Update to Google’s AI weather model improves forecast accuracy
谷歌的WeatherNext 3 AI天气模型通过整合特定地点的物理信息（如陆地/海洋状态和地表海拔）提高了预报准确性。这种方法通过训练历史气象站数据，使高层大气状况的准确性提高了约5%，相当于增加了大约六小时的准确预报提前期。特定地点的地表温度准确性也提高了高达30%。WeatherNext 3现在为谷歌服务（包括搜索、Gemini和地图）提供预报信息，并且在这些指标上普遍优于ECWMF模型。
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发布当时偏移：UTC+02026年9月8日 18:00 UTC
收录当时偏移：UTC+02026年9月9日 21:00 UTC
- 发布
- 2026年9月8日 18:00
- 收录
- 2026年9月9日 21:00
- 来源类型
- 媒体报道
- 档位
- 专业媒体
- 信源状态
- 正常
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讨论趋势
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Unlike traditional models that use physical properties in a location to simulate physical processes, machine-learning models are largely black boxes that train on past patterns and spit out predictions of future patterns. But WeatherNext 3 is adding a tiny bit of physical information to calculate surface temperature and dew point at any specific location you want to pull up. It checks whether that point is land or ocean and uses its surface elevation. By training on past weather station data tagged with that information, the team says they get better forecast predictions.
Some oddities
The white paper shows some results to document forecast performance improvements over WeatherNext 2, as well as the European Centre for Medium-Range Weather Forecasts (ECMWF) AI model.
They note a roughly 5 percent improvement in upper atmosphere condition accuracy over their previous model, for example, which they say equates to about six more hours of accurate forecast lead time. And their change to calculating surface temperature for a specific location improved accuracy by up to 30 percent. They’re generally beating the ECWMF model on these metrics as well.
There is one curious exception that the paper doesn’t even guess at the cause of. For a number of variables, their comparison to the initial six-hours-ahead forecast from the other models shows WeatherNext 3 doing worse before pulling ahead for the rest of a 15-day forecast.
Larger-scale patterns are also not without some weirdness. You can see the shape of the model’s grid in some predictions, like the map of precipitation showing some distinctly hexagonal blobs. Their method of generating multiple surface temperature forecasts to represent the range of possible outcomes also has a habit of producing snapshots where the global average temperature is higher or lower. Normally, you would want to see the average be consistent, with local-scale variability that averages out across the globe.
Overall, the team says their new model “represents a major step forward for AI-based weather predictions by going beyond relying purely on analysis and utilizing information-dense, low-latency observation data.”
WeatherNext 3 is now the source of forecast information across Google services, including Search, Gemini, and Maps.