  A deep learning platform has been developed to forecast the occurrence of the
low visibility events or hazes. It is trained by using multi-decadal daily
regional maps of various meteorological and hydrological variables as input
features and surface visibility observations as the targets. To better preserve
the characteristic spatial information of different input features for
training, two branched architectures have recently been developed for the case
of Paris hazes. These new architectures have improved the performance of the
network, producing reasonable scores in both validation and a blind forecasting
evaluation using the data of 2021 and 2022 that have not been used in the
training and validation.
