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 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.

天气临近预报包括在短期内以高空间分辨率预测气象成分。由于其对许多人类活动的影响，准确临近预报最近受到了广泛关注。在本文中，我们将临近预报问题视为使用卫星图像的图像到图像的转换问题。我们引入了 Broad-UNet，一种基于核心 UNet 模型的新型架构，以有效解决这个问题。特别是，所提出的 Broad-UNet 配备了非对称并行卷积以及 Atrous Spatial Pyramid Pooling (ASPP) 模块。通过这种方式，Broad-UNet 模型通过组合多尺度特征，同时使用比核心 UNet 模型更少的参数来学习更复杂的模式。所提出的模型应用于两个不同的临近预报任务，即 降水图和云量临近预报。获得的数值结果表明，与其他检查的架构相比，引入的 Broad-UNet 模型执行更准确的预测。