Possible errors in flux measurements due to limited digitalization

Foken, Thomas; Babel, Wolfgang; Thomas, Christoph

doi:10.5194/amt-12-971-2019

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…When low resolution problems were simulated by rounding values (S R i=1,2,3 ), the bias was less than 2%. Similar results were reported by VM97 and more recently by Foken et al (2019). On the contrary, when low resolution problems were simulated by replacing values via LOCF, a significant bias was introduced in correlation estimates, whose amount depends not only on the number of contaminated data, but also on the degree of serial dependence.…”

Section: Low Signal Resolution (Lsr) Testsupporting

confidence: 84%

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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Section: Low Signal Resolution (Lsr) Testsupporting

confidence: 84%

A robust data cleaning procedure for eddy covariance flux measurements

Vitale¹,

Fratini²,

Bilancia³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…A significant difference is the use of either open path (LI-7500, LiCor Biosciences, USA) or closed path (LI-6262, LiCor Biosciences, USA) gas analysers, although this should not have an influence on the time series or for comparison of the stations (Ocheltree and Loescher, 2007;Haslwanter et al, 2009;J€ arvi et al, 2009). The change of the digitalisation of the electrical signals for carbon dioxide and water vapour concentrations, which was 12-bit for the LI-6262 and 16- bit for the next generation of analysers, has only a negligible effect on annual sums, and only very low respiration fluxes in winter time differ slightly (Foken et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Selected breakpoints of net forest carbon uptake at four eddy-covariance sites

Foken

Babel

Munger

et al. 2021

Tellus B: Chemical and Physical Meteorology

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“…β varies with scalar and with atmospheric conditions. Typical β values obtained from measurements and simulations (Wyngaard and Moeng, 1992;Businger and Oncley, 1990;Oncley et al, 1993;Pattey et al, 1993;Baker et al, 1992;Gao, 1995;Milne et al, 1999;Katul et al, 1996;Baker, 2000;Ammann and Meixner, 2002;Held et al, 2008) are around 0.55 but range from ca. 0.4 to ca.…”

Section: Relaxed Eddy Accumulation (Rea)mentioning

confidence: 99%

True eddy accumulation trace gas flux measurements: proof of concept

Siebicke

Emad

2019

Atmos. Meas. Tech.

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Abstract. Micrometeorological methods to quantify fluxes of atmospheric constituents are key to understanding and managing the impact of land surface sources and sinks on air quality and atmospheric composition. Important greenhouse gases are water vapor, carbon dioxide, methane, and nitrous oxide. Further important atmospheric constituents are aerosols, which impact air quality and cloud formation, and volatile organic compounds. Many atmospheric constituents therefore critically affect the health of ecosystems and humans, as well as climate. The micrometeorological eddy covariance (EC) method has evolved as the method of choice for CO2 and water vapor flux measurements using fast-response gas analyzers. While the EC method has also been used to measure other atmospheric constituents including methane, nitrous oxide, and ozone, the often relatively small fluxes of these constituents over ecosystems are much more challenging to measure using eddy covariance than CO2 and water vapor fluxes. For many further atmospheric constituents, eddy covariance is not an option due to the lack of sufficiently accurate and fast-response gas analyzers. Therefore, alternative flux measurement methods are required for the observation of atmospheric constituent fluxes for which no fast-response gas analyzers exist or which require more accurate measurements. True eddy accumulation (TEA) is a direct flux measurement technique capable of using slow-response gas analyzers. Unlike its more frequently used derivative, known as the relaxed eddy accumulation (REA) method, TEA does not require the use of proxies and is therefore superior to the indirect REA method. The true eddy accumulation method is by design ideally suited for measuring a wide range of trace gases and other conserved constituents transported with the air. This is because TEA obtains whole air samples and is, in combination with constituent-specific fast or slow analyzers, a universal method for conserved scalars. Despite the recognized value of the method, true eddy accumulation flux measurements remain very challenging to perform as they require fast and dynamic modulation of the air sampling mass flow rate proportional to the magnitude of the instantaneous vertical wind velocity. Appropriate techniques for dynamic mass flow control have long been unavailable, preventing the unlocking of the TEA method's potential for more than 40 years. Recently, a new dynamic and accurate mass flow controller which can resolve turbulence at a frequency of 10 Hz and higher has been developed by the first author. This study presents the proof of concept that practical true eddy accumulation trace gas flux measurements are possible today using dynamic mass flow control, advanced real-time processing of wind measurements, and fully automatic gas handling. We describe setup and methods of the TEA and EC reference flux measurements. The experiment was conducted over grassland and comprised 7 d of continuous flux measurements at 30 min flux integration intervals. The results show that fluxes obtained by TEA compared favorably to EC reference flux measurements, with coefficients of determination of up to 86 % and a slope of 0.98. We present a quantitative analysis of uncertainties of the mass flow control system, the gas analyzer, and gas handling system and their impact on trace gas flux uncertainty, the impact of different approaches to coordinate rotation, and uncertainties of vertical wind velocity measurements. Challenges of TEA are highlighted and solutions presented. The current results are put into the context of previous works. Finally, based on the current successful proof of concept, we suggest specific improvements towards long-term and reliable true eddy accumulation flux measurements.

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Possible errors in flux measurements due to limited digitalization

Cited by 6 publications

References 19 publications

A robust data cleaning procedure for eddy covariance flux measurements

A robust data cleaning procedure for eddy covariance flux measurements

Selected breakpoints of net forest carbon uptake at four eddy-covariance sites

True eddy accumulation trace gas flux measurements: proof of concept

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