  Not only can discovering patterns and insights from atmospheric data enable
more accurate weather predictions, but it may also provide valuable information
to help tackle climate change. Weather4cast is an open competition that aims to
evaluate machine learning algorithms' capability to predict future atmospheric
states. Here, we describe our third-place solution to Weather4cast. We present
a novel Variational U-Net that combines a Variational Autoencoder's ability to
consider the probabilistic nature of data with a U-Net's ability to recover
fine-grained details. This solution is an evolution from our fourth-place
solution to Traffic4cast 2020 with many commonalities, suggesting its
applicability to vastly different domains, such as weather and traffic.
