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Title: Optimization of an enclosed gas analyzer sampling system for measuring eddy covariance fluxes of H2O and CO2

Journal Article · · Atmospheric Measurement Techniques (Online)
 [1];  [2];  [3];  [4];  [2];  [1];  [5]
  1. National Ecological Observatory Network, Boulder, CO (United States); Univ. of Colorado, Boulder, CO (United States)
  2. LI-COR Biosciences, Lincoln, NE (United States)
  3. Univ. of Colorado, Boulder, CO (United States); National Center for Atmospheric Research, Boulder, CO (United States)
  4. Univ. of Colorado, Boulder, CO (United States)
  5. National Ecological Observatory Network, Boulder, CO (United States)

Several initiatives are currently emerging to observe the exchange of energy and matter between the earth's surface and atmosphere standardized over larger space and time domains. For example, the National Ecological Observatory Network (NEON) and the Integrated Carbon Observing System (ICOS) are set to provide the ability of unbiased ecological inference across ecoclimatic zones and decades by deploying highly scalable and robust instruments and data processing. In the construction of these observatories, enclosed infrared gas analyzers are widely employed for eddy covariance applications. While these sensors represent a substantial improvement compared to their open- and closed-path predecessors, remaining high-frequency attenuation varies with site properties and gas sampling systems, and requires correction. Here, we show that components of the gas sampling system can substantially contribute to such high-frequency attenuation, but their effects can be significantly reduced by careful system design. From laboratory tests we determine the frequency at which signal attenuation reaches 50 % for individual parts of the gas sampling system. For different models of rain caps and particulate filters, this frequency falls into ranges of 2.5–16.5 Hz for CO2, 2.4–14.3 Hz for H2O, and 8.3–21.8 Hz for CO2, 1.4–19.9 Hz for H2O, respectively. A short and thin stainless steel intake tube was found to not limit frequency response, with 50 % attenuation occurring at frequencies well above 10 Hz for both H2O and CO2. From field tests we found that heating the intake tube and particulate filter continuously with 4 W was effective, and reduced the occurrence of problematic relative humidity levels (RH > 60 %) by 50 % in the infrared gas analyzer cell. No further improvement of H2O frequency response was found for heating in excess of 4 W. These laboratory and field tests were reconciled using resistor–capacitor theory, and NEON's final gas sampling system was developed on this basis. The design consists of the stainless steel intake tube, a pleated mesh particulate filter and a low-volume rain cap in combination with 4 W of heating and insulation. In comparison to the original design, this reduced the high-frequency attenuation for H2O by ≈ 3/4, and the remaining cospectral correction did not exceed 3 %, even at high relative humidity (95 %). The standardized design can be used across a wide range of ecoclimates and site layouts, and maximizes practicability due to minimal flow resistance and maintenance needs. Lastly, due to minimal high-frequency spectral loss, it supports the routine application of adaptive correction procedures, and enables largely automated data processing across sites.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
7094866
OSTI ID:
1258740
Journal Information:
Atmospheric Measurement Techniques (Online), Vol. 9, Issue 3; ISSN 1867-8548
Publisher:
European Geosciences UnionCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 16 works
Citation information provided by
Web of Science

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Cited By (5)

Comparison of Lyman-alpha and LI-COR infrared hygrometers for airborne measurement of turbulent fluctuations of water vapour journal January 2018
Quantifying the uncertainty of eddy covariance fluxes due to the use of different software packages and combinations of processing steps in two contrasting ecosystems journal January 2016
Eddy covariance flux errors due to random and systematic timing errors during data acquisition journal January 2018
eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5 journal January 2017
High-quality eddy-covariance CO2 budgets under cold climate conditions text January 2017

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