Short-range visibility forecast by means of neural-network modelling: a case-study

Summary A neural-network approach to a hetero-associative prognostic problem in the field of meterology is presented. In particular, the application to the short-range visibility forecast of a neural back-propagation model with adaptive training and test procedures is considered. Our case-study leads to the following results: we achieve a better theoretical understanding of the behaviour of neural networks handling meteorological data (for example, the phenomenon of ?network overfitting? and a seasonal-change influence on the performance of the model are recognised); the forecast performances of the network are always better than the persistence and subjective forecasts (in particular, the forecast of the visibility tendency is very good).