Our code is available at https://github.com/martaskrt/qdeq.

Modern deep learning approaches haveshown promising results inmeteorological applications like precipitation nowcasting, synthetic radar generation, front detection and several others. Inorder toeffectively train and validate these complex algorithms, large and diverse datasets containing high-resolution imagery are required. Petabytes of weather data, such as from the Geostationary Environmental SatelliteSystem(GOES)andtheNext-Generation Radar(NEXRAD) system, are available to the public; however, the size and complexity of these datasets isahindrance todeveloping and training deep models.

Have you retrained or moved careers due to your previous career path being at risk of an artificial intelligence takeover? Please include as much detail as possible. Did you have a dream profession that you have decided not to pursue because of fears it will be thwarted by AI? Optional Please include as much detail as possible.

Our environment is riddled with sensory stimuli that are noisy, ambiguous, and often incomplete, necessitating organisms to handle uncertainty in their sensory observations.

Many attempts have been made throughout the last thirty years.