  Forecasting severe weather conditions is still a very challenging and
computationally expensive task due to the enormous amount of data and the
complexity of the underlying physics. Machine learning approaches and
especially deep learning have however shown huge improvements in many research
areas dealing with large datasets in recent years. In this work, we tackle one
specific sub-problem of weather forecasting, namely the prediction of
thunderstorms and lightning. We propose the use of a convolutional neural
network architecture inspired by UNet++ and ResNet to predict thunderstorms as
a binary classification problem based on satellite images and lightnings
recorded in the past. We achieve a probability of detection of more than 94%
for lightnings within the next 15 minutes while at the same time minimizing the
false alarm ratio compared to previous approaches.
