Projects per year
Abstract / Description of output
Eddy covariance has been the de facto method of analyzing scalar turbulent transport data. To refine the information available from these data, we derive a simplified version of the turbulent scalar-transport equation for the surface layer, which employs a more explicit form of signal decomposition and dispenses with Reynolds averaging in favour of an averaging operator based on the relevant scalar-flux driving variables. The resulting method, termed functional covariance, provides five areas of improvement in flux estimation: (i) Better representation of surface fluxes through closer correspondence of turbulent exchange with variations in the driving variables. (ii) An approximate 25% reduction in flux uncertainty resulting from improved independence of turbulent-flux samples. (iii) Improved data retention through less onerous quality control (stationarity) testing. (iv) Improved estimation of low-frequency flux contributions through reduced uncertainty and avoidance of driving-variable nonstationarity. (v) Potential elimination of flux-storage estimation when state driving-variables are used to define the functional-covariance flux averaging. We describe the important considerations required for application of functional covariance, apply both functional- and eddy-covariance methods to an example dataset, compare the resulting eddy- and functional-covariance esti- mates, and demonstrate the aforementioned benefits of functional covariance.
Original language | English |
---|---|
Article number | 10.1007/s10546-019-00474-z |
Pages (from-to) | 373–408 |
Journal | Boundary-Layer Meteorology |
Volume | 173 |
Early online date | 10 Sept 2019 |
DOIs | |
Publication status | Published - 1 Dec 2019 |
Keywords / Materials (for Non-textual outputs)
- Functional Covariance
- Eddy Covariance
- surface layer
- micrometeorology
Fingerprint
Dive into the research topics of 'A Functional Approach to Vertical Turbulent Transport of Scalars in the Atmospheric Surface Layer'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Generating Regional Emissions Estimates with a Novel Hierarchy of Observations and Upscaled Simulation Experiments (GREENHOUSE)
Williams, M., Mencuccini, M., Moncrieff, J. & Reay, D.
1/04/13 → 31/03/17
Project: Research
-
The influence of forest management on C sequestration and trace gas exchange.
Moncrieff, J., Mencuccini, M. & Smith, K.
2/06/03 → 31/05/05
Project: Research