Weather data is a system where we have a really good understanding of the underlying physics but can’t do enough computation to simulate them in a way that’s detailed enough to make truly accurate predictions.
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.
One fundamental problem we have with weather forecasts is that our input data for the starting point is fairly sparse. GFS calculates the forecast on a grid with 13 km horizontal resolution and 64 vertical layers. We don't have accurate weather information at that resolution from all over the globe, so the starting point for the forecasts is a combination of previous simulations and interpolated observational data.
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.
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/)