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DeepMind's WeatherNext model achieves breakthrough forecasting cyclones
- bhavansig 2mo agoFrom 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."
- jen729w 2mo agoI just discovered typhoon/cyclone predictions and they're insane. I get mine via https://zoom.earth 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/ 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/ https://zoom.earth/storms/chan-hom-2026/
- trescenzi 2mo agoIf 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/ https://www.tropicaltidbits.com/
- algo_trader 2mo agoI am getting into cyclone predictions (for maritime scheduling) Is there a basic/freemium resource for past events? Mostly just very coarse spatial/temporal maps of past events
- auspiv 2mo agohttps://www.nhc.noaa.gov/ https://www.nhc.noaa.gov/
- netcraft 2mo agoFor atlantic basin hurricanes (and the occasional one that could impact Hawaii he also does fantastic youtube videos
- moktonar 2mo agoThey should try to forecast earthquakes, that would really be a breakthrough If anything better than random comes out
- Yokolos 2mo agoIs this even feasible with our current sensor data?
- mattlondon 2mo agoGoogle 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.
- Aboutplants 2mo agoThis 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
- phoghed 2mo ago> 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.
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- SubiculumCode 2mo agoI don't think the parent was shitting on Google
- concinds 2mo agoAnd it's been built into every Android phone for years, for free. While Apple is still completely Missing In Action.
- pingou 2mo agoIt seems especially useful for cargo ships, with better predictions they could save some fuel and be safer.
- embedding-shape 2mo agoWake me up once commercial airplanes can take advantage of this and take us across the Atlantic in less than 5 hours.
- notfromhere 2mo agoplanes fly above the weather, so kinda irrelevant. you can cross the atlantic fast with something like the Concorde
- embedding-shape 2mo agoWell, that explains why even cyclones don't make us faster! Obviously the technology I'm talking about would involve the planes going into the cyclone so plane can go faster.
- fallingbananna 2mo agoI 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?
- embedding-shape 2mo agoNo disrespect taken :) It was (obviously) a joke, I'm not seriously waiting for us to hurl airplanes through cyclones to make air travel faster.
- burnt-resistor 2mo agoPredict Wind has a sailing ship course auto router.
- fcanesin 2mo agoMaybe 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"
- trescenzi 2mo agoIronically typhoon forecasting, at this moment, is more valuable. These predictions are matters of life, death, and billions of dollars in damage.
- sweezyjeezy 2mo agoValuable, agreed. But lucrative?
- gniv 2mo agoAre Sol and Fable lucrative? I suspect they also are valuable (to clients) but not lucrative (yet).
- quicekuru 2mo agoI think both have value, but in opposite ways. While WeatherNext prevents costs, models like fable or sol "create profit". 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?
- gniv 2mo agoYes of course. My comment was facetious.
- allannienhuis 2mo agoI'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
- snake_doc 2mo ago> 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
- derbOac 2mo ago"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?
- alpaca9 2mo agoYou can't, and it's one of the biggest problems when trying to use AI for anything.
- hn974izqdv 2mo ago[flagged]
- dgellow 2mo agoThis is really cool, please more of this from the AI folks! That’s way more impactful and interesting than another coding agent
- E-Reverance 2mo agoI get what you generally mean but it’s worth emphasizing that a coding agent probably helped with setting this up
- _alternator_ 2mo agoAccurate 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).
- deleted 2mo ago[deleted]
- tcumulus 2mo agoEverything 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 https://arxiv.org/abs/2212.12794
- polairscience 2mo agoYou 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...
- geertj 2mo agoI would imagine this would be trained on actual historical weather data instead?
- plantain 2mo agoHistorical weather data is discrete. You need continuous state for weather modelling which is currently achieved through conventional reforecasts using those historical observations.
- RandomLensman 2mo agoFrom a quick read: ECMWF and IBTrACS data - the former is model based (with measurement data crunched), the latter purely observational.
- sunshinesnacks 2mo agoPretty much all of the AI weather prediction models are trained on ECMWF ERA5, which is kinda like a numerical weather prediction model run to forecast at t=0. ERA5 is historical weather data, but it’s a “reanalysis” of it.
- throw310822 2mo agoNext step: steering them. (As in Permutation City's "Operation Butterfly".)
- purplemoonx 2mo agoPredicting 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.
- vasco 2mo ago> what the slope of the beach is today (many people don't even know this changes drastically daily and why it is important So why is it important? As far as I know the slope changes AFTER the weather not before as a prediction mechanism but happy to learn
- purplemoonx 2mo agoIt changes constantly on a daily cycle and also on a larger seasonal cycle. Slope defines the water depth and what kind of beach it is - long flat beach means shallow water, steep beach means deep water - it affects if you can you fish there, route lagoon systems and seasonal floodwater, do sports like surfing and skimboarding which are highly dependent on it to the point you can only do it some times of day only part of the year, and other things like whether or not it’s safe to be on that day for visitors. In California, a beach can be flat and inches deep in the Spring at low tide, and be 20ft deep in Winter at high tide with a massive hill you can’t even stand on with basically a river running thru it. That same massive hill can turn into a literal cliff drop off 25ft to a flat beach below overnight. So you come back 24h later and it’s a massive cliff now. Come back 8 hours after that and it’s like a massive flat puddle revealing hundreds of yards of exposed land. It’s not random either, it happens in cycles that you observe if you go there daily to fish or surf, but none of the weather or surf apps can ever predict it. A great example is the amazing skimboarding conditions in Laguna Beach and Santa Cruz during the fall (October, November) there's nothing like it anywhere else in the world. Another good example is surfing the winter swell in Santa Cruz, when a good majority of the beach disappears underwater for months. A wave you're surfing on Halloween is where someone will be laying out tanning in June.
- pbronez 2mo agoCool 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.
- noduerme 2mo agoAsk Gemini why google maps doesn't have a weather layer. Its justifications are defensive rubbish, even for Gemini.
- kashifr 2mo agoCheck out my pytorch reproduction of the paper here for those interested: https://github.com/NVIDIA/physicsnemo/pull/1660 https://github.com/NVIDIA/physicsnemo/pull/1660
- HardCodedBias 2mo agoAnd 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.
- vickychijwani 2mo agoAre you being sarcastic? Even if “Google is struggling so badly” (which it really is not - the narrative will flip again at some point), these efforts will have a lasting impact on the world. Not everything good is about bringing in revenue.
- PunchTornado 2mo agoshareholders are pretty unhappy about demis. think about alphafold. huge investments from the company, tens of billions. at a critical time. and absolutely 0 revenue. it got demis a nobel though. as a shareholder you'd be unhappy too.
- phi0 2mo agoAs a shareholder I am up 75% in a year. Alphafold gained experience makes them better suited to succeed with Isomorphic Labs than anyone else. Research on improved translation gave us the transformer. If you think AI will win but Google will continue failing, there so many better places to allocate your capital right now.
- PunchTornado 2mo agoI guess so. Probably i was being greedy. But there is a feeling in investment community that google open sources too much
- vickychijwani 2mo agoThis line of thought strikes me as very short-term. I’m already a shareholder and employee of the company, but that’s beside the point.
- ronnieron 2mo agoSOTA to be abandoned for something that makes money.
- domeinhornMOT 2mo ago[flagged]
- imthenitto 2mo ago[flagged]
- wafngar 2mo agoSeems 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-ensemble-ai-forecasts-become-operational https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs... https://www.nature.com/articles/s44387-026-00073-7 https://www.nature.com/articles/s44387-026-00073-7
- ycui7 2mo agoon 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.
- PunchTornado 2mo agowhich for google shareholders is pretty bad. we don't get anything from them releasing this.
- ElijahLynn 2mo agoWhat a beautiful outcome of this age of AI!!! And they are open sourcing it too!! #HappyNews
- ObscureScience 2mo agoIt 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, ...)
- ghm2199 2mo ago> 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?
- burnt-resistor 2mo agoSounds a like a model Predict Wind needs.
- InvertedRhodium 2mo agoHuh, 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.
- pseudocoup 2mo agocall me a cynic, but in the age of government underinvestment in science, isn’t this how google could actually change the world?
- counters 2mo agoNo, because 100% of the data that Google trains their models on comes from federal research investment.
- rdli 2mo agoI couldn't find a client of any sort for WeatherNext, so I had Claude write a simple one: https://github.com/richarddli/weatherodds https://github.com/richarddli/weatherodds.
- leoh 2mo agoNice!