Estimation of wind speed using regional frequency analysis based on linear‐moments

Abstract

The quantiles of annual maximum wind speed (AMWS) can be estimated for different meteorological stations of interest by using at-site frequency analysis and extreme value theory. These estimates are of immense importance for the codification of wind speed. However, the historical 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 quantiles estimates regarding policy implications. To cope with the problem, an approach of Regional Frequency Analysis (RFA) is opted here. In this study, RFA of AMWS using linear-moments (Lmoments) is carried out by considering 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. Quantiles' estimates calculated from this study can be used in codified structural designs for policy implications. K E Y W O R D S linear-moments, Monte Carlo simulation, quantile estimates, wind speed

Retracted: “Economic regulation of energy costs when integrated into distribution networks of industrial enterprises”

“…As mentioned in 80% of studies, parameter estimation method (PEM) and MLM are used to estimate at least one distribution parameter. [26][27][28][29][30][31][32]27,34,[38][39][40][41][42][43][44][45][46][47][48][49]50,[51][52][53][55][56][57][58][59][60][61][62][63]65,67,68,71,79 MLM is an optimization process for determining the estimation of different distribution functions. 80 MOM method was applied 22% in all studies to estimate the distribution of the parameters.…”

“…In this study, the L-moment method (LMOM) [60][61][62] was used to evaluate the parameters of the partial distribution model for wind speed data. Since Hosking 63 defines the L-moment, the L-moment is the probability of some linear arrangement of statistics.…”

Estimation of regional and at-site quantiles of extreme winds under flood index procedure

“…When determining an appropriate equilibrium, precise calculation of the anticipated maximum wind speeds is critical. These estimations are often in the form of the quantiles value , the extreme wind events that surpass once on average after every T-year return period [ 13 ].…”

High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalized extreme value distributions

“…They often require climate and/or geographical covariates (see e.g. Fawad et al, 2018;Forestieri et al, 2018, for recent work) and work in three steps: (i) selecting explanatory covariates, (ii) grouping sites with similar covariates, and (iii) testing the homogeneity of the groups obtained. Covariates are selected for their ability to explain the precipitation distribution (Evin et al, 2016;Ouarda et al, 2008).…”