  Nowcasting is a field of meteorology which aims at forecasting weather on a
short term of up to a few hours. In the meteorology landscape, this field is
rather specific as it requires particular techniques, such as data
extrapolation, where conventional meteorology is generally based on physical
modeling. In this paper, we focus on cloud cover nowcasting, which has various
application areas such as satellite shots optimisation and photovoltaic energy
production forecast.
  Following recent deep learning successes on multiple imagery tasks, we
applied deep convolutionnal neural networks on Meteosat satellite images for
cloud cover nowcasting. We present the results of several architectures
specialized in image segmentation and time series prediction. We selected the
best models according to machine learning metrics as well as meteorological
metrics. All selected architectures showed significant improvements over
persistence and the well-known U-Net surpasses AROME physical model.
