Coordination Funds

天气预报、季节模拟和气候预测的质量在很大程度上取决于陆地-大气(L-A)反馈的充分再现。这些反馈是与动量、能量和质量交换相关的高度复杂的过程和变量网络的结果。在理解进程和反馈方面仍然存在重大挑战，这一倡议将解决这一问题。陆地-大气反馈倡议(LAFI)是一个由来自大气、农业和土壤科学以及生物地球物理、水文学和神经信息学的研究人员组成的跨学科联盟，提出了一种先进研究方法的新组合。LAFI的首要目标是通过独特的协同观测和来自微伽马(约.2米)到中-伽马(约2千米)的尺度，从日尺度到季节尺度。LAFI由一个紧密交织的项目网络组成，这些项目解决了作为目标和假设提出的六个研究挑战：1)替代相似理论，2)陆面非均质性的影响，3)蒸发蒸腾的分配，4)夹带的理解，5)L-A反馈的协同特征，以及6)可能通过特别野外观测来研究的干旱或热浪。这12个项目的协作将由深度学习、传感器协同和升级以及拉菲多模型实验的三个横向工作组促进。我们的研究重点包括：a)在两个以农业土地利用为主的地点观测到L-A系统的过程和反馈：i)霍恩海姆大学的土地-大气反馈观测站通过全球无与伦比的仪器协同作用得到加强，例如，首次结合水稳定同位素测量、光纤分布式温度传感器和扫描激光雷达系统；ii)德国气象局林登堡-理查德·阿伯·曼气象台(MOL-RAO)为研究干旱和热浪的统计数据提供长期数据集。B)对L-A系统中的过程研究不足和了解不足，C)L-A系统模式的改进和应用，包括对植被和水稳定同位素的先进表示的大涡度尺度。D)应用深度学习方法来确定流程描述中的关键因素和潜在的新因素。基于这一研究成分的组合，我们将使用各种基于过程的度量来表征L-A系统变量在整个植被时期的多维相空间，以达到拉菲的首要目标。在紧密交织的协作研究的推动下，LAFI研究部门将获得对L-A反馈的关键新见解，以及表征中欧农业区耦合强度的过程。

The quality of weather forecasts, seasonal simulations, and climate projections depends critically on the adequate representation of land-atmosphere (L-A) feedbacks. These feedbacks are the result of a highly complex network of processes and variables related to the exchange of momentum, energy, and mass. Significant challenges persist in understanding processes and feedbacks, which this initiative will address. The Land-Atmosphere Feedback Initiative (LAFI) is an interdisciplinary consortium of researchers from atmospheric, agricultural, and soil sciences as well as from bio-geo-physics, hydrology, and neuroinformatics proposing a novel combination of advanced research methods. The overarching goal of LAFI is to understand and quantify L-A feedbacks via unique synergistic observa-tions and model simulations from the micro-gamma (approx. 2 m) to the meso-gamma (approx. 2 km) scales across diurnal to seasonal time scales. LAFI consists of a network of closely intertwined projects addressing six research challenges formulated as objectives and hypotheses on 1) alternative similarity theories, 2) the impact of land-surface heterogeneity, 3) partitioning evapotranspiration, 4) understanding entrainment, 5) synergistic characterization of L-A feedback, and 6) droughts or heatwaves potentially investigated by ad-hoc field observations. Collaboration across the twelve projects will be fostered by three Cross Cutting Working Groups on Deep Learning, Sensor Synergy and Upscaling, as well as the LAFI Multi-model Experiment. Our research highlights include: A) The observation of L-A system processes and feedbacks at two sites dominated by agricultural land use: I) The Land-Atmosphere Feedback Observatory (LAFO) at the University of Hohenheim enhanced by a worldwide-unparalleled synergy of instruments, e.g., combining for the first time measurements of water stable isotopes, fiber-optic distributed temperature sensors, and scanning lidar systems, II) the Meteorological Observatory Lindenberg-Richard Aßmann Observatory (MOL-RAO) of the German Meteorological Service providing a long-term data set for studying the statistics of droughts and heatwaves. B) Under-studied and poorly understood processes in the L-A system, C) Improvement and application of L-A system models down to the large eddy scales with advanced representation of vegetation and water stable isotopes. D) Application of deep learning (DL) methods for identifying key and potentially novel factors in process descriptions. Based on this combination of research components, we will characterize the multi-dimensional phase space of L-A system variables with various process-based metrics over an entire vegetation period in order to reach the overarching LAFI goal. Fueled by the tightly interwoven collaborative research, the LAFI research unit will gain crucial new insights into L-A feedbacks and processes for the characterization of coupling strengths over agricultural regions in central Europe.