> To do better than the HRRR, with significantly less computational effort must be causing a lot of meteorologists a degree of discomfort. It doesn't cause any

> To do better than the HRRR, with significantly less computational effort must be causing a lot of meteorologists a degree of discomfort. It doesn't cause any such discomfort, because in the grand scheme of things, while this is still very impressive and interesting work, it's still a toy in comparison with what tool like the HRRR is typically used for. Reported increases in performance are skewed towards the first few hours of the forecast, where all CAMs generally have some issues because of inconsistencies between the assimilated model initial state and the real-world (e.g. small deficits in the structure of convective systems at the initial state can dominate precipitation forecast skill at short lead times), and where traditional nowcasting systems are already significantly superior. There's little evidence reported that this modeling paradigm can even fundamentally tackle the most critical aspects of short-term mesoscale/convective forecasting, which is hysteresis - the initiation of convection and the structure it takes on in different environments. This is _by far_ the most important way that the HRRR is used to aid in short-range forecasting. In the long arc of things, the community is very, very excited to see how novel approaches involving things like generative AI could lead to next-generation warn-on-forecast systems and large ensembles. And while this paper is a cool early step in this direction, it's a very small one in the bigger picture.