EAGER: Collaborative Analysis of Doppler Lidar Data from Canopy Horizontal Array Turbulence Study (CHATS)

EAGER：对冠层水平阵列湍流研究 (CHATS) 的多普勒激光雷达数据进行协作分析

• 批准号: 1230055
• 负责人: Ronald Calhoun
• 金额: $ 3.15万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-15 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1230055&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Analysis; Doppler; Lidar

Like many sources of remotely-sensed measurements of atmospheric boundary layer structure and motions, Doppler lidar data present a formidable analysis challenge both owing to their voluminous and highly detailed nature and potential for contamination by interfering signals such as those returned from the earth's underlying surface and/or plant canopies. This tightly-scoped research effort will support collaborations between a principal investigator who is well versed in lidar measurements and a canopy-induced turbulence expert at NSF's National Center for Atmospheric Research to pursue improved techniques for accurate yet cost-effective means for more automated processing of lidar data previously collected with NSF support during the Canopy Horizontal Array Turbulence Study (CHATS) in 2007.The intellectual merit of this effort centers on development and application of idealized models of expected coherent structures (such as roll vortices and other phenomena evident in large-eddy simulations and via other means) to provide a labor-saving framework in which raw radial velocity measurements may be more intelligently processed and interpreted. This will in-turn support extraction of improved representations of coherent structures above plant canopies that are responsible for exchange of momentum and trace gases (such as CO2) with the planetary boundary layer. The availability of tandem in-canopy, high-resolution turbulent flux measurements obtained during CHATS to evaluate this previously unproven analysis approach represents a potentially high-payoff research approach. Broader impacts of this effort include improved downstream knowledge of biogeochemical cycles and sources of low-level atmospheric turbulence, as well as enhancements to the lead investigator's classroom education efforts.

与大气边界层结构和运动遥感测量的许多来源一样，多普勒激光雷达数据提出了一项艰巨的分析挑战，这既是因为其数量庞大和非常详细的性质，也是因为其可能受到干扰信号的污染，例如从地球下垫面和/或植物冠层返回的信号。 这项范围紧密的研究工作将支持一位精通激光雷达测量的首席研究员与NSF国家大气研究中心的冠层诱导湍流专家之间的合作，以寻求改进技术，实现准确但具有成本效益的方法，以便更自动化地处理先前在冠层水平阵列湍流研究（CHATS）期间由NSF支持收集的激光雷达数据。这项工作的智力价值集中在预期相干结构的理想化模型的开发和应用上（例如在大涡模拟中和通过其他手段明显的滚转涡和其他现象），以提供一个节省劳动力的框架，在该框架中，原始径向速度测量可以更智能地处理和解释。 这将反过来支持提取植物冠层上方相干结构的改进表示，这些结构负责与行星边界层交换动量和痕量气体（如CO2）。 在CHATS期间获得的串联冠层内高分辨率湍流通量测量的可用性，以评估这种以前未经证实的分析方法，代表了一种潜在的高回报的研究方法。 这一努力的更广泛影响包括改进下游对地球化学循环和低空大气湍流来源的了解，以及加强首席研究员的课堂教育工作。