  Rainfall prediction at the kilometre-scale up to a few hours in the future is
key for planning and safety. But it is challenging given the complex influence
of climate change on cloud processes and the limited skill of weather models at
this scale. Following the set-up proposed by the \emph{weather4cast} challenge
of NeurIPS, we build a two-step deep-learning solution for predicting rainfall
occurrence at ground radar high spatial resolution starting from coarser
resolution weather satellite images. Our approach is designed to predict future
satellite images with a physics-aware ConvLSTM network, which is then converted
into precipitation maps through a U-Net. We find that our two-step pipeline
outperforms the baseline model and we quantify the benefits of including
physical information. We find that local-scale rainfall predictions with good
accuracy starting from satellite radiances can be obtained for up to 4 hours in
the future.
