Eddy covariance flux errors due to random and systematic timing errors during data acquisition

Fratini, Gerardo; Sabbatini, Simone; Ediger, Kevin; Riensche, B.; Burba, George; Nicolini, Giacomo; Vitale, Domenico; Papale, Dario

doi:10.5194/bg-15-5473-2018

Cited by 7 publications

(9 citation statements)

References 25 publications

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“…Lag window limits (from 1.5 to 3.8 s) were determined based on the nominal time lag of 2.6 s calculated from the flow rate and tube dimensions. More flexibility was given to the upper end of the lag window as time lags have been found to be longer than the nominal time lag (Massman, 2000;Gerdel et al, 2017).…”

Section: Time Lag Determinationmentioning

confidence: 99%

“…Studies on ecosystem COS flux measurements with the EC technique are still limited (Asaf et al, 2013;Billesbach et al, 2014;Maseyk et al, 2014;Commane et al, 2015;Gerdel et al, 2017;Wehr et al, 2017;Yang et al, 2018;Kooijmans et al, 2019;Spielmann et al, 2019), and there is no standardized flux processing protocol for COS EC fluxes. Table 1 summarizes the processing steps used in earlier studies.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike for CH 4 or N 2 O, there are no sudden bursts or sinks expected for COS, and in that sense some processing steps for COS are more like those for CO 2 (e.g., despiking, storage change correction, and friction velocity, u * , filtering). Gerdel et al (2017) describe the issues of different detrending methods, high-frequency spectral correction, time lag determination, and u * filtering. However, there has not been any study comparing different methods for time lag determination or high-frequency spectral correction in terms of their effects on COS fluxes.…”

Section: Introductionmentioning

confidence: 99%

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Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

et al. 2020

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Abstract. Carbonyl sulfide (COS) flux measurements with the eddy covariance (EC) technique are becoming popular for estimating gross primary productivity. To compare COS flux measurements across sites, we need standardized protocols for data processing. In this study, we analyze how various data processing steps affect the calculated COS flux and how they differ from carbon dioxide (CO2) flux processing steps, and we provide a method for gap-filling COS fluxes. Different methods for determining the time lag between COS mixing ratio and the vertical wind velocity (w) resulted in a maximum of 15.9 % difference in the median COS flux over the whole measurement period. Due to limited COS measurement precision, small COS fluxes (below approximately 3 pmol m−2 s−1) could not be detected when the time lag was determined from maximizing the covariance between COS and w. The difference between two high-frequency spectral corrections was 2.7 % in COS flux calculations, whereas omitting the high-frequency spectral correction resulted in a 14.2 % lower median flux, and different detrending methods caused a spread of 6.2 %. Relative total uncertainty was more than 5 times higher for low COS fluxes (lower than ±3 pmol m−2 s−1) than for low CO2 fluxes (lower than ±1.5 µmol m−2 s−1), indicating a low signal-to-noise ratio of COS fluxes. Due to similarities in ecosystem COS and CO2 exchange, we recommend applying storage change flux correction and friction velocity filtering as usual in EC flux processing, but due to the low signal-to-noise ratio of COS fluxes, we recommend using CO2 data for time lag and high-frequency corrections of COS fluxes due to the higher signal-to-noise ratio of CO2 measurements.

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Section: Time Lag Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

et al. 2020

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“…when collecting data in analog format, diagnostic information is simply not available (Fratini et al, 2018). It is therefore useful to devise tests to detect instrument-related situations that are likely to generate systematic errors in resulting fluxes.…”

Section: Detection Of Instrumental Issuesmentioning

confidence: 99%

A robust data cleaning procedure for eddy covariance flux measurements

Vitale¹,

Fratini²,

Bilancia³

et al. 2019

Preprint

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<p><strong>Abstract.</strong> Integration of long-term eddy covariance (EC) flux datasets over regional and global scales requires high degree of comparability of flux data measured at different stations, which entails not only similar-performing instrumentation and their appropriate deployment, but also standardized and reproducible data processing and quality control (QC) procedures. This work focuses on the latter topic and, in particular, on the development of a robust data cleaning procedure. The proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error in the data, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values without losing in efficacy and, at the same time, to avoid the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality. Tests development was rooted on advanced time series analysis techniques that consider the stochastic properties of both raw, high-frequency EC data and of flux time series, such as complex dynamics, high persistence and possible presence of stochastic trends. The performance of each proposed test is evaluated by means of Monte Carlo simulations on synthetic datasets, whereas their impact on observed times series was evaluated on a selection of EC datasets distributed by the ICOS research infrastructure. Simulation results evidenced that the proposed tests have a better performance compared to alternative existing QC routines, showing lower false positive and false negative error rates. The application of the proposed tests on real datasets led to an effective cleaning of EC flux data retaining the maximum number of good quality data. Although there is still room for improvement, in particular with the development of new QC tests, we think that the proposed data cleaning procedure can serve as a basis towards a unified QC strategy for EC datasets which i) includes only completely data-driven routines and is therefore suitable for automatic and centralized data processing pipelines, ii) guarantees results reproducibility and iii) is flexible and scalable to accommodate new and additional tests that makes the approach also suitable for other greenhouse gases.</p>

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“…Other quality classification schemes of this sort have been proposed and used (e.g. Kruijt et al, 2004;Göckede et al, 2008;Thomas et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A robust data cleaning procedure for eddy covariance flux measurements

et al. 2020

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The sources of systematic error responsible for introducing significant biases in the eddy covariance (EC) flux computation are manifold, and their correct identification is made difficult by the lack of reference values, by the complex stochastic dynamics, and by the high level of noise characterizing raw data. This work contributes to overcoming such challenges by introducing an innovative strategy for EC data cleaning. The proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values, ensuring scalability of the whole process. The selection of best performing tests was carried out by means of Monte Carlo experiments, whereas the impact on real data was evaluated on data distributed by the Integrated Carbon Observation System (ICOS) research infrastructure. Results evidenced that the proposed procedure leads to an effective cleaning of EC flux data, avoiding the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality. We expect that the proposed data cleaning procedure can serve as a basis towards a unified quality control strategy for EC datasets, in particular in centralized data processing pipelines where the use of robust and automated routines ensuring results reproducibility constitutes an essential prerequisite.

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Eddy covariance flux errors due to random and systematic timing errors during data acquisition

Cited by 7 publications

References 25 publications

Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

A robust data cleaning procedure for eddy covariance flux measurements

A robust data cleaning procedure for eddy covariance flux measurements

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