Flux calculation of short turbulent events – comparison of three methods

Schaller, Carsten; Göckede, Mathias; Foken, Thomas

doi:10.5194/amt-10-869-2017

Cited by 46 publications

(80 citation statements)

References 42 publications

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“…This similarity of result is consistent with observations from Schaller et al (2017). The real-valued Mexican hat wavelet , based on the second derivative of the Gaussian probability function, was chosen here to estimate the momentum and dust fluxes over nonstationary small time periods as such wavelet leads to a good resolution in time.…”

Section: Wavelet Methodssupporting

confidence: 78%

“…Recently, Schaller et al (2017) demonstrated the performance of the wavelet technique compared to the EC one at estimating the flux of methane during nocturnal short turbulent events. Our analysis is based on the guide and wavelet tools proposed by Torrence and Compo (1998) in line with the analysis of Schaller et al (2017). Our analysis is based on the guide and wavelet tools proposed by Torrence and Compo (1998) in line with the analysis of Schaller et al (2017).…”

Section: Wavelet Methodsmentioning

confidence: 99%

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Scaling of Dust Flux With Friction Velocity: Time Resolution Effects

Dupont

2020

JGR Atmospheres

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The intermittency of aeolian soil erosion has motivated studies to extend to small time resolution (less than 15-30 min) the scaling property of erosion fluxes with the friction velocity u * . However, common methods used to estimate u * such as the law of the wall or the eddy covariance are only valid for stationary conditions, that is, long periods of 15-30 min. Unlike these methods, the wavelet transform method is applicable for nonstationary conditions. Here, this method is used to investigate the scaling property of the dust flux F wd with u * at 1-min and 10-s resolutions, during erosion events. At 15-min resolution, u * and F wd estimated from the wavelet transform are identical to those obtained by eddy covariance. At smaller resolution, u * and F wd exhibit strong fluctuations around their 15-min trend, reflecting the nonstationarity of the flow and the intermittency of dust emission, respectively. While the 15-min resolution F wd appears as correlated with u * as with the mean wind speed U, with decreasing resolution, F wd becomes less correlated with u * but still significantly correlated with U. Our results suggest that u * is a suitable scaling parameter of F wd over usual 15-to 30-min periods with the advantage of being height independent compared to U. However, at small time resolution, U becomes more relevant to scale F wd , the surface friction velocity becoming hardly accessible due to the absence of a constant momentum flux layer. Our study demonstrates the suitability of the wavelet transform to estimate dust fluxes at small time resolution or during nonstationary events.

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Section: Wavelet Methodssupporting

confidence: 78%

Section: Wavelet Methodsmentioning

confidence: 99%

Scaling of Dust Flux With Friction Velocity: Time Resolution Effects

Dupont

2020

JGR Atmospheres

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“…The Morlet wavelet is the standard choice for eddy covariance calculations, giving reasonable localization in both time and frequency domains (Schaller et al, 2017). The CWT is well suited for airborne fluxes, offering several advantages over traditional EA and FFT.…”

Section: Continuous Wavelet Transform Fluxmentioning

confidence: 99%

The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology

et al. 2018

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Abstract. The exchange of trace gases between the Earth's surface and atmosphere strongly influences atmospheric composition. Airborne eddy covariance can quantify surface fluxes at local to regional scales (1-1000 km), potentially helping to bridge gaps between top-down and bottomup flux estimates and offering novel insights into biophysical and biogeochemical processes. The NASA Carbon Airborne Flux Experiment (CARAFE) utilizes the NASA C-23 Sherpa aircraft with a suite of commercial and custom instrumentation to acquire fluxes of carbon dioxide, methane, sensible heat, and latent heat at high spatial resolution. Key components of the CARAFE payload are described, including the meteorological, greenhouse gas, water vapor, and surface imaging systems. Continuous wavelet transforms deliver spatially resolved fluxes along aircraft flight tracks. Flux analysis methodology is discussed in depth, with special emphasis on quantification of uncertainties. Typical uncertainties in derived surface fluxes are 40-90 % for a nominal resolution of 2 km or 16-35 % when averaged over a full leg (typically 30-40 km). CARAFE has successfully flown two missions in the eastern US in 2016 and 2017, quantifying fluxes over forest, cropland, wetlands, and water. Preliminary results from these campaigns are presented to highlight the performance of this system.

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“…Because highly non-steady state conditions were expected for CH 4 fluxes at this observation site, which pose a serious violation of the basic assumptions linked to the eddy-covariance method (Foken and Wichura, 1996), we applied a waveletbased calculation method as a second flux processing approach in addition to the standard eddy-covariance data processing. 20 Schaller et al (2017b) have developed a method for wavelet-based flux computation that offers the possibility of determining exact fluxes with a user-defined time resolution that can be as low as about 1 minute. Within the context of this study, we applied their calculation tool with a continuous wavelet transform using the Mexican hat wavelet (WV M h ), which provides an excellent resolution in the time domain.…”

Section: Raw Data Processing and Flux Calculationmentioning

confidence: 99%

“…The focus of the present study is on the interpretation of CH 4 emission events detected by a wavelet software package (Schaller et al, 2017b, a), which has already successfully been applied to the non-steady state fluxes during a solar eclipse 15 . This approach, which builds on the raw data sampled by EC towers, allows us to resolve fluxes not only over 30 minute averaging periods, but also for an averaging interval of 1 minute.…”

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confidence: 99%

Answer to Anonymous Referee #2

Schaller¹

2018

Preprint

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Methane (CH 4 ) emissions from biogenic sources, such as Arctic permafrost wetlands, are associated with large uncertainties because of the high variability of fluxes in both space and time. This variability poses a challenge to monitoring CH 4 fluxes with the eddy covariance (EC) technique, because this approach requires stationary signals from spatially homogeneous sources. Episodic outbursts of CH 4 emissions, i.e. outgassing in the form of bubbles from oversaturated groundwater or surfacewater, are particularly challenging to quantify. Such events typically last for only a few minutes, which is much 5 shorter than the common averaging interval for eddy covariance (30 minutes). The steady state assumption is jeopardized, which potentially leads to a non-negligible bias in the CH 4 flux. We tested and evaluated a flux calculation method based on wavelet analysis, which, in contrast to regular EC data processing, does not require steady-state conditions and is allowed to obtain fluxes over averaging periods as short as 1 minute. We demonstrate that the occurrence of extreme CH 4 flux events over the summer season followed a seasonal course with a maximum in early August, which is strongly correlated with the 10 maximum soil temperature. Statistics on meteorological conditions before, during, and after the detected events revealed that it is atmospheric mixing that triggered such events rather than CH 4 emission from the soil. By investigating individual events in more detail, we identified various mesoscale processes like gravity waves, low-level jets, weather fronts passing the site, and cold-air advection from a nearby mountain ridge as the dominating processes. Overall, our findings demonstrate that wavelet analysis is a powerful method for resolving highly variable flux events on the order of minutes. It is a reliable reference to 15 evaluate the quality of EC fluxes under non-steady-state conditions.

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Flux calculation of short turbulent events – comparison of three methods

Cited by 46 publications

References 42 publications

Scaling of Dust Flux With Friction Velocity: Time Resolution Effects

Scaling of Dust Flux With Friction Velocity: Time Resolution Effects

The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology

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