Introducing WeatherNext 3, our most advanced and accurate global weather AI model
Introducing WeatherNext 3, our most advanced and accurate global weather AI model
隆重推出 WeatherNext 3：我们迄今为止最先进、最精准的全球天气 AI 模型
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.
每天，天气都在影响着数十亿人的决策。有些决策很简单，比如出门前带上一把伞，但有些决策则影响深远。风、雨以及热浪和干旱等极端天气事件，会对农业、全球供应链、清洁能源生产和国民经济产生连锁反应。
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.
近年来，人工智能彻底改变了天气预报，通过利用历史记录，其预测速度和准确性均优于传统方法。然而，预测高度局部化且快速变化的天气仍然是一项挑战。之前的模型往往缺乏足够的空间分辨率，且难以整合来自卫星等来源的实时天气数据。
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.
今天，Google DeepMind 和 Google Research 隆重推出 WeatherNext 3。根据 Brightband 的独立实时评估，这是迄今为止最先进、最精准的全球天气模型。我们的模型直接从实时观测数据中学习，能够针对对人们影响最大的天气事件提供及时且更具局部性的预测。通过利用原始卫星数据每小时生成高分辨率预报，我们的模型让全球 Google 产品用户都能获取可靠的天气预报。
Rapid weather prediction at unprecedented resolution
前所未有的高分辨率快速天气预报
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.
预报的实用性往往取决于细节，以及它在时间和空间上的解析精度。WeatherNext 3 以多种空间分辨率生成每小时预报，从宏观的全球风向模式到局部的地形特征，始终保持物理一致性。
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.
借助 WeatherNext 3，我们可以以 5 公里的分辨率可视化关键地表变量（如温度和湿度），以 10 公里的分辨率可视化其他地表变量，并以 25 公里的分辨率可视化大气变量（如风速）。总体而言，这提供的全球天气图景比我们之前的模型 WeatherNext 2 清晰约五倍，后者是在 25 公里的网格上以 6 小时为间隔生成预报的。
Real-world data at continuous global scale
持续的全球规模真实世界数据
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.
WeatherNext 3 最大的飞跃在于其学习来源。大多数 AI 天气模型（包括 WeatherNext 2）都是基于数值天气预报 (NWP) 模型的数据进行训练的。尽管 NWP 模型很有用，但它们是复杂的、由超级计算机驱动的物理模拟，存在六小时的数据滞后。这种滞后可能导致降雨或地表温度等快速变化变量出现偏差。
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.
通过摄入实时、全球静止卫星数据的马赛克图像，我们的新模型获得了对大气层丰富且持续更新的视角。这使得模型能够每小时生成一次新的预报，每一次预报都基于最新的卫星观测数据，分辨率高达 5 公里。
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.
这一点至关重要，因为关键天气往往发展迅速。当风暴、锋面或降水系统突然出现时，我们快速的更新周期和更高的分辨率能够提供更早、更详细的洞察，从而帮助推动有效的应对措施。