frequency analysis(RFA)and at-site frequency analysis(ASFA).However,the his-torical data of wind speed at the number of meteorological stations are sometimes unavailable and often insufficient due to the shorter length,especially in developing countries like Pakistan.The scarcity of the data increases the uncertainty of the es-timates of the quantiles with regard to policy implications.To address the problem,the approaches of RFA and ASFA are opted in this thesis,which are mainly based on my two SCI papers.We use linear-moments(L-moments)to analyze regional frequency of AMWS for wind speed data of nine meteorological stations of province Punjab,Pakistan.No station is found to be discordant.A single homogenous region is constituted from these nine stations using a subjective approach based on their geographical locations.Heterogeneity measures justify that these nine stations of Punjab form a single homogeneous region.Regional quantiles estimates are found through the most appropriate probability distribution among generalized normal(GNO),generalized logistic(GLO),Pearson Type 3(P3),generalized Pareto(GPA),Weibull(WEI),log Pearson Type 3(LP3)and generalized extreme value(GEV)distributions.Z-statistic and L-moment ratio diagram suggest that GLO and GNO distributions are better choices than others.Robustness of both distributions is evaluated through relative bias(RB)and relative root mean square error(RRMSE).Findings indicate that overall,GLO distribution is better than GNO.Further,we also find at-site quantiles from dimensionless quantities(regional quantiles)using the sample mean and median as scaling factors.At-site frequency analysis of AMWS was carried out at nine stations in Pak-istan,located in Punjab and Khyber Pakhtunkhwa Province.Multiparameter Prob-ability Distributions(PDs),such as Generalized logistic(GLO),Generalized Ex-treme Value(GEV),Generalized Normal(GNO),Generalized Pareto(GPA),Weibul-1(WEI),Pearson type 3(P3),Log Pearson type 3(LP3);and two parameter PDs,such as Logistic(LOG),Normal(NOR),Gumbel(GUM),Exponential(EXP),and Uniform(UNI)were used to determine the most suitable distributions for the nine stations.The method of L-moments was used for estimating parameters of the distributions.The Kolmogorov-Smirnov(KS)test,Anderson-Darling(AD)test,Minimum L-Kurtosis(ML-K)Difference Criterion,and L-ratio diagrams showed that four distributions,namely GEV,GNO,GPA,and GLO were the most suitable distributions for different stations and were superior to the two-parameter distribu-tions.The quantile estimates(design estimates)from multiparameter PDs provide information on how fast the maximum wind will pass through a certain place in the future and hence are important for policy makers and planners in the construction of different structures.The Multivariate Diebold-Mariano(DM)test was applied to check the accuracy of design estimates from the best fitted PDs and results indicated that they were significantly different.The organization of the thesis is as follows.Chapter 1:In this chapter,we highlighted the wind speed and its importance;it also covers the significance and objectives of the study.Chapter 2:In chapter 2,we provide a brief review of literature related to regional frequency analysis and at-site frequency analysis,with parameter estimation and selection of PDs.Chapter 3:First,we provide a brief overview of the methodology of regional frequency analysis based on linear moments.Then we apply the method of the regional frequency to estimate the quantiles of the annual maximum wind speed for the nine stations of Punjab,Pakistan.The potential homogeneous region for annual maximum wind speed is identified by using geographical locations.The robustness of the best regional probability distributions are tested by using relative bias,and relative root mean square error.Further,the at-site quantiles(by using the sample mean and sample median as scaling factor)are calculated from regional quantiles for best fitted regional probability distributions,and their accuracy are compared by using standard error.Chapter 4:In this chapter,we focused on the at-site frequency of annual maximum wind speed of nine stations located in Punjab and Khyber Pakhtunkhwa Province,Pakistan.It also covers the underlying assumptions of at-site frequency analysis.Several different probability distributions(three and two parameters)were fitted to annual maximum wind speed of nine stations.Goodness of fit tests includ-ing numerical and graphical methods were used for the assessments of best fitted probability distributions.In addition,Multivariate Diebold test were applied to check the accuracy of relative absolute error for best fitted probability distribution.Chapter 5:Finally,chapter 5 summarizes the main conclusions made from the analyzed data,and recommendations for practical applications and future research are also given.