Can Data Mining Help Eddy Covariance See the Landscape? A Large-Eddy Simulation Study
Can Data Mining Help Eddy Covariance See the Landscape? A Large-Eddy Simulation Study
复制标题
DOI：
10.1007/s10546-020-00513-0
复制
发表时间：
2020-04
影响因子：
4.3
通讯作者：
Ke Xu;M. Sühring;S. Metzger;D. Durden;A. Desai
中科院分区：
文献类型：
--
作者：
Ke Xu;M. Sühring;S. Metzger;D. Durden;A. Desai
涡度相关通量是地球系统模型和遥感数据的基本基准。然而，两个挑战，阻止模式数据相互比较充分利用涡动协方差通量。第一个挑战源于涡度协方差测量的不同和可变的空间代表性，或足迹偏差和瞬时性。第二个起源于一个非封闭的能量平衡的现象，使用涡动协方差测量，假设是由于不明中尺度流或欠采样的热点通量塔，等等。以前的研究表明，这两个问题可以通过建立多个塔或通过应用时空校正方法，如环境响应函数（ERF）方法来缓解。在这里，我们要问：（1）我们需要多少个涡流通量塔来充分纠正位置偏差，关闭能量预算，并对区域域进行采样？(2)先进的时空校正方法是否可以降低塔密度，同时仍然对区域通量域进行充分采样？此外，（3）电流变场方法反演地表通量变化的精度如何？为了回答这些问题，我们使用的数据从一个大涡模拟的大气流动以上的异质表面捕获的虚拟塔测量合奏。我们计算了涡度相关通量的空间和时空方法。空间涡动协方差法每15 km2约一个塔捕获89%的规定的总表面能通量，而时空方法只需要一个塔每40 km2捕获95%的表面能。为了捕获97%的能量，应用ERF方法进一步将所需的塔密度降低到每200平方公里一个塔，这是时空整流和纳入中尺度流的结果。这种方法也使检索的显热通量的表面空间变化。研究结果为未来基于通量塔集群的观测系统和尺度感知数据产品的设计提供了参考。
Eddy-covariance fluxes serve as an essential benchmark for Earth system models and remote sensing data. However, two challenges prevent model-data intercomparisons from fully utilizing eddy-covariance fluxes. The first challenge stems from the differing and variable spatial representativeness of the eddy-covariance measurements, or footprint bias and transience. The second originates from the phenomenon of a non-closed energy balance using eddy-covariance measurements, hypothesized to result from unaccounted mesoscale flows or under-sampling of hot spots by flux towers, among others. Previous studies have suggested that these two problems can be mitigated by either building multiple towers or by applying space–time rectification approaches, such as the environmental response function (ERF) approach. Here we ask: (1) How many eddy-flux towers do we need to sufficiently rectify location bias, close the energy budget, and sample the regional domain? (2) Can an advanced space–time rectification approach reduce the tower density, while still adequately sampling the regional flux domain? Furthermore, (3) How accurately can the ERF approach retrieve the surface-flux variation? To answer these questions, we used data from a large-eddy simulation of atmospheric flows above a heterogeneous surface as captured by an ensemble of virtual tower measurements. We calculated eddy-covariance fluxes by spatial and spatio-temporal methods. The spatial eddy-covariance method captured 89% of the prescribed total surface energy flux with about one tower per 15 km2, while the spatio-temporal method required only one tower per 40 km2to capture 95% of surface energy. To capture 97% of energy, applying the ERF approach further reduced the required tower density to one tower per 200 km2, as a result of space–time rectification and incorporating mesoscale flows. This approach also enabled retrieving the surface spatial variation of the sensible heat flux. The results provide a reference for informing and designing future observation systems based on flux tower clusters, and scale-aware data products.