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学习熟练的中期全球天气预报
Remi Lam1, Alvaro Sanchez-Gonzalez1, Matthew Willson1
1Google DeepMind, London, UK.
Science (New York, N.Y.)
|November 14, 2023
概括
机器学习天气模型GraphCast可以在不到一分钟的时间内准确地预测全球10天的天气. 它的性能优于传统系统, 提高了恶劣天气预报和高效的气候建模.
科学领域:
- 气象学和气候科学
- 人工智能
- 动态系统建模
背景情况:
- 准确的中期全球天气预报对于社会和经济部门至关重要.
- 传统的数值天气预测模型通过更多的计算能力提高了准确性,但没有直接利用历史数据.
- 现有的方法在速度和直接利用历史天气模式方面存在局限性.
研究的目的:
- 推出基于机器学习 (ML) 的新天气预报方法GraphCast.
- 展示GraphCast能够以高分辨率和速度预测未来10天的全球天气变量.
- 用最先进的运行天气预报系统来评估GraphCast的性能.
主要方法:
- 开发了GraphCast,这是一款直接使用历史天气再分析数据进行训练的机器模型.
- 使用图形神经网络架构来处理和学习时空天气模式.
- 在10天的预测时间内,对1380个验证目标进行了预测准确性的评估.
主要成果:
- 在不到一分钟的时间里, GraphCast 以0.25度的分辨率实现了数百个天气变量的全球预报.
- 在90%的验证目标上,ML模型显著优于最准确的操作决定性系统.
- 在预测包括热带气旋,大气河流和极端温度等恶劣天气事件方面,
结论:
- 在准确高效的中期全球天气预报方面,
- 基于机器学习的方法为传统的数字天气预报提供了强大的替代方案,有效地利用历史数据.
- 这项工作突显了机器学习在模拟地球天气等复杂动态系统方面的潜力.
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