Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Regional Frequency Analysis of Extreme Wind in Pakistan Using Robust Estimation Methods

Version 1 : Received: 3 July 2023 / Approved: 4 July 2023 / Online: 6 July 2023 (09:14:44 CEST)

How to cite: Salman, M.; Alnazi, T.A.; Alshawarbeh, E.; Ahmad, I. Regional Frequency Analysis of Extreme Wind in Pakistan Using Robust Estimation Methods. Preprints 2023, 2023070383. https://doi.org/10.20944/preprints202307.0383.v1 Salman, M.; Alnazi, T.A.; Alshawarbeh, E.; Ahmad, I. Regional Frequency Analysis of Extreme Wind in Pakistan Using Robust Estimation Methods. Preprints 2023, 2023070383. https://doi.org/10.20944/preprints202307.0383.v1

Abstract

The quantile estimation of extreme wind speed is needed in various environmental fields such as climatology, design of structures, renewable energy sources and agricultural operations. These calculations are crucial for the coding of wind speed. In this study, the required wind speed series of 16 stations in Khyber Pakhtunkhwa, Pakistan, was obtained from the NASA official website and measured in meters per second (m/s) at a 10-meter distance. A Regional Frequency Analysis of 16 AMWS stations was performed using L-moments. The quantile estimates of extreme wind speed are needed for various areas of interest using Regional Frequency Analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meter per second (m/s). A Regional Frequency Analysis of AMWS using L-moments is performed utilizing wind speed data from sixteen sites (16) in Pakistan's Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions are chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Keywords

Linear-Moments; Monte Carlo Simulation; Quantile Estimates; Wind Speed

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.