I wouldn't need infinite resources, just a practical integration.
For one, I always thought it would be informative for things like game engines to have a reference point. How fast to streams typically flow in this type of environment? What tree species are even in this geo?
That's pretty cool! Would be great to learn more about your app and how the wave/tide prediction was working. Is there some place to read more about this?
Re question 1: LLMs are already working pretty well for video generation (e.g. see Sora). You can also think of weather as some sort of video generation problem where you have hundreds of channels (one for each variable). So this is not inconsistent with other LLM success stories from other domains.
Re question 2: Simulations don't need to be explainable. Being able to simulate simply means being able to provide a resonable evolution of a system given some potential set of initial conditions and other constraints. Even for physics-based simulations, when run at huge scale like with weather, it's debatable to what degree they are "interpretable".
Agree looking at ensembles is super essential in this context and this is what the end of our blogpost is meant to highlight. At the same time, a good control run is also a prerequisite for good ensembles.
Re NeuralGCM, indeed, our post should have said "*most* of these models". Definitely proves that combining ML and physics models can work really well. Thanks for your comments!
Volcanoes are a tricky one. There are a few volcanic eruptions in historical data, but it's unclear if this is enough to predict reasonably well how such future eruptions (especially at unseen locations) will affect the weather. Would be fun to look at some events and see what the model is doing. Thanks for the suggestion!
Re where do we start. A lot of organisations across different sectors need better weather predictions or simulations that depend on weather. Measuring the skill of such models is a relatively standard procedure and people can check the numbers.
Not immediately, but we will consider open sourcing some of our future work. At least, we definitely plan to be very open with our metrics and how well (or bad) our models are doing.
