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

TL;DR

Broad-UNet is a novel multi-scale deep learning architecture that improves short-term weather nowcasting accuracy by efficiently capturing complex patterns in satellite imagery using asymmetric convolutions and ASPP modules.

Contribution

This paper introduces Broad-UNet, a new architecture that enhances the core UNet model with multi-scale feature learning and fewer parameters for weather nowcasting tasks.

Findings

Broad-UNet outperforms existing architectures in precipitation map prediction.

The model effectively captures multi-scale features with fewer parameters.

It demonstrates improved accuracy in cloud cover nowcasting.

Abstract

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.