[{"data":1,"prerenderedAt":30},["ShallowReactive",2],{"nr-en-google-weathernext-3-satellite-ai-forecasts":3},{"slug":4,"title":5,"dek":6,"date":7,"time":8,"publishedAt":9,"updated":10,"updatedAt":10,"dateFmt":11,"updatedFmt":10,"kind":12,"tier":13,"author":14,"authorName":15,"topics":16,"tracker":22,"trackerLabel":23,"headlineStat":24,"image":25,"ogImage":26,"imageAlt":5,"csv":10,"minutes":27,"words":28,"html":29},"google-weathernext-3-satellite-ai-forecasts","Google WeatherNext 3: AI Replaces Physics Simulations – Weather Forecasts Now Five Times More Detailed","Google Research and DeepMind unveil an AI weather model that ditches classical physics simulations and learns directly from live satellite data. The result: hourly forecasts with five-kilometer resolution instead of the previous 25 kilometers.","2026-09-07","07:35","2026-09-07T07:35:00+02:00","","September 7, 2026","news","standard","ideal-syka","Ideal Syka",[17,18,19,20,21],"Weather forecasting","AI research","Machine learning","Google DeepMind","Energy industry","\u002Fstand-der-ki","Progress at the AI frontier","5 km instead of 25 km resolution, hourly forecasts","\u002Fnewsroom\u002Fimg\u002Fgoogle-weathernext-3-satellite-ai-forecasts.webp","\u002Fog-nr\u002Fgoogle-weathernext-3-satellite-ai-forecasts.en.png",3,555,"\u003Cp>Google Research and DeepMind have unveiled a research breakthrough with \u003Cstrong>WeatherNext 3\u003C\u002Fstrong>, fundamentally rethinking weather forecasting: The AI model abandons classical physics simulations entirely and instead trains directly on live data from geostationary satellites. The result is five times sharper spatial resolution and hourly updates instead of six-hour cycles.\u003C\u002Fp>\n\u003Ch2>Quick facts\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>WeatherNext 3\u003C\u002Fstrong> delivers hourly forecasts with up to \u003Cstrong>5-kilometer resolution\u003C\u002Fstrong> – five times more detailed than its predecessor (25 km)\u003C\u002Fli>\n\u003Cli>The model learns directly from \u003Cstrong>live satellite data\u003C\u002Fstrong> instead of classical numerical weather predictions, which carry a \u003Cstrong>6-hour delay\u003C\u002Fstrong>\u003C\u002Fli>\n\u003Cli>Particularly benefits underserved regions in \u003Cstrong>Africa, Latin America, and the Asia-Pacific region\u003C\u002Fstrong>\u003C\u002Fli>\n\u003Cli>Precipitation forecasts are up to \u003Cstrong>50 percent more accurate\u003C\u002Fstrong> and are set to feed into Google services like Search, Maps, and Gemini\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>How the old system held back accuracy\u003C\u002Fh2>\n\u003Cp>Previous AI weather models, including \u003Cstrong>WeatherNext 2\u003C\u002Fstrong>, were trained on data from numerical weather prediction (NWP). These simulations run on supercomputers and introduce a \u003Cstrong>six-hour delay\u003C\u002Fstrong>, according to Google – a critical disadvantage when weather changes rapidly, such as sudden storms or rain bands. Errors in temperature and precipitation forecasts were the consequence.\u003C\u002Fp>\n\u003Cp>WeatherNext 3 reverses this process: The model processes \u003Cstrong>live data from geostationary satellites\u003C\u002Fstrong> and generates a new forecast every hour based on the most current observations. This allows rapidly developing weather events to be detected earlier and more accurately.\u003C\u002Fp>\n\u003Ch2>Spatial details that were previously lost\u003C\u002Fh2>\n\u003Cp>The new resolution makes a significant practical difference. WeatherNext 3 represents temperature and humidity at \u003Cstrong>5 kilometers\u003C\u002Fstrong>, other surface values at \u003Cstrong>10 kilometers\u003C\u002Fstrong>, and atmospheric quantities like wind speed at \u003Cstrong>25 kilometers\u003C\u002Fstrong>. The model additionally trains on data from individual weather stations to capture local features such as coastlines, valleys, and mountain ranges.\u003C\u002Fp>\n\u003Cp>This becomes clear in comparisons: While WeatherNext 2 delivers a pixelated representation in a temperature forecast over Britain, WeatherNext 3 captures local topography. Mountain ranges and coastlines that merge into crude blocks in the 25-kilometer grid are now resolved. For precipitation over the northwestern United States, WeatherNext 3 resolves narrow rain bands much more sharply and comes closer to radar measurements.\u003C\u002Fp>\n\u003Ch2>Special benefits for underserved regions\u003C\u002Fh2>\n\u003Cp>The higher resolution and elimination of computational costs from classical regional models represent major progress for regions that previously lacked access to accurate forecasts. Google explicitly names \u003Cstrong>Latin America, Africa, and the Asia-Pacific region\u003C\u002Fstrong> as beneficiaries.\u003C\u002Fp>\n\u003Cp>Additionally, WeatherNext 3 calculates specific data for renewable energy – a value-add for solar and wind power plants that depend on precise forecasts. The improved precipitation predictions, which are up to 50 percent more accurate, are set to feed into Google services like Search, Maps, and Gemini.\u003C\u002Fp>\n\u003Ch2>What this means for German enterprises\u003C\u002Fh2>\n\u003Cp>For German weather and energy companies, WeatherNext 3 could become relevant – both as a potential data source and as a benchmark for their own models. Accuracy in precipitation and wind is critical for wind farm operators and energy providers. How and whether Google makes the data commercially available remains unclear. At the same time, a trend is evident: major tech companies increasingly favor \u003Cstrong>AI over classical physics simulation\u003C\u002Fstrong>, raising questions about validation and interpretability of forecasts – a point German regulators should monitor.\u003C\u002Fp>\n\u003Ch2>Sources\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fthe-decoder.de\u002Fweathernext-3-liefert-stuendliche-wettervorhersagen-mit-fuenf-kilometern-aufloesung-statt-25\u002F\">The Decoder (DE)\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fthe-decoder.com\u002Fgoogles-weathernext-3-ditches-physics-simulations-and-learns-weather-directly-from-live-satellite-data\u002F\">The Decoder\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cem>Editorially owned by \u003Ca href=\"\u002Fen\u002Fautor\u002Fideal-syka\">Ideal Syka\u003C\u002Fa>. Sources and method: \u003Ca href=\"\u002Fen\u002Fredaktion\">Newsroom & method\u003C\u002Fa>. Tips and corrections: \u003Ca href=\"mailto:ai@i6eal.de\">ai@i6eal.de\u003C\u002Fa>.\u003C\u002Fem>\u003C\u002Fp>\n",1788762393479]