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6. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M. Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D’Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., Cinti, Bruno De, Grandcourt, Agnes de, Ligne, Anne De, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, Tommasi, Paul di, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, Dios, Victor Resco de, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario

Published
2020
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16. Pětileté zkušenosti z ECMO programu regionálního kardiocentra bez kardiochirurgie.

Author

Karásek, Jiří, Polášek, Rostislav, Horák, David, Seiner, Jiří, Hraboš, Vladimír, Horák, Jan, Tomašov, Pavel, Krejbichová, Kateřina, Zýková, Ivana, Sedlák, Pavel, Morman, Dušan, and Ošťádal, Petr

Abstract

Copyright of Intervencní a Akutní Kardiologie is the property of SOLEN sro and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2020
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17. Estimating the storage term in eddy covariance measurements: the ICOS methodology.

Author

Montagnani, Leonardo, Grünwald, Thomas, Kowalski, Andrew, Mammarella, Ivan, Merbold, Lutz, Metzger, Stefan, Sedlák, Pavel, and Siebicke, Lukas

Subjects
ANALYSIS of covariance, ECOSYSTEM management, CARBON & the environment, GREENHOUSE gases & the environment, GREENHOUSE gas protocol
Abstract

In eddy covariance measurements, the storage flux represents the variation in time of the dry molar fraction of a given gas in the control volume representative of turbulent flux. Depending on the time scale considered, and on the height above ground of the measurements, it can either be a major component of the overall net ecosystem exchange or nearly negligible. Instrumental configuration and computational procedures must be optimized to measure this change at the time step used for the turbulent flux measurement. Three different configurations are suitable within the Integrated Carbon Observation System infrastructure for the storage flux determination: separate sampling, subsequent sampling and mixed sampling. These configurations have their own advantages and disadvantages, and must be carefully selected based on the specific features of the considered station. In this paper, guidelines about number and distribution of vertical and horizontal sampling points are given. Details about suitable instruments, sampling devices, and computational procedures for the quantification of the storage flux of different GHG gases are also provided. [ABSTRACT FROM AUTHOR]

Published
2018
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18. Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations.

Author

Sabbatini, Simone, Mammarella, Ivan, Arriga, Nicola, Fratini, Gerardo, Graf, Alexander, Hörtnagl, Lukas, Ibrom, Andreas, Longdoz, Bernard, Mauder, Matthias, Merbold, Lutz, Metzger, Stefan, Montagnani, Leonardo, Pitacco, Andrea, Rebmann, Corinna, Sedlák, Pavel, Šigut, Ladislav, Vitale, Domenico, and Papale, Dario

Subjects
ANALYSIS of covariance, CARBON dioxide & the environment, EDDY flux, ECOSYSTEM management, GREENHOUSE gases & the environment, GREENHOUSE gas protocol
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 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 terms 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. [ABSTRACT FROM AUTHOR]

Published
2018
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19. On the development of benchmarking for eddy covariance data quality checking schemes.

Author

Šigut, Ladislav, Sedlák, Pavel, Fischer, Milan, Jocher, Georg, Pavelka, Marian, and Mauder, Matthias

Subjects
*DATA quality, *BENCHMARKING (Management), *EDDIES, *ELECTRONIC data processing
Abstract

With the development of a standardized eddy covariance (EC) data processing software it is now possible to create a high-throughput post-processing chain that allows to assess various statistics extracted from the datasets. The effect of changes in quality checking (QC) procedures can be thus effectively analysed. It is still difficult to establish what constitutes an effective QC test or filter, in other words, how exactly to define such filtering efficiency. One of the main obstacles is the design of such filters that can focus on issues that span from very specific problems to general tests of basic assumptions of the EC method. Here we propose a framework to benchmark different QC schemes based on a combination of three main measures extracted from the dataset – 1) difference in the aggregated EC fluxes, 2) change in the aggregated uncertainty and 3) data exclusion fraction. It is clear that relaxing of QC cannot be justified simply by the effort to retain data coverage in case of frequent spurious records. However, inclusion of a filter that increases exclusion fraction while the post-processed data do not differ in aggregated fluxes and uncertainties can be hardly accepted. In order to push this topic forward it would be also beneficial to provide easily available and reliable QC tools for the EC community and collect the feedback. This was already accomplished by publishing the R package openeddy (https://github.com/lsigut/openeddy) and related workflows. The feedback can help to make the codes more robust and also draw attention to new QC related issues not addressed before. The software could be therefore a platform for an exchange of experience from EC measurements across different ecosystem types and environmental conditions. Such an overview is otherwise difficult to obtain as the QC benchmarking requires supplemental information typically not included in tower network databases. Here we present the QC benchmarking results for sites from the Czech Carbon Observation System (CzeCOS) covering different ecosystems that represent different QC challenges. We show that, even though most of the QC procedures are generally applicable, site specific adaptation of filters is useful in some cases.This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the CzeCOS program, grant number LM2015061, and within the National Sustainability Program I (NPU I), grant number LO1415. [ABSTRACT FROM AUTHOR]

Published
2019

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