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Artificial Neural Networks (ANN) are efficient tools to derive solar UVradiation from measured meteorological parameters such as global radiation,aerosol optical depths and atmospheric column ozone. The ANN model has beentested with different combinations of data from the two sites Potsdam andLindenberg, and used to reconstruct solar UV radiation at eight Europeansites by more than 100 years into the past. Special emphasis will be givento the discussion of small-scale characteristics of input data to the ANNmodel.  Annual totals of UV radiation derived from reconstructed daily UV valuesreflect interannual variations and long-term patterns that are compatiblewith variabilities and changes of measured input data, in particular globaldimming by about 1980/1990, subsequent global brightening, volcanic eruptioneffects such as that of Mt. Pinatubo, and the long-term ozone decline sincethe 1970s. Patterns of annual erythemal UV radiation are very similar atsites located at latitudes close to each other, but different patterns occurbetween UV radiation at sites in different latitude regions

Long-term solar UV radiation reconstructed by ANN modelling with emphasis on spatial characteristics of input data