TY - JOUR

T1 - The high-frequency response correction of eddy covariance fluxes - Part 1: An experimental approach and its interdependence with the time-lag estimation

AU - Peltola, Olli

AU - Aslan, Toprak

AU - Ibrom, Andreas

AU - Nemitz, Eiko

AU - Rannik, Ullar

AU - Mammarella, Ivan

PY - 2021

Y1 - 2021

N2 - The eddy covariance (EC) technique has emerged as the prevailing method
to observe the ecosystem–atmosphere exchange of gases, heat and
momentum. EC
measurements require rigorous data processing to derive the fluxes that
can be used to analyse exchange processes at the ecosystem–atmosphere
interface. Here we show that two common post-processing steps (time-lag
estimation via cross-covariance maximisation and correction for limited
frequency response of the EC measurement system) are interrelated, and
this should be accounted for when processing EC gas flux data. These
findings
are applicable to EC systems employing closed- or enclosed-path gas
analysers which can be approximated to be linear first-order sensors.
These EC
measurement systems act as low-pass filters on the time series of the
scalar χ (e.g. CO2, H2O), and this induces a time lag
(tlpf) between vertical wind speed (w) and scalar χ time series which is additional to the travel time of the gas signal in the
sampling line (tube, filters). Time-lag estimation via cross-covariance maximisation inadvertently accounts also for tlpf and hence
overestimates the travel time in the sampling line. This results in a phase shift between the time series of w and χ, which distorts the
measured cospectra between w and χ and hence has an effect on the correction for the dampening of the EC flux signal at high frequencies. This
distortion can be described with a transfer function related to the phase shift (Hp)
which is typically neglected when processing EC
flux data. Based on analyses using EC data from two contrasting
measurement sites, we show that the low-pass-filtering-induced time lag
increases
approximately linearly with the time constant of the low-pass filter,
and hence the importance of Hp
in describing the high-frequency
flux loss increases as well. Incomplete description of these processes
in EC data processing algorithms results in flux biases of up to 10 %,
with
the largest biases observed for short towers due to the prevalence of
small-scale turbulence. Based on these findings, it is suggested that
spectral
correction methods implemented in EC data processing algorithms are
revised to account for the influence of low-pass-filtering-induced time
lag.

AB - The eddy covariance (EC) technique has emerged as the prevailing method
to observe the ecosystem–atmosphere exchange of gases, heat and
momentum. EC
measurements require rigorous data processing to derive the fluxes that
can be used to analyse exchange processes at the ecosystem–atmosphere
interface. Here we show that two common post-processing steps (time-lag
estimation via cross-covariance maximisation and correction for limited
frequency response of the EC measurement system) are interrelated, and
this should be accounted for when processing EC gas flux data. These
findings
are applicable to EC systems employing closed- or enclosed-path gas
analysers which can be approximated to be linear first-order sensors.
These EC
measurement systems act as low-pass filters on the time series of the
scalar χ (e.g. CO2, H2O), and this induces a time lag
(tlpf) between vertical wind speed (w) and scalar χ time series which is additional to the travel time of the gas signal in the
sampling line (tube, filters). Time-lag estimation via cross-covariance maximisation inadvertently accounts also for tlpf and hence
overestimates the travel time in the sampling line. This results in a phase shift between the time series of w and χ, which distorts the
measured cospectra between w and χ and hence has an effect on the correction for the dampening of the EC flux signal at high frequencies. This
distortion can be described with a transfer function related to the phase shift (Hp)
which is typically neglected when processing EC
flux data. Based on analyses using EC data from two contrasting
measurement sites, we show that the low-pass-filtering-induced time lag
increases
approximately linearly with the time constant of the low-pass filter,
and hence the importance of Hp
in describing the high-frequency
flux loss increases as well. Incomplete description of these processes
in EC data processing algorithms results in flux biases of up to 10 %,
with
the largest biases observed for short towers due to the prevalence of
small-scale turbulence. Based on these findings, it is suggested that
spectral
correction methods implemented in EC data processing algorithms are
revised to account for the influence of low-pass-filtering-induced time
lag.

U2 - 10.5194/amt-14-5071-2021

DO - 10.5194/amt-14-5071-2021

M3 - Journal article

SN - 1867-1381

VL - 14

SP - 5071

EP - 5088

JO - Atmospheric Measurement Techniques

JF - Atmospheric Measurement Techniques

IS - 7

ER -