Photographic Visualization of Weather Forecasts with Generative Adversarial Networks

TL;DR

This paper introduces a novel method using conditional GANs to generate realistic photographic visualizations of future weather conditions from current webcam images, enhancing weather forecast communication.

Contribution

The paper presents a new GAN-based approach to synthesize future weather images that are realistic, seamless, and match NWP model forecasts, improving visualization of weather predictions.

Findings

Generated images are indistinguishable from real images for users.

Images match NWP forecasts in at least 89% of cases.

Sequences show seamless transition and visual continuity.

Abstract

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.