Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors

活生生的生物圈与大气过程在多种尺度上相互作用。要理解这些过程，需要综合多种观测数据，以便与大气模型中的理论进行比较。但是，所有的观测结果都与所有模型的比例不匹配。因此，地表通量的空间和时间尺度对我们如何评估关于大气模式子格子内发生的事情的理论以及如何将这些反馈到更大尺度的动力学中是基本的。Chequamegon异质生态系统能量平衡研究由高密度扩展探测器阵列(CheeseHead)实现，是一项密集的实地活动，专门设计用于解决长期存在的难题，即大气边界层响应对地表-大气热量和水交换的空间异质性尺度的作用。智力优势：高密度观测网络与大涡模拟(LES)和机器学习尺度实验相结合，以更好地理解亚中尺度响应，并改进分网格过程的数值天气和气候预测公式。该项目将在低层大气对地表非均质性作出反应的尺度上推进非均质陆面属性和通量的时空尺度方法和理论。Cheesehead的目标是提供前所未有的观测密度和仪器可靠性，以测试和开发关于空间异质性和大气反馈的假设。广泛的影响：该实验产生的知识促进了地表通量测量和建模的科学，与许多科学应用相关，如数值天气预报、气候变化、能源和计算流体动力学。这项研究将培养下一代陆地大气研究生和本科生。通过与UCAR(大学大气研究公司)全球计划、诺斯兰学院和当地学区合作，通过在附近学校和大学的初中、高中和本科生参与，包括现场支持外展和教师培训。观察和模型数据库将立即提供给社区和公众，供公众用于进一步的科学进步。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The living biosphere interacts with atmospheric processes at a multitude of scales. Understanding these processes requires integration of multiple observations for comparison to theories embedded in atmospheric models. But, all observations mismatch the scale of all models. Therefore, spatial and temporal scaling of surface fluxes is fundamental to how we evaluate theories on what happens within the sub-grid of atmospheric models and how those feed back onto larger scale dynamics. The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD) is an intensive field-campaign designed specifically to address long-standing puzzles regarding the role of atmospheric boundary-layer responses to scales of spatial heterogeneity in surface-atmosphere heat and water exchanges.Intellectual Merit:The high-density observing network is coupled to large eddy simulation (LES) and machine-learning scaling-experiments to better understand sub-mesoscale responses and improve numerical weather and climate prediction formulations of sub-grid processes. This project will advance spatiotemporal scaling methods for heterogeneous land surface properties and fluxes and theories on the scales at which the lower atmosphere responds to surface heterogeneity. CHEESEHEAD aims to provide a level of observation density and instrumentation reliability never previously achieved to test and develop hypotheses on spatial heterogeneity and atmosphere feedbacks.Broader Impacts:The experiment generates knowledge that advances the science of surface flux measurement and modeling, relevant to many scientific applications such as numerical weather prediction, climate change, energy resources, and computational fluid dynamics. The research will train next generation land-atmosphere graduate and undergraduate students. Field support outreach and teacher training is included via middle, high school, and undergraduate student involvement at nearby schools and colleges in coordination with UCAR's (University Corporation for Atmospheric Research) GLOBE program, Northland College, and local school districts. The database of observations and models will be made immediately available to the community and public for general use for further scientific advancement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.