TY - JOUR

T1 - Can Data Mining Help Eddy Covariance See the Landscape?

T2 - A Large-Eddy Simulation Study

AU - Xu, Ke

AU - Sühring, Matthias

AU - Metzger, Stefan

AU - Durden, David

AU - Desai, Ankur R.

N1 - Publisher Copyright: © 2020, Springer Nature B.V.

PY - 2020/7/1

Y1 - 2020/7/1

N2 - 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 km2 to 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.

AB - 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 km2 to 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.

KW - Eddy covariance

KW - Energy balance

KW - Footprint

KW - Large-eddy simulation

KW - Upscaling

UR - http://www.scopus.com/inward/record.url?scp=85083427010&partnerID=8YFLogxK

U2 - 10.1007/s10546-020-00513-0

DO - 10.1007/s10546-020-00513-0

M3 - Article

AN - SCOPUS:85083427010

VL - 176

SP - 85

EP - 103

JO - Boundary-Layer Meteorology

JF - Boundary-Layer Meteorology

SN - 0006-8314

IS - 1

ER -