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do you want a causal weather model or one that can predict the weather well?
by SpaceManNabs 2y ago
do you want a causal weather model or one that can predict the weather well?
- jacobgkau 2y agoWhen climate change occurs (which it is), we're going to want a causal one so we can actually make a forecast instead of predicting only based on past (unchanged) data.
- hotstickyballs 2y agoWhat does a causal weather model even mean?
- IshKebab 2y agoHe meant one that is physically based and interpretable, rather than a black box based on pattern matching. But I'll take the accurate black box any day, at least for weather forecasting. Climate modelling is a totally different thing.
- labster 2y agoAccurate black box is fine until it blows a forecast and people die, and you want to fix it for the next time. Take the situation with Hurricane Otis — what do you do if an AI doesn’t detect cyclogenesis 24-48 hours beforehand? Are we sure tuning the model to detect this event will improve forecast skill in general, or will it make it worse?
- nradov 2y agoWeather forecasting models will gradually improve over time but it's unreasonable to expect perfect accuracy. That is mathematically impossible in a chaotic system. Some people will always die in extreme weather events.
- SpaceManNabs 2y agoDoesn't exactly answer your question, but maybe this link will give enough intuition so that i can be lazy and hand-wave away an answer https://en.wikipedia.org/wiki/The_Book_of_Why#Chapter_1:_The_Ladder_of_Causation https://en.wikipedia.org/wiki/The_Book_of_Why#Chapter_1:_The...
- SpaceManNabs 2y agoahhhh i was hoping someone wouldn't mention this. i wanted to add a caveat but it made my comment look ugly lol. but like someone else says weather and climate models forecast on different scales and for different purposes usually.
- ted_dunning 2y agoNothing prevents people from training new models. And note that these new models based on machine learning are already better at predicting extreme events. This is because existing models are not built entirely from first principles but rather include a lot of heuristics to describe important phenomena like cloud formation and albedo effects. That means that traditional models are just as rooted in weather-as-it-was as the machine learning models are. The big difference is that it takes a lot of work to identify the dependencies in traditional models while it takes less work to retrain the machine learning model.
- counters 2y agoThe current crop of AI weather models captures a surprisingly robust array of "core dynamics". E.g., recent work by Hakim and Masanam [1] reproduce classic dynamical core test cases on AI weather models and show that in many cases they robustly capture the expected dynamical responses to these highly idealized setups - setups far outside the bounds of what the models are trained on. What this means is that the causal or dynamical mechanisms producing a particular flow regime can very directly be inferred from the model. [1]: https://journals.ametsoc.org/view/journals/aies/3/3/AIES-D-23-0090.1.xml https://journals.ametsoc.org/view/journals/aies/3/3/AIES-D-2...
- SpaceManNabs 2y agothanks for providing evidence that these two things don't need to contradict.