SmaAt-UNet: Precipitation nowcasting using a small attention-UNet architecture

Kevin Trebing, Tomasz Stanczyk, Siamak Mehrkanoon*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

111 Citations (Web of Science)


Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere. A downside of numerical weather prediction is that it is lacking the ability for short-term forecasts using the latest available information. By using a data-driven neural network approach we show that it is possible to produce an accurate precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient convolutional neural networks-based on the well known UNet ar-chitecture equipped with attention modules and depthwise-separable convolutions. We evaluate our ap-proaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France. The experimental results show that in terms of prediction perfor-mance, the proposed model is comparable to other examined models while only using a quarter of the trainable parameters. ? 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( )

Original languageEnglish
Pages (from-to)178-186
Number of pages9
JournalPattern Recognition Letters
Publication statusPublished - May 2021


  • Domain adaptation
  • Neural networks
  • Kernel methods
  • Coupling regularization

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