What does AI refer to here? Presumably weather models have been using all sorts of advanced machine learning for decades now, so what’s AI about this that wasn’t AI previously?
What does AI refer to here? Presumably weather models have been using all sorts of advanced machine learning for decades now, so what’s AI about this that wasn’t AI previously?
They're using a graph neural network. From the article - "The team leveraged Google DeepMind's GraphCast model as an initial foundation and fine-tuned the model using NOAA's own Global Data Assimilation System analyses".
> so what’s AI about this that wasn’t AI previously?
The weather models used today are physics-based numerical models. The machine learning models from DeepMind, ECMWF, Huawei and others are a big shift from the standard, numerical approach used for the last decades.
Thanks, I guess my assumption that ML was widely used in forecasting is wrong.
So are they essentially training a neural net on a bunch of weather data and getting a black box model that is expensive to train but comparatively cheap to run?
Are there any other benefits? Like is there a reason to believe it could be more accurate than a physics model with some error bars?
> Are there any other benefits? Like is there a reason to believe it could be more accurate than a physics model with some error bars?
Surprisingly, the leading AI-NWP forecasts are more accurate than their traditional counterparts, even at large scales and long lead times (i.e. the 5-day forecast).
The reason for this is not at all obvious, to the point I'd call it an open question in the literature. Large-scale atmospheric dynamics are a well-studied domain, so physics-based models essentially have to be getting "the big stuff" right. It's reasonable to think that AI-NWP models are doing a better job at sub-grid parameterizations and local forcings because those are the 'gaps' in traditional NWP, but going from "improved modelling of turbulence over urban and forest areas" (as a hypothetical example) to "improvements in 10,000 km-scale atmospheric circulation 5 days later" isn't as certain.
Machine learning _has_ been widely used in weather forecasting, but in a different way than these models. Going back to the 1970's, you never just take the output of a numerical weather model and call it a forecast. We know that limitations in the models' resolution and representation of physical processes lead to huge biases and missed details that cause the forecast to disagree with real world observations. So a standard technique has been to post-process model outputs, calibrating them for station observations where available. You don't need super complex ML to really dramatically improve the quality or skill of the forecast in this manner; typically multiple linear regressions with some degree of feature selection and other criteria will capture most of the variance, especially when you pool observation stations together.
Do these ML models replace the numerical approach completely? A lot of numerical methods are iterative. If the ML model can produce a good initial guess, it might make convergence of an iterative process quite a bit quicker…
Reading the article could have helped with this.
I don’t think it fully addresses the question (maybe I missed something).
AI refers to whatever would have been called "Machine Learning" five years ago.
> Presumably weather models have been using all sorts of advanced machine learning for decades now
This isn't actually true, unless you're considering ML to be just linear regression, in which case we have been using "AI" for >100 years. "Advanced ML" with NN is what's being showcased here.
This ambiguity resulted in some very funny drama on Bluesky: https://bsky.app/profile/nws.noaa.gov/post/3ma754dbtuj2t
Holy shit lmao. Like the wokest tumblr crowd focused into a laser for 2025. How do these guys get through life? Exhausting existence.