DeepMind weather model outperforms government forecasters in accuracy benchmarks
Developed over time first seen 2 months ago
Google DeepMind and Google Research have released WeatherNext 3, a new AI-driven weather forecasting model that has topped independent accuracy benchmarks and outperformed both rival AI systems and traditional government forecasters. Google says the model will begin powering weather data across Search, Google Maps and Gemini, marking a significant step in the shift from costly, slow physics-based supercomputer forecasting towards faster, cheaper deep-learning approaches that now match or exceed official forecasting agencies on core measures such as temperature, wind speed and humidity.
On Operational WeatherBench, an independent testing platform built by startup Brightband, WeatherNext 3 beat AI models from Microsoft, Nvidia and the ECMWF, as well as official forecasts from the US National Weather Service and the ECMWF itself. With 2.4 times more parameters than its predecessor, the model can forecast key variables at a resolution of 5km (down from 15–25km), has improved rainfall predictions by 60%, and now issues hourly forecasts rather than the standard six-hourly updates. It has also been trained to target predictions to specific weather stations, such as individual airports, allowing more granular, verifiable forecasts and building on earlier DeepMind advances such as cyclone-path visualisation.
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In short
- Google's WeatherNext 3 AI model beats government and rival AI forecasts
- It will power weather data in Search, Maps and Gemini
- Offers 5km resolution, hourly updates and 60% better rain forecasts
How this story developed
- 2 months ago Further detail emerged that WeatherNext 3 fuses machine learning with real-time satellite imagery and historical weather data, and will also be opened up to researchers via Google Cloud alongside its consumer rollout.
- 2 months ago Google unveiled WeatherNext 3, an AI weather forecasting model from DeepMind that topped the Operational WeatherBench benchmark and will roll out across Search, Maps and Gemini, offering 5km hourly resolution and 60% better rainfall predictions than its predecessor.
Full account
Google has unveiled WeatherNext 3, a new artificial intelligence weather forecasting system developed jointly by Google DeepMind and Google Research, which the company says produces sharper, more frequent and more accurate forecasts than its predecessor and, in independent testing, than established government meteorological services. The system has reportedly topped Operational WeatherBench, a benchmarking tool built by the forecasting startup Brightband that compares AI and traditional models on measures such as temperature, wind speed and humidity, outperforming rival AI systems from Microsoft, Nvidia and the European Centre for Medium-Range Weather Forecasting (ECMWF), as well as conventional forecasts issued by the ECMWF and the US National Weather Service.
Conventional forecasting has long depended on government-run supercomputers that simulate atmospheric physics by solving vast systems of equations, an approach that is highly accurate but computationally expensive and relatively slow to update. Machine-learning alternatives, which have proliferated since the ECMWF released decades of historical weather data in 2018, instead learn statistical patterns from past observations, allowing forecasts to be generated far more quickly. Google says WeatherNext 3 pushes this further by drawing on live satellite observations rather than relying solely on the archived, standardised datasets that most AI weather models are trained on, refreshing its forecasts every hour instead of the six-hour cycle typical of earlier systems.
In practical terms, Google says the new model can resolve certain variables, such as temperature and atmospheric moisture, at a grid resolution of around five kilometres, compared with 25 kilometres for its previous model, WeatherNext 2 — a roughly fivefold sharpening that the company says is especially valuable for tracking fast-moving rain and snow systems. Reported gains in rainfall prediction vary depending on the comparison being drawn: one account cites precipitation forecasts up to 50 percent more accurate than prior benchmarks a day or more ahead, while another cites a 60 percent improvement in rainfall evaluation specifically against WeatherNext 2. Google says the use of satellite data should also benefit regions with sparse ground-based rain-gauge networks, particularly outside the US and Europe, where forecasting has historically been weaker.
Beyond day-to-day weather prediction, Google says WeatherNext 3 has been built to forecast conditions relevant to renewable energy generation, including wind speeds at the roughly 100-metre height of a turbine, a capability researchers linked to the company's own rising electricity demands from AI data centres. The model is also set to be woven into consumer-facing Google products, including Search, Maps and its Gemini assistant, as well as being made available to researchers and developers via Google's cloud platform. Coverage of the launch diverged somewhat in focus: one outlet emphasised the technical leap in resolution and its implications for renewable-energy planning, while another placed greater weight on the model's competitive benchmark results and its imminent rollout across Google's own products; the two accounts also gave differing job titles for the Google researchers quoted, Samier Merchant and Ferran Alet.
Where outlets differ
From the full account, which is AI-written from the sources:
Precipitation accuracy improvement figures differ: one source cites up to 50% more accurate forecasts a day or more out, the other cites a 60% improvement specifically versus WeatherNext 2 — likely different baselines rather than a contradiction.
The Verge emphasises technical detail (resolution, satellite data use, renewable-energy/wind forecasting tied to Google's own energy needs) while TechCrunch emphasises competitive benchmarking against government forecasters and rollout into Google consumer products (Search, Maps, Gemini).
Job titles given for the Google researchers differ between outlets: Samier Merchant is described as a 'research engineer' by The Verge and a 'senior staff engineer' by TechCrunch; Ferran Alet is described as a 'research scientist' by The Verge and a 'staff research scientist manager' by TechCrunch.
Steelman: both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
Advocates of this shift welcome AI weather models as a genuine leap forward in a field where accuracy saves lives and money: faster, higher-resolution and more frequently updated forecasts mean better warnings for storms, floods and heatwaves, achieved at a fraction of the computing cost of traditional supercomputer simulations. They argue that independent, transparent benchmarking such as Operational WeatherBench keeps the competition honest, and that if a well-resourced private lab can outperform government agencies, the sensible response is to adopt or license the better tool rather than cling to institutional pride, ultimately benefiting the public through Search, Maps and Gemini at no extra cost to users.
The case against
Others, while not disputing the benchmark results, urge caution about ceding such critical public infrastructure to a private company's proprietary model. They point out that national weather services carry statutory duties, public accountability and continuity obligations that a commercial product does not, and that dependence on a single corporation's black-box system for life-safety forecasting raises legitimate questions about transparency, long-term access, pricing power and what happens if incentives or ownership change. For them, government forecasters remain essential as an independently verifiable, publicly accountable backbone, even if AI tools are used to complement rather than replace that capability.
Explained simply
Google has developed an artificial intelligence system, called WeatherNext 3, designed to predict the weather. It was built by DeepMind and Google Research, the same teams behind other well-known AI projects, and it works very differently from the way forecasts have traditionally been made. Rather than running enormous physics-based simulations on supercomputers, it uses machine learning trained on weather data to generate predictions, which is generally quicker and cheaper to run.
For decades, the most trusted forecasts have come from national weather agencies, such as the US National Weather Service, and international bodies like the European Centre for Medium-Range Weather Forecasts (ECMWF), all using physics-based modelling. In recent years, tech companies including Google, Microsoft and Nvidia have been building rival AI systems that learn patterns from historical weather data instead, and these are increasingly being tested against the official forecasters to see which approach is more accurate.
This matters because weather forecasts affect everything from daily travel plans to farming, aviation and disaster preparedness, so improvements in accuracy or detail have real practical value. It also reflects a broader trend of AI being used to tackle large-scale scientific and public-service tasks once dominated by government institutions, raising questions about the future role of state-run forecasting agencies versus commercial technology firms.
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