  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 architecture
equipped with attention modules and depthwise-separable convolutions. We
evaluate our approaches 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 performance, the
proposed model is comparable to other examined models while only using a
quarter of the trainable parameters.
