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When 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 (unchange

When 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 ago
What does a causal weather model even mean?
- IshKebab 2y ago
  He 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 ago
    Accurate 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 ago
      Weather 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 ago
  Doesn'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 ago
ahhhh 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 ago
Nothing 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.

When 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 ago

What does a causal weather model even mean?

IshKebab 2y ago
He 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 ago
  Accurate 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 ago
    Weather 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 ago
Doesn'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 ago

ahhhh 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 ago

Nothing 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.