Publications

Photographic Visualization of Weather Forecasts with Generative Adversarial Networks

Christian Sigg, Flavia Cavallaro, Tobias Günther and Martin R. Oswald

Artificial Intelligence for the Earth Systems, 2022

Abstract

Outdoor webcam images jointly visualize many aspects of the past and present weather. As they are also easy to interpret, they are consulted by meteorologists and the general public alike. Weather forecasts, in contrast, are communicated as text, pictograms or charts, each focusing on separate aspects of the future weather. We therefore introduce a method that uses photographic images to also visualize weather forecasts. This is a challenging task, because photographic visualizations of weather forecasts should look real and 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 even experts struggle to distinguish real from generated images, achieving only a 59% accuracy. The generated images match the atmospheric, ground and illumination conditions visible in the true future images in 67 up to 99% of cases. Nowcasting sequences of generated images achieve a seamless transition from observation to forecast and attain good visual continuity.

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