General circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained o...
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on ...
值得一提的是,NeuralGCM 不仅在准确度方面达到甚至超过了现有传统数值天气预报模型和其他机器学习(ML)模型;在速度上也是“遥遥领先”,可以在30 秒计算时间内生成 22.8 天大气模拟;且可以比传统模型节省数量级的计算量。 相关研究论文以“Neural general circulation models for weather and climate”为题,已发表在权威...
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Neural general circulation models for weather and climate New Google AI weather and climate model improves accuracy Google’s new weather prediction system combines AI with traditional physics 本文转载自IT之家,转载目的在于传递更多信息,并不代表本站赞同其观点和对其真实性负责。如涉及作品内容、版权和其它问...
相关研究以「Neural general circulation models for weather and climate」为题,于 7 月 22 日发布在《Nature》上。 NeuralGCM:物理与智能的融合 NeuralGCM是Google Research团队提出的一项创新技术,它将基于物理的大气循环模型与神经网络巧妙结合,旨在通过深度学习算法提升气候预测的精度与效率。该模型的核心在于两个关...
相关研究以「Neural general circulation models for weather and climate」为题,于 7 月 22 日发布在《Nature》上。 论文链接:https://www.nature.com/articles/s41586-024-07744-y NeuralGCM 架构 NeuralGCM 将基于物理的大气循环模型与用于小规模过程的神经网络相结合。
大气环流模型(GCM,General Circulation Models)是基于物理的模拟器,将大尺度动力学的数值求解器与云形成等小尺度过程的调整表现相结合,是天气和气候预测的基础。 在NeuralGCM之前,基于再分析数据训练的机器学习模型在确定性天气预报方面已经取得了与传统大气环流模型相当或更好的技能,然而,这些模型未对天气、气候的集合...
2. #传统天气预测、气候模拟,正在被 AI 颠覆。 由 Google Research 研究团队及其合作者开发的人工智能(AI)模型 NeuralGCM,将天气预测和气候模拟提升到了新高度。相关研究论文以"Neural general circulation models for weather and climate"为题,已发表在周一的权威科学期刊 Nature 上。谷歌团队称,NeuralGCM 对 1-15...
Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground. Geosci. Model Dev. 12, 2797–2809 (2019). Article ADS Google Scholar Rasp, S. et al. WeatherBench: a benchmark data set for data‐driven weather ...