Weather forecasting does create ensemble models to help constrain their forecasts. They’ll adjust some of their inputs in each model, mainly as a way of embedding the uncertainty in the measured data, then run that model and see if it changed.

This resembles AI on one level, but it’s at a dramatically different scale. An ensemble may contain a few hundred runs at most, but an AI needs tens of thousands of data points at minimum. In order to make predictions like what google is saying they can do, they’d need to train on billions or maybe trillions of data points.

This is still fundamentally different than ensemble modeling though. Ensembles are physically informed and the perturbations are based on real assumptions. Each model in an ensemble is based on validated physics equations. An AI model would undermine that completely. You can’t possibly describe the underlying equations because there aren’t any, so you can’t analyze its accuracy or propose a more accurate model, you’re just stuck with a bunch of coefficients that you’ll never understand.

I’ve worked in climate modeling, and this kind of AI work is nothing more than an electricity sink for at least a decade, maybe forever.