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This work describes a data cleaning procedure for the detection of eddy covariance fluxes affected by systematic errors. We believe that the proposed procedure can serve as a basis toward a unified quality control strategy suitable for the centralized data processing pipelines, where the use of completely data-driven and scalable procedures that guarantee high-quality standards and reproducibility of the released products constitutes an essential prerequisite.
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BG | Articles | Volume 17, issue 6
Biogeosciences, 17, 1367–1391, 2020
https://doi.org/10.5194/bg-17-1367-2020
Biogeosciences, 17, 1367–1391, 2020
https://doi.org/10.5194/bg-17-1367-2020

Research article 19 Mar 2020

Research article | 19 Mar 2020

A robust data cleaning procedure for eddy covariance flux measurements

Domenico Vitale et al.

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Cited articles

Aubinet, M., Chermanne, B., Vandenhaute, M., Longdoz, B., Yernaux, M., and Laitat, E.: Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes, Agr. Forest Meteorol., 108, 293–315, https://doi.org/10.1016/S0168-1923(01)00244-1, 2001. a
Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy Covariance, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-2351-1, 2012. a
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M.: Time series analysis: forecasting and control, John Wiley & Sons, Hoboken, New Jersey, https://doi.org/10.2307/2284112, 2015. a
Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I.: STL: A seasonal-trend decomposition, J. Off. Stat., 6, 3–73, 1990. a
Delpierre, N., Berveiller, D., Granda, E., and Dufrêne, E.: Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest, New Phytol., 210, 459–470, https://doi.org/10.1111/nph.13771, 2015. a
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Short summary
This work describes a data cleaning procedure for the detection of eddy covariance fluxes affected by systematic errors. We believe that the proposed procedure can serve as a basis toward a unified quality control strategy suitable for the centralized data processing pipelines, where the use of completely data-driven and scalable procedures that guarantee high-quality standards and reproducibility of the released products constitutes an essential prerequisite.
Citation
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Final-revised paper
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