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

Maybe was this that was the last drop for Sundar.

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 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.

https://zoom.earth/storms/dolphin-2026/

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.

https://zoom.earth/storms/chan-hom-2026/

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."

This is really cool, please more of this from the AI folks! That’s way more impactful and interesting than another coding agent

Check out my pytorch reproduction of the paper here for those interested: https://github.com/NVIDIA/physicsnemo/pull/1660

> One key limitation of our approach is in how uncertainty is handled. We focused on deterministic forecasts and compared against HRES, but the other pillar of ECMWF’s IFS, the ensemble forecasting system, ENS, is especially important for 10+ day forecasts. The non-linearity of weather dynamics means there is increasing uncertainty at longer lead times, which is not well-captured by a single deterministic forecast. ENS addresses this by generating multiple, stochastic forecasts, which model the empirical distribution of future weather, however generating multiple forecasts is expensive. By contrast, GraphCast’s MSE training objective encourages it to express its uncertainty by spatially blurring its predictions, which may limit its value for some applications.

As someone who has learned bayesian statistics in social sciences, isn't this a big deal? There is a reason why risk estimates need to be well understood and *explainable* for certain fields like this. Are you willing to bet a government response should issue an evacuation order 30 miles from the center of a hurricane at location X if the model can't tell you why it produced an uncertainty estimate there — or worst the model changed its mind later?

Seems to be the same methodology as the ECMWF AI ENS operational since mid last year:

https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs...

https://www.nature.com/articles/s44387-026-00073-7

Huh, I was just watching a YouTube video that claimed the limited ability of China to predict weather in the Taiwan Strait is a factor in what makes it hard to invade.

It would be interesting to research more into parameter analysis to learn more about what parameters a trained network has "extracted" that has the strongest predictive power; assuming the model itself has such preductive power. I guess I'm imagining some kind of alternative to embeddings where you get answers like: Predicted percipitation P(accumulated humidity over n days, net wind vector over n days, humity today, temperature today, ...)

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.

Crazy

I couldn't find a client of any sort for WeatherNext, so I had Claude write a simple one: https://github.com/richarddli/weatherodds.

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).

What a beautiful outcome of this age of AI!!! And they are open sourcing it too!!

#HappyNews

They should try to forecast earthquakes, that would really be a breakthrough If anything better than random comes out

It seems especially useful for cargo ships, with better predictions they could save some fuel and be safer.

Next step: steering them. (As in Permutation City's "Operation Butterfly".)

on one side, deepmind makes a lot of advancement in science related application. but, on the commercial side, they struggle to compete with other major LLM providers.

And this is why GDM has to go. It's crazy that when Google is struggling so badly that efforts like this that have no path to revenue at all were funded.

GDM management really thought that they were some kind of charity. UNREAL.

SOTA to be abandoned for something that makes money.

Sounds a like a model Predict Wind needs.

call me a cynic, but in the age of government underinvestment in science, isn’t this how google could actually change the world?

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.

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.

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.

This was my immediate hope too, as fault line resident

Is this even feasible with our current sensor data?