I am dearly hoping that they are using the current "AI" craze to talk up the machine learning methods they have presumably been using for a decade at this point, and not that they have actually integrated an LLM into a weather model.
Same. I hope this was written by hardened greybeards who have dedicated their lives to weather prediction and atmospheric modeling, and have "weathered" a few funding cycles.
Graphcast (the model this is based on) has been validated in weather models for a while[1]. It uses transformers, much like LLMs. Transformers are really impressive at modeling a variety of things and have become very common throughout a lot of ML models, there's no reason to besmirch these methods as "integrating an LLM into a weather model"
It’s not an LLM, but it is genAI. It’s based on the same idea of predict-the-next-thing, but instead of predicting words it predicts the next state of the atmosphere from the current state.
The GraphCast paper says "GraphCast is implemented using GNNs" without explaining that the acronym stands for Graph Neural Networks. It contrasts GNNs to the " convolutional neural network (CNN)" and "graph attention network." (GAN?) It doesn't really explain the difference between GAN and a GNN. I think LLMs are GANs. So no, it's not an LLM in a weather model, but it's very similar to an LLM in terms of how it is trained.
Interestingly, while this model is based on a Google Deepmind AI weather model, it's based on a model from 2023 (GraphCast) rather than the WeatherNext 2 model which has grabbed headlines as of late. I'd imagine it takes a while to integrate and test everything, explaining the gap.
Google Research and Google DeepMind also build their models for Google's own TPU hardware. It's only natural for them, but weather centres can't buy TPUs and can't / don't want to be locked to Google's cloud offerings.
For Gencast ('WeatherNext Gen', I believe), the repository provides instructions and caveats (https://github.com/google-deepmind/graphcast/blob/main/docs/...) for inference on GPU, and it's generally slower and more memory intensive. I imagine that FGN/WeatherNext 2 would also have similar surprises.
Training is also harder. DeepMind has only open-sourced the inference code for its first two models, and getting a working, reasonably-performant training loop written is not trivial. NOAA hasn't retrained its weights from scratch, but the fine-tuning they did re: GFS inputs still requires the full training apparatus.
I've seen the Microsoft Aurora team make a compelling argument that weather is an interesting contradiction of the AI-energy-waste narrative. Once deployed at scale, inference with these models is actually a sizable energy/compute improvement over classical simulation and forecasting methods. Of course it is energy intensive to train the model, but the usage itself is more energy efficient.
These are available on Weatherbell[1] (which requires a subscription) now except for the HGEFS ensemble model which I'm guessing will probably be added later. AIGFS is on tropical tidbits which should be free for some stuff[5]. I believe some of the research on this is mentioned in these two[2][3] videos from NOAA weather partners site. They also talk about some of the other advances in weather model research.
One of the big benefits of both the single run (AIGFS) and ensemble (AIGEFS) models is the speed and (less) computation time required. Weather modeling is hard and these models should be used as complementary to deterministic models as they all have their own strengths and weaknesses. They run at the same 0.25 degree resolution as the ECMWF AIFS models which were introduced earlier this year and have been successful[4].
Edit: Spring 2025 forecasting experiment results is available here[6].
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?
These look like staging MVP releases with a full rollout planned for the future. They are only including a few parameters at every 6 hours which is barely interesting to anyone with their feet on the ground.
how about working with Weather Underground to validate predicted weather at ground level? Here in Southern CO would be a perfect place to try this. Weather Underground has thousands of volunteer backyard weather stations, including mine.
I understand that aviation safety is certainly a primary concern for NWS/NOAA but ground level forecasts are also very important for public safety.
Apparently it seems to be impossible with these files and the best AI right now to answer the simple question, will it rain in midtown Manhattan tomorrow?
Protip: Any time you read "AI" in a news article, substitute the phrase "faster, more numerous, and confidently incorrect." I don't think we need "confidently incorrect" weather models. Who is asking for this?
How well do these predict extremes/outliers? Given that I expect these are more "ML" type models, these are somewhat limited to interpolation, rather than extrapolation?
Where I am the last couple of years, the EU model out performed the US model. The local stations tend to show both when sever weather is on its way to the area.
We know how the current admin views science and with the cuts to NOAA done this year, I expect that trend to continue and widen. At least where I am, we get to see both.
I know someone pursuing a degree in meteorology at well known university for the subject and I asked that person if they are being taught about these and other AI weather models, about how they work, how to evaluate them for effectiveness, etc.
The answer: AI is not even covered, at least at the undergrad level. This is just a sample of one, so are any other universities educating future meteorologists on this subject?
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[ 2.7 ms ] story [ 63.7 ms ] thread[1] https://github.com/google-deepmind/graphcast
For Gencast ('WeatherNext Gen', I believe), the repository provides instructions and caveats (https://github.com/google-deepmind/graphcast/blob/main/docs/...) for inference on GPU, and it's generally slower and more memory intensive. I imagine that FGN/WeatherNext 2 would also have similar surprises.
Training is also harder. DeepMind has only open-sourced the inference code for its first two models, and getting a working, reasonably-performant training loop written is not trivial. NOAA hasn't retrained its weights from scratch, but the fine-tuning they did re: GFS inputs still requires the full training apparatus.
A quick search didn't turn up anything about the model's skill or resolution, though I'm sure the data exists.
One of the big benefits of both the single run (AIGFS) and ensemble (AIGEFS) models is the speed and (less) computation time required. Weather modeling is hard and these models should be used as complementary to deterministic models as they all have their own strengths and weaknesses. They run at the same 0.25 degree resolution as the ECMWF AIFS models which were introduced earlier this year and have been successful[4].
Edit: Spring 2025 forecasting experiment results is available here[6].
[1] https://www.weatherbell.com/
[2] https://www.youtube.com/watch?v=47HDk2BQMjU
[3] https://www.youtube.com/watch?v=DCQBgU0pPME
[4] https://www.ecmwf.int/en/forecasts/dataset/aifs-machine-lear...
[5] https://www.tropicaltidbits.com/analysis/models/
[6] https://repository.library.noaa.gov/view/noaa/71354/noaa_713...
This makes me skeptical that it isn’t just politicized Trumpian nonsense.
https://www.nco.ncep.noaa.gov/pmb/products/gens/
https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_s...
I understand that aviation safety is certainly a primary concern for NWS/NOAA but ground level forecasts are also very important for public safety.
It may be a fools errand but makes for an extremely interesting research project. http://climatesight.app if you’re interested in climate markets.
We know how the current admin views science and with the cuts to NOAA done this year, I expect that trend to continue and widen. At least where I am, we get to see both.
The answer: AI is not even covered, at least at the undergrad level. This is just a sample of one, so are any other universities educating future meteorologists on this subject?
I suspect the nail in the coffin was the hurricane season, where NOAA’s model was basically beat by every major AI model. [0]
The GFS also just had its worst year in predicting hurricane paths since 2005. [1] That’s not a trend you want to continue.
[0] https://arstechnica.com/science/2025/11/googles-new-weather-...
[1] https://www.local10.com/weather/hurricane/2025/11/03/this-hu...