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I can assure you that anyone who touches numerical simulations of any kind (physicists, engineers, chemists, biophysicts...etc) has tried their hand at ML based

I can assure you that anyone who touches numerical simulations of any kind (physicists, engineers, chemists, biophysicts...etc) has tried their hand at ML based surrogate models in the last 5 years, so it's not like they aren't being tested. From my experience, they aren't very robust. Weather modeling is actually one of the very few areas where it seems to work half decently.

testfoobar 1mo ago
Why does it work for weather at all? Is there something that the mathematical models are over-simulating? Is weather easier to predict than we thought? Just curious what the intuition is to regarding the success of ML weather modelling...
- DrScientist 1mo ago
  I suspect it's the massively rich, detailed, accurate, multimodal data. For example, you have satellite images, coupled with real on the ground measurements of windspeed, sunlight, air pressure and precipitation going back decades.
- SirHumphrey 1mo ago
  There is just A LOT of data available- usually an order of magnitude more than in any other related problem. And general weather forecasts are not that hard - we have semi useful forecasts for more than 50 years. It’s when you want to do something special: long range, nowcasting of convective storm, other extreme weather etc. that is hard. And even then it’s as much a problem of input data accuracy than the models themselves.
  - micro2588 1mo ago
    Traditional physics based weather models also rely heavily on physical parameterization for sub grid scale processes (think clouds, microphysics of rain sleet snow, etc) so even the deterministic physics models are learned approximations from data.
    - jeffbee 1mo ago
      traditional physics-based weather models also rely heavily on humans looking at the output and the evaluation of the output to discard wacky runs. Let's not pretend that existing physical models of the atmosphere stay on the rails all the time.
      - micro2588 1mo ago
        When you say runs that is an ensemble model forecast which is different than a deterministic model forecast. Any type of weather model sensitive to initial input state errors will have outlier model runs.

testfoobar 1mo ago

Why does it work for weather at all? Is there something that the mathematical models are over-simulating? Is weather easier to predict than we thought? Just curious what the intuition is to regarding the success of ML weather modelling...

DrScientist 1mo ago
I suspect it's the massively rich, detailed, accurate, multimodal data. For example, you have satellite images, coupled with real on the ground measurements of windspeed, sunlight, air pressure and precipitation going back decades.
SirHumphrey 1mo ago
There is just A LOT of data available- usually an order of magnitude more than in any other related problem. And general weather forecasts are not that hard - we have semi useful forecasts for more than 50 years. It’s when you want to do something special: long range, nowcasting of convective storm, other extreme weather etc. that is hard. And even then it’s as much a problem of input data accuracy than the models themselves.
- micro2588 1mo ago
  Traditional physics based weather models also rely heavily on physical parameterization for sub grid scale processes (think clouds, microphysics of rain sleet snow, etc) so even the deterministic physics models are learned approximations from data.
  - jeffbee 1mo ago
    traditional physics-based weather models also rely heavily on humans looking at the output and the evaluation of the output to discard wacky runs. Let's not pretend that existing physical models of the atmosphere stay on the rails all the time.
    - micro2588 1mo ago
      When you say runs that is an ensemble model forecast which is different than a deterministic model forecast. Any type of weather model sensitive to initial input state errors will have outlier model runs.