  Weather forecast plays an essential role in multiple aspects of the daily
life of human beings. Currently, physics based numerical weather prediction is
used to predict the weather and requires enormous amount of computational
resources. In recent years, deep learning based models have seen wide success
in many weather-prediction related tasks. In this paper we describe our
experiments for the Weather4cast 2021 Challenge, where 8 hours of
spatio-temporal weather data is predicted based on an initial one hour of
spatio-temporal data. We focus on SmaAt-UNet, an efficient U-Net based
autoencoder. With this model we achieve competent results whilst maintaining
low computational resources. Furthermore, several approaches and possible
future work is discussed at the end of the paper.
