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

陆-气（L-A）反馈的表征需要对土壤、土地覆盖物和大气进行详细的研究，直到包含大气边界层的较低的自由对流层。目前几乎所有的观测站和观测网络都只关注L-A系统的子系统。研究单位土地-大气反馈倡议（LAFI）的项目P1的目标是在霍恩海姆大学的土地-大气反馈观测站（LAFO）实现一个全面的实地项目，以缩小这些观测差距。在LAFO，我们将实现所有三个分区中关键变量的同步分析，以及它们在异质景观中的水平分布特征。此外，我们将分析在DWD的MOL-RAO收集的20年数据集，涵盖最相关的L-A系统变量，用于推导统计L-A反馈指标，表面通量和能量预算闭合，特别强调极端条件，如异常潮湿和干燥的年份。在LAFO，在至少一个植被期（2025年春季至秋季），我们将执行一般操作期（GOP）与一系列的五个强化观测期（IOPS）相结合，以表征L-A反馈过程。除了完善的测量技术，如通量室和涡度协方差系统以及多普勒激光雷达系统，我们将进一步采用新的技术。其中包括用于区分蒸发和蒸腾的水稳定同位素传感器，用于测量温度和风的土壤、冠层和表层光纤分布式传感，辐射、温度和湿度的冠层剖面，绘制地表特性的无人驾驶飞行器，和高分辨率扫描拉曼-和差分吸收激光雷达系统与多普勒激光雷达一起，以获得表面层和大气边界层的三维湍流特性，特别强调关于夹带过程。P1与所有其他项目直接相关，并作为LAFI的协调中心。P2-P6和P11将有助于传感器协同作用，并分析在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数据进行反馈度量和过程表示的比较以及模型评估。P10将使用深度学习方法分析MOL-RAO长期和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，所有跨部门工作组都依赖于项目1的意见。通过LAFI GOP和IOP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.