Sigh... don't make me bind a hotkey to paste my spiel about how these non-primitive-equation-based models are bad at long tails and freak weather...
Ed.: too late! https://news.ycombinator.com/item?id=40577332
Ed.: too late! https://news.ycombinator.com/item?id=40577332
If these methods work, they'll likely improve our ability to model long tails. Traditional NWP is extremely expensive, so cutting-edge models can have either high resolution xor large ensembles. You need high resolution for the detail, but you need large ensembles to see into the tails of the distribution; it's a persistent problem.
In inference, ML-based models run a bit over two orders of magnitude faster than traditional NWP, with the gains split between running on GPUs (possibly replicable) and fantastic levels of numerical intensity thanks to everything being matrix-matrix products (much harder to replicate with conventional algorithms). That opens a lot of freedom to expand ensemble sizes and the like.
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.
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 :)
So while GPUs are ready to crunch the numbers, we don't actually have the numbers yet.