  Outdoor webcam images are an information-dense yet accessible visualization
of past and present weather conditions, and are consulted by meteorologists and
the general public alike. Weather forecasts, however, are still communicated as
text, pictograms or charts. We therefore introduce a novel method that uses
photographic images to also visualize future weather conditions.
  This is challenging, because photographic visualizations of weather forecasts
should look real, be free of obvious artifacts, and should match the predicted
weather conditions. The transition from observation to forecast should be
seamless, and there should be visual continuity between images for consecutive
lead times. We use conditional Generative Adversarial Networks to synthesize
such visualizations. The generator network, conditioned on the analysis and the
forecasting state of the numerical weather prediction (NWP) model, transforms
the present camera image into the future. The discriminator network judges
whether a given image is the real image of the future, or whether it has been
synthesized. Training the two networks against each other results in a
visualization method that scores well on all four evaluation criteria.
  We present results for three camera sites across Switzerland that differ in
climatology and terrain. We show that users find it challenging to distinguish
real from generated images, performing not much better than if they guessed
randomly. The generated images match the atmospheric, ground and illumination
conditions of the COSMO-1 NWP model forecast in at least 89 % of the examined
cases. Nowcasting sequences of generated images achieve a seamless transition
from observation to forecast and attain visual continuity.
