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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 24, issue 8
Ann. Geophys., 24, 2105–2114, 2006
https://doi.org/10.5194/angeo-24-2105-2006
© Author(s) 2006. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 24, 2105–2114, 2006
https://doi.org/10.5194/angeo-24-2105-2006
© Author(s) 2006. This work is distributed under
the Creative Commons Attribution 3.0 License.

  13 Sep 2006

13 Sep 2006

Determination of daily solar ultraviolet radiation using statistical models and artificial neural networks

F. J. Barbero1, G. López2, and F. J. Batlles1 F. J. Barbero et al.
  • 1Departamento de Física Aplicada, Universidad de Almería, 04120, Almería, Spain
  • 2Departamento de Ingeniería Eléctrica y Térmica, E.P.S., Universidad de Huelva, 21819, Huelva, Spain

Abstract. In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain), a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA), a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%). The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%). This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.

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