  A deep learning model is presented to nowcast the occurrence of lightning at
a five-minute time resolution 60 minutes into the future. The model is based on
a recurrent-convolutional architecture that allows it to recognize and predict
the spatiotemporal development of convection, including the motion, growth and
decay of thunderstorm cells. The predictions are performed on a stationary
grid, without the use of storm object detection and tracking. The input data,
collected from an area in and surrounding Switzerland, comprise ground-based
radar data, visible/infrared satellite data and derived cloud products,
lightning detection, numerical weather prediction and digital elevation model
data. We analyze different alternative loss functions, class weighting
strategies and model features, providing guidelines for future studies to
select loss functions optimally and to properly calibrate the probabilistic
predictions of their model. Based on these analyses, we use focal loss in this
study, but conclude that it only provides a small benefit over cross entropy,
which is a viable option if recalibration of the model is not practical. The
model achieves a pixel-wise critical success index (CSI) of 0.45 to predict
lightning occurrence within 8 km over the 60-min nowcast period, ranging from a
CSI of 0.75 at a 5-min lead time to a CSI of 0.32 at a 60-min lead time.
