Coordination Funds

天气预报、季节模拟和气候预估的质量在很大程度上取决于陆地-大气反馈的充分体现。这些反馈是与动量、能量和质量交换相关的过程和变量的高度复杂网络的结果。在理解过程和反馈方面仍然存在重大挑战，这是本计划将要解决的问题。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学、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察和模型模拟，从微伽马（约）的角度来理解和量化L-A反馈。2米)到中伽马(约。2公里)横跨日至季节时间尺度。LAFI由一个紧密交织的项目网络组成，旨在解决以下六个研究挑战：1)替代相似性理论，2)陆地表面异质性的影响，3)分配蒸散，4)理解携带，5)L-A反馈的协同表征，6)通过特别现场观测可能调查的干旱或热浪。这12个项目的合作将由深度学习、传感器协同和升级以及LAFI多模型实验三个跨领域工作组促进。我们的研究重点包括：A)在两个以农业用地为主导的地点观察L-A系统过程和反馈；1)霍恩海姆大学的陆地-大气反馈观测站（LAFO）得到了世界上无与伦比的仪器协同作用的加强，例如，首次将水稳定同位素测量、光纤分布式温度传感器和扫描激光雷达系统结合起来；2)德国气象局的林登堡-理查德·阿斯曼气象台（molo - rao）提供了长期数据集，用于研究干旱和热浪的统计数据。B) L-A系统过程的研究不足和认识不足；C) L-A系统模式的改进和应用，直至大涡尺度，植被和水稳定同位素的先进表示。D)应用深度学习（DL）方法识别过程描述中的关键因素和潜在的新因素。基于这一研究组成部分的组合，我们将在整个植被周期内用各种基于过程的指标来表征L-A系统变量的多维相位空间，以达到总体的LAFI目标。在紧密交织的合作研究的推动下，LAFI研究单位将获得对L-A反馈和过程的关键新见解，以表征中欧农业地区的耦合强度。

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.