It does seem like this is one of those domains where new AI models could thrive. From my understanding, the amount and variety of data necessary to make these models work is huge. In addition to historical data, you've got constant satellite data, weather stations on the ground for data collection, weather balloons going high into the atmosphere multiple times daily per ___location, Doppler radars tracking precipitation, data from ships and other devices in the ocean measuring temps and other info, and who knows what else.
It's incredible that we are able to predict anything this far into the future, but the complexity seems like it lends itself to this kind of black box approach.
*This is all speculation, so I'd be grateful if someone more knowledgeable could tell if if I'm mistaken about these assumptions. It's an interesting topic.
The real reason why this is a ___domain where AI models can thrive is actually because we already have extremely complex, well-developed, physics-based simulations of the atmosphere which can take all of these observations and do useful things with them. So the task we currently ask AI models to do with regards to weather forecasting is actually much, much simpler than what you're proposing - we're simply asking the AI models to emulate what these existing models are capable of doing.
Actually doing useful things with the observations alone is a different story altogether. It's the fact that we've been developing computer simulations of the weather and climate since the 1950's that is the key enabler here.
It's incredible that we are able to predict anything this far into the future, but the complexity seems like it lends itself to this kind of black box approach.
*This is all speculation, so I'd be grateful if someone more knowledgeable could tell if if I'm mistaken about these assumptions. It's an interesting topic.