SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture
By Kevin Trebing, Siamak Mehrkanoon. Pattern Recognition Letters, 2020
459 citations recorded by the source corpus.
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 \textit{SmaAt-UNet}, an efficient convolutional neural networks based on the well known UNet architecture equipped with attention modules and depthwise-separable convolutions. We evaluate our approach on a real-life dataset using precipitation maps from the region of the Netherlands. The experimental results show that in terms of accuracy the proposed model is comparable to other examined models while only using a quarter of the trainable parameters.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/b27f82b84ffd0894f649279946354acc760d2bb4
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Citations in the corpus
- Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks (2021, background): “[30] apply attention modules and show a reduction in model parameter size while maintaining performance.”