DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

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• Hacker News
• > 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

  by snake_doc
• "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?

  by derbOac
• 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.

  by pbronez
• 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, ...)
  by ObscureScience
• 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.
  by InvertedRhodium
• 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

  by wafngar
• > 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?

  by ghm2199
• Check out my pytorch reproduction of the paper here for those interested: https://github.com/NVIDIA/physicsnemo/pull/1660
  by kashifr
• This is really cool, please more of this from the AI folks! That’s way more impactful and interesting than another coding agent
  by dgellow
• I get what you generally mean but it’s worth emphasizing that a coding agent probably helped with setting this up
  by E-Reverance
• 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."
  by bhavansig
• 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/
  by jen729w
• 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.

  1: https://www.tropicaltidbits.com/

  by trescenzi
• 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"

  by fcanesin
• he got promoted to chief scientist and them being an expert at all modeling will really help compared to being llm only
  by nbardy
• Ironically typhoon forecasting, at this moment, is more valuable. These predictions are matters of life, death, and billions of dollars in damage.
  by trescenzi
• 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
  by tcumulus
• everything in AI is not focused on LLM, if you think so then that's because you are in LLM bubble. The big idea with LLM is that it's generative AI, the generative could be anything! Not just large languages, we have seen break through in image generation, video, audio, but guess what. Anything that you have enough data and given data you can predict what comes next can have gen AI applied, so we are seeing it with physical actions so robots get trained to generate the next move, and I think the same thing applies to weather forecast. It's predictable too given enough data
  by segmondy
• Is there any website publishing these forecasts? I imagine NWS/NOAA isn’t doing anything different yet on their public websites.
  by hammock
• LLM’s are an evolutionary deadend with the power and resource demands to make and run them for diminishing returns, but that may be okay as their own reasoning capabilities are big enough, and spur investment into the supporting infrastructure for them
  by yieldcrv
• 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.

  by pbronez
• 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...
  by polairscience
• Rumor is that part of the disruption at GDM these past few months also involved people not wanting to be bound to strictly LLM research.
  by WarmWash
• For what it's worth, that paper is a spiritual successor to Keisler (2022) which was the first published work that took this approach: https://arxiv.org/abs/2202.07575
  by counters
• I can assure you that anyone who touches numerical simulations of any kind (physicists, engineers, chemists, biophysicts...etc) has tried their hand at ML based surrogate models in the last 5 years, so it's not like they aren't being tested. From my experience, they aren't very robust. Weather modeling is actually one of the very few areas where it seems to work half decently.
  by numbers_guy