Acknowledgement
The authors are grateful for the National Meteorological Information Center of China for the data support.
References
- Alam, J., Muzzammil, M. and Khan, M.K. (2016), "Regional flood frequency analysis: comparison of L-moment and conventional approaches for an Indian catchment", ISH J. Hydraul. Eng., 22(3), 247-253. https://doi.org/10.1080/09715010.2016.1177739.
- Aydin, D. (2018), "Alternative robust estimation methods for parameters of Gumbel distribution: an application to wind speed data with outliers", Wind Struct., 26(6), 383-395. https://doi.org/10.12989/was.2018.26.6.383.
- Duan, C., Wang, Z., Dong, S. and Zhenkun, L. (2018), "Wind characteristics and wind energy assessment in the Barents Sea based on ERA-Interim reanalysis", Oceanologc. Hydrobio. Studies, 47(4), 415-428. https://doi.org/10.1515/ohs-2018-0039.
- Fawad, M., Ahmad, I., Nadeem, F.A., Yan, T. and Abbas, A. (2018), "Estimation of wind speed using regional frequency analysis based on linear-moments", Int. J. Climatol., 38(12) 4431-4444. https://doi.org/10.1002/joc.5678.
- Fawad, M., Yan, T., Chen, L., Huang, K. and Singh, V.P. (2019), "Multiparameter probability distributions for at-site frequency analysis of annual maximum wind speed with L-Moments for parameter estimation", Energy., 181(8), 724-737. https://doi.org/10.1016/j.energy.2019.05.153.
- Ge, Y. and Xiang, H. (2002), "Statistical study for mean wind velocity in Shanghai area", J. Wind Eng. Ind. Aerod., 90(12-15), 1585-1599. https://doi.org/10.1016/S0167-6105(02)00272-6.
- Haddad, K. (2021), "Selection of the best fit probability distributions for temperature data and the use of L-moment ratio diagram method: a case study for NSW in Australia", Theoretic. Appl. Climatol., 143(3), 1261-1284. https://doi.org/10.1007/s00704-020-03455-2.
- Hosking, J.R.M. (1990), "L-moments: analysis and estimation of distributions using linear combinations of order statistics", J. Roy. Statist. Soc. Ser. B., 52(1), 105-124. https://doi.org/10.2307/2345653.
- Hosking, J.R.M. and Wallis, J.R. (1997), Regional frequency analysis: an approach based on L-moments, Cambridge University Press, Cambridge, Britain.
- Huang, M., Li, Q., Xu, H., Lou, W. and Lin, N. (2018), "Nonstationary statistical modeling of extreme wind speed series with exposure correction", Wind Struct., 26(3), 129-146. https://doi.org/10.12989/was.2018.26.3.129.
- Khosravi, A., Machado, L. and Nunes, R.O. (2018), "Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil", Appl. Energy., 224(8), 550-566. https://doi.org/10.1016/j.apenergy.2018.05.043.
- Laio, F., Di Baldassarre, G. and Montanari, A. (2009), "Model selection techniques for the frequency analysis of hydrological extremes", Water Resource Res., 45(7). https://doi.org/10.1029/2007WR006666.
- Lee, B., Ahn, D., Kim, H. and Ha, Y. (2011), "An estimation of the extreme wind speed using the Korea wind map", Renew Energy., 42(1), 4-10. https://doi.org/10.1016/j.renene.2011.09.033.
- Liu, L. and Hu, F. (2019), "Long-term Correlations and Extreme Wind Speed Estimations", Advan. Atmos. Sci., 36(10), 1121-1128. https://doi.org/10.1007/s00376-019-9031-z.
- Modarres, R. (2008), "Regional maximum wind speed frequency analysis for the arid and semi-arid regions of Iran", J. Arid Environ., 72(7), 1329-1342. https://doi.org/10.1016/j.jaridenv.2007.12.010.
- Moreno, S.R. and Dos Santos Coelho, L. (2018), "Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System", Renew Energy., 126(10), 736-754. https://doi.org/10.1016/j.renene.2017.11.089.
- Ouarda, T.B.M.J., Charron, C. and Chebana, F. (2016), "Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study", Energy Converse Manage., 124(9), 247-265. https://doi.org/10.1016/j.enconman.2016.07.012.
- Ozkan, R., Sen, F. and Balli, S. (2020), "Evaluation of wind loads and the potential of Turkey's south west region by using lognormal and gamma distributions", Wind Struct., 31(4), 299-309. https://doi.org/10.12989/was.2020.31.4.299.
- Pandey, M.D., Gelder, P.H.A.J. and Vrijling, J.K. (2001), "Assessment of an L-Kurtosis-based criterion for quantile estimation", J. Hydrol. Eng., 6(4), 284-292. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:4(284).
- Seshaiah, C.V. and Sukkiramathi, K. (2016), "A mathematical model to estimate the wind power using three parameter Weibull distribution", Wind Struct., 22(4), 393-408. https://doi.org/10.12989/was.2016.22.4.393.
- Soukissian, T.H. and Tsalis, C. (2018), "Effects of parameter estimation method and sample size in metocean design conditions", Ocean Eng., 169 19-37. https://doi.org/10.1016/j.oceaneng.2018.09.017.
- Staid, A., Pinson, P. and Guikema, S.D. (2015), "Probabilistic maximum-value wind prediction for offshore environments", Wind Energy., 18(10), 1725-1738. https://doi.org/10.1002/we.1787.
- Sukkiramathi, K. and Seshaiah, C.V. (2020), "Analysis of wind power potential by the three-parameter Weibull distribution to install a wind turbine", Energy Explor. Exploit., 38 158-174. https://doi.org/10.1177/0144598719871628.
- Sukkiramathi, K., Rajkumar, R. and Seshaiah, C.V. (2020), "Evaluation of wind power potential for selecting suitable wind turbine", Wind Struct., 31(4), 311-319. https://doi.org/10.12989/was.2020.31.4.311.
- Sukkiramathi, K., Rajkumar, R. and Seshaiah, C.V. (2020), "Mathematical representation to assess the wind resource by three parameter Weibull distribution", Wind Strcut., 31(5), 419-430. https://doi.org/10.12989/was.2020.31.5.419.
- Tosunoglu, F. (2018), "Accurate estimation of T year extreme wind speeds by considering different model selection criterions and different parameter estimation methods", Energy., 162(11), 813-824. https://doi.org/10.1016/j.energy.2018.08.074.
- Um, M., Joo, K., Nam, W. and Heo, J. (2017), "A comparative study to determine the optimal copula model for the wind speed and precipitation of typhoons", Int. J. Climatol., 37(4), 2051-2062. https://doi.org/10.1002/joc.4834.
- Wei, T. and Song, S. (2019), "Utilization of the copula-based composite likelihood approach to improve design precipitation estimates accuracy", Water Resource. Manage., 33(15), 5089-5106. https://doi.org/10.1007/s11269-019-02416-3.
- Yu, I., Kim, J. and Jeong, S. (2016), "Development of probability wind speed map based on frequency analysis", Spatial Information Res., 24(5), 577-587. https://doi.org/10.1007/s41324-016-0054-6.
- 2004Specification (2004), Wind-Resistent Design Specification for Highway Bridges (JTG/T D60-01-2004), China communication press, China.
- 2018Specification (2018), Wind-Resistent Design Specification for Highway Bridges (JTG/T 3360-01-2018), China communication press, China.