  Weather nowcasting consists of predicting meteorological components in the
short term at high spatial resolutions. Due to its influence in many human
activities, accurate nowcasting has recently gained plenty of attention. In
this paper, we treat the nowcasting problem as an image-to-image translation
problem using satellite imagery. We introduce Broad-UNet, a novel architecture
based on the core UNet model, to efficiently address this problem. In
particular, the proposed Broad-UNet is equipped with asymmetric parallel
convolutions as well as Atrous Spatial Pyramid Pooling (ASPP) module. In this
way, The the Broad-UNet model learns more complex patterns by combining
multi-scale features while using fewer parameters than the core UNet model. The
proposed model is applied on two different nowcasting tasks, i.e. precipitation
maps and cloud cover nowcasting. The obtained numerical results show that the
introduced Broad-UNet model performs more accurate predictions compared to the
other examined architectures.
