The weather models that run on super computers and predict 3D detailed atmospheric conditions. This is just image analytics. These models could predict, for example, that new precip will develop. This just tracks the path of existing weather.
"Ensemble numerical weather prediction (NWP) systems, which simulate coupled physical equations of the atmosphere to generate multiple realistic precipitation forecasts, are natural candidates for nowcasting as one can derive probabilistic forecasts and uncertainty estimates from the ensemble of future predictions7. For precipitation at zero to two hours lead time, NWPs tend to provide poor forecasts as this is less than the time needed for model spin-up and due to difficulties in non-Gaussian data assimilation8,9,10."
Sounds like the detailed models are too heavy for this particular job, and that the existing methods to deal with it are too coarse. And there's lots of training data, so it's a really natural place to drop in a generative model.
It's a special corner case of weather forecasting, but a real result.
Just think of the images as a PCA/dimensionality reduction. Instead of running a model on 3D atmospheric conditions, you run predictions based on a 2D slice of it. Enough of the information remains embedded in the radar images that predictions have similar accuracy to crunching numbers on higher dimensions.