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WeatherNext 2: Our most advanced weather forecasting model
- xd1936 11mo agoPricing, I think? https://developers.google.com/maps/billing-and-pricing/pricing#environment-pricing https://developers.google.com/maps/billing-and-pricing/prici...
- LogicFailsMe 11mo agoThis year, the wild variance in hourly weather reports on my phone has really been something. I attributed it to likely budget cuts as a result of DOGE, but if those forecasts came from Google itself the whole time, all is clear now.
- politelemon 11mo agoI find that unlikely, my forecasts for much of Europe and East Asia have been consistently accurate.
- dylan604 11mo agoHow do DOGE implemented budget cuts affect European or East Asian forecasts? Those are not the forecasts that someone suspecting departmental DOGEing to be a fault.
- NoiseBert69 11mo agoIf the US does less data gathering (balloon starts, buoy maintenance, setting up weather huts in super remote sites, etc.) it will affect all forecasts. Models all use a "current world state" of all sensors available to bootstrap their runs. Similar thing happened during the beginning of Covid-19: they are using modified cargo/passenger planes to gather weather data during their routine trips. Suddenly this huge data source was gone (but was partially replaced by the experimental ADM-Aeolus satellite - which turned out to be a huge global gamer changer due to its unexpected high quality data)
- integralid 11mo agoBut GP said they only USED TO blame DOGE, and blame Google now?
- LogicFailsMe 11mo agoYeah... So you know that's not the United States right? Though judging by the down votes, it's quite triggering for some and I can't say which side when I pivot from blaming DOGE to blaming bad AI. Curious(tm)... And I say that as a huge fan of AI, but being vocally self-critical is an important attribute for professional success in AI and elsewhere.
- binsquare 11mo agoI find it interesting that they quantify the improvement on speed and number of forecast-ed scenarios but lack details on how it results in improved accuracy of the forecast per: ``` WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour. This breakthrough is enabled by a new model that can provide hundreds of possible scenarios. ``` As an end user, all I care is that there's one accurate forecasted scenario.
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- NoiseBert69 11mo agoAs a end user I also want to see the variance to get a feeling of the uncertainty. Quite a lot of weather sites offer this data in an easily eatable visual format.
- mmooss 11mo agoThat would be great - do you recommend any sites?
- NoiseBert69 11mo agohttps://www.meteoblue.com/en/weather/10-days/new-york_united-states_5128581 https://www.meteoblue.com/en/weather/10-days/new-york_united... https://www.meteoblue.com/en/weather/forecast/multimodelensemble/new-york_united-states_5128581 https://www.meteoblue.com/en/weather/forecast/multimodelense... also https://weather.us/forecast/5128581-new-york/ensemble/euro-range/temperature https://weather.us/forecast/5128581-new-york/ensemble/euro-r...
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- Sanzig 11mo agoIndeed. The most important benchmark is accuracy and how well it stacks up against existing physics-based models like GFS or ECMWF. Sure, those big physics-based models are very computationally intensive (national weather bureaus run them on sizeable HPC clusters), but you only need to run them every few hours in a central location and then distribute the outputs online. It's not like every forecaster in a country needs to run a model, they just need online access to the outputs. Even if they could run the models themselves, they would still need the mountains of raw observation data that feeds the models (weather stations, satellite imagery, radars, wind profilers...). And these are usually distributed by... the national weather bureau of that country. So the weather bureau might as well do the number crunching as well and distribute that.
- jakewins 11mo agoIs anyone aware of good sources of higher resolution models? Hourly resolution like this model provides doesn’t help much now that energy markets have moved to 15-min and 5-min resolution.
- trillic 11mo agoHRRR is 15 min res updated hourly. It's not that resolution all the way out only 18 hours I think.
- vaughnegut 11mo agoWindy allows you to select your model. For that reason it's my go to for accuracy. Different models have different strengths, though. Some are shorter range (72h) or longer range (1-3 weeks). Some are higher resolution for where you live (the size of an area which it assigns a forecast to, so your forecast is more local). Some governments will have their own weather model for your country that is the most accurate for where you live. What I did for a long time was use Windy and use HDRPS (a Canadian short range model with a higher resolution in Canada so I have more accurate forecasts). Now I just use the government of Canada weather app. I genuinely wonder what the weather Channel, iPhone/Android official weather apps, etc. use under the hood for global models. My gut says ECMWF (a European model with global coverage) mixed with a little magic.
- chneu 11mo agoWindy or Ventusky. Both really solid.
- mlmonkey 11mo agoHow does one use weather data in an energy market, if you don't mind my asking?
- bitdivision 11mo agoSeems like it would be pretty useful to forecast the supply of renewables (wind, solar, maybe some hydro).
- timenotwasted 11mo agoIt feels like real weather AI|Forecast|whatever_you_want_to_call_it is still far, far away. Maybe it's just the consumer aspect of weather apps but I don't feel as if I get any more accurate data now than I did back when my parents turned to the daily weather channel for the forecast. Still a lot of clear days when rain was predicted or the even more dreaded torrential downpour when it was supposed to be sunny and clear. Obviously all I have is anecdata for what I'm mentioning here but from a consumer perspective I don't feel like these model enhancements are really making average folks feel as if weather is any more understood than it was decades ago.
- jstummbillig 11mo agoNo need for anecdata! We have the data: https://ourworldindata.org/weather-forecasts https://ourworldindata.org/weather-forecasts tdlr: Weather forecasts have improved a lot
- timenotwasted 11mo agoThat's actually really helpful to understand better, thank you!
- baron816 11mo agoI remember when it was a trope that the weatherman was always wrong and that the weather was the prototypal thing that was inherently “unpredictable”.
- deanputney 11mo agoI've found this to be more related to poor representation of the data than inaccurate data. For example on Apple's Weather app, a "rainy" day means a high chance of rain at any point during the day. If it's 80% chance of rain at 5am and sunny the rest of the day– that counts as rainy. You can see an hourly report for more info, and generally this is pretty accurate. You have to learn how to find the right data, know your local area, and interpret it yourself. Then you have to consider what effects this has on your plans and it gets more complicated. Finding a window to walk the dog, choosing a day to go sailing, or determining conditions for backcountry skiing all have different requirements and resources. What I'd like AI to do is know my own interests and highlight what the forecast means for me.
- atonse 11mo agoAnyone know whether we can use this to simulate hurricanes/floods in particular areas, instead of looking at real existing data and helping model an existing hurricane as it's happening? (which is definitely more important and impactful, but the simulation angle is the one I happen to be curious about at the moment). Like if I wanted to simulate whether something like Hurricane Melissa would've gone through a handful of southern US states, what would the effect have been, from an insurance or resiliency standpoint.
- counters 11mo agoThat's not really what a weather model "does."
- cryptoz 11mo ago15 years later and still no word from Google if they will use the barometers in Android devices to assimilate surface pressure data. It has been shown that this can improve forecast accuracy. I think IBM may be doing it with their weather apps, but Google/Apple would have dramatically more data available. Apple even bought Dark Sky, which purported to do this but never released any information - so I doubt they really did do it. And if they did, I doubt Apple continued the practice. Been waiting a long time to hear Google announce they'll use your barometer to give you a better forecast. Still waiting I guess.
- Majromax 11mo ago> 15 years later and still no word from Google if they will use the barometers in Android devices to assimilate surface pressure data. For WeatherNext, the answer is 'no'. The paper (https://arxiv.org/abs/2506.10772 https://arxiv.org/abs/2506.10772) describes in detail what data the model uses, and direct assimilation of user barometric data is not on the list.
- counters 11mo agoThe community has mostly abandoned SPO data. It's extraordinarily difficult to use this data because of social issues like PII and technical ones like QA/QC. But even more importantly, there's very little compelling evidence that the data makes much of any difference whatsoever in real forecasts.
- AceJohnny2 11mo agoIs this the same model as provided the most accurate hurricane predictions this season? https://arstechnica.com/science/2025/11/googles-new-weather-model-impressed-during-its-first-hurricane-season/ https://arstechnica.com/science/2025/11/googles-new-weather-...
- lysecret 11mo agoIm pretty deep into this topic and what might be interesting to an outsider is that the leading models like neuralgcm/weathernext 1 before as well as this model now are all trained with a "crps" objective which I haven't seen at all outside of ml weather prediction. Essentially you add random noise to the inputs and train by minimizing the regular loss (like l1) and at the same time maximizing the difference between 2 members with different random noise initialisations. I wonder if this will be applied to more traditional genai at some point.
- rytill 11mo agoWhat is the goal of doing that vs using L2 loss?
- lysecret 11mo agoTo encourage diversity between the different members in an ensemble. I think people are doing very similar things for MOE networks but im not that deep into that topic.
- sunshinesnacks 11mo agoThe goal of using CRPS is to produce an ensemble that is a good probabilistic forecast without needing calibration/post processing. [edit: "without", not "with"]
- counters 11mo agoTo add to the existing answers - L2 losses induce a "blurring" effect when you autoregressively roll out these models. That means you not only lose import spatial features, you also truncate the extrema of the predictions - in other terms, you can't forecast high-impact extreme weather with these models at moderate lead times.
- lysecret 11mo agoYes very good point this to me is one of the most magical elements of this loss how it suddenly makes the model "collapse" on one output and the predictions become sharp.
- bilsbie 11mo agoWhere can I use this? I’ve been trying to find hyperlocal forecasts like darksky used to be.
- paxys 11mo ago> We're now taking our research out of the lab and putting it into the hands of users. WeatherNext 2's forecast data is now available in Earth Engine and BigQuery. We’re also launching an early access program on Google Cloud’s Vertex AI platform for custom model inference. > By incorporating WeatherNext technology, we’ve now upgraded weather forecasts in Search, Gemini, Pixel Weather and Google Maps Platform’s Weather API. In the coming weeks, it will also help power weather information in Google Maps.
- jadbox 11mo agoGoogle Maps has... weather predictions?
- batrat 11mo agoif you search for a city usually it shows the current weather, but I've seen in some cities there is also a 7 day forecast
- abdullahkhalids 11mo agoIf you want to accurately predict times for future trips, you need weather predictions.
- jachee 11mo agoApple integrated the hyperlocal darksky stuff into their native Weather app. It had a few growing pains, but it's as good as it ever was, imho.
- baxtr 11mo agoAgreed. The one thing I’d like them to improve are the precipitation maps though. They just feel awkward and unreliable.
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- lampiaio 11mo agoReminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."
- empath75 11mo agoThere is a fairly compelling argument that divination in the ancient world was not a useless waste of time, as is commonly assumed, but that having either a process or a person that can make essentially random choices for them allowed people to make hard, consequential decisions where they might otherwise be paralyzed, especially when the penalty for not acting was worse than making a mistake.
- twoodfin 11mo agoFascinating. I suppose it also encourages developing adaptable strategies that accommodate imperfect information, vs. succumbing to wishful thinking or other forms of cognitive bias.
- OisinMoran 11mo agoI've also read that a source of randomness like that could help prevent things like over-extracting some land
- tekkk 11mo agoNever thought of that. Probably a bit too generous given that it could be just as well waste of time and resources, nevermind the bias of the voodoo doctor. Most of it was just weirdly provided therapy I suppose to relieve stress. But it is funny that humans put a great lot of weight on social contracts and being given explicit orders, maybe even publicly, must help pursuing action instead of rumination. Especially in a world where things seemed to happen randomly anyway.
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- Tannic 11mo ago[dead]
- bigtones 11mo agoGoogles weather prediction engine is already very good, and the new hurricane model was breathtakingly good this season when tested against actual hurricane paths. Meanwhile, the US Government Global Forecasting System continues to get worse. https://arstechnica.com/science/2025/11/googles-new-weather-model-impressed-during-its-first-hurricane-season/ https://arstechnica.com/science/2025/11/googles-new-weather-...
- jasonmarks_ 11mo ago> Global Forecasting System continues to get worse What do you mean?
- tylervigen 11mo agoIt's the subtitle of the article they linked to. But to expand: the US flagship forecast model just had its worst year predicting hurricanes since 2005. The trend of errors over the last few years hasn't been great.
- jasonmarks_ 11mo agoMore objectively it reads as if none of the models performed well outside of 24 hours with a significant uptick in inaccuracy after 72 hours.
- tylervigen 11mo agoI disagree with your logic. Increased mean error 72 hours out (vs 24 hours out) is not an indication that GFS is getting worse over time. At that scale it’s obviously getting better over time; 24 hours out is further in the future than 72 hours out. However, an increase in the mean error at the same time out year over year (or between 2005 and 2025) is an indication of an issue, and that’s what we see.
- toephu2 11mo agoI can't wait! So where's the app? could not find 'WeatherNext 2' in the appstore. iOS default weather app is notoriously very inaccurate.
- rootusrootus 11mo agoDoesn't the iOS app mostly just channel info directly from other sources like the local gov't weather service? I suppose maybe they tried to put some intelligence into it when they bought Dark Sky. That seems about the time it started trying to predict rainfall in the next few minutes. Which hasn't ever worked for me.
- CaliforniaKarl 11mo agoFor folks who are interested, I suggest checking out "The weather machine: a journey inside the forecast" by Andrew Blum[0]. It's a great read into the history of weather forecasting pre-Covid. [0]: https://search.worldcat.org/title/1153659005 https://search.worldcat.org/title/1153659005
- lucb1e 11mo agoSounds interesting! I was listening to the 5-minute audiobook preview and it starts right up my alley, but then devolves into meta chatter about how the author wrote this other book about the people that built the internet, how they learn the most by touching the machines and talking to the creators (as if you learn something about meteorology from walking up to a weather server). 'Must just be the intro' I thought, but then one review of the five that I found says (translated) "This book talks about people who deal with meteorology, their age, their physical appearance, their biography, their clothes, the meal he took with them. It describes places where observatories are located. It does not explain the weather phenomena or how to predict them." Many nonfiction books have it to some extent and it's usually fine (like 5% of the content, either relevant or easy to pass into one ear and out the other), but this sounds like it takes up a good chunk of the book with who's-whos and (former) meteorological celebrities What's your take on this? Does it spend more than, say, 20% talking about the people as compared to the content matter about weather forecast mechanisms and innovations?
- mmooss 11mo agoOn what geometric surfaces do weather models run? Spheriods? Spheres? Projections on planes? Geoids?? Weather is three-dimensional and I would guess that the difference between sphere and (appropriate) spheroid could impact predictions. It seems possible that, at least for local and hyperlocal forecasts, geoids would be worthwhile. But as you go from plane -> sphere -> spheroid -> geoid, computing resources must increase pretty quickly. And even if a geoid is used, that doesn't mean the weather user sees a geoid or section of geoid. Every consumer weather application displays a plane, afaict. Maybe nautical or aeronatautical weather maps display spheres?
- iFred 11mo agoThe German “ICON” model uses a spheroid shape that escapes me right now, but each grid cell is effectively a triangle. Most others are gridded squares.
- nipperkinfeet 11mo agoHopefully this will help! I've found that all weather apps and websites have been completely inaccurate this year.
- kachapopopow 11mo ago1) this is for 1 hour~ prediction 2) it's for CRPS not for normal people
- counters 11mo agoThese models don't predict CRPS - it's just the loss that they're optimized against. They still produce a normal weather forecast with gridded variables.
- kachapopopow 11mo agohuh, I guess I shouldn't believe every HN commenter that sounds like they know what they're talking about
- cco 11mo agoI've found Google's default Weather app to be quite poor starting around six months ago. Consistently off by two to five degrees. On the advice of someone here on hackernews I tried out weawow, and though it is a terrible name it is _very_ accurate. So much better and consistent. Love it so far.
- drsalt 11mo agomaybe apple can learn from this. i keep getting 0% chance of rain and then it rains
- sora2video 11mo ago[dead]
- rkagerer 11mo agoI noticed my local weather forecasts from Google search have gotten significantly less accurate these days. Like, they consistenly called for freezing seasonal overnight lows many weeks before it was remotely probable. You'd get better predictions asking anyone who's lived here a couple years. In fairness, I'm in a region that's notoriously difficult to forecast, but the popular non-Google sources seem to be generating better predictions. I wonder if the rollout of this new model is related (either occurred and made it worse, or will come and make it better). I'd love to get some hard data. Are there any sites out there where you can compare past performance of different prediction models at a very localized scale?
- agos 11mo agocomparing model performance for a location seems such an obvious thing but it's basically non existent, I wonder why
- eugmill 11mo agoOpen-meteo has a free non commercial api. Their api exploration page is super powerful and can pull and graph model-specific forecast data. It doesn't have weathernext but it does seem to have many of the big models if you want to geek out. Does take some figuring out though. https://open-meteo.com/ https://open-meteo.com/
- robotguy 11mo agoNot affiliated, just thought it was a neat idea when I stumbled upon it during some hobby weather research last year: ForecastAdvisor will show you the accuracy of the major weather forecasters, including AerisWeather, Foreca, Microsoft, the National Weather Service, OpenWeather, The Weather Channel, Wetter.com, WeatherBit, World Weather Online, and others. They also provide links to your city's weather forecast from all the other weather forecasters, so you can compare for yourself. https://www.forecastadvisor.com/ https://www.forecastadvisor.com/
- rkagerer 11mo agoThat sounds perfect, I wish it were available outside the US as well.
- moktonar 11mo agoI’ve read the article and skimmed the paper, but they don’t seem to say if the model has to be retrained every few days or every now and then. Because if it’s trained on marginals it seems to me that the model essentially learns to forecast weather at each location (a bit like locals do), but weather patterns might change over time so I guess they’ll have to retrain it. The retraining frequency might be a deal breaker if for example you have to train it for 3 wall lock days every, say, 7 days.