{"slug": "google-deepmind-plans-to-bring-weathernext-3-forecasts-to-search-maps-and-gemini", "title": "Google DeepMind plans to bring WeatherNext 3 forecasts to Search, Maps and Gemini", "summary": "Google DeepMind and Google Research announced WeatherNext 3, an AI weather forecasting model, on September 3, with plans to distribute its forecasts through Google Search, the Gemini app, Google Maps, the Google Maps Platform Weather API, Earth Engine, and Google Cloud. The model, led by weather science lead Ferran Alet, claims up to a 60% improvement in Continuous Ranked Probability Score against NASA's IMERG precipitation observations, though independent verification is pending. WeatherNext 3 will be integrated into consumer products and developer services, expanding beyond research use.", "body_md": "# Google DeepMind plans to bring WeatherNext 3 forecasts to Search, Maps and Gemini\n\n**The joint Google DeepMind and Google Research project is moving into consumer products, developer APIs and cloud datasets, where company-reported benchmark gains will meet real weather.**\n\nBy [Ryan Merket](/author/ryan-merket)\n· Published\n\nPrimary source: [TechCrunch](https://techcrunch.com/2026/09/03/googles-latest-ai-weather-model-gives-you-no-excuse-to-forget-your-umbrella/)\n\n## Why it matters\n\nWeatherNext 3 would give a joint Google DeepMind and Google Research forecasting project distribution through Google's consumer products, developer APIs and cloud services. Independent testing will need to show whether Google's reported gains hold during rare, dangerous weather as the planned integrations reach users.\n\nGoogle plans to distribute WeatherNext 3 forecasts through Search, the Gemini app, Google Maps, the Google Maps Platform Weather API, Earth Engine and Google Cloud. [Ferran Alet](https://alet-etal.com/?ref=runtimewire), [Google DeepMind](https://deepmind.google/?ref=runtimewire)'s weather science lead, and researchers across Google DeepMind and [Google Research](https://research.google/?ref=runtimewire) [announced the model](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/?ref=runtimewire) on September 3.\n\nAlet works in London and earned a computer science PhD from MIT CSAIL after completing degrees in mathematics and engineering physics through Barcelona's UPC-CFIS. Moving WeatherNext 3 into Google's product network would put his weather forecasting research in front of consumers, software developers and companies rather than leaving it inside research papers and benchmark tables.\n\nThe project sits inside Google DeepMind, the laboratory [co-founded and led by Demis Hassabis](https://deepmind.google/about/?ref=runtimewire). DeepMind was founded in London in 2010, acquired by Google in 2014 and combined with Google Brain in 2023 to form the current organization.\n\nThe practical change is distribution: Google intends to place WeatherNext 3 forecasts in Search, Gemini and Maps, while exposing them through the Maps Platform Weather API, Earth Engine and Google Cloud services.\n\n### Google's previous model provides a technical baseline\n\nThe public [Earth Engine dataset](https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_weathernext_3_0_0_0p1deg?ref=runtimewire) is listed at 0.1-degree resolution.\n\nGoogle's documentation provides a baseline for the previous generation. [WeatherNext 2](https://developers.google.com/weathernext/guides/models?ref=runtimewire) uses a 0.25-degree global grid, roughly 30 kilometers at the equator, initializes every six hours and produces 64 ensemble members.\n\nThe [public WeatherNext repository](https://github.com/google-deepmind/weathernext?ref=runtimewire) documents daily WeatherNext 2 forecast feeds through Google Cloud, including BigQuery. It separately provides pretrained weights and sample data in a Google Cloud Storage bucket.\n\n[Google says WeatherNext 3 improves Continuous Ranked Probability Score by up to 60%](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/?ref=runtimewire) against NASA's IMERG precipitation observations under Google's stated evaluation setup. That figure is not an independent measure of universal forecast accuracy.\n\nBrightband's [Operational WeatherBench methodology](https://owb.brightband.com/methodology?ref=runtimewire) lists GraphCast, WeatherNext 2, WeatherNext 3, ECMWF's IFS ENS and AIFS ENS, Microsoft Aurora 1.5, NVIDIA Atlas, GFS, GEFS, HRES and climatology. It says WeatherNext 2 and WeatherNext 3 are initialized from ECMWF IFS analysis and separates deterministic scoring of ensemble means from probabilistic scoring of ensemble members. Fields are evaluated on a fixed 0.25-degree global grid, except WeatherNext 3 surface fields, which are scored at 0.1 degree. Forecasts are evaluated every six hours through 360 hours, or 15 days.\n\n### Proprietary observations are already part of the competition\n\nWindBorne, a weather intelligence company with its own atmospheric sensing network, feeds proprietary measurements from autonomous balloons into [WeatherMesh-6](https://windbornesystems.com/blog/introducing-wm-6?ref=runtimewire).\n\nIn an evaluation covering July 2025 through March 2026 at 0.25-degree resolution, [WindBorne says WeatherMesh-6 produced up to 38% lower ensemble-mean root mean square error than ECMWF's physics-based IFS and up to 32% lower error than ECMWF's AIFS](https://windbornesystems.com/blog/introducing-wm-6?ref=runtimewire). The company compared WeatherMesh-6's 128-member ensemble with 51-member IFS and AIFS ensembles and defined lower RMSE as higher accuracy. WindBorne said it could not directly compare WeatherMesh-6 with Google's FGN system, making its Google comparison indirect. These are company-reported results and have not been independently verified.\n\nMicrosoft is also advancing an AI weather model with hourly output. Microsoft [released Aurora 1.5](https://www.microsoft.com/en-us/research/blog/aurora-1-5-extending-open-foundation-models-for-weather-and-earth-system-applications/?ref=runtimewire) in July 2026 with forecasts covering 22 weather variables and probabilistic ensembles. Comparisons among these systems depend on the selected variables, lead times, observations and error metrics.\n\n### Alet's research reaches Google's existing products\n\nAlet's [research page](https://alet-etal.com/?ref=runtimewire) traces work spanning machine learning for science, GraphCast, GenCast and WeatherNext's cyclone models. RuntimeWire [reported in August](https://runtimewire.com/article/google-deepmind-open-sources-weathernext-cyclone-models) that Google DeepMind's release included WeatherNext 2 and WeatherNext Cyclones.\n\nWeatherNext 3 would carry that work into Google's distribution network. Consumers are slated to receive its forecasts through Search, Gemini and Maps, while developers would access them through the Maps Platform Weather API.\n\nGoogle's [WeatherNext guidance](https://developers.google.com/weathernext?ref=runtimewire) directs users to the relevant local meteorological agency or national weather service for official forecasts and warnings.\n\nScientific caution still applies to extreme events. [Research on record-breaking weather](https://arxiv.org/abs/2508.15724?ref=runtimewire) found that physics-based numerical models could outperform leading AI systems on some unprecedented heat, cold and wind events. AI models can smooth rare outcomes or underestimate conditions poorly represented in their training data.\n\nIndependent evaluations will need to determine whether Google's reported gains persist during rare, dangerous conditions. Once the planned integrations launch across Search, Maps, Gemini, APIs and cloud datasets, forecast errors could reach products and users directly, giving Alet's group a test that research benchmarks alone cannot provide.", "url": "https://wpnews.pro/news/google-deepmind-plans-to-bring-weathernext-3-forecasts-to-search-maps-and-gemini", "canonical_source": "https://runtimewire.com/article/google-weathernext-3-hourly-ai-forecasts-search-maps-gemini", "published_at": "2026-09-03 17:22:43+00:00", "updated_at": "2026-09-03 17:56:46.565464+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-products", "ai-research"], "entities": ["Google DeepMind", "Google Research", "WeatherNext 3", "Ferran Alet", "Google Maps", "Gemini", "Earth Engine", "Google Cloud"], "alternates": {"html": "https://wpnews.pro/news/google-deepmind-plans-to-bring-weathernext-3-forecasts-to-search-maps-and-gemini", "markdown": "https://wpnews.pro/news/google-deepmind-plans-to-bring-weathernext-3-forecasts-to-search-maps-and-gemini.md", "text": "https://wpnews.pro/news/google-deepmind-plans-to-bring-weathernext-3-forecasts-to-search-maps-and-gemini.txt", "jsonld": "https://wpnews.pro/news/google-deepmind-plans-to-bring-weathernext-3-forecasts-to-search-maps-and-gemini.jsonld"}}