Abstract
In this paper, annual precipitation data sets from five old rain gauge stations (Bushehr, Isfahan, Meshed, Tehran, and Jask) in Iran were fitted to nonparametric kernel function by using rectangular, triangular, and Gaussian or normal as kernel functions. The smoothing parameter was calculated by four methods including rule of thumb, Adamowski criterion, least squares cross-validation, and Sheater and Jones plug-in. The Adamowski criterion showed a better performance compared to other methods due to goodness of fit tests. The results of these proposed nonparametric methods will be then compared to the results of the parametric density functions including normal, two and three parameter log-normal, two parameter gamma, Pearson and log-Pearson type 3, Gumbel or extreme value type 1 and also Fourier series method which were applied by a previous study for the same stations. It was concluded that the annual precipitation data were fitted to nonparametric methods better than parametric methods.
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Acknowledgments
This paper was performed when the first author was a visiting professor at the Department of Agricultural Engineering–Water, The Oceanic and Atmospheric Research Center, Faculty of Agriculture, University of Shiraz, Shiraz, Iran. Hence, the kind support and services provided by the head and staffs of the mentioned center is gratefully appreciated. The authors wish to thank the anonymous reviewers whose suggested comments improved the paper.
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Haghighat jou, P., Akhoond-Ali, A.M. & Nazemosadat, M.J. Nonparametric kernel estimation of annual precipitation over Iran. Theor Appl Climatol 112, 193–200 (2013). https://doi.org/10.1007/s00704-012-0727-6
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DOI: https://doi.org/10.1007/s00704-012-0727-6