Abstract
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.
Highlights
Ultraviolet (UV) radiation extends for wavelengths from 100 nm to 400 nm
The biological effects of UV radiation vary enormously with wavelength; by convention, the ultraviolet spectrum has been further subdivided into three regions: UV-C (100–280 nm), UV-B (280–315 nm) and UVA (315–400 nm)
The UV climatology at a specific site depends primarily on the time of day and day of the year, secondly on cloudiness and thereafter on the type and amount of aerosols (Foyo-Moreno et al, 2003)
Summary
Ultraviolet (UV) radiation extends for wavelengths from 100 nm to 400 nm. The biological effects of UV radiation vary enormously with wavelength; by convention, the ultraviolet spectrum has been further subdivided into three regions: UV-C (100–280 nm), UV-B (280–315 nm) and UVA (315–400 nm). An attempt to reduce the effect of the site and its climatology on the UV-global relationship involves the utilisation of hemispherical transparency indices (or clearness indices) KtUV and Kt (Martınez-Lozano et al, 1994) These clearness indices are defined as Kt = Hg/Hg0 and KtUV =HUV /HUV 0, where Hg and HUV are, respectively, the daily global solar broadband radiation (typically in the spectral range 0.3–3 μm) and the daily global solar UV radiation on horizontal surfaces at ground level, and Hg0 and HUV 0 are the corresponding horizontal daily extraterrestrial radiations. Linear or polynomial expressions, and fuzzy inference techniques, relating the UV and the global clearness indices, have been derived for different sites for hourly and/or daily data All these studies show that absorption and scattering of solar radiation by clouds and aerosols affect the relationship between the clearness indices, KtUV and Kt , showing a local dependence mainly on the dominant type and amount of clouds. This method allows ANN to determine the relative importance of the various input variables considered in calculating UV values, leading to the removal of those inputs which do not significantly explain any variance in the UV measurements
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