引力波动量流动从1公里全球ECMWF综合预测系统
Aman Gupta1, Aditi Sheshadri2, Valentine Anantharaj3
1Department of Earth System Science, Stanford University, Stanford, USA. ag4680@stanford.edu.
Scientific data
|August 21, 2024
概括
这项研究使用高分辨率模型数据量化大气重力波 (GW) 动量流. 经过验证的结果促进了对大气动态的理解,并为机器学习模拟提供了数据.
科学领域:
- 大气科学 大气科学
- 地质物理学 地质物理学
- 气候建模气候模型
背景情况:
- 对大气循环研究来说,了解中等尺度变化,如重力波 (GWs),至关重要.
- 全球,精细分辨率的观测和模拟数据对GWs是有限的.
- 气候模型经常使用参数化来模拟GW效应.
研究的目的:
- 从高分辨率模型模拟中获得的来自大气GW的动量流呈现.
- 为了对这些流量与重新分析数据进行验证.
- 为GW研究和机器学习应用提供有价值的数据集.
主要方法:
- 从综合预报系统 (IFS) 模型中使用1公里分辨率的"自然运行" (XNR1K).
- 应用了赫尔姆霍尔茨分解来计算从约1.5 petabyte数据的区域和南部动量流.
- 与ERA5再分析数据对计算流量进行了验证.
主要成果:
- IFS模型成功地产生了可靠的GW动量流量分布.
- 该模型捕获了中尺度的动态变化,显示与ERA5再分析的良好一致.
- 经过验证的数据集证明了该模型在模拟GW影响方面的能力.
结论:
- 该研究提供了GW动量流量的验证数据集.
- 这一数据集可以增强对GW-行星波相互作用和GW演变的理解.
- 这些数据作为机器学习模型模拟GWs的高质量培训材料.
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