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Volume 10, issue 9 | Copyright
Geosci. Model Dev., 10, 3189-3206, 2017
https://doi.org/10.5194/gmd-10-3189-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model description paper 31 Aug 2017

Model description paper | 31 Aug 2017

eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

Stefan Metzger et al.
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Ammann, C., Brunner, A., Spirig, C., and Neftel, A.: Technical note: Water vapour concentration and flux measurements with PTR-MS, Atmos. Chem. Phys., 6, 4643–4651, https://doi.org/10.5194/acp-6-4643-2006, 2006.
Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy covariance: A practical guide to measurement and data analysis, Springer, Dordrecht, Heidelberg, London, New York, 438 pp., 2012.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W. U. K., Pilegaard, K., Schmid, H., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Billesbach, D. P.: Estimating uncertainties in individual eddy covariance flux measurements: A comparison of methods and a proposed new method, Agr. Forest. Meteorol., 151, 394–405, https://doi.org/10.1016/j.agrformet.2010.12.001, 2011.
Boettiger, C.: An introduction to Docker for reproducible research, with examples from the R environment, Operat. Syst. Rev., 49, 71–79, https://doi.org/10.1145/2723872.2723882, 2015.
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We apply the development and systems operations software development model to create the eddy4R–Docker open-source, flexible, and modular eddy-covariance data processing environment. Test applications to aircraft and tower data, as well as a software cross validation demonstrate its efficiency and consistency. Key improvements in accessibility, extensibility, and reproducibility build the foundation for deploying complex scientific algorithms in an effective and scalable manner.
We apply the development and systems operations software development model to create the...
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