Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3189-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-3189-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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
CORRESPONDING AUTHOR
National Ecological Observatory Network, Battelle, 1685 38th Street, Boulder, CO 80301, USA
University of Wisconsin-Madison, Dept. of Atmospheric and Oceanic Sciences, 1225 West Dayton Street, Madison, WI 53706, USA
David Durden
National Ecological Observatory Network, Battelle, 1685 38th Street, Boulder, CO 80301, USA
Cove Sturtevant
National Ecological Observatory Network, Battelle, 1685 38th Street, Boulder, CO 80301, USA
Hongyan Luo
National Ecological Observatory Network, Battelle, 1685 38th Street, Boulder, CO 80301, USA
Natchaya Pingintha-Durden
National Ecological Observatory Network, Battelle, 1685 38th Street, Boulder, CO 80301, USA
Torsten Sachs
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Andrei Serafimovich
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Jörg Hartmann
Alfred Wegener Institute – Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany
Jiahong Li
LI-COR Biosciences, 4647 Superior Street, Lincoln, NE 68504, USA
Ke Xu
University of Wisconsin-Madison, Dept. of Atmospheric and Oceanic Sciences, 1225 West Dayton Street, Madison, WI 53706, USA
Ankur R. Desai
University of Wisconsin-Madison, Dept. of Atmospheric and Oceanic Sciences, 1225 West Dayton Street, Madison, WI 53706, USA
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Total article views: 7,715 (including HTML, PDF, and XML)
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- Temperature-dependent emissions dominate aerosol and ozone formation in Los Angeles E. Pfannerstill et al. https://doi.org/10.1126/science.adg8204
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- Surface-atmosphere exchange in a box: Making the control volume a suitable representation for in-situ observations S. Metzger https://doi.org/10.1016/j.agrformet.2017.08.037
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- Bigleaf—An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data J. Knauer et al. https://doi.org/10.1371/journal.pone.0201114
- The Terrestrial Carbon Sink T. Keenan & C. Williams https://doi.org/10.1146/annurev-environ-102017-030204
- Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape S. Paleri et al. https://doi.org/10.1029/2022JD037138
- AmeriFlux BASE data pipeline to support network growth and data sharing H. Chu et al. https://doi.org/10.1038/s41597-023-02531-2
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- The eddy-covariance storage term in air: Consistent community resources improve flux measurement reliability K. Xu et al. https://doi.org/10.1016/j.agrformet.2019.107734
- Geospatial coherence of surface-atmosphere fluxes in the upper Great Lakes region J. Poe et al. https://doi.org/10.1016/j.agrformet.2020.108188
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- Upscaling surface energy fluxes over the North Slope of Alaska using airborne eddy-covariance measurements and environmental response functions A. Serafimovich et al. https://doi.org/10.5194/acp-18-10007-2018
- Can Data Mining Help Eddy Covariance See the Landscape? A Large-Eddy Simulation Study K. Xu et al. https://doi.org/10.1007/s10546-020-00513-0
- Intercomparison of eddy-covariance software for urban tall-tower sites C. Lan et al. https://doi.org/10.5194/amt-17-2649-2024
33 citations as recorded by crossref.
- Basic and extensible post-processing of eddy covariance flux data with REddyProc T. Wutzler et al. https://doi.org/10.5194/bg-15-5015-2018
- Pandemic restrictions in 2020 highlight the significance of non-road NOx sources in central London S. Cliff et al. https://doi.org/10.5194/acp-23-2315-2023
- Spatially and temporally resolved measurements of NOx fluxes by airborne eddy covariance over Greater London A. Vaughan et al. https://doi.org/10.5194/acp-21-15283-2021
- The AmeriFlux network: A coalition of the willing K. Novick et al. https://doi.org/10.1016/j.agrformet.2017.10.009
- An Ecosystem-Scale Flux Measurement Strategy to Assess Natural Climate Solutions K. Hemes et al. https://doi.org/10.1021/acs.est.0c06421
- Temperature-dependent emissions dominate aerosol and ozone formation in Los Angeles E. Pfannerstill et al. https://doi.org/10.1126/science.adg8204
- Towards an enhanced metric for detecting vertical flow decoupling in eddy covariance flux observations O. Peltola et al. https://doi.org/10.1016/j.agrformet.2024.110326
- Surface-atmosphere exchange in a box: Making the control volume a suitable representation for in-situ observations S. Metzger https://doi.org/10.1016/j.agrformet.2017.08.037
- Remote sensing northern lake methane ebullition M. Engram et al. https://doi.org/10.1038/s41558-020-0762-8
- An Open‐Source Python Library for Varying Model Parameters and Automating Concurrent Simulations of the National Water Model A. Raney et al. https://doi.org/10.1111/1752-1688.12973
- Quantifying vertical heterogeneity of forest gross primary production using hyperspectral and LiDAR data Z. Shi et al. https://doi.org/10.1016/j.jag.2026.105094
- Bigleaf—An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data J. Knauer et al. https://doi.org/10.1371/journal.pone.0201114
- The Terrestrial Carbon Sink T. Keenan & C. Williams https://doi.org/10.1146/annurev-environ-102017-030204
- Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape S. Paleri et al. https://doi.org/10.1029/2022JD037138
- AmeriFlux BASE data pipeline to support network growth and data sharing H. Chu et al. https://doi.org/10.1038/s41597-023-02531-2
- Generating Spatially Robust Carbon Budgets From Flux Tower Observations A. Griebel et al. https://doi.org/10.1029/2019GL085942
- The ASK-16 motorized glider: an airborne eddy covariance platform to measure turbulence, energy, and matter fluxes I. Wiekenkamp et al. https://doi.org/10.5194/amt-18-749-2025
- Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia J. Shaw et al. https://doi.org/10.1029/2021GB007261
- Surface–atmosphere fluxes of volatile organic compounds in Beijing W. Acton et al. https://doi.org/10.5194/acp-20-15101-2020
- InnFLUX – an open-source code for conventional and disjunct eddy covariance analysis of trace gas measurements: an urban test case M. Striednig et al. https://doi.org/10.5194/amt-13-1447-2020
- Measurements of traffic-dominated pollutant emissions in a Chinese megacity F. Squires et al. https://doi.org/10.5194/acp-20-8737-2020
- Toward understanding the contribution of waterbodies to the methane emissions of a permafrost landscape on a regional scale—A case study from the Mackenzie Delta, Canada K. Kohnert et al. https://doi.org/10.1111/gcb.14289
- Ozone deposition to a coastal sea: comparison of eddy covariance observations with reactive air–sea exchange models D. Loades et al. https://doi.org/10.5194/amt-13-6915-2020
- From NEON Field Sites to Data Portal: A Community Resource for Surface–Atmosphere Research Comes Online S. Metzger et al. https://doi.org/10.1175/BAMS-D-17-0307.1
- A robust data cleaning procedure for eddy covariance flux measurements D. Vitale et al. https://doi.org/10.5194/bg-17-1367-2020
- Surface-atmosphere exchange in a box: Space-time resolved storage and net vertical fluxes from tower-based eddy covariance K. Xu et al. https://doi.org/10.1016/j.agrformet.2017.10.011
- The eddy-covariance storage term in air: Consistent community resources improve flux measurement reliability K. Xu et al. https://doi.org/10.1016/j.agrformet.2019.107734
- Geospatial coherence of surface-atmosphere fluxes in the upper Great Lakes region J. Poe et al. https://doi.org/10.1016/j.agrformet.2020.108188
- Observational partitioning of water and CO2 fluxes at National Ecological Observatory Network (NEON) sites: a 5-year dataset of soil and plant components for spatial and temporal analysis E. Zahn & E. Bou-Zeid https://doi.org/10.5194/essd-16-5603-2024
- Eddy covariance measurements highlight sources of nitrogen oxide emissions missing from inventories for central London W. Drysdale et al. https://doi.org/10.5194/acp-22-9413-2022
- Upscaling surface energy fluxes over the North Slope of Alaska using airborne eddy-covariance measurements and environmental response functions A. Serafimovich et al. https://doi.org/10.5194/acp-18-10007-2018
- Can Data Mining Help Eddy Covariance See the Landscape? A Large-Eddy Simulation Study K. Xu et al. https://doi.org/10.1007/s10546-020-00513-0
- Intercomparison of eddy-covariance software for urban tall-tower sites C. Lan et al. https://doi.org/10.5194/amt-17-2649-2024
Saved (final revised paper)
Latest update: 19 Aug 2026
Short summary
We apply the
development and systems operationssoftware 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 operationssoftware development model to create the...
