A neural network model for visibility nowcasting from surface observations: results and sensitivity to physical input variables

A neural network model recently developed for fog nowcasting from surface observations is summarized in its features, paying attention to its particular learning structure (weighted least-squares training), introduced because of the non-constant errors associated with the estimation of visibility values. We apply it to a winter forecast of meteorological visibility in Milan (Italy). The performance of this model is presented and shown to be always better than persistence and climatology. Finally, we introduce a bivariate analysis and a network pruning scheme, and discuss the possibility of identifying the more significant physical input variables for a correct very short-range forecast of visibility.