Reading through the paper and...

This seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value.

I imagine a lot of predictive sciences are ultimately about mixing together a bunch of inputs to attempt to decipher some output. Do we end up being able to take stuff from here and figure out some new ideas about modelling the climate as a whole?