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NOAA deploys new generation of AI-driven global weather models
- margalabargala 10mo agoI 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.
- idontwantthis 10mo agoHopefully they weren’t all forced out this year. The NOAA had massive cuts.
- trueismywork 10mo agoNCAR is being dismantled as we speak.
- derbOac 10mo agoI suspect the names of those perpetrating this kind of destruction will become synonymous with ignorance and intellectual cowardice.
- username223 10mo agoSame. 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.
- sigmar 10mo agoGraphcast (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" [1] https://github.com/google-deepmind/graphcast https://github.com/google-deepmind/graphcast
- lynndotpy 10mo agoA 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. 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. [1] https://www.nature.com/articles/s41586-018-0438-y https://www.nature.com/articles/s41586-018-0438-y [2] https://www.nature.com/articles/s41586-019-1582-8 https://www.nature.com/articles/s41586-019-1582-8
- sdenton4 10mo agoNot all transformers are LLMs.
- lynndotpy 10mo agoYes, that is not in contention. Not all transformers are LLMs, not all neural networks are transformers, not all machine learning methods are neural networks, not all statistical methods are machine learning. I'm not saying this is an LLM, margalabargala is not saying this is an LLM. They only said they hoped that they did not integrate an LLM into the weather model, which is a reasonable and informed concern to have. Sigmar is correctly pointing out that they're using a transformer model, and that transformers are effective for modeling things other than language. (And, implicitly, that this _isn't_ adding a step where they ask ChatGPT to vibe check the forecast.)
- Legend2440 10mo agoIt’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.
- adamweld 10mo agoIt is in fact one of the least generalized forms of "AI" out there. A model focused solely on predicting weather.
- astrange 10mo ago"gen" stands for "generative". If you read the GenCast paper they call it a generative AI - IIRC it's an autoregressive GNN plus a diffusion model. Which is surprising to me because I didn't think it would work for this; they're bad at estimating uncertainty for instance.
- Majromax 10mo ago> Which is surprising to me because I didn't think it would work for this; they're bad at estimating uncertainty for instance. FGN (the model that is 'WeatherNext 2'), FourCastNet 3 (NVIDIA's offering), and AIFS-CRPS (the model from ECMWF) have all moved to train on whole ensembles, using a cumulative ranked probability score (CRPS) loss function. Minimizing the CRPS minimizes the integrated square differences of the cumulative density function between the prediction and truth, so it's effectively teaching the model to have uncertainty proportional to its expected error. GenCast is a more classic diffusion-based model trained on a mean-squared-error-type loss function, much like any of the image diffusion models. Nonetheless it performed well.
- lukeschlather 10mo agoThe 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.
- astrange 10mo ago> I think LLMs are GANs. They aren't, but both of them are transformer models. nb GAN usually means something else (Generative Adversarial Network).
- lukeschlather 10mo agoI used GAN to mean graph attention network in my comment, which is how the GraphCast paper defines transformers. https://arxiv.org/pdf/2212.12794 https://arxiv.org/pdf/2212.12794 I was looking at this part in particular: > And while Transformers [48] can also compute arbitrarily long-range computations, they do not scale well with very large inputs (e.g., the 1 million-plus grid points in GraphCast’s global inputs) because of the quadratic memory complexity induced by computing all-to-all interactions. Contemporary extensions of Transformers often sparsify possible interactions to reduce the complexity, which in effect makes them analogous to GNNs (e.g., graph attention networks [49]). Which kind of makes a soup of the whole thing and suggests that LLMs/Graph Attention Networks are "extensions to transformers" and not exactly transformers themselves.
- astrange 10mo agoOh yeah, GNN (graph neural network) is the common term, "graph attention network" is pretty confusing because a GAN is a totally different architecture. (Well, not necessarily architecture. Training method?)
- optimalsolver 10mo agoYou're absolutely right! That was a category 5. Thanks for pointing that out.
- Workaccount2 10mo agoInterestingly, 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.
- sigmar 10mo agoI've been assuming that, unlike graphcast, they have no intention to make weathernext 2 open source.
- tndl 10mo agoThat seems to be the case from what I've heard.
- Majromax 10mo agoGoogle 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/cloud_vm_setup.md#running-inference-on-gpu 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.
- username223 10mo agoWhatever it is, it seems like it might be roughly competitive with ECMWF, the state of the art when it comes to global weather models: https://www.epic.noaa.gov/ai/eagle-verification/ https://www.epic.noaa.gov/ai/eagle-verification/ A quick search didn't turn up anything about the model's skill or resolution, though I'm sure the data exists.
- ryuuchin 10mo agoThey run at 0.25 degree resolution (same as ECMWF AIFS models).
- apawloski 10mo agoI'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.
- goodolddays9090 10mo ago[dead]
- klysm 10mo agoIt's definitely interesting that some neural nets can reduce compute requirements, but that's certainly not making a dent on the LLM part of the pie.
- lukeschlather 10mo agoSam Altman has made a lot of grandiose claims about how much power he's going to need to scale LLMs, but the evidence seems to suggest the amount of power required to train and operate LLMs is a lot more modest than he would have you believe. (DeepSeek reportedly being trained for just $5M, for example.)
- lovich 10mo agoI saw a claim that DeepSeek had piggybacked off of some aspect of training that ChatGPT had done, and so that cost needed to be included when evaluating DeepSeek. This training part of LLMs is still mostly Greek to me, so if anyone could explain that claim as true or false and the reasons why, I’d appreciate it
- TomatoCo 10mo agoSo LLMs predict the next token. Basically, you train them by taking your training data that's N words long and, for X = 1 to N, and optimizing it to predict token X using tokens 1 to X-1. There's no reason you couldn't generate training data for a model by getting output from another model. You could even get the probability distribution of output tokens from the source model and train the target model to repeat that probability distribution, instead of a single word. That'd be faster, because instead of it learning to say "Hello!" and "Hi!" from two different examples, one where it says hello and one where it says hi, you'd learn to say both from one example that has a probability distribution of 50% for each output. Sometimes DeepSeek said it's name is ChatGPT. This could be because they used Q&A pairs from ChatGPT for training or because they scraped conversations other people posted where they were talking to ChatGPT. Or for unknown reasons where the model just decided to respond that way, like mixing up some semantics of wanting to say "I'm an AI" and all the scraped data referring to AI as ChatGPT. Short of admission or leaks of DeepSeek training data it's hard to tell. Conversely, DeepSeek really went hard into an architecture that is cheap to train, using a lot of weird techniques to optimize their training process for their hardware. Personally, I think they did. Research shows that a model can be greatly improved with a relatively-small set of high quality Q&A pairs. But I'm not sure the cost evaluation should be influenced that much, because the ChatGPT training price was only paid once, it doesn't have to be repaid for every new model that cribs its answers.
- ryuuchin 10mo agoThese 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]. [1] https://www.weatherbell.com/ https://www.weatherbell.com/ [2] https://www.youtube.com/watch?v=47HDk2BQMjU https://www.youtube.com/watch?v=47HDk2BQMjU [3] https://www.youtube.com/watch?v=DCQBgU0pPME https://www.youtube.com/watch?v=DCQBgU0pPME [4] https://www.ecmwf.int/en/forecasts/dataset/aifs-machine-learning-data https://www.ecmwf.int/en/forecasts/dataset/aifs-machine-lear... [5] https://www.tropicaltidbits.com/analysis/models/ https://www.tropicaltidbits.com/analysis/models/ [6] https://repository.library.noaa.gov/view/noaa/71354/noaa_71354_DS1.pdf https://repository.library.noaa.gov/view/noaa/71354/noaa_713...
- tndl 10mo agoReally exciting to see NOAA finally make some progress on this front, but the AIGFS suite likely won't outperform ECMWF's AIFS suite any time soon. The underlying architecture between AIFS and GraphCast/AIGFS is pretty similar (both GNNs), so there won't likely be a model-level improvement. And most of ECMWF's edge lies in its superior 4DVar data assimilation process. AIGFS is still being initialized on NOAA's hybrid 4DEnVar assimilation process as far as I understand it, which is still not as good as straight up 4DVar unfortunately.
- kkylin 10mo agoCame here to say this -- looks like the data assimilation is still done the "old fashioned" way. I wonder how long that will last?
- padjo 10mo agoWhat 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?
- tomww 10mo agoThey'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.
- padjo 10mo agoThanks, 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?
- Majromax 10mo ago> 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.
- CalChris 10mo agoNeil Jacobs, Ph.D This makes me skeptical that it isn’t just politicized Trumpian nonsense.
- luc_ 10mo agoI wonder if the new models consider land use change and emissions from aggressive datacenter development and model training...
- adamredwoods 10mo agoIs there a primer for reading these files? https://www.nco.ncep.noaa.gov/pmb/products/gens/ https://www.nco.ncep.noaa.gov/pmb/products/gens/ https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gefs.php https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_s...
- jasonmarks_ 10mo agoNo, not really. If you are just looking to work with the data you want to read about extracting from grib2 format. One of the faster ways off the ground is to use the Pywgrib2_s python package and iterate against the model files using python to extract the fields that are interesting. I have a container on docker hub that has pywgrib compiled with all its dependencies if you want to tinker. pywgrib https://www.cpc.ncep.noaa.gov/products/people/lxu/cookbook/about/ https://www.cpc.ncep.noaa.gov/products/people/lxu/cookbook/a... containerized https://hub.docker.com/repository/docker/jmarks213/containerized_wgrib2/general https://hub.docker.com/repository/docker/jmarks213/container...
- counters 10mo agoIt's far, far simpler for users to simply use eccodes[1], particularly as implemented in xarray[2]. [1]: https://github.com/ecmwf/eccodes https://github.com/ecmwf/eccodes [2]: https://docs.xarray.dev/en/stable/index.html https://docs.xarray.dev/en/stable/index.html
- jasonmarks_ 10mo agoThat is another viable path, no more no less.
- jasonmarks_ 10mo agoThese 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.
- DaveZale 10mo agohow 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.
- cramcgrab 10mo agoApparently 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?
- defrost 10mo agoTake an umbrella if you're concerned. What is possible is to know with near certainty the rough tonnage of water that will fall across a wide area grain region in an upcoming week. Useful for the reliable production of grain (timing seeding, harvesting, spraying, etc) in the millions of tonnes.
- ryandrake 10mo agoProtip: 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?
- Incipient 10mo agoHow 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?
- gvkhna 10mo agoWorking on AI driven weather predictions to make money on prediction markets. The accuracy of WeatherNext 2 is astounding. It may be a fools errand but makes for an extremely interesting research project. http://climatesight.app http://climatesight.app if you’re interested in climate markets.
- cl42 10mo agoHave you considered launching your own weather prediction market instead? Parametric insurance, energy traders, etc could be good markets.
- gvkhna 10mo agoNo I haven’t but I think the lack of liquidity as a chicken and egg is a huge barrier to entry in these markets specifically. They are small right now but there are climate derivatives on the Chicago mercantile exchange so this isn’t a new concept I think. Could you tell me more? https://discord.gg/HPpN42SKQ https://discord.gg/HPpN42SKQ
- lisp2240 10mo agoAll these years later and we still don’t have the minute-accurate forecasts that Dark Sky had before Apple shut it down. Living in the future sucks.
- Tempest1981 10mo agoMy friend's kids would ride their bikes to school in the morning, and on rainy days, check Dark Sky to find the driest time window. It was usually quite accurate.
- vitorgrs 10mo agoIf you mean minute-accurate forecast for the next 4-6 years... That's called Nowcasting, and yes, it exists. Bing Weather have it, ACCU Weather as well. Rain viewer too. I believe Google already implemented on Pixel Weather at least. IMO the best of these are Bing Weather and Rain Viewer, both provide rich maps showing where the rain it's going and all too. And how much.
- NetMageSCW 10mo agoApple definitely broke something when they incorporated Dark Sky into their weather ecosystem and it isn’t nearly as good in my locality.
- Terretta 10mo agoApple purports to still have this, but it is indeed less reliable than Dark Sky. However, the author of Carrot for iOS implemented his own flavor of this* and it's remarkably decent. * According to Gruber interview around the same time Carrot introduced an entire Broadway musical about the conflict between the Carrot AI and her Maker (the dev). Which, while made with AI, is rather more listenable than the typical weather app.
- matt3210 10mo agoDoes ai mean LLM here or just normal software?
- jmclnx 10mo agoWhere 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.
- gazabbqparty 10mo ago[flagged]
- JoeDaDude 10mo agoI 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?
- NetMageSCW 10mo agoAre meteorologists even the right people to be training on how to produce and improve better modeling of weather?
- tylervigen 10mo agoThis is big news. For decades, NOAA’s model has basically just been a huge Fortran physics simulation. Now they are making the leap to AI. 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-model-impressed-during-its-first-hurricane-season/ https://arstechnica.com/science/2025/11/googles-new-weather-... [1] https://www.local10.com/weather/hurricane/2025/11/03/this-hurricane-season-two-forecast-models-stand-out-but-for-very-different-reasons/ https://www.local10.com/weather/hurricane/2025/11/03/this-hu...