To be extremely pedantic, their general approach isn't new. Folks in the meteorology community have been toying with generative modeling approaches for the past

To be extremely pedantic, their general approach isn't new. Folks in the meteorology community have been toying with generative modeling approaches for the past five years. For example [1] used GANs for super-resolution reconstruction of radar-derived imagery and if you skim the past 4-5 AI conferences at the American Meteorological Society Annual Meeting you'll see multiple people working with similar (albeit simpler - usually weather folks aren't domain experts in AI) modeling approaches. The DeepMind work is fantastic. The media spin isn't - and I don't mean DM's PR team, I mean the opinions shouted from the rooftops across blogs and popular media (including the MIT Technology Review [2]). DM's technique still falls squarely in the domain of extrapolation from recent imagery - exactly what some commenters here are pointing out was developed decades ago. There's little evidence that the new approach can robustly handle the development of new convection or non-linear evolution of mesoscale systems. That's obvious in the animation that's being shared - within the envelope of the linear system over the UK, the structure of storm cells is highly persistent and the overall motion is linear. But you can readily identify areas of unrealistic growth/decay (usually attributed to numerical diffusion in pure image processing techniques, e.g. semi-lagrangian advection of the background OF field). That matters because the practical application(s) of precipitation nowcasting are really limited to things like, "it will rain in XX minutes at location YY". As long as there is rain on the radar, that problem is 'solved' about as precisely as you would ever need. IMHO the biggest innovation here relates to the computational efficiency of the approach. Probably a total beast to train the DGMR system, but inferences in a handful of seconds? That's awesome - it opens up new possibilities for _analysis_ (e.g. sampling a large ensemble from the latent space of plausible future states of the radar imagery and producing highly-tuned probabilistic forecasts or incorporating stochastic mechanism that may yield more realistic projections of cellular growth/decay within linear systems) which have thus far been computationally intractable. The next leap forward in nowcasting is convective initiation. That would be a legitimate game changer in meteorology. [1]: https://www.mdpi.com/2073-4433/10/9/555/htm https://www.mdpi.com/2073-4433/10/9/555/htm [2]: https://www.technologyreview.com/2021/09/29/1036331/deepminds-ai-predicts-almost-exactly-when-and-where-its-going-to-rain/ https://www.technologyreview.com/2021/09/29/1036331/deepmind...