As far as I understand, these are still hand designed algorithms using a tiny fraction of possible weather data. Impressive for old school methods. Would be even more awesome to see how far ML could take the state of the art.
Machine learning is all about finding an unknown function that underlies known data. This is sort of the opposite issue: we know the underlying function but can’t compute it.
That said, ML models are being applied to situations where the whether data doesn’t translate directly into known physical quantities, like satellite images (see https://developmentseed.org/projects/hurricane-intensity/)
So even if we had a forecast engine that would perfectly simulate everything given some start state, we wouldn't have enough input data to have an accurate start state.