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.

翻译：已开发出一种深度学习平台，用于预测低能见度事件或雾霾的发生。该平台使用数十年每日区域气象和水文变量图作为输入特征，以地面能见度观测值作为目标进行训练。为更好地保留不同输入特征的特征空间信息进行训练，近期针对巴黎雾霾案例开发了两种分支架构。这些新架构提升了网络性能，在验证集以及使用未参与训练和验证的2021-2022年数据进行的盲测评估中，均获得了合理评分。