  We use a conditional deep convolutional generative adversarial network to
predict the geopotential height of the 500 hPa pressure level, the two-meter
temperature and the total precipitation for the next 24 hours over Europe. The
proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018
with the goal to predict the associated meteorological fields in 2019. The
forecasts show a good qualitative and quantitative agreement with the true
reanalysis data for the geopotential height and two-meter temperature, while
failing for total precipitation, thus indicating that weather forecasts based
on data alone may be possible for specific meteorological parameters. We
further use Monte-Carlo dropout to develop an ensemble weather prediction
system based purely on deep learning strategies, which is computationally cheap
and further improves the skill of the forecasting model, by allowing to
quantify the uncertainty in the current weather forecast as learned by the
model.
