The limitations of the non-physics-based models is under rapid exploration and the bounds of their utility will be a lot more clear in a year or two. For now, they seem to outperform physics-based models in larger scales. The future may be a hybrid: https://arxiv.org/abs/2407.06100v2
Well, as I said, I would expect them to outperform at large scales, specifically because they're learning and memoising large, stable patterns (ed. in the sense of teleconnections) at low wavenumbers.
I hope they have a switch to turn it off if we ever mess up and go back to a single-cell Hadley configuration :)
Hybrid models are almost certainly the future, i.e., developing structural equations and filling in data or modeling gaps with relatively small NNs for approximation.
Hybrid models have the conceptual edge, but it's not yet obvious that they'll become the dominant AI forecasting paradigm.
The hybridization acts as a strong regularizer. This is a good thing, but it's not yet obvious that it's a necessary thing for short to medium-term forecasts. There seems to be enough extant data that pure learning models figure out the dynamics relatively easily.
Hybrid models are more obviously appropriate if we think about extending forecasts to poorly-constrained environments, like non-modern climates or exoweather. You can run an atmospheric model like WRF but with parameters set for Mars (no, not for colonization, but for understanding dust storms and the like), and we definitely don't have enough data to train a "Mars weather predictor."
The difficulty in training NeuralGCM is that one has to backpropagate over many dynamics steps (essentially all the time between data snapshots) to train the NN parameterizations. That's very memory-intensive, and for now NeuralGCM-like models run at coarser resolutions than fully-learned peers.
a senior weather modeler that is US-based but works on some of the Euro models spoke recently about the use of AI from his perspective. The comments were more than half communication skills with the audience, so it was not a math lecture. Overall I believe that this UC Berkeley speaker said almost exactly what the parent-post says.
