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

J.G. Fernandez, S. Mehrkanoon*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Web of Science)

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 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. (C) 2021 The Authors. Published by Elsevier Ltd.
Original languageEnglish
Pages (from-to)419-427
Number of pages9
JournalNeural Networks
Volume144
DOIs
Publication statusPublished - 1 Dec 2021

Keywords

  • Satellite imagery
  • Precipitation forecasting
  • Cloud cover forecasting
  • Deep learning
  • Convolutional neural network
  • U-net
  • NEURAL-NETWORK

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