eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

Metzger, Stefan; Durden, David; Sturtevant, Cove; Luo, Hongxia; Pingintha‐Durden, Natchaya; Sachs, Torsten; Serafimovich, Andrei; Hartmann, Jörg; Li, Jiahong; Xu, Ke; Desai, Ankur R.

doi:10.5194/gmd-10-3189-2017

Cited by 48 publications

(38 citation statements)

References 48 publications

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“…A detailed description of flux estimation using eddy covariance is provided by Aubinet et al (2012). Here fluxes were calculated using the eddy4R routines (Metzger et al, 2017). The flux (Fx) of each compound was determined by calculating the covariance function between the vertical wind velocity (w) and the VOC mixing ratio (VMRx):…”

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confidence: 99%

Surface–atmosphere fluxes of volatile organic compounds in Beijing

Acton¹,

Huang²,

Davison³

et al. 2020

Preprint

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Abstract. Air pollution in Beijing has a major impact on public health and is therefore of concern to both policy makers and the general public. Volatile organic compounds (VOCs) are emitted from both anthropogenic and biogenic sources in urban environments and play an important role in atmospheric chemistry and hence atmospheric pollution through the formation of secondary organic aerosol and tropospheric ozone. Fluxes and mixing ratios of VOCs were recorded in two field campaigns as part of the Air Pollution and Human Health in a Chinese Megacity (APHH) project at the Institute of Atmospheric Physics (IAP) meteorological tower in central Beijing. These measurements represent the first eddy covariance flux measurements of VOCs in Beijing giving a top down estimation of VOC emissions from a central area of the city. These were then used to validate the Multi-resolution Emission Inventory for China (MEIC). The APHH winter and summer campaigns took place in November and December 2016 and May and June 2017 respectively. The largest VOC fluxes observed were of small oxygenated compounds such as methanol, ethanol&#8201;+&#8201;formic acid and acetaldehyde, with average emission rates of 8.02, 3.88 and 1.76&#8201;nmol&#8201;m&#8722;2&#8201;s&#8722;1 respectively recorded in the summer campaign. In addition a large flux of isoprene was observed in the summer with an average flux of 4.63&#8201;nmol&#8201;m&#8722;2&#8201;s&#8722;1. While oxygenated VOCs made up 60&#8201;% of the molar VOC flux measured, when fluxes were scaled by ozone formation potential and peroxyacyl nitrate (PAN) formation potential the high reactivity of isoprene and monoterpenes meant that these species represented 30 and 28&#8201;% of the flux contribution to ozone and PAN formation potential respectively. Comparison of measured fluxes with the emission inventory showed that the inventory failed to capture VOC emission at the local scale.

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confidence: 99%

Surface–atmosphere fluxes of volatile organic compounds in Beijing

Acton¹,

Huang²,

Davison³

et al. 2020

Preprint

Self Cite

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“…For the latent heat flux only 6 % of the data are less than 0 W m −2 or more than 110 W m −2 . We interpret these as a spurious yet systematic process that the machine learning technique cannot yet de- (Lin et al, 1983) Longwave radiation Rapid Radiative Transfer Model (Mlawer et al, 1997)…”

Section: Environmental Response Functions Of Energy Fluxesmentioning

confidence: 99%

Upscaling surface energy fluxes over the North Slope of Alaska using airborne eddy-covariance measurements and environmental response functions

et al. 2018

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Abstract. The objective of this study was to upscale airborne flux measurements of sensible heat and latent heat and to develop high-resolution flux maps. In order to support the evaluation of coupled atmospheric–land-surface models we investigated spatial patterns of energy fluxes in relation to land-surface properties. We used airborne eddy-covariance measurements acquired by the Polar 5 research aircraft in June–July 2012 to analyze surface fluxes. Footprint-weighted surface properties were then related to 21 529 sensible heat flux observations and 25 608 latent heat flux observations using both remote sensing and modeled data. A boosted regression tree technique was used to estimate environmental response functions between spatially and temporally resolved flux observations and corresponding biophysical and meteorological drivers. In order to improve the spatial coverage and spatial representativeness of energy fluxes we used relationships extracted across heterogeneous Arctic landscapes to infer high-resolution surface energy flux maps, thus directly upscaling the observational data. These maps of projected sensible heat and latent heat fluxes were used to assess energy partitioning in northern ecosystems and to determine the dominant energy exchange processes in permafrost areas. This allowed us to estimate energy fluxes for specific types of land cover, taking into account meteorological conditions. Airborne and modeled fluxes were then compared with measurements from an eddy-covariance tower near Atqasuk. Our results are an important contribution for the advanced, scale-dependent quantification of surface energy fluxes and they provide new insights into the processes affecting these fluxes for the main vegetation types in high-latitude permafrost areas.

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“…For measurements over tall canopies in forested and urban environments, it has been shown that 30 min averaging intervals are quite suitable for surface layer measurements and that averaging periods up to 1 h can be feasible. Longer averaging periods often suffer from nonstationary conditions (Moncrieff et al, 2004). Averaging periods that are too short will systematically lead to an underestimation of the measured flux (e.g., Massman and Clement, 2004).…”

Section: Introductionmentioning

confidence: 99%

InnFLUX – an open-source code for conventional and disjunct eddy covariance analysis of trace gas measurements: an urban test case

et al. 2020

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Abstract. We describe and test a new versatile software tool for processing eddy covariance and disjunct eddy covariance flux data. We present an evaluation based on urban non-methane volatile organic compound (NMVOC) measurements using a proton transfer reaction quadrupole interface time-of-flight mass spectrometer (PTR-QiTOF-MS) at the Innsbruck Atmospheric Observatory. The code is based on MATLAB® and can be easily configured to process high-frequency, low-frequency and disjunct data. It can be applied to a wide range of analytical setups for NMVOC and other trace gas measurements, and is tailored towards the application of noisy data, where lag time corrections become challenging. Several corrections and quality control routines are implemented to obtain the most reliable results. The software is open source, so it can be extended and adjusted to specific purposes. We demonstrate the capabilities of the code based on a large urban dataset collected in Innsbruck, Austria, where three-dimensional winds and ambient concentrations of NMVOCs and auxiliary trace gases were sampled with high temporal resolution above an urban canopy. Concomitant measurements of 12C and 13C isotopic NMVOC fluxes allow testing algorithms used for determination of flux limits of detection (LOD) and lag time analysis. We use the high-frequency NMVOC dataset to generate a set of disjunct data and compare these results with the true eddy covariance method. The presented analysis allows testing the theory of disjunct eddy covariance (DEC) in an urban environment. Our findings confirm that the disjunct eddy covariance method can be a reliable tool, even in complex urban environments when fast sensors are not available, but that the increase in random error impedes the ability to detect small fluxes due to higher flux LODs.

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eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

Cited by 48 publications

References 48 publications

Surface–atmosphere fluxes of volatile organic compounds in Beijing

Surface–atmosphere fluxes of volatile organic compounds in Beijing

Upscaling surface energy fluxes over the North Slope of Alaska using airborne eddy-covariance measurements and environmental response functions

InnFLUX – an open-source code for conventional and disjunct eddy covariance analysis of trace gas measurements: an urban test case

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