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.<p>One of the big benefits of both the single run and ensemble AIGFS 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].<p>[1] <a href="https://www.weatherbell.com/" rel="nofollow">https://www.weatherbell.com/</a><p>[2] <a href="https://www.youtube.com/watch?v=47HDk2BQMjU" rel="nofollow">https://www.youtube.com/watch?v=47HDk2BQMjU</a><p>[3] <a href="https://www.youtube.com/watch?v=DCQBgU0pPME" rel="nofollow">https://www.youtube.com/watch?v=DCQBgU0pPME</a><p>[4] <a href="https://www.ecmwf.int/en/forecasts/dataset/aifs-machine-learning-data" rel="nofollow">https://www.ecmwf.int/en/forecasts/dataset/aifs-machine-lear...</a><p>[5] <a href="https://www.tropicaltidbits.com/analysis/models/" rel="nofollow">https://www.tropicaltidbits.com/analysis/models/</a>
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 <i>improvement</i> over classical simulation and forecasting methods. Of course it is energy intensive to train the model, but the usage itself is more energy efficient.
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?
> Presumably weather models have been using all sorts of advanced machine learning for decades now<p>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.
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".<p>> so what’s AI about this that wasn’t AI previously?<p>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.
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.
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.
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"<p>[1] <a href="https://github.com/google-deepmind/graphcast" rel="nofollow">https://github.com/google-deepmind/graphcast</a>
A lot of shiny new "AI" features being shipped are language models being placed where they don't belong. It's reasonable to be skeptical here, not just because of the AI label, but especially for the troubled history of neural-network based ML methods for weather prediction.<p>Even before LLMs got big, a lot of machine learning research being published were models which underperformed SOTA (which was the case for weather modeling for a long time!) or models which are far far larger than they need to be (e.g. this [1] Nature paper using 'deep learning' for aftershock prediction being bested by this [2] Nature paper using one neuron.<p>[1] <a href="https://www.nature.com/articles/s41586-018-0438-y" rel="nofollow">https://www.nature.com/articles/s41586-018-0438-y</a><p>[2] <a href="https://www.nature.com/articles/s41586-019-1582-8" rel="nofollow">https://www.nature.com/articles/s41586-019-1582-8</a>
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.
Hopefully they weren’t all forced out this year. The NOAA had massive cuts.
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.
I wonder if the new models consider land use change and emissions from aggressive datacenter development and model training...
Neil Jacobs, Ph.D<p>This makes me skeptical that it isn’t just politicized Trumpian nonsense.
Whatever it is, it seems like it might be roughly competitive with ECMWF, the state of the art when it comes to global weather models: <a href="https://www.epic.noaa.gov/ai/eagle-verification/" rel="nofollow">https://www.epic.noaa.gov/ai/eagle-verification/</a><p>A quick search didn't turn up anything about the model's skill or resolution, though I'm sure the data exists.