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.
There's also the efficiency argument from new capability: even a tiny bit better weather forecast is highly economically valuable (and saves a lot of wasted energy) if it means that 1 city doesn't have to evacuate because of an erroneous hurricane forecast, say. But how much would it cost to do that with the rivals? I don't know but I would guess quite a lot.
And one of the biggest ironies of AI scaling is that where scaling succeeds the most in improving efficiency, we realize it the least, because we don't even think of it as an option. An example: a Transformer (or RNN) is not the only way to predict text. We have scaling laws for n-grams and text perplexity (most famously, from Jeff Dean et al at Google back in the 2000s), so you can actually ask the question, 'how much would I have to scale up n-grams to achieve the necessary perplexity for a useful code writer competitive with Claude Code, say?' This is a perfectly reasonable, well-defined question, as high-order n-grams could in theory write code without enough data and big enough lookup tables, and so it can be answered. The answer will look something like 'if we turned the whole earth into computronium, it still wouldn't be remotely enough'. The efficiency ratio is not 10:1 or 100:1 but closer to ∞:1. The efficiency gain is so big no one even thinks of it as an efficiency gain, because you just couldn't do it before using AI! You would have humans do it, or not do it at all.
> even a tiny bit better weather forecast is highly economically valuable (and saves a lot of wasted energy) if it means that 1 city doesn't have to evacuate because of an erroneous hurricane forecast
Here is the NOAA on the improvements:
> 8% better predictions for track, and 10% better predictions for intensity, especially at longer forecast lead times — with overall improvements of four to five days.(1)
I’d love someone to explain what these measurements mean though. Does better track mean 8% narrower angle? Something else? Compared to what baseline?
And am I reading this right that that improvement is measured at the point 4-5 days out from landfall? What’s the typical lead time for calling an evacuation, more or less than four days?
To have a competitive code writer with ngrams you need more than to "scale up the ngrams" you need to have a corpus that includes all possible codes that someone would want to write. And at that point you'd be better off with a lossless full text index like an r-index. But, the lack of any generalizability in this approach, coupled with its markovian features, will make this kind of model extremely brittle. Although, it would be efficient. You just need to somehow compute all possible language before hand. tldr; language models really are reasoning and generalizing over the domain they're trained on.
Obviously much simpler Neural Nets, but we did have some models in my domain whose role was to speed up design evaluation.
Eg you want to find a really good design. Designs are fairly easy to generate, but expensive to evaluate and score. Understand we can quickly generate millions of designs but evaluating one can take 100ms-1s. With simulations that are not easy to GPU parallelize. We ended up training models that try to predict said score. They don’t predict things perfectly, but you can be 99% sure that the actual score designs is within a certain distance of said score.
So if normally you want to get the 10 best design out of your 1 million, we can now first have the model predict the best 1000 and you can be reasonably certain your top 10 is a subset of these 1000. So you only need to run your simulation on these 1000.
Sam 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.)
If its more energy efficient it is doing something different there is no guarantee that its more accurate long term. Weather is horrible difficult to predict and we are only just alright at it. If LLM are guessing at the same rate we are calculating but I am doubtful
"Area for future improvement: developers continue to improve the ensemble’s ability to create a range of forecast outcomes."
Someone else noted the models are fairly simple.
My question is "what happens if you scale up to attain the same levels of accuracy throughout? Will it still be as efficient?"
My reading is that these models work well in other regions but I reserve a certain skepticism because I think it's healthy in science, and also because I think those ultimately in charge have yet to prove reliable judges of anything scientific.
> "it's more efficient if you ignore the part where it's not"
Even when you include training, the payoff period is not that long. Operational NWP is enormously expensive because high-resolution models run under soft real-time deadlines; having today's forecast tomorrow won't do you any good.
The bigger problem is that traditional models have decades of legacy behind them, and getting them to work on GPUs is nontrivial. That means that in a real way, AI model training and inference comes at the expense of traditional-NWP systems, and weather centres globally are having to strike new balances without a lot of certainty.
It's more efficient anyway because the inference is what everyone will use for forecasting. Researchers will be using huge amounts of compute to develop better models, but that's also currently the case, and it isn't the majority of weather simulation use.
There's an interesting parallel to Formula One, where there are limits on the computational resources teams can use to design their cars, and where they can use an aerodynamic model that was previously trained to get pretty good outcomes with less compute use in the actual design phase.
I suggest reading up on fixed costs vs variable costs and why it is generally preferable to push costs to fixed.
Assuming you’re not throwing the whole thing out after one forecast, it is probably better to reduce runtime energy usage even if it means using more for one-time training.
I mean that’s cute, but surely you can add up the two parts (single training plus globally distributed inference) and understand that the net efficiency would be an improvement?
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].
Really 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.
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"
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.
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.
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.
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.
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.
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?
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…
> 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.
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?
No, 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.
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.
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.
No 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.
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 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?
If 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.
Apple 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.
My 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.
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?
These models actually outperform traditional methods on many fronts, including accuracy a lot of the time. They are technically generative AI models, but they're definitely not LLMs.
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.
There's also the efficiency argument from new capability: even a tiny bit better weather forecast is highly economically valuable (and saves a lot of wasted energy) if it means that 1 city doesn't have to evacuate because of an erroneous hurricane forecast, say. But how much would it cost to do that with the rivals? I don't know but I would guess quite a lot.
And one of the biggest ironies of AI scaling is that where scaling succeeds the most in improving efficiency, we realize it the least, because we don't even think of it as an option. An example: a Transformer (or RNN) is not the only way to predict text. We have scaling laws for n-grams and text perplexity (most famously, from Jeff Dean et al at Google back in the 2000s), so you can actually ask the question, 'how much would I have to scale up n-grams to achieve the necessary perplexity for a useful code writer competitive with Claude Code, say?' This is a perfectly reasonable, well-defined question, as high-order n-grams could in theory write code without enough data and big enough lookup tables, and so it can be answered. The answer will look something like 'if we turned the whole earth into computronium, it still wouldn't be remotely enough'. The efficiency ratio is not 10:1 or 100:1 but closer to ∞:1. The efficiency gain is so big no one even thinks of it as an efficiency gain, because you just couldn't do it before using AI! You would have humans do it, or not do it at all.
> even a tiny bit better weather forecast is highly economically valuable (and saves a lot of wasted energy) if it means that 1 city doesn't have to evacuate because of an erroneous hurricane forecast
Here is the NOAA on the improvements:
> 8% better predictions for track, and 10% better predictions for intensity, especially at longer forecast lead times — with overall improvements of four to five days.(1)
I’d love someone to explain what these measurements mean though. Does better track mean 8% narrower angle? Something else? Compared to what baseline?
And am I reading this right that that improvement is measured at the point 4-5 days out from landfall? What’s the typical lead time for calling an evacuation, more or less than four days?
(1)https://www.noaa.gov/news/new-noaa-system-ushers-in-next-gen...
To have a competitive code writer with ngrams you need more than to "scale up the ngrams" you need to have a corpus that includes all possible codes that someone would want to write. And at that point you'd be better off with a lossless full text index like an r-index. But, the lack of any generalizability in this approach, coupled with its markovian features, will make this kind of model extremely brittle. Although, it would be efficient. You just need to somehow compute all possible language before hand. tldr; language models really are reasoning and generalizing over the domain they're trained on.
Now that we’ve saved infinite energy all carbon tax credit markets are unnecessary! Big win for the climate! pollutes
Obviously much simpler Neural Nets, but we did have some models in my domain whose role was to speed up design evaluation.
Eg you want to find a really good design. Designs are fairly easy to generate, but expensive to evaluate and score. Understand we can quickly generate millions of designs but evaluating one can take 100ms-1s. With simulations that are not easy to GPU parallelize. We ended up training models that try to predict said score. They don’t predict things perfectly, but you can be 99% sure that the actual score designs is within a certain distance of said score.
So if normally you want to get the 10 best design out of your 1 million, we can now first have the model predict the best 1000 and you can be reasonably certain your top 10 is a subset of these 1000. So you only need to run your simulation on these 1000.
Heuristical branch-and-bound
It'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.
Sam 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.)
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And an LLM can be more energy efficient than a human -- and that's precisely when you should use it.
That's precisely when, (insert hand wavy motion), we should use any of this.
If its more energy efficient it is doing something different there is no guarantee that its more accurate long term. Weather is horrible difficult to predict and we are only just alright at it. If LLM are guessing at the same rate we are calculating but I am doubtful
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This jumped out at me as well - very interesting that it actually reduces necessary compute in this instance
The press statement is full of stuff like this:
"Area for future improvement: developers continue to improve the ensemble’s ability to create a range of forecast outcomes."
Someone else noted the models are fairly simple.
My question is "what happens if you scale up to attain the same levels of accuracy throughout? Will it still be as efficient?"
My reading is that these models work well in other regions but I reserve a certain skepticism because I think it's healthy in science, and also because I think those ultimately in charge have yet to prove reliable judges of anything scientific.
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"it's more efficient if you ignore the part where it's not"
> "it's more efficient if you ignore the part where it's not"
Even when you include training, the payoff period is not that long. Operational NWP is enormously expensive because high-resolution models run under soft real-time deadlines; having today's forecast tomorrow won't do you any good.
The bigger problem is that traditional models have decades of legacy behind them, and getting them to work on GPUs is nontrivial. That means that in a real way, AI model training and inference comes at the expense of traditional-NWP systems, and weather centres globally are having to strike new balances without a lot of certainty.
It's more efficient anyway because the inference is what everyone will use for forecasting. Researchers will be using huge amounts of compute to develop better models, but that's also currently the case, and it isn't the majority of weather simulation use.
There's an interesting parallel to Formula One, where there are limits on the computational resources teams can use to design their cars, and where they can use an aerodynamic model that was previously trained to get pretty good outcomes with less compute use in the actual design phase.
I suggest reading up on fixed costs vs variable costs and why it is generally preferable to push costs to fixed.
Assuming you’re not throwing the whole thing out after one forecast, it is probably better to reduce runtime energy usage even if it means using more for one-time training.
I mean that’s cute, but surely you can add up the two parts (single training plus globally distributed inference) and understand that the net efficiency would be an improvement?
[dead]
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].
[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...
Really 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.
Came here to say this -- looks like the data assimilation is still done the "old fashioned" way. I wonder how long that will last?
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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"
[1] https://github.com/google-deepmind/graphcast
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.
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
[2] https://www.nature.com/articles/s41586-019-1582-8
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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.
It is in fact one of the least generalized forms of "AI" out there. A model focused solely on predicting weather.
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You're absolutely right! That was a category 5. Thanks for pointing that out.
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.
> I think LLMs are GANs.
They aren't, but both of them are transformer models.
nb GAN usually means something else (Generative Adversarial Network).
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Hopefully they weren’t all forced out this year. The NOAA had massive cuts.
NCAR is being dismantled as we speak.
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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.
inb4 it’s actually an intern maintaining a 3000+ line markdown file
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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 been assuming that, unlike graphcast, they have no intention to make weathernext 2 open source.
That seems to be the case from what I've heard.
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?
2 replies →
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…
2 replies →
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.
This 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-...
[1] https://www.local10.com/weather/hurricane/2025/11/03/this-hu...
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?
Are meteorologists even the right people to be training on how to produce and improve better modeling of weather?
Is there a primer for reading these files?
https://www.nco.ncep.noaa.gov/pmb/products/gens/
https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_s...
No, 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/a... containerized https://hub.docker.com/repository/docker/jmarks213/container...
It's far, far simpler for users to simply use eccodes[1], particularly as implemented in xarray[2].
[1]: https://github.com/ecmwf/eccodes [2]: https://docs.xarray.dev/en/stable/index.html
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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: 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.
They run at 0.25 degree resolution (same as ECMWF AIFS models).
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.
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.
Working 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 if you’re interested in climate markets.
Have you considered launching your own weather prediction market instead?
Parametric insurance, energy traders, etc could be good markets.
No 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
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 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?
All 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.
If 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.
Did you mean 4-6 hours?
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No.
Apple 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.
Apple definitely broke something when they incorporated Dark Sky into their weather ecosystem and it isn’t nearly as good in my locality.
My 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.
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?
Take 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.
I wonder if the new models consider land use change and emissions from aggressive datacenter development and model training...
Does ai mean LLM here or just normal software?
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Neil Jacobs, Ph.D
This makes me skeptical that it isn’t just politicized Trumpian nonsense.
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?
These models actually outperform traditional methods on many fronts, including accuracy a lot of the time. They are technically generative AI models, but they're definitely not LLMs.
If it were LLM I’d agree, but that’s not the case here.