Observation and investigation of the land-atmosphere system, atmospheric boundary layer processes, and fluxes

陆地-大气（L-A）反馈的特征需要对土壤、土地覆盖和大气进行详细的研究，包括大气边界层在内的自由对流层。目前几乎所有的观测站和观测网都只关注L-A系统的子组成部分。研究单位陆地-大气反馈倡议（LAFI）项目P1的目标是在霍亨海姆大学的陆地-大气反馈观测站（LAFO）实现一个针对异质农田的综合实地计划，以弥补这些观测差距。在LAFO，我们将实现对所有三个隔间的关键变量的同时分析，并在异质景观中描述它们的水平分布。此外，我们将分析在DWD的molo - rao收集的20年数据集，包括最相关的L-A系统变量，以推导统计L-A反馈度量、地表通量和能量收支关闭，并特别强调极端条件，如异常潮湿和干燥年。在LAFO，在至少一个植被期（2025年春季-秋季），我们将执行一般操作期（GOP）与一系列五个密集观察期（IOPS）相结合，以表征L-A反馈过程。除了完善的测量技术，如通量室和涡流相关系统以及多普勒激光雷达系统，我们将进一步采用新技术。其中包括用于区分蒸发和蒸腾的水稳定同位素传感器，用于测量土壤、冠层和表层温度和风的光纤分布式传感器，用于测量辐射、温度和湿度的冠层剖面，用于绘制地表特性的无人驾驶飞行器，以及高分辨率扫描拉曼和差分吸收激光雷达系统以及多普勒激光雷达，以获得地面层和大气边界层的三维湍流特性，特别强调夹带过程。P1与所有其他项目直接相关，并作为LAFI的焦点。P2-P6和P11将有助于传感器协同，并分析不同科学方面收集的数据。P7-P9将对整个GOP进行建模，并使用P1数据来比较反馈指标和过程表示以及模型评估。P10将使用深度学习方法分析MOL-RAO长期数据和LAFI GOP和IOP数据。在P1中进行的测量为实现所有六个目标和解决LAFI的所有假设提供了必要的基础。同样，所有的跨领域工作组都依赖于P1的观察结果。通过LAFI GOP和IOPs的设计和性能，我们期望在农业景观的L-A反馈和过程中有新的见解。

The characterization of Land-Atmosphere (L-A) feedback requires detailed studies of the soil, the land cover, and the atmosphere up to lower free troposphere encompassing the atmospheric boundary layer. Almost all current observatories and observational networks focus only on subcomponents of the L-A system. The objective of project P1 of the Research Unit Land-Atmosphere Feedback Initiative (LAFI) is to realize a comprehensive field program over heterogeneous farmland at the Land-Atmosphere Feedback Observatory (LAFO) of the University of Hohenheim that closes these observational gaps. At the LAFO, we will realize the simultaneous profiling of key variables in all three compartments and the characterization of their horizontal distributions in a heterogeneous landscape. Moreover, we will analyze a 20-year data set collected at the MOL-RAO of the DWD covering the most relevant L-A system variables for the derivation of statistical L-A feedback metrics, surface fluxes, and the energy-budget closure with special emphasis on extreme conditions such as exceptionally wet and dry years. At LAFO, over at least one vegetation period (spring–fall 2025), we will perform a General Operations Period (GOP) in combination with a series of five Intensive Observations Periods (IOPS) to characterize L-A feedback processes. Besides well-established measurement techniques such as flux chambers and eddy covariance systems as well as Doppler lidar systems we will further employ new techniques. These include water stable isotope sensors for differentiating between evaporation and transpiration, fiber-optic distributed sensing in the soil, in the canopy, and in the surface layer for temperature and wind measurements, canopy profiling of radiation, temperature, and moisture, unmanned aerial vehicles to map land surface properties, and high-resolution scanning Raman- and differential absorption lidar systems together with Doppler lidars to derive 3D turbulent properties of the surface layer and the atmospheric boundary layer with special emphasis on entrainment processes. P1 is of immediate relevance to all other projects and serve as a focal point of LAFI. P2-P6, and P11 will contribute to the sensor synergy and analyze the data collected under different scientific aspects. P7-P9 will model the whole GOP and use the P1 data for comparisons of feedback metrics and process representations as well as model evaluation. P10 will analyze the MOL-RAO long-term and the LAFI GOP and IOP data using deep learning methods. The measurements performed in P1 provide the essential basis to reach the all six objectives and to address all the hypotheses of LAFI. Similarly, all cross-cutting working groups rely on the observations of P1. By means of the design and the performance of the LAFI GOP and IOPs we expect new insights in L-A feedbacks and processes over agricultural landscapes.