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Photographic Visualization of Weather Forecasts with Generative Adversarial Networks

arXiv 2203.15601 ↗First posted 29 Mar 2022cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

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.

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References · 18

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6 more
  1. 2018Self-Attention Generative Adversarial Networkscited by 809
  2. 2018Generative Image Inpainting with Contextual Attentioncited by 580
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  6. 2020Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Networkcited by 70

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