The weather forecasts that people rely upon are currently generated through a complex set of stages, each taking several hours to run on bespoke supercomputers. Aside from daily usage, the development, maintenance and deployment of these complex systems requires significant time and large teams of experts.
More recently, research by Huawei, Google, and Microsoft has demonstrated that one component of this pipeline, the numerical solver (which calculates how weather evolves over time), can be replaced with AI resulting in faster and more accurate predictions. This combination of AI and traditional approaches is now being deployed by the European Centre for Medium Range Weather Forecasts.
But with Aardvark, researchers have replaced the entire weather prediction pipeline with a single, simple machine learning model. The new model takes in observations from satellites, weather stations and other sensors and outputs both global and local forecasts. This fully AI driven approach means that predictions are now achievable in minutes on a desktop computer.
When using just 10% of the input data of existing systems, Aardvark already outperforms the United States national GFS forecasting system on many variables and it is also competitive with United States Weather Service forecasts that use input from dozens of weather models and analysis by expert human forecasters.
One of the most exciting aspects of Aardvark is its flexibility and simple design. Because it learns directly from data it can be quickly adapted to produce bespoke forecasts for specific industries or locations, be that predicting temperatures for African agriculture or wind speeds for a renewable energy company in Europe.
This contrasts to traditional weather prediction systems where creating a customised system takes years of work by large teams of researchers.
This capability has the potential to transform weather prediction in developing countries where access to the expertise and computational resources required to develop conventional systems is not typically available.
Professor Richard Turner, Lead Researcher for Weather Prediction at the Alan Turing Institute and Professor of Machine Learning in the Department of Engineering at the University of Cambridge, said: “Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries.”
“Importantly, Aardvark would not have been possible without decades of physical-model development by the community, and we are particularly indebted to ECMWF for their ERA5 dataset which is essential for training Aardvark.”
Anna Allen, lead author from the University of Cambridge, said “These results are just the beginning of what Aardvark can achieve. This end-to-end learning approach can be easily applied to other weather forecasting problems, for example hurricanes, wildfires, and tornadoes. Beyond weather, its applications extend to broader Earth system forecasting, including air quality, ocean dynamics, and sea ice prediction.”
Matthew Chantry, Strategic Lead for Machine Learning at ECMWF said: “We have been thrilled to collaborate on this project which explores the next generation of weather forecasting systems — part of our mission to develop and deliver operational AI-weather forecasting while openly sharing data to benefit science and the wider community. It is essential that academia and industry work together to address technological challenges and leverage new opportunities that AI offers. Aardvark’s approach combines both modularity with end-to-end forecasting optimisation, ensuring effective use of the available datasets.”
Dr. Chris Bishop, Technical Fellow and Director, Microsoft Research AI for Science, said: “Aardvark represents not only an important achievement in AI weather prediction but it also reflects the power of collaboration and bringing the research community together to improve and apply AI technology in meaningful ways.”
Dr Scott Hosking, Director of Science and Innovation for Environment and Sustainability at The Alan Turing Institute, said: “Unleashing AI’s potential will transform decision-making for everyone from policymakers and emergency planners to industries that rely on accurate weather forecasts. Aardvark’s breakthrough is not just about speed, it’s about access. By shifting weather prediction from supercomputers to desktop computers, we can democratize forecasting, making these powerful technologies available to developing nations and data-sparse regions around the world.”
Next steps for Aardvark include developing a new team within the Alan Turing Institute led by Professor Richard Turner, exploring the potential to deploy Aardvark in the global south and integrating the technology into the Institute’s wider work to develop high-precision environmental forecasting for weather, oceans and sea ice.
link to paper: https://www.nature.com/articles/s41586-025-08897-0
Reference: Allen, A., et al. 2025. ‘End-to-end data-driven weather prediction’, Nature, DOI: 10.1038/s41586-025-08897-0
What are all the D students going to for a job.
They can take the openings being created by the deportation of illegal aliens. Agriculture, landscaping, some construction roles.
See what Aardvark’s track record is.
Two partial answers, after going to Nature and reading the prepublication abstract.
Hype ho! Weather is intrinsically unpredictable on the scale of ten days out but sometimes much sooner. NWS says I have a 50% chance of rain tonight, which will be right whatever happens, but it is not a forecast.
That just means that if 100 things fall out of the sky, 50 of them will be rain. That right there is weather math. Boom!
Oh man… Now I wanna know what the other 50 things falling out of the sky are going to be. Gotta forecast that includes that?
Highly dependent on conditions. Could be hail, could be snow, could be frogs…
Most of them will be micrometeorites, some will be frozen water from commercial aircraft toilets.
25 cats and 25 dogs.
Rud, this is a slide from a presentation I made to a university group
.Dr Jennifer Marohasy – who has a PhD from the University of Queensland in Australia had been studying weather well before the 2011 floods and became interested in forecasting. She turned to Prof. John Abbot (at CQU), who studied chemistry at Imperial College London and later received a PhD from McGill University in Canada, and was also interested in forecasting with Artificial Neural Networks (ANN) a type of Artificial Intelligence (AI)
They wrote their first paper on ANN in 2012
Abbot J., & J. Marohasy, 2012. Application of artificial neural networks to rainfall forecasting in Queensland , Australia. Advances in Atmospheric Sciences, Volume 29, Number 4, Pages 717-730. doi: 10.1007/s00376-012-1259-9 .
They had written some 15 peer reviewed papers since 2012
Around 2012 (after a number of published papers) They proposed their method to the Australian Bureau of Meteorology , which was not interested. “They didn’t argue with the fact that our forecasts were actually more accurate for the 17 locations in Queensland with long historical records,” In a recent interview (with Freedom Research on the net)
Marohasy notes, adding that they were also told that historical data could no longer be used to forecast the future weather conditions, as the climate had changed and was on a new trajectory.
In a recent paper ( J. Abbot & J. Marohasy, Int. J. Sus. Dev. Plann. Vol. 12, No. 7 (2017) 1117–1131 )
Abbot & Marohasy write “Until May 2013, official forecasts were based on a statistical scheme using an El Niño Southern Oscillation (ENSO) index as the primary predictor. The BOM switched to the use of the Predictive Atmospheric Model for Australia (POAMA), a general circulation model (GCM), in June 2013. GCMs, however, do not generally perform well at forecasting rainfall, despite substantial efforts to enhance skill over three decades”
A feature of both the BOM models is that the predictions are in percentage of the mean.
Other slides were of my 132 yr record of rainfall both in a table and in a graph(there is no trend). Slides of BOM 3 month forecasts and the actual for the periods showing how far BOM is out.
Rud did you know monthly rainfall is close to a Poisson distribution (in which the SD is close to the average). Rainfall is not chaotic (as someone below writes) it is periodic. Other slides had data on floods in Brisbane tied to SOI & IPO. There is no evidence of climate change in SEQld and certainly no connection with CO2. I also had slides of the predictions for some Qld sites from A &M showing forecasts to be within 90% of actual rainfall (in mms) upto 3 years. Please do some more research on ANN and AI.
Are model temperature predictions any better than the historical average?
I checked Weatherbug for my zip code yesterday. It said that on Tuesday it would only be cloudy. I thought, yes, maybe I can mow. Now it says rainy.
10 days my big white …
As I understand it, a long range forecast is up to 10 days out, short range is up to 3 days out.
If the 10 day forecast is, say, 90% chance of rain, that means they project a 90% chance it will rain somewhere in the forecast area.
If the 3 day forecast is, say, 90% chance of rain, that means they project it will rain some in 90% of the forecast area.
(Of course, both projections can change as the days count down to the present.)
PS If my understanding is inaccurate, I welcome corrections.
I may have misunderstood/misremembered what a NWS meteorologist told me years ago. (Where I worked we reported our daily rainfall to his office. I was one of the contacts.)
My zip code is a small area, 98277, North Whidbey Island. This was only two days out, and still changed in less than 24 hours. It’s rubbish.
Yep – totally agree Jeff. When it comes to rain I find the radar invaluable as it often shows the forecast to be inaccurate. And yesterday it supposed to be cloudy all day but there was plenty of sun.
I’ve had weather apps tell me it was sunny and clear, when in reality it was overcast from horizon to horizon.
Jeff, gezza, I was asking if my understanding of 10 day vs 3 day forecast from a meteorologist viewpoint was accurate, not weather …er… whether or not the forecast itself was accurate. 😎
Sounds like they to arrange some testing against the real future forecast.
Let it input the same data used by forecasters and then compare the forecast.
If it really is consistently more accurate, they have a marketable product.
My guess is there is nothing wrong with MET except for the liars and cheaters running it. If those same people were running Aardvark we would have the same problems. Even machines need supervisors, if you have crappy supervisors you will have a crappy operation.
Good luck with AI and weather forecasting. Remember, the first rule of weather forecasting is “look out of the window”. When I google something to do with geology, Nevada, and gold I get a useful answer 1/3 of the time, nonsense 1/3 of the time, and utter BS 1/3 of the time. I hope it doesn’t rain on your parade.
My sense is that weather forecasters do an adequate job of forecasting daily temperatures. If they are off by a couple of degrees nobody is going to get too upset.
However, precipitation is entirely different. One doesn’t want to be caught without an umbrella (bumper-shoot?) when it is needed. One doesn’t want to plan a picnic or backyard party and have it rained out.
Yet, my experience tells me that, perhaps except for Mediterranean climates, more people are disappointed by precipitation forecasts than are pleased. Part of the problem is that I’ve never seen a definition of what is meant by a 50% chance of rain. Does that mean there is an equal chance of rain/no rain in the forecast area, or does it mean that half the area can expect rain, albeit they can’ say what parts will experience the rain. Does it mean that only 50% of the forecasters predict rain? Is a 50% forecast a place holder for no decision until it gets closer to the forecast day? It is hard to be wrong when terms aren’t defined.
Clyde, a local (Raleigh) forecaster recently answered exactly that question for a viewer and said it was the first one: equal chance of rain or no rain.
That may or may not apply to other forecasters – that was this one talking about his own forecasts.
Went and read the prepublication paper abstract at Nature, posted 20 March 2025.
Aardvark Weather AI shows local skill out to 10 days using no numerical weather processing (NWP, aka weather models). BUT that is only as good as present NWP local skill.
So the abstract ends ‘with more funding, Aardvark may get better in the future’. In my opinion, much ado about nothing.
“shows local skill out to 10 days”
Define “skill”. Most weather forecasts are no better than horoscopes.
In the paper, ‘skill’ means the same result as the alternative NWP forecast. So, two horoscopes of equal ‘skill’.
“When the moon is in the Seventh House
And Jupiter aligns with Mars
Then rain will flood the area …”
So, if this holds up, individuals would be able to do local forecasts out to maybe 10 days on a laptop. That would be a game changer. If, in fact it allows good local resolution.
It will still be blind to the indirect solar forcing of NAO/AO anomalies driving mid latitude heat and cold waves, which can be predicted centuries ahead.
https://docs.google.com/document/d/e/2PACX-1vQemMt_PNwwBKNOS7GSP7gbWDmcDBJ80UJzkqDIQ75_Sctjn89VoM5MIYHQWHkpn88cMQXkKjXznM-u/pub
There will be heat and cold waves centuries from now.
See, done.
Glendower: “I can call spirits from the vasty deep.
Hotspur: “Why, so can I, or so can any man; But will they come when you do call for them?”
Everyone talks about the weather, etc.
I wouldn’t count on it being better, the liers in the field are insidious.
The full article is here. It doesn’t claim to give more accurate local forecasts than NWP (eg the MO). It just needs less computer time. It does claim more accurate global forecasts, but who does global forecasts anyway? Or at least, who looks at them.
Once you bought the computer, it’s use time doesn’t matter much in weather forecasting.
It may mean that the forecasters won’t need to upgrade computers as often.
What in the world is a global forecast? There is no global weather.
There is global weather, just as there is US weather, UK weather etc. You see that on TV weather maps, with fields of temperature, pressure, winds etc.
A regional forecast starts from a global one, but seeking better resolution. So it uses the global as a boundary condition. Since the boundary is further away, accuracy there is not so critical. Aadvark also provides the global forecast, and because of less computer time, they can do a better one. BUt it doesn’t help much with the regional.
I see it is a low resolution global map of local weather. Hard to see how one could repeatedly measure accuracy on such a thing to say that one way was more accurate than another. Especially since we do not know what the actual local weather is on most of the globe.
Who lives their daily lives in “global” weather?
and as Nick says –
“who looks at them”?
Weather conditions are entirely local, almost locality by locality.
If one is flying from NY to SF for a business meeting, they may want to know what clothes to pack, and they may want to double-check that they packed some air-sickness pills if there is going to be storms between airports. Therefore, there is a need for local weather forecasts over the expanse of a continent.
Try https://www.ventusky.com/?p=45;-90;3&l=rain-3h
50% chance of rain or snow, temperatures either above or below freezing, and a chance of partly cloudy skies. Plan accordingly.
Thanks Nick.
Another unintelligible cut-and-paste masquerading as an article from Watts explained.
Another unintelligible cut-and-paste masquerading as a comment from TheFinalNail self explanatory.
“It doesn’t claim to give more accurate local forecasts than NWP”
Stokes may not be very accurate, but he is quick to come up with an excuse to support his position.
Here is their graph of error vs HRES, a NWP forecast, temp in top row. Low is good. You can see that for CONUS and Europe, HRES does slightly better, except around the tenth day. In West Africa and Pacific, Aardvark does better. That is because, as they say, poorer countries just use the global data without downscaling, and the AARDVARK global is better.
From the full article –
“Raw data and resulting processed products are fed into a data assimilation system which combines these with an initial guess from a previous forecast to generate a global approximation of the current state of the atmosphere.”
You really are a gullible wee GHE disciple, aren’t you? A global approximation based on a guess?
Two things – it is not possible to predict the future state of the atmosphere any more skilfully than a smart 12 year old, and –
Adding CO2 to air does not make it hotter (just in case anyone thinks it does. Some people are gullible enough to believe in the GHE!).
The forecasts will be based on … what? With any observation feedback in almost-real time?
No AI can do what the young lady in the picture above can do.
You have her name and phone number, then!
In the last 20 years, the Boards of many large companies, with Board members not particularly skilled in computer matters, have accepted a vague proposition that computing should replace people in jobs when it can, with the advantage that computers cost less than people. Fewer people also means fewer offices, therefore many small local branches have been closed.
The next stage is now in progress. Artificial Intelligence is starting to replace the present combination of computers and people, with the inspiration to make commerce even freer of staff than now.
This is all fine if several assumptions are proved to be correct. I concentrate here on whether or to what degree computers can replace people in retail trade, like banking or merchandise purchasing, for examples.
Now, the human brain is a rather complex organ, capable of concepts like emotion, justice and special pleading, that are currently alien to retail sales computing. There will be a clash of person and computer when the computer is not programmed, or cannot be programmed because of lack of anticipation of a circumstance, so a solution has to be found when the client person desires something that the computer cannot or will not deliver.
Mostly, at present, such tussles are solved by computer advice to suck it up, followed by advice to contact a help desk. Often, the help desk is initially another computer function, with the result that it is increasingly common to find no person-to-person communication. There are situations, millions of them, where a person requires an answer that the computer cannot provide. People are becoming rather unfriendly to being herded like cattle instead of being treated with personal civility, respect, understanding. The concept of programming for exceptions is unworkable when all exceptions are not anticipated. Currently, they are not.They might never be.
From this unhappy base, the move is now growing towards even more remote computing with AI. While it is easy to see some AI benefits, few are really comprehending or discussing AI unsolved problems.
HAL: “I’m sorry, Dave. I’m afraid I can’t do that.”
Geoff S
Whether or not Aardvark forecasts better than current forecasting systems, AI forecasting will improve weather forecasting. Google’s GraphCast is already doing better than legacy systems. From the GraphCast website:
GraphCast predicts weather conditions up to 10 days in advance more accurately and much faster than the industry gold-standard weather simulation system – the High Resolution Forecast (HRES), produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).
Once again proving my point that the NOAA and NWS no longer need to be in the business of forecasting weather (and “climate”). They should focus on observations and data collection only and let private systems like GraphCast analyze and predict. NOAA has a herd of scientists analyzing global warming. Most of them, if not all, are afflicted with global-warming-catastrophe groupthink. Lay off all the analysts, save money, and get NOAA and its subsidiaries out of the politics of global warming.
Can it forecast floods like the Met Office does?
A copy of Old Moore’s Almanac and a pine cone is better than the Met office.
Say it with me kids, AI does what it is programed to do, just like every other computer system.
Has it yet projected an extreme weather event?
I am (of course) skeptical. The system presumably uses several decades of ERA Reanalysis data. The trouble is that with even 50 years of global reanalyses, the range of combinations of weather situations around the world interacting is nowhere near to those possible in the future. They don’t cover the range of possible situations. So, yeah, such a system could approach what a physical model can provide, and more likely could usefully augment those physical models, but I don’t see them replacing physics-based models running from ever-changing combinations of initial conditions. But I could be wrong.
“more likely could usefully augment those physical models”
I think you are spot on here. The AI group that I oversee at a national lab is looking into how AI can be used with models to reduce the amount of data needed for training. This, in principle, would reduce the amount of compute time and (hopefully) increase accuracy. The ability to forecast out more than 7 to 10 days will probably not change.
Isn’t 10 days ahead close to the theoretical limit, based on Chaos Theory?
Bill, I don’t know of any theoretical limit based on chaos theory. “Forecasting” is assumption generally based on the past.
“I predict the Sun will rise tomorrow” is an example.
In a fully deterministic chaotic system, the present determines the future, but the approximate present does not determine the approximate future. A trivial example is that the wind speed and direction in 30 seconds is determined by the present state of the universe, but you cannot “predict” it with any precision. There might be a lull, or a gust, the direction change briefly (or not so briefly), and so on.
The daily weather report for Nepal during the tourist season used to be “Mainly fair throughout the Kingdom.” And it generally was.