论文标题
Graphcast：学习熟练的中等范围全球天气预测
GraphCast: Learning skillful medium-range global weather forecasting
论文作者
论文摘要
全球中等范围的天气预报对于许多社会和经济领域的决策至关重要。传统的数值天气预测使用增加的计算资源来提高预测准确性，但不能直接使用历史天气数据来改善基础模型。我们介绍了一种基于机器学习的方法，称为“ Graphcast”，该方法可以直接从重新分析数据进行培训。它预测数百个天气变量在10天内，在全球0.25度分辨率的10天内，一分钟不到一分钟。我们表明，Graphcast在1380个验证目标中有90％的表现明显优于最准确的操作确定性系统，并且其预测支持更好的严重事件预测，包括热带气旋，大气河流和极端温度。 Graphcast是准确有效的天气预报的关键进步，并有助于实现机器学习对复杂动力学系统进行建模的希望。
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but cannot directly use historical weather data to improve the underlying model. We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting, and helps realize the promise of machine learning for modeling complex dynamical systems.
