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 ecosys tem 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 differ ent steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve compara bility 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 uncertain ties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised pro cessing 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 process ing method

Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations / Sabbatini, S; Mammarella, I; Arriga, N; Fratini, G; Graf, A; Hörtnagl, L; Ibrom, A; Longdoz, B; Mauder, M; Merbold, L; Metzger, S; Montagnani, L; Pitacco, A; Rebmann, C; Sedlák, P; Šigut, L; Vitale, D; Papale, D. - In: INTERNATIONAL AGROPHYSICS. - ISSN 0236-8722. - 32:(2018), pp. 495-515. [10.1515/intag-2017-0043]

Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations

Vitale D;
2018

Abstract

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 ecosys tem 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 differ ent steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve compara bility 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 uncertain ties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised pro cessing 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 process ing method
2018
ICOS; protocol; method standardisation
01 Pubblicazione su rivista::01a Articolo in rivista
Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations / Sabbatini, S; Mammarella, I; Arriga, N; Fratini, G; Graf, A; Hörtnagl, L; Ibrom, A; Longdoz, B; Mauder, M; Merbold, L; Metzger, S; Montagnani, L; Pitacco, A; Rebmann, C; Sedlák, P; Šigut, L; Vitale, D; Papale, D. - In: INTERNATIONAL AGROPHYSICS. - ISSN 0236-8722. - 32:(2018), pp. 495-515. [10.1515/intag-2017-0043]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1665180
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