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| Titel |
Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection |
| VerfasserIn |
B. Langford, W. Acton, C. Ammann, A. Valach, E. Nemitz |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 10 ; Nr. 8, no. 10 (2015-10-12), S.4197-4213 |
| Datensatznummer |
250116633
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| Publikation (Nr.) |
 copernicus.org/amt-8-4197-2015.pdf |
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| Zusammenfassung |
| All eddy-covariance flux measurements are associated with random
uncertainties which are a combination of sampling error due to natural
variability in turbulence and sensor noise. The former is the principal
error for systems where the signal-to-noise ratio of the analyser is high,
as is usually the case when measuring fluxes of heat, CO2 or H2O.
Where signal is limited, which is often the case for measurements of other
trace gases and aerosols, instrument uncertainties dominate. Here, we are
applying a consistent approach based on auto- and cross-covariance functions
to quantify the total random flux error and the random error due to
instrument noise separately. As with previous approaches, the random error
quantification assumes that the time lag between wind and concentration
measurement is known. However, if combined with commonly used automated
methods that identify the individual time lag by looking for the maximum in
the cross-covariance function of the two entities, analyser noise
additionally leads to a systematic bias in the fluxes. Combining data sets
from several analysers and using simulations, we show that the method of
time-lag determination becomes increasingly important as the magnitude of
the instrument error approaches that of the sampling error. The flux bias
can be particularly significant for disjunct data, whereas using a
prescribed time lag eliminates these effects (provided the time lag does not
fluctuate unduly over time). We also demonstrate that when sampling at
higher elevations, where low frequency turbulence dominates and covariance
peaks are broader, both the probability and magnitude of bias are magnified.
We show that the statistical significance of noisy flux data can be
increased (limit of detection can be decreased) by appropriate averaging of
individual fluxes, but only if systematic biases are avoided by using a
prescribed time lag. Finally, we make recommendations for the analysis and
reporting of data with low signal-to-noise and their associated errors. |
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