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| | The use of different processing options lead to different flux estimates. Discrepancies in flux estimates are caused by systematic errors introduced by methods used in the raw-data processing stage. Since there are not tools to establish a priori which is the best combination of processing options providing unbiased flux estimates, the viable solution, proposed by Sabbatini et al. (2018) and implemented here, involves a multiple processing scheme where EC flux data are calculated according to different combinations of methods. | | The use of different processing options lead to different flux estimates. Discrepancies in flux estimates are caused by systematic errors introduced by methods used in the raw-data processing stage. Since there are not tools to establish a priori which is the best combination of processing options providing unbiased flux estimates, the viable solution, proposed by Sabbatini et al. (2018) and implemented here, involves a multiple processing scheme where EC flux data are calculated according to different combinations of methods. |
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| − | In particular, , EC fluxes are calculated according to four different processing schemes resulting from a combination of block average (ba) or linear detrending (ld) and double rotation (dr) or planar fit<ref>Wilczak, J. M., Oncley, S. P., & Stage, S. A. (2001). Sonic anemometer tilt correction algorithms. ''Boundary-Layer Meteorology'', 99(1), 127-150.</ref> (pf) processing options (for details see Aubinet et al, 2012)<ref>Aubinet, M., Vesala, T., & Papale, D. (Eds.) (2012). ''Eddy covariance: a practical guide to measurement and data analysis''. Springer Science & Business Media.</ref>. All other processing options remain unchanged: maximum cross-covariance method for time lag determination, spectral correction method proposed by Fratini et al. (2012)<ref>Fratini, G., Ibrom, A., Arriga, N., Burba, G., & Papale, D. (2012). Relative humidity effects on water vapour fluxes measured with closed-path eddy-covariance systems with short sampling lines. Agricultural and forest meteorology, 165, 53-63.</ref>, statistical tests by Vickers and Mahrt (1997)<ref>Vickers, D., & Mahrt, L. (1997). Quality control and flux sampling problems for tower and aircraft data. Journal of Atmospheric and Oceanic Technology, 14(3), 512-526.</ref> and by Foken and Wichura (1996)<ref>Foken, T., & Wichura, B. (1996). Tools for quality assessment of surface-based flux measurements. Agricultural and forest meteorology, 78(1-2), 83-105.</ref> for data quality control, method by Finkelstein and Sims (2001)<ref>Finkelstein, P. L., & Sims, P. F. (2001). Sampling error in eddy correlation flux measurements. Journal of Geophysical Research: Atmospheres, 106(D4), 3503-3509.</ref> to estimate random uncertainty.
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| − | To reduce the computational runtime, the implementation of the four processing schemes above is performed in parallel mode in the gCube Virtual Research Environment (VRE). The processing path is defined as in [[Science Demonstrator 2: The Eddy Covariance Fluxes of GHGs (Use Case IC 13)#Figure1|Figure 1]]. When using EC raw data from a single observation tower, the estimated computational time required for a NRT run is about 4 minutes, similar to those required for the run of a single processing scheme.
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| − | <span id="Figure1">[[File:Image2018-9-28_9-35-16.png|center]]</span>
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| − | <center>'''<span style="color: #BBCE00">Figure 1. EC data processing path</span>'''</center>
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| − | == <span style="color: #BBCE00">Advantages</span> ==
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| − | The implementation of a multiple processing schemes as illustrated above and the direct management and use of metadata according to international standard in the eddy covariance community constitutes a novelty in the context of EC data analysis. The main advantage of the multiple processing is twofold. From one hand, it offers the possibility of an extensive evaluation of the effect each method has on flux data estimation. On the other, by combining the output results as described by Sabbatini et al. (2018), it is possible to obtain more consistent estimates of the uncertainty associated to EC fluxes.. The direct use of metadata instead ensure the needed flexibility for a large use of the tool if the new sensors are added in the system.
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| − | The efficiency of parallel computing implemented in the VRE, drastically reduces the computational runtime required to obtain flux estimates from different processing options schemes. This constitutes a clear advantage for any user and in particular, for RIs aiming at analyzing routinely large amount of data.
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| − | Although, here we selected only 4 processing option schemes, the efficiency of parallel computing implemented in the VRE offers the possibility to increase the number of processing schemes suitable for the EC data processing and also post-processing steps.
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| − | This might considerably improve our understanding about the performance of methods developed for EC raw-data processing and about interpretation of resulting fluxes.
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| − | == <span style="color: #BBCE00">Link to the Demonstrator</span> ==
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| − | [[File:Screen_Shot_2019-04-29_at_14.35.56.png|700px|link=https://youtu.be/hod2WksKzV8]]
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| − | Youtube video is at: https://youtu.be/hod2WksKzV8
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| − | == <span style="color: #BBCE00">Contributors</span> ==
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| − | * Dr Domenico Vitale, University of Tuscia, [mailto:domvit@unitus.it domvit@unitus.it]
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| − | * Dr Dario Papale, University of Tuscia, [mailto:darpap@unitus.it darpap@unitus.it]
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| − | * Dr Leonardo Candela, CNR-ISTI, [mailto:leonardo.candela@isti.cnr.it leonardo.candela@isti.cnr.it]
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| − | * Dr Gianpaolo Coro, CNR-ISTI, [mailto:gianpaolo.coro@isti.cnr.it gianpaolo.coro@isti.cnr.it]
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| − | == <span style="color: #BBCE00">Reference</span> ==
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| − | <references />
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| | [[Category:Science Demonstrators]] | | [[Category:Science Demonstrators]] |
