Weather prediction is the realm of relatively well understood differential equations based on physical properties. Where deep learning can play is in inference of what's going on between observations on the map and timeline.
The problem is you need to define "decent spatial and time resolution." We regularly run kilometer-scale global models that eschew convective parameterizations and instead directly simulate those scales of motion. Over smaller domains we run models pushing beyond LES scales.
Deep learning applications in the field haven't come anywhere even _close_ to tackling these niches of the field yet. SOTA DL-based forecasting tools run at quarter-degree resolution, if even that, and the field hasn't even begun to run hierarchical or multi-scale models coupling coarse models to mesoscale or finer-grained. Hell - you'd be hard-pressed to even find mesoscale DL weather simulations in the first place! So it's a big, big stretch to suggest that DL can achieve "the same quality results" when no one has even offered a cursory glance at an AI application bordering on the SOTA in NWP.
I don't. Weather prediction is a physical process with (I assume) fairly well known laws. That's the wheelhouse of traditional algorithms, not deep learning.
But the "known laws" are too convolved with chaotic behavior, which is why making predictions from them is really hard.
There's an analogy to the 90s dream of replacing fluid dynamics with its differential equations with cellular automata. The claim made about the former is that the "variables in the equations represent real things in the world", but in the end it is all just a story. If the cellular automata/neural modelling can tell a better story ....
> But the "known laws" are too convolved with chaotic behavior, which is why making predictions from them is really hard.
This canard always gets thrown out but the application here is flawed in two very big ways. First, "chaotic behavior" does not mean "unpredictable behavior." Modern numerical weather forecasting already deals with this through ensemble modeling techniques and other approaches designed to capture the statistics of the evolution of the weather, not just a single deterministic state. Anyone selling you a deterministic weather forecast from a single model is robbing you.
Second, DL will suffer the same challenges here because many AI-based weather forecasting tools are auto-regressive, where a model output is used to seed the next step of the forecast. So the AI approach doesn't actually escape this hypothetical limitation (in fact it might compound it badly).
Good points, of which I was aware (mostly). I was trying to dispel the "just because you know the laws of physics you can make predictions based on them (alone)" tone of the GP. But thanks for doing a better job at that than I did.
> in the end it is all just a story. If the cellular automata/neural modelling can tell a better story ....
Uhm no, it is all fluid dynamics. You are correct that weather is highly sensitive to initial conditions, but you are incorrect to conclude that that means DNNs are somehow better at dealing with that.
The only help they might be is in correcting systematic modelling and measurement errors, or speeding up computation (by guessing).
FD is just one of the stories we have to tell that allows us to predict and therefore engineer certain aspects of the universe in which we find ourselves. But it's no more than a story, no matter how fit it may be for the purpose.
Also, the claim would not be that DNNs are better at handling initial conditions, but rather than they are better at spotting patterns (ok, ok, call them correlations) than either ourselves or the systems we've built based on pre-conceived physical models.
