Long tails and freak weather are the hottest topics of research in the area of data-driven weather forecasting. ECMWF, highlighted in this article, is attempting to extend its ML forecast system to ensemble predictions (https://www.ecmwf.int/en/about/media-centre/aifs-blog/2024/e...).
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 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.
How do ML models do with chaotic systems in general? I am a beginner in terms of ML, but based on my limited knowledge it seems like ML models would generally do fairly poorly with chaotic systems. Then again, maybe with enough data it doesn't really matter.
I think there is potential in this approach, but I don't think we have the training data yet. We have 100+ years of storm tracks, but we don't have 100+ years of surface observations at the mesonet granularity (like, every city has a reliable weather station). We only measure the upper atmosphere from a few points twice a day. I think that the "chaos" and "butterfly effect" type influences can be controlled, but probably not without really granular surface data, including over the ocean.
So while GPUs are ready to crunch the numbers, we don't actually have the numbers yet.
Ed.: too late! https://news.ycombinator.com/item?id=40577332