Details

Autor(en) / Beteiligte

• Fernández, Jesús García
• Mehrkanoon, Siamak

Titel

Broad-UNet: Multi-scale feature learning for nowcasting tasks

Ist Teil von

• Neural networks, 2021-12, Vol.144, p.419-427

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. •The novel Broad-UNet model is proposed as an extension of the core UNet model.•The model contains parallel convolutions and Atrous Spatial Pyramid Pooling module.•The model learns more complex patterns by combining multi-scale features.•The model requires fewer parameters than the core UNet model.•The model outperforms other models in precipitation and cloud cover nowcasting tasks.