DeepMind's WeatherNext AI gives forecasters an extra day to predict cyclones
DeepMind's WeatherNext model achieves breakthrough forecasting cyclones
In a Nature paper, Google DeepMind and Google Research, with the National Hurricane Center and UK Met Office, show their WeatherNext model predicts cyclone track, intensity, and wind structure with state-of-the-art accuracy, gaining over 24 hours of lead time—roughly a decade of meteorological progress. The model, now open-sourced, helped forecast Hurricane Melissa's 2025 landfall and runs 1,000-member ensembles in under a minute on a TPU.
On average, our model gives forecasters an extra day’s worth of predictive accuracy: our three-day forecasts are as good as what prior models were able to provide for only the next two days.
- tcumulus
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
- fcanesin
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"
- jen729w
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.
- dgellow
This is really cool, please more of this from the AI folks! That’s way more impactful and interesting than another coding agent
- bhavansig
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."
- ghm2199
> 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?
- kashifr
Check out my pytorch reproduction of the paper here for those interested: https://github.com/NVIDIA/physicsnemo/pull/1660
- purplemoonx
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.