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.
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/)