Abstract:In recent years there has been a substantial increase in attempts to model radiative flux of ultraviolet radiation, UV. In this paper we present the development of an artificial neural network (ANN) model that can be used to estimate solar UV erythemal irradiance, UVER, based on optical air mass, ozone columnar content and broadband solar irradiance. The study was developed at seven stations in the Iberian Peninsula using data recorded at A Coruña, Málaga, Murcia and Santander, during 2000-2003; at Madrid in the period at Valencia in 2000 at Valencia in , 2001 at Valencia in and 2003 and at Zaragoza, from 2001 to 2003. The UVER observations are recorded as half-hour average values. The measurements were performed in the framework of the Spanish UV-B radiometric network operated and maintained by the Spanish Meteorological Institute. In order to train and validate the Multi-layer Perceptron neural networks, independent subsets of data were extracted from the complete database at each station. The networks developed at each place when applied to an independent data set recorded at the same location provide estimates with mean bias deviation less than 1% and root mean square deviation below 17% for all the sites. The generalization network developed using data registered at A Coruña, Madrid, Murcia and Zaragoza provides estimates at all the locations with RMSD below 19%. According to these results, the use of solar broadband irradiances for the estimation of ultraviolet erythemal irradiance provides a tool that can solve the difficulties associated to the retrieval of appropriate information on the cloud field by human observers. In this sense, the proposed method seems appropriate to use the widespread networks of solar broadband irradiance to obtain ultraviolet erythemal irradiance data sets in places where this radiative flux is not measured or to extend back in time the existing data sets.