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Boundary-Layer Meteorology

, Volume 170, Issue 1, pp 69–93 | Cite as

Trade-Offs in Flux Disaggregation: A Large-Eddy Simulation Study

  • Matthias SühringEmail author
  • Stefan Metzger
  • Ke Xu
  • Dave Durden
  • Ankur Desai
Research Article

Abstract

Airborne flux measurements allow us to quantify the surface–atmosphere exchange over heterogeneous land surfaces. While often applied to regional-scale fluxes, it is also possible to infer component fluxes emanating from different surface patches from the measurement via disaggregation strategies. Here, we emulate flux disaggregation strategies by conducting an ensemble of virtual flight measurements within a set of large-eddy simulations over idealized surface heterogeneities and under different flow regimes. The resulting patch surface fluxes are compared with the prescribed patch surface fluxes in the simulation. To calculate fluxes along the flight legs, we apply traditional eddy-covariance and space–frequency (wavelet) methods. We show that the patch fluxes are captured best with the space–frequency method, where the disaggregation error is almost invariant of the segment length. For the eddy-covariance method, however, the error strongly depends on the segment length, with largest random and systematic errors for shorter segments. Furthermore, we determine a trade-off between a permissible disaggregation error and a sufficient resolution of the heterogeneous surface signals. Among our set-ups, an optimal segment length is determined to be 3–4 km for the eddy-covariance method, while with the space–frequency method even shorter segment lengths of a few hundreds of metres can be chosen, which enables sufficient isolation of signals from surface patches and the resolution of small-scale surface heterogeneity.

Keywords

Aircraft measurement Eddy covariance Flux disaggregation Flux footprint Wavelet decomposition 

Notes

Acknowledgements

We thank the reviewers for their valuable comments and remarks that helped to improve the quality of the manuscript. All simulations were performed on the Cray XC40 at The North-German Supercomputing Alliance (HLRN), Hannover/Berlin. M. Sühring is funded by the German Federal Ministry of Education and Research (BMBF) (Grant 01LP1601A) within the framework of Research for Sustainable Development (FONA; www.fona.de). S. Metzger and D. Durden are funded by the National Ecological Observatory Network. The National Ecological Observatory Network is a project sponsored by the National Science Foundation and managed under cooperative agreement by Battelle Ecology, Inc. This material is based upon work supported by the National Science Foundation [Grant DBI-0752017]. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. A.R. Desai and K. Xu acknowledge support of NSF AGS-1822420, Dept of Energy Ameriflux Network Management Project, Battelle Ecology, Inc. contact #3010-0401-000. NCL (The NCAR Command Language (Version 6.4.0) [software]. (2017). Boulder, Colorado: UCAR/NCAR/CISL/TDD.  https://doi.org/10.5065/D6WD3XH5) has been used for data analysis and visualization. Simulation data and analysis scripts will be provided upon request.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Institut für Meteorologie und KlimatologieLeibniz Universität HannoverHannoverGermany
  2. 2.National Ecological Observatory NetworkBoulderUSA
  3. 3.Department of Atmospheric and Oceanic SciencesUniversity of Wisconsin-MadisonMadisonUSA

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