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| Hauptseite > Publikationsdatenbank > Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations > print |
| Hauptseite > Publikationsdatenbank > Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations > print |
| 001 | 859074 | ||
| 005 | 20210131030851.0 | ||
| 024 | 7 | _ | |a 10.1515/intag-2017-0043 |2 doi |
| 024 | 7 | _ | |a 0236-8722 |2 ISSN |
| 024 | 7 | _ | |a 2300-8725 |2 ISSN |
| 024 | 7 | _ | |a 2128/21160 |2 Handle |
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| 037 | _ | _ | |a FZJ-2019-00025 |
| 082 | _ | _ | |a 640 |
| 100 | 1 | _ | |a Sabbatini, Simone |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations |
| 260 | _ | _ | |a Lublin |c 2018 |b IA PAS |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1547479961_23129 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a The eddy covariance is a powerful technique to estimate the surface-atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO2, water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in tenns of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g. ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-the-art processing methods. |
| 536 | _ | _ | |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) |0 G:(DE-HGF)POF3-255 |c POF3-255 |f POF III |x 0 |
| 536 | _ | _ | |a ICOS - Integrated Carbon Observation System (211574) |0 G:(EU-Grant)211574 |c 211574 |f FP7-INFRASTRUCTURES-2007-1 |x 1 |
| 536 | _ | _ | |a IDAS-GHG - Instrumental and Data-driven Approaches to Source-Partitioning of Greenhouse Gas Fluxes: Comparison, Combination, Advancement (BMBF-01LN1313A) |0 G:(DE-Juel1)BMBF-01LN1313A |c BMBF-01LN1313A |f Nachwuchsgruppen Globaler Wandel 4+1 |x 2 |
| 536 | _ | _ | |a TERENO - Terrestrial Environmental Observatories (TERENO-2008) |0 G:(DE-HGF)TERENO-2008 |c TERENO-2008 |f TERENO-2008 |x 3 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Mammarella, Ivan |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Arriga, Nicola |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Fratini, Gerardo |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Graf, Alexander |0 P:(DE-Juel1)129461 |b 4 |u fzj |
| 700 | 1 | _ | |a Hörtnagl, Lukas |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Ibrom, Andreas |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Longdoz, Bernard |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Mauder, Matthias |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Merbold, Lutz |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Metzger, Stefan |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Montagnani, Leonardo |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Pitacco, Andrea |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Rebmann, Corinna |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Sedlák, Pavel |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Šigut, Ladislav |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Vitale, Domenico |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Papale, Dario |0 P:(DE-HGF)0 |b 17 |
| 773 | _ | _ | |a 10.1515/intag-2017-0043 |g Vol. 32, no. 4, p. 495 - 515 |0 PERI:(DE-600)2235638-1 |n 4 |p 495 - 515 |t International agrophysics |v 32 |y 2018 |x 2300-8725 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/859074/files/Sabbatini_graf_2018_IntAgrophys_ICOS.pdf |
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