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Please provide your feedback on this Science Demonstrator using the questionnaire at https://survey2.icos-cp.eu/ENVRIplus-evaluator!

Overview

Eddy covariance (EC) fluxes calculation involves a complex set of data processing steps that, beyond the knowledge of the technique, requires a considerable amount of computational resources. This might constitute a constraint for RIs (e.g. ICOS) that aim to simultaneously process raw data sampled at multiple sites in Near Real Time (NRT) mode (i.e. provide each day fluxes estimates relative to the previous day). This demonstrator is the implementation results of Use Case IC_13, and it showcases the service solution that integrates the gCube service to optimize the processing of EC data based on 4 different processing schemes resulting from a combination of block average (ba) or linear detrending (ld) and double rotation (dr) or planar fit (pf) processing options and make this available to other RIs.

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The EC technique involves high-frequency sampling (e.g. 10 or 20 Hz) of wind speed and scalar atmospheric concentration data, and yields vertical turbulent fluxes. EC fluxes are computed within a finite averaging time (normally 30 mins)  from the covariance estimates between instantaneous deviations in vertical wind speed and gas concentration (e.g. CO2) from their respective mean values, multiplied by the mean air density (see Aubinet et al., 2012)[2].

Despite the simplicity of this idea, a number of practical difficulties arise in transforming high-frequency data into reliable half-hourly flux measurements. To cope with these issues, here we used the tools implemented by the EddyPro® Fortran code (LI-COR Biosciences, 2017, Fratini and Mauder, 2014)[3] an open source software application available for free download at https://www.licor.com/env/products/eddy_covariance/eddypro.html. The choice of EddyPro® software is motivated by i) the availability of different methods for data quality control and processing (e.g. coordinate rotation, time series detrending,  time lag determination,  spectral corrections, flux random uncertainty quantification, etc.), ii) the availability of the source code and iii) the fact that the software is based on a community developed set of tools.

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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 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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Figure 1.EC data processing path

Advantages

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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This might considerably improve our understanding about the performance of methods developed for EC raw-data processing and about interpretation of resulting fluxes.

Link to the Demonstrator

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Youtube video is at: https://youtubeyoutu.combe/watch?v=hod2WksKzV8height460

Contributors

Reference

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[1]Sabbatini S. et al (2018). Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations, International Agrophysics.

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