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WeatherNext 3
Paper [pdf]: https://storage.googleapis.com/deepmind-media/papers/weathernext_3.pdf https://storage.googleapis.com/deepmind-media/papers/weather...
- bahmboo 29d agoThey even have an interactive world map with all the layers. It's so cool when an announcement comes with the actual goods.
- ssl-3 25d agoI'm not seeing that kind of demo. I just see a video, a bunch of future-tense words about what could be done by someone, and the requisite hype. Link? edit: The link for the demo is as thus, https://deepmind.google.com/science/weatherlab https://deepmind.google.com/science/weatherlab
- trainingonme 25d agoClick on "Explore Weather Lab" at the bottom of the page
- ssl-3 25d agoThank you! That works. Here's a URL for the demo for those who -- you know -- like to click on links and see stuff happen: https://deepmind.google.com/science/weatherlab https://deepmind.google.com/science/weatherlab
- tidbeck 25d agoAfter signing in I get a 404. 404. That’s an error. The requested URL was not found on this server. That’s all we know.
- zamadatix 25d agoClicking "Try WeatherNext 3" at the top of the page takes you to the section with different ways to use it.
- ssl-3 25d agoNegative. Clicking on any appearance of "Try WeatherNext 3" brings me to this nonsense: WeatherNext 3 is being integrated into the Google products and tools that billions of people rely on – like Search, Maps, and Gemini. The model is also available for enterprise use, across a range of different applications. Gain access to high-resolution forecasts, without model setup. Including real-time operational data and historical forecasts. Try WeatherNext 3 in BigQuery, Earth Engine, Google Maps Platform and Google Cloud Storage. --- None of those words are links or demos. It's just noise.
- bogzz 25d agoIt's literally right below that-- the "Explore Weather Lab" button.
- adrianmonk 25d agoThe button label is "Explore Weather Lab", isn't it? (I'm not trying to be pedantic, but if someone is having trouble finding the button, the exact text is helpful.) Also, here's where the button takes you: https://deepmind.google.com/science/weatherlab https://deepmind.google.com/science/weatherlab
- bogzz 25d agoApologies, was walking and distracted. Corrected my comment.
- zamadatix 25d agoYou're saying these buttons took you to different pages than what was described? https://i.imgur.com/IVv4y0n.png https://i.imgur.com/IVv4y0n.png Keep in mind both button are "the demo". One is for trying out the API yourself, the other is for seeing a pre-made dashboard.
- tcumulus 28d agoPaper: https://storage.googleapis.com/deepmind-media/papers/weathernext_3.pdf https://storage.googleapis.com/deepmind-media/papers/weather... TLDR: the input data for the model now also includes real-time observations (satellite and weather stations) on top of the typical (re)analysis data, improving model resolution, run frequency and timestep frequency.
- dang 25d agoWe'll put that link in the toptext as well. Thanks!
- NostraDavid 26d agoIn the energy world, this should be such a boon over the classic NWP (Numerical Weather Prediction; complex ML models), but I've not seen it implementated. Anyone with experience of these models over classic NWP?
- BlackRabbit1 25d agoA few weather forecast sites in Europe have them on their websites. It's just another forecast you can select and compare in an ensemble. Problem is that the WN3 grid is still quite rough (5km) - but a that's a brutal improvement for many places compared to many other global models. Quite a few country-scale models go down to a 1-2km grid nowadays. This is very helpful in complex geography like mountains and alleys.
- dist-epoch 25d agoYou could use this model as another input into the country-scale models.
- anakaine 25d agoThis would not likely be a great idea since you reduce your ability to understand inputs except for a few parameters. Explainable inputs become very important for many down the line processes used by government and industry alike, because said inputs and their predictive certainty can be quite informative, even critical, for accurate mesoscale prediction. If model members were available, with all the usual measures, thats a fantastic place to start looking at serious inclusion. It doesnt seem thats the case, however.
- counters 25d ago> Problem is that the WN3 grid is still quite rough (5km) - but a that's a brutal improvement for many places compared to many other global models. That's a pretty apples-and-oranges comparison. One would almost always use a high-resolution regional model if you needed certain details for different forecasting applications like renewable energy. It's also worth noting that the 5km outputs are from a model decoder head that was trained against temperature and dewpoint at surface stations. According to the Rasp et al (2026) preprint, this head was designed for continuous sampling; the choice of a 5km grid is arbitrary. What we don't actually know is how well the model handles shocks like a frontal passage or impacts from things like outflow from storms - or even evaporative cooling from precipitation. We are limited to the output that DeepMind publishes; we can't run the model and stress test these things on our own. That's all a long way to say that the 5km resolution is (a) limited to temperature fields, and (b) we don't know if the "additional" resolution has any impact whatsoever on the phenomena that one would typically use a mesoscale-resolving forecast for.
- RockstarSprain 25d agoIs there a convenient way to use this on iOS? AFAIK Google Search and Maps only show rather basic information.
- dmix 25d agoI searched a bit and seems like the answer is no besides this web app https://deepmind.google.com/science/weatherlab https://deepmind.google.com/science/weatherlab > WeatherNext 3 will power weather features in Google Search, the Gemini app and Google Maps, as well as the Google Maps Weather API and Google Earth Engine. https://dataconomy.com/2026/09/04/weathernext-3-ai-forecasts/ https://dataconomy.com/2026/09/04/weathernext-3-ai-forecasts... So I assume the main way would be googling "weather Los Angeles" and it will be powered by the WeatherNext 3 models They also open sourced the last one and are doing B2B/enterprise arrangements so maybe other weather apps are experimenting with it.
- dandaka 25d agoLast time they have only shipped the model on web in mobile...
- mcr70 24d agoMobile app would be awesome.
- mrd3v0 25d ago"Weather Lab is not available in your country or region at this time." cool...
- hankbond 25d agoThe exploration page is really easy to use. Having more reliable weather predictions is a genuinely useful product.
- mkroman 25d agoI disagree.. Feels very sloppy to me: - Time is UTC rather than local by default. - Units are imperial rather than metric (i.e. not basing it on users locale) - The concept of init time is not initially clear at all, so it's confusing when you click the calendar icon to see the weather forecast for a future date only to see it isn't an option, you have to use the slider. - When changing the sidebar to the "detailed" view, most of the hovering element is cut off by the container so you can't actually read it. And it took longer to write this comment than it did to find these issues. It's fine since it's clearly marked as experimental, but I disagree that it's "really easy to use" :p
- ssl-3 24d agoThe legend and map colors seem strange, too. I haven't really put my finger on how or why (and I'm not going to spend much time on it), but: Everything seems dark and desaturated, as if the the only clue we have (color!) for matching things up has been deliberately reduced. And then: It sure does feel like the legend has even more of whatever-that-is going on than the map does. I find it difficult to look at the map and understand the information it relays at the same time.
- sarcasimo 24d agoTime and units are toggled from the 3 dot menu. https://i.imgur.com/Vi6tvGW.png https://i.imgur.com/Vi6tvGW.png Init time makes sense to me, it's when the forecast/model is initialized, letting you look back in the past to watch the change to the present and future. The sidebar thing is weird, but it also can expand out.
- yzmtf2008 24d agoTime being UTC and init times are both meteorological standards, so I'm not sure why those are problems.
- throw0101a 25d agoThere's reduced data available for initial conditions, so I wonder how that will effect things: * https://apnews.com/article/weather-forecasts-worsen-doge-trump-cuts-tornado-da573a044916c06cebcdb92b1f1452e6 https://apnews.com/article/weather-forecasts-worsen-doge-tru... * https://www.independent.co.uk/news/world/americas/us-politics/doge-noaa-weather-usa-b3035937.html https://www.independent.co.uk/news/world/americas/us-politic... A good book on the history of forecasting, The Weather Machine: A Journey Inside the Forecast: * https://www.andrewblum.net/the-weather-machine-2 https://www.andrewblum.net/the-weather-machine-2
- yreg 24d agoDon't plenty of android devices have barometers? I expect Google being Google and recording all that data unless you opt out. Would that be practical for weather forecasting or not really?
- throw0101a 24d ago> Would that be practical for weather forecasting or not really? What are the conditions at 5000 feet, 10000, etc? What is the location of the jet stream and its strength? The reduced number of (e.g.) weather balloons is hindering forecasts (per the links).
- Majromax 24d agoA barometer will give you surface pressure, but that's a field that tends to vary relatively slowly over the surface of the Earth. The calibrated weather stations that exist at every airstrip do a reasonable job of providing these conditions over land, and the residual of "pressure from phones" probably won't help all that much. The data that would be most valuable to initial conditions is upper-atmosphere winds -- this is the kind of data given by weather balloons. In clear air there's no great way to measure this from either the ground or from space. One important supplemental data source here are aviation reports, from planes flying at altitude and particularly trans-oceanic routes. When air traffic was largely curtailed during the early phase of the Covid pandemic, weather forecasting suffered a bit for the lack of data (see eg https://www.ecmwf.int/en/about/media-centre/news/2020/drop-aircraft-observations-could-have-impact-weather-forecasts https://www.ecmwf.int/en/about/media-centre/news/2020/drop-a...).
- anakaine 25d agoThe online viewer could really, really use Wind Direction as a compass bearing. Its super important considering wildfire/bushfire, air quality, ocean-going conditions, and a myriad of other things. It is produced as a set of vectors during model creation, so would be very useful to see.
- bahmboo 25d agoI agree but this seems like a demo for the underlying data set. The data is the product and they want to sell or use it downstream.
- bastawhiz 25d agoIt's great that they're working on this, but I am puzzled at how absolutely awful the forecasts are in the Google weather app for my area. The forecast will show no rain, the radar view shows nothing, meanwhile it's pouring outside and every other app I check shows it. I know I can't expect it to be perfect, but being terribly wrong even one in ten times is enough to tarnish its reputation permanently.
- gonzalohm 25d agoSame experience. I moved to Windy and I just look at the weather radar and make my own prediction. I guesstimate that it has less than 50% accuracy for my area
- timmg 25d agoSame experience. As a former Googler, I wouldn't at all be surprised if this is an integration that is "planned" -- but just not done yet. And some good handful of people are planning to wring a promo out of work. "Implemented weather UI in Android that is 63% more accurate." ;)
- MPSimmons 25d agoWeather 2 doesn't seem to have been an ensemble model. Weather 3 is, so theoretically it can get more accurate outcomes by taking the probabilistic analysis of several models concurrently to determine the most likely weather conditions. I'm building a tool right now that uses an ensemble to determine wind gust likelihood, which is useful for safety critical work on construction sites and the like.
- counters 24d agoWeatherNext 2 was based on the FGN architecture described in [1]. It was explicitly designed and trained to produce ensemble forecasts (it was trained in such a way that the output ensemble optimized a CRPS metrics). In fact, it was a set of 4 different model weights, each of which was seeded with a random noise vector to produce an array of 16 forecasts for a total of 64 ensemble members. WeatherNext 3 trimmed that down from 4 to 2 separate model weights to use. [1]: https://www.nature.com/articles/d41586-026-02643-w https://www.nature.com/articles/d41586-026-02643-w
- bilsbie 25d agoIt’s crazy we’ll never have forecasts as good as dark sky again. What kind of magic were they doing?
- altcognito 25d agoThe only forecasted rainfall for local areas within a very short timeframe. This made it possible to utilize simple factors to predict the near future. It is built into Apple Weather after Apple purchased them.
- isubkhankulov 25d agoIt feels like dark sky was more proactive with push notifications
- notfromhere 24d agoits nowhere near as good as it was in Dark Sky. the apple weather app is pretty bad where I'm at.
- boringg 24d agoWhile that might be true but apple weather still fails compared to dark sky. I still don't use it for radar like I did with dark sky - they effed up the UI.
- eclipticplane 24d ago> It is built into Apple Weather after Apple purchased them. Did Apple lobotomize the tech when they integrated it, or lose access to whatever upstream data Dark Sky had access to? Apple Weather is comically bad (though not nearly as bad as Google's weather searches).
- guywithabike 24d agoI use it in Oregon and it's remarkably accurate at predicting rain, both timing and severity.
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- witweb 24d agoThere is a great article from the German Meteorological Service on WeatherNext 3 and how AI-based weather models will probably co-exist with physics-based ones. https://www.dwd.de/DE/wetter/thema_des_tages/2026/9/6.html https://www.dwd.de/DE/wetter/thema_des_tages/2026/9/6.html (German only)
- roryirvine 24d agoThis was posted recently on Show HN: https://nickleenders.github.io/verisky-scoreboard/?city=london https://nickleenders.github.io/verisky-scoreboard/?city=lond... which tracks performance of the main models (including the AI ones) in various locations. There's also https://nickleenders.github.io/verisky-scoreboard/history.html?v=combined https://nickleenders.github.io/verisky-scoreboard/history.ht... which tracks the trends over time.
- counters 24d agoThere's another tool which seemed to coordinate with the launch of WeatherNext 3 last week, the "Operational WeatehrBench" from Brightband -> https://owb.brightband.com/ https://owb.brightband.com/ It features a couple of AI and NWP model forecasts for comparison.
- paidx 24d ago[flagged]
- rtpg 24d agoReading through the paper and... This seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of predictive sciences are ultimately about mixing together a bunch of inputs to attempt to decipher some output. Do we end up being able to take stuff from here and figure out some new ideas about modelling the climate as a whole?
- desterothx 24d agoSure we do, you can follow the weights so to say, to see which data turns out to be more important vs less important for predictions of a higher quality
- Majromax 24d ago> The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") In a high-level view, it's the result of specialized decoding heads. Traditionally one would take gridded forecast outputs, then process those with comprehensible actions like "find all local pressure minima in the ocean, then filter to ones which correspond to warm cores, etc." to infer (diagnose) the presence of a cyclone. One problem with this is that gridded forecasts suffer from known biases and tradeoffs. For example, a forecast on a ~25km grid is just on the edge of being able to represent the eye of a hurricane (50km scales), and it certainly can't accurately represent the sharp transition of wind in the eyewall. That means that the forecast winds are almost certainly a smoothed (and therefore less intense) version of what observers would see. The WN2 approach (paper: https://www.nature.com/articles/s41586-026-10953-2 https://www.nature.com/articles/s41586-026-10953-2) adds a direct readout head to the model: given latent-space access to the full forecast, it tries to predict the bona-fide cyclone observations (https://www.ncei.noaa.gov/products/international-best-track-archive https://www.ncei.noaa.gov/products/international-best-track-...). It's kind of like a post-processing or bias correction (see for example https://www.ecmwf.int/en/about/media-centre/aifs-blog/2026/ai-tropical-cyclone-forecasts https://www.ecmwf.int/en/about/media-centre/aifs-blog/2026/a..., which applies in physical space), but by having access to the model latent space and by being included in model training it is (probably!) higher-quality than a pure, after-the-fact approach.
- spareloom 24d ago[flagged]
- ankithg 24d ago[flagged]
- tomrod 24d agoSweet. This will be a great layer for our hazard prediction engine.
- darktoto 24d agoNice, coming from a company mainly impacted by the country currently considered the least trustworthy on climate change
- dotinvictim 24d agoI am wasting money on gemini pro sub release gemini 4 already
- mathgeek 24d ago> As our weather grows more extreme, the stakes grow higher. Never let a good crisis go to waste for marketing, especially if the product you're promoting is contributing to exacerbating it.
- Simoluis 24d ago[dead]