在生物圈-大气界面的高频流中因果相互作用:结构性行为
Leila Constanza Hernandez Rodriguez1, Praveen Kumar1,2
1Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, USA.
Chaos (Woodbury, N.Y.)
|December 7, 2023
概括
高频旋协变数据揭示了大气变量之间的复杂因果关系. 这项研究引入了一种新的方法,使用定向环形图来分析这些高频相互作用.
科学领域:
- 大气科学 大气科学
- 环境物理 环境物理
- 数据科学数据科学数据科学
背景情况:
- 高频旋共变度 (EC) 测量以10 Hz的频率捕获陆地与大气之间的相互作用.
- 传统的光谱分析侧重于15-60分钟的时间尺度,往往忽略了高频相互依存.
- 多变量EC数据包含丰富的信息,包括风,湿度,温度和CO2等变量相互作用.
研究的目的:
- 超越传统的EC数据的光谱分析.
- 探索和量化高频互动变量之间的因果依赖关系.
- 开发一种方法框架,以了解流系统中的因果结构演变.
主要方法:
- 利用来自美国中西部一个农业场地的10 Hz EC数据.
- 使用定向非循环图 (DAG) 量化高频互依关系.
- 应用基于距离的分类和k-means集群来分析随时间推移的DAG结构演变.
主要成果:
- 识别了明确的集群,代表了类似的因果依赖结构.
- 证明了在因果相互作用中表征结构相似性和差异的能力.
- 在晴天和日食期间观察到因果结构的动态变化.
- 该方法有效地选择了无偏见的集群数量.
结论:
- 开发的基于DAG的方法为分析乱系统中高频因果关系提供了强大的框架.
- 这种方法提高了对因果依赖如何在复杂的环境数据中表现的理解.
- 该研究强调了高频EC数据在揭示复杂大气动态方面的潜力.
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