Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The original Graphcast paper is worth a read if you think this is interesting: https://arxiv.org/abs/2212.12794
You say this as if you don't need he MWP models to train the AI models? The accuracy of the AI Prediction depends entirely on the quality of the training dataset...
Historical weather data is discrete. You need continuous state for weather modelling which is currently achieved through conventional reforecasts using those historical observations.
Insightful paper, thanks for sharing. Two things stand out to me.
First, it reinforces that you want methods that get better with more data. It emphasizes that the current approach cannot improve based on historic data - that’s the opportunity that ML based approaches exploit.
Second, it highlights that mature legacy solutions are tough competitors. They benefit from extensive tuning and real world feedback. Even when you have a genuinely better approach, it will take meaningful time & effort to achieve the current standard.
Patience and solid long term strategy are needed to make progress in these situations. You need confidence that your approach will win long term, backed by enough money & time to prove yourself correct.
I know this is uncharitable and I am wrong but I am having trouble coming up with concrete scenarios where you die with 2 days notice but survive with 3. I am nonethless a believer that more accurate forecasting has value.
Could you imagine a scenario where from warning to complete evacuation takes more than two days? Evacuating a whole area is a hard task, particularly once you start looking at more complex problems (elderly, prisons, hospitals).
I feel like the details of this are highly dependent on the confidence of the warning; moving large numbers of people (particularly elderly) will result in some deaths regardless. I guess more time to do it should help though.
I've always assumed that insurance and/or government departments that would spend money due to storms would be the ones funneling money to these sorts of efforts. It's not exactly something you can easily sell directly to individuals who would benefit. It would be pretty dystopian for them to sell subscriptions for an extra 24 hrs notice on the next typhoon :P
Predicting big weather events is not that hard even with 50 year old technology.
What's hard is predicting details, like exactly where it will rain, what the slope of the beach is today (many people don't even know this changes drastically daily and why it is important), wave height, ocean depth today where people swim, water temperature, shorebreak, and knowing with certainty when rain becomes ice/sleet/snow and what routes will be affected, accurate wind speed, accurate temperature throughout different parts of the region, and what the weather next week will be.
We can't do any of those things with conventional equipment, but we can with training data and algorithms. So I'm very excited about the role of algorithmic prediction in weather, but not for the kind we already know how to forecast (without AI) but being able to glean useful insights that matter to people who live, work and play in the weather.
I just discovered typhoon/cyclone predictions and they're insane. I get mine via https://zoom.earth (whose iPhone app is terrific).
Here's a selection from Typhoon Dolphin, currently sitting off the east coast of China.
Dolphin continues its slow, trochoidal Z motion, generally heading westward deeper into the East China Sea. Over the past 12 hours, the system completed another cyclonic loop and has decelerated, exhibiting continued meandering prior to establishing a sustained westward track.
The erratic motion witnessed over the past two days is attributable to a weak steering environment produced by a break in the subtropical ridge 2 over Korea, combined with the dynamics where the inner core is cocooned within a much larger parent circulation.
While the general steering pattern is weak, a mesoscale deep-layer ridge is seen building over southern Japan.
Here's Chan-hom, which threatens to make my birthday a windy day here in northern Japan.
Intensity guidance is in good agreement overall. However, the JTWC forecast is placed lower than all the guidance save for Google DeepMind over the next 36 hours, before joining the consensus envelope (which peaks at 95 km/h (50 knots) at 60 hours) through the remainder of the forecast.
If you’re just getting into this tropical tidbits[0] is my go to for more raw data. Less pretty than zoom earth but also an interesting place to see what the models are predicting on each of their runs which is then interesting to compare to actual forecast guidance.
From the tagline in the article: "WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model."
Cool how they integrated both huge machine-scale data and smaller human-curated data for this project.
> The model was co-trained on two distinct data modalities: global weather dynamics and expert-curated historical cyclone observations. By training end-to-end on nearly 20 terabytes of global atmospheric data and the historical IBTrACS database spanning nearly 5,000 historical storms, the model learns complex atmospheric patterns and how to model extreme weather.
> We can now generate a single 15-day forecast in less than a minute on a TPU, empowering forecasters to quickly evaluate the probability distribution of potentially devastating tail-risks.
"This has surprised scientists, and it remains an open research question to fully understand how our models produce such accurate predictions at this resolution."
Also crazy.
Seems important to understand why something does what it does, in the very least to know when it might not?
Forecasting earthquakes via ML should be possible but is very strongly limited by data. We have ~50 years of reasonably good seismological catalogs for most of the world. The seismic cycle (the sequence of major earthquake, reloading, major earthquake on a single section of fault) is generally thousands of years except at the fastest-slipping faults. There are very few sections of faults where we have seismological observations of multiple events, and for >90% of faults, we don't even know when the last earthquake was. There are geologic methods to help with this, but they are labor intensive and often yield error bars of hundreds to thousands of years, because the earthquakes don't produce radiocarbon signatures directly; the geologists use e.g. charcoal older and younger layers as available to bracket the timing, and many faults do not have suitable geologic sites to preserve the earthquake deformation and bracket the timing.
I do think it's possible that thorough exploration of the data that do exist can yield broader patterns that apply to many regions, but earthquake behavior has a lot of complexities and different fault systems may behave differently.
A lot of the hope is for coupling physical simulators to ML and the existing datasets to better understand the physics and then work from there, but this is typically cutting-edge HPC work, which limits the pace of research and the number of researchers.
Google has the early warning system that gives people maybe 20-30s to e.g. turn off gas, stop vehicles, get under something solid. There was a lot of news recently about how this saved many thousands of lives in Venezuela I think it was.
But hey let's all keep shitting on Google because their coding agent is slightly worse than SOTA.
This needs to be tied to a whole house shutoff system because if I get an alert I’m not thinking about shutting off my gas or water. Having a system that shut those off immediately would be great
Accurate weather forecasting has been one of the major achievements of the 20th and 21st century. Computing power is a central piece of this story, but it's also important to remember that the government infrastructure in place to collect ground-truth current weather data is utterly critical to these model's successes. From launching weather balloons to running global weather-monitoring satellites, the scientists and systems at NOAA/NWS (and in this case, the UK counterparts) provide critical expertise and data.
I say this because it seems that earlier announcements where industrial deep neural nets "outperformed NOAA" likely encouraged the slash-and-burn Trump administration in its gutting of critical activities and centers of expertise at NOAA. The impression that industry can predict weather better than the government agencies totally misses that the industrial models utterly rely on government data for inputs. In fact, almost all weather reports you see---weather.com, TV, etc.---are just lightly repackaged products that NOAA provides for free on weather.gov (which you can access for free without ads).
I don't mean to be disrespectful... but, why would you consider tech intended to save lives and resources less of a deal, than slightly faster flights over the Atlantic?
First, it reinforces that you want methods that get better with more data. It emphasizes that the current approach cannot improve based on historic data - that’s the opportunity that ML based approaches exploit.
Second, it highlights that mature legacy solutions are tough competitors. They benefit from extensive tuning and real world feedback. Even when you have a genuinely better approach, it will take meaningful time & effort to achieve the current standard.
Patience and solid long term strategy are needed to make progress in these situations. You need confidence that your approach will win long term, backed by enough money & time to prove yourself correct.
Demis: "I have a new amazing breakthrough"
Sundar: "Great! We really need a answer to Sol and Fable"
Demis: "They are completely owned in typhoon forecasting"
I can think of 10 examples how one could make money with fable. With WeatherNext? Only 10 examples of preventing costs.
Taking this, maybe naive, thought further, profits have no upper limit (except resources) while costs can only save so much?
What's hard is predicting details, like exactly where it will rain, what the slope of the beach is today (many people don't even know this changes drastically daily and why it is important), wave height, ocean depth today where people swim, water temperature, shorebreak, and knowing with certainty when rain becomes ice/sleet/snow and what routes will be affected, accurate wind speed, accurate temperature throughout different parts of the region, and what the weather next week will be.
We can't do any of those things with conventional equipment, but we can with training data and algorithms. So I'm very excited about the role of algorithmic prediction in weather, but not for the kind we already know how to forecast (without AI) but being able to glean useful insights that matter to people who live, work and play in the weather.
Here's a selection from Typhoon Dolphin, currently sitting off the east coast of China.
https://zoom.earth/storms/dolphin-2026/Here's Chan-hom, which threatens to make my birthday a windy day here in northern Japan.
https://zoom.earth/storms/chan-hom-2026/1: https://www.tropicaltidbits.com/
Is there a basic/freemium resource for past events? Mostly just very coarse spatial/temporal maps of past events
> The model was co-trained on two distinct data modalities: global weather dynamics and expert-curated historical cyclone observations. By training end-to-end on nearly 20 terabytes of global atmospheric data and the historical IBTrACS database spanning nearly 5,000 historical storms, the model learns complex atmospheric patterns and how to model extreme weather.
Crazy
Also crazy.
Seems important to understand why something does what it does, in the very least to know when it might not?
I do think it's possible that thorough exploration of the data that do exist can yield broader patterns that apply to many regions, but earthquake behavior has a lot of complexities and different fault systems may behave differently.
A lot of the hope is for coupling physical simulators to ML and the existing datasets to better understand the physics and then work from there, but this is typically cutting-edge HPC work, which limits the pace of research and the number of researchers.
But hey let's all keep shitting on Google because their coding agent is slightly worse than SOTA.
Reminder, we can do two or even more things. In fact, we can even simultaneously hold contradictory opinions.
I say this because it seems that earlier announcements where industrial deep neural nets "outperformed NOAA" likely encouraged the slash-and-burn Trump administration in its gutting of critical activities and centers of expertise at NOAA. The impression that industry can predict weather better than the government agencies totally misses that the industrial models utterly rely on government data for inputs. In fact, almost all weather reports you see---weather.com, TV, etc.---are just lightly repackaged products that NOAA provides for free on weather.gov (which you can access for free without ads).
Obviously the technology I'm talking about would involve the planes going into the cyclone so plane can go faster.