Unlike traditional methods, which incorporate a priori knowledge of how the atmosphere works, the researchers used what they are calling a 'physics-free' approach that interprets the problem of weather prediction as solely an image-to-image translation problem. As such, the trained CNN? by the team?—a U-Net?—only approximates atmospheric physics from the training examples provided to it.
For training the U-Net, multispectral satellite images were used. Data collected over the continental US from the year 2017 to 2019 was used for the initial training. Specifically, the data was split into chunks of four weeks where the last week was used as the evaluation dataset while the rest of the weeks were used for the training dataset.