Google DeepMind and Google Research Launch WeatherNext 3 Global Weather AI Model

Google DeepMind and Google Research Launch WeatherNext 3 Global Weather AI Model Google DeepMind and Google Research have introduced WeatherNext 3, an AI weather forecasting model that integrates real-time satellite and weather station data to deliver hourly global forecasts at resolutions up to 5 kilometers. WeatherNext 3 delivers forecasts roughly five times sharper than WeatherNext 2, outputting surface variables at 5 km resolution and updating hourly. The model bypasses standard numerical weather prediction (NWP) 6-hour lag by learning directly from live geostationary satellite mosaics and sparse weather station observations. Precipitation modeling shows Continuous Ranked Probability Score (CRPS) improvements up to 60% against IMERG and 30% against MRMS. Tailored variables support clean energy planning, including 100-meter wind speeds, high-resolution cloud cover, and solar radiation levels. High Resolution and Direct Observational Training WeatherNext 3 visualizes key surface variables such as temperature and moisture at 5-kilometer resolution, additional surface variables at 10 kilometers, and atmospheric variables like wind speed at 25 kilometers. Unlike traditional AI weather models reliant on physics-based simulations with 6-hour data lags, WeatherNext 3 ingests global geostationary satellite mosaics and sparse weather station data to account for localized topography and rapid atmospheric changes. Precipitation Accuracy and Renewable Energy Metrics Trained on NASA's satellite-based IMERG data alongside a proprietary global precipitation reanalysis from satellite radar, WeatherNext 3 improves medium-range global precipitation forecasts by up to 60% on CRPS metrics against IMERG and up to 10% against rain gauge measurements. The model also introduces dedicated outputs for clean energy management, forecasting 100-meter turbine-height wind speeds, high-resolution cloud cover, and solar radiation. Google Product and Cloud Integration WeatherNext 3 is integrated across Google Search, the Gemini app, Google Maps, and the Google Maps Platform Weather API, providing day-ahead precipitation forecasts up to 50% more accurate. Forecast data is also made available to researchers and commercial users via Google Earth Engine, BigQuery, and bulk download on Google Cloud Storage.

Google DeepMind 与 Google Research 推出全球气象 AI 模型 WeatherNext 3 Google DeepMind 联合 Google Research 正式发布全球气象 AI 模型 WeatherNext 3。该模型结合实时静止卫星数据与气象站实测，实现了逐小时更新和最高 5 公里的空间分辨率，并显著提升了降水与清洁能源变量的预测精度，现已整合至 Google 旗下多款产品与云端平台。 模型支持逐小时更新，关键地表变量分辨率达 5 公里，较上一代 WeatherNext 2 的清晰度提升约 5 倍 直接基于实时静止卫星马赛克和稀疏气象站观测数据训练，摆脱了传统数值天气预报（NWP）的 6 小时数据延迟 采用 NASA IMERG 等数据训练降水，中程预报评估中 CRPS 评分相比基准提升最高达 60% 针对清洁能源提供 100 米高度风速、高分辨率云层覆盖和太阳辐射预测 高分辨率与实时观测驱动 传统数值预报（NWP）因依赖耗时巨大的超算物理模拟而存在 6 小时数据延迟，容易对快速变化的地表变量产生偏差。WeatherNext 3 引入 1 小时静止卫星马赛克数据，并直接利用稀疏气象站观测进行训练，消除了过度平滑与像素化现象。其地表关键变量如温度和湿度分辨率达 5 公里，大气变量如风速为 25 公里，能够精细解析局部地形差异，使拉美、非洲和亚太等传统高分辨率预报资源不足的地区受益。 降水精度与新能源变量预测 针对全球气象模型长期难以精准捕捉的降水问题，WeatherNext 3 结合 NASA 的 IMERG 卫星数据和基于卫星雷达的全球降水再分析数据。在中程全球预报中，针对 IMERG 的连续分级概率评分（CRPS）提升最高达 60%，针对 MRMS 提升 30%，针对雨量计观测提升 10%。此外，模型面向清洁能源场景专门推出了轮毂高度（100 米）风速、高分辨率云量及太阳辐射预测，辅助电网和新能源企业预估发电量。 生态系统整合与应用落地 WeatherNext 3 已开始全面支持 Google 核心生态，包括 Google 搜索、Gemini 应用、Google 地图、Maps Platform Weather API 和 Earth Engine。提前一天及以上的降水预报准确度提升最高达 50%。相关全球预报数据已开放支持通过 BigQuery、Earth Engine 查询或经由 Google Cloud Storage 批量下载，供开发者和研究人员直接集成。

Sep 03, 2026 | Our flagship AI weather forecasting model now includes real-time satellite data, hourly refreshes, higher resolution, precise precipitation forecasting, and clean energy variables. It’s now integrated across Search, Gemini, Maps, Google Maps Platform, and Cloud. The WeatherNext team Your browser does not support the audio element. Listen to article [[duration]] minutes This content is generated by Google AI. Generative AI is experimental Every day, the weather influences billions of decisions. Some are as simple as grabbing an umbrella before heading out the door, but others are far more consequential. Wind, rain, and extreme weather events, like heatwaves and droughts, have cascading impacts across agriculture, global supply chains, clean energy production, and national economies. In recent years, AI has revolutionized weather forecasting, using historical records to make faster and more accurate predictions than traditional methods. Yet predicting highly local and rapidly changing weather has remained a challenge. Previous models often lacked sufficient spatial resolution, and struggled to incorporate real-time weather data from sources like satellites. Today, Google DeepMind and Google Research are introducing WeatherNext 3 , the most advanced and accurate global weather model to date, according to independent live evaluations by Brightband. Our model learns directly from real-time observations, enabling it to provide timely and more localized predictions for the weather events that impact people the most. By using raw satellite data to produce a forecast every hour in high resolution, our model makes reliable forecasts accessible across Google products worldwide. Rapid weather prediction at unprecedented resolution A forecast's utility often comes down to detail and how finely it resolves both time and space. WeatherNext 3 generates hourly forecasts at multiple spatial resolutions, maintaining physical consistency from broad global wind patterns all the way down to local topography. With WeatherNext 3, we can visualize key surface variables — like temperature and moisture — at a 5-kilometer resolution, other surface variables at 10 kilometers, and atmospheric variables, like wind speed, at 25 kilometers. Overall, this provides a global weather picture roughly five times sharper than our previous model, WeatherNext 2, which produced forecasts on a 25-kilometer grid in 6-hour increments. Figure 1: The end-to-end WeatherNext 3 system architecture. The model ingests live 1-hour geostationary satellite mosaics alongside traditional historical analysis to feed a single, flexible Functional Generative Network (FGN) mesh transformer. It outputs dense gridded fields, discrete cyclone tracks, and predicts station-level sparse coordinates natively. Figure 2: Comparison of 2-meter temperature forecasts over the UK. WeatherNext 2 (left) at 25-kilometer (0.25°) resolution vs. WeatherNext 3 (right) at a native 5-kilometer (0.05°) resolution. WeatherNext 3 resolves the intricate local topography, preventing the pixelated, over-smoothed thermal representations seen in older models. Real-world data at continuous global scale WeatherNext 3's biggest leap forward is what it learns from. Most AI weather models, including WeatherNext 2, are trained on data from numerical weather prediction (NWP) models. Although useful, NWP models are complex, supercomputer-driven physics simulations that carry a six-hour data lag. This lag can lead to biases for fast-changing variables like rain or surface temperature. By ingesting a mosaic of live, global geostationary satellite data, our new model gains a rich, continuously updating view of the atmosphere. This allows the model to generate a new forecast every hour, each one grounded in the most recent satellite observations available, at up to 5-kilometer resolution. This is important because critical weather develops fast. When storms, fronts, or precipitation systems materialize suddenly, our rapid update cycle and higher resolution provides earlier, more detailed insights needed to help drive an effective response. Some variables, like temperature and humidity, can fluctuate dramatically over just a few kilometers, which is particularly relevant for communities near coastlines, valleys, or mountain ranges. Traditional models struggle here because they train on representations of the atmosphere that lack detail and miss extreme local variations. To address this, WeatherNext 3 instead trains directly on sparse weather station observation data. This allows us to make global forecasts on a 5-kilometer grid that account for regional details like topography. This breakthrough is particularly vital for regions across Latin America, Africa, and Asia-Pacific that have historically been underserved by high-resolution forecasting due to the immense supercomputing costs of traditional regional models. It brings localized, high-fidelity forecasting to billions of people and local businesses in these areas. Beyond improved resolution and forecast frequency, our model introduces predictions specifically engineered for renewable energy production. The model forecasts 100-meter wind speeds (roughly at turbine-height) for precise wind-energy output, alongside high-resolution cloud cover and sun radiation levels to help solar farms estimate how much light they will receive on the ground. This data is crucial for global clean energy planning, allowing grid operators and renewables developers to accurately predict how much power their clean energy assets will generate and match it with consumer demand. Precipitation forecasting at breakthrough accuracy Global weather models notoriously struggle to accurately predict precipitation. Rain and snow systems are driven by fast-moving cloud processes on tiny scales that are hard to model accurately using traditional physics-based simulations. Consequently, AI forecasts often produce blurry estimates or miss the boundaries of severe storms entirely. To solve this, we train our model on two exceptionally high-quality sources of precipitation data: NASA’s satellite-based Integrated Multi-satellite Retrievals for GPM (IMERG) and our own global precipitation reanalysis based on satellite radar. The result is a significant leap in precipitation forecasting accuracy. In medium-range global forecasts, evaluations against baselines show a Continuous Ranked Probability Score (CRPS) improvement of up to 60% against IMERG, 30% for MRMS, and 10% against rain gauge measurements for early lead times. Figure 3: Medium-range probability of precipitation (PoP > 1mm) forecast comparison. WeatherNext 2 (left) at 25-kilometer resolution shows a highly diffused and pixelated precipitation footprint. WeatherNext 3 (middle) at 11-kilometer resolution closely mirrors the actual satellite ground truth (right), accurately capturing the sharp, convective bands of the weather systems. Research applied across the ecosystem Our primary goal is to advance weather intelligence to make it universally useful — whether for an emergency responder tracking sudden wind shifts, an air traffic controller planning flight paths, or a farmer managing crops. To bring these breakthroughs out of the lab and into the real world, we’re integrating WeatherNext 3 across Google’s core ecosystem and beyond: High-resolution forecast data: We’re making global weather predictions, updated hourly and ready to integrate into your workflows with no model setup required. This enables researchers, developers and businesses to query the data in BigQuery and Earth Engine, or bulk-download from Google Cloud Storage. Available globally : WeatherNext 3 will begin powering weather experiences within Google Search, Gemini app, Google Maps, Google Maps Platform Weather API, and Google Earth Engine starting today. The update dramatically improves longer term forecasts. When planning a day or more ahead, people will see up to 50% more accurate precipitation forecasts — with the greatest improvements in regions where forecasts have historically been less reliable. So if you’re packing for a weekend trip or deciding the best day for an outdoor activity, you’ll now get more accurate predictions to help you plan. The atmosphere will always retain a degree of unpredictability. However, by training on real-world observations and bypassing traditional modeling constraints, WeatherNext 3 brings us closer to a future where forecasts truly match what is happening on the ground. To learn more about geospatial platforms and AI work at Google, check out Google Earth Engine , AlphaEarth Foundations , and Earth AI . Disclaimer: For official weather forecasts, severe weather warnings, and public safety advisories, please refer to your local meteorological agency or national weather service. Learn more about WeatherNext 3 Read our paper Build with WeatherNext 3 Explore Weather Lab to see WeatherNext 3 visualized in real-time See where WeatherNext 3 ranks on independent live leaderboards from Brightband. Get the latest news from Google in your inbox Sign up for our newsletters with product updates, event information, special offers, and more. Your information will be used in accordance with Google's privacy policy. You may opt out at any time.