the Creative Commons Attribution 4.0 License.

the Creative Commons Attribution 4.0 License.

# True eddy accumulation – Part 1: Solutions to the problem of non-vanishing mean vertical wind velocity

### Anas Emad

### Lukas Siebicke

The true eddy accumulation (TEA) method provides direct measurements of ecosystem-level turbulent fluxes for a wide range of atmospheric constituents. TEA utilizes conditional sampling to overcome the requirement for a fast sensor response demanded by the state-of-the-art eddy covariance (EC) method.

The TEA method is formulated under the assumption of ideal conditions with a zero mean vertical wind velocity during the averaging interval. However, this idealization is rarely met under field conditions. Additionally, unlike in EC, this assumption cannot be imposed in post-processing due to the real-time nature of sampling and the absence of high-frequency measurements of the scalar. Consequently, fluxes measured with the TEA method are biased with a non-turbulent advective term that scales with the scalar mean concentration.

Here, we explore the magnitude of this biased advective term and potential ways
to minimize or remove it.
We propose a new formulation to calculate TEA fluxes that minimizes the bias term.
The new formulation shows that the magnitude of the error is constrained to
$\stackrel{\mathrm{\u203e}}{w}/\stackrel{\mathrm{\u203e}}{\left|w\right|}$ when the stationarity criterion is fulfilled.
Here, *w* is the vertical wind velocity, and the overbar denotes time averaging.
The error is shown to be dependent on the asymmetry of atmospheric transport,
represented by the coefficient *α*_{c}.
Two methods of estimating the coefficient *α*_{c} are proposed: a
probabilistic treatment of turbulent transport and a method utilizing the
assumption of scalar similarity.
We show how other formulas for calculating the TEA flux are linked to the new
formulation and explore the different corrections in a numerical simulation.

The new formulation avoids the direct dependence of the bias term on the scalar background concentration. This result increases confidence in applying the TEA method to measuring fluxes of atmospheric constituents. This is particularly relevant to scalars with a large background concentration and a small flux. This paper is Part 1 of a two-part series on true eddy accumulation.

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Micrometeorological methods provide noninvasive, in situ, integrated, and continuous point measurements for ecosystem fluxes on a scale ideal for ecosystem study (Baldocchi et al., 1988; Baldocchi, 2014). Among micrometeorological methods, eddy covariance (EC) has become the de facto method for measuring ecosystem fluxes for the past 40 years. The EC method is the most direct micrometeorological method. It is also relatively easy to set up and operate. These features have led to the wide use and adoption of the EC method at hundreds of sites worldwide, including several regional and global flux measurement networks such as ICOS and FLUXNET (Hicks and Baldocchi, 2020).

The EC method depends on the fast measurement of vertical wind velocity and the
scalar concentration (such as an atmospheric constituent).
The requirement for fast measurement frequency (10 to 20 Hz) limits the
application of the method to a handful of atmospheric constituents for which fast
gas analyzers are available.
Alternative methods that work for slow gas analyzers include
(i) signal downsampling methods (Lenschow et al., 1994), such as
disjunct eddy accumulation (H. J. I. Rinne et al., 2000; Turnipseed et al., 2009) and
disjunct eddy covariance (Rinne and Ammann, 2012),
and (ii) indirect methods such as
flux gradient methods (e.g., J. Rinne et al., 2000)
which depend on the Monin–Obukhov similarity theory
(Monin and Obukhov, 1954)
and relaxed eddy accumulation (REA) which assumes
flux-variance similarity
(Businger and Oncley, 1990).
The true eddy accumulation (TEA) method (Desjardins, 1977)
is the most direct and mathematically equivalent alternative to eddy
covariance among accumulation methods.
Unlike EC, the TEA method requires the concentration measurements to be
carried out once every averaging interval (30 min) (Businger and Oncley, 1990).
The TEA method is formulated under ideal conditions assuming a zero mean
vertical wind velocity during the averaging interval.
This assumption is almost never met under field conditions, and
it is not possible to enforce in post-processing due to lacking high-frequency
information on the scalar concentration.
As a result, the non-vanishing
vertical mean velocity will contribute to a systematic error in the flux.
Nonzero mean vertical wind velocity is a source of error for all eddy
accumulation methods, including TEA (Hicks and McMillen, 1984),
relaxed eddy accumulation
(REA) (Pattey et al., 1993; Businger and Oncley, 1990; Bowling et al., 1998), and disjunct
eddy accumulation (DEA) (Turnipseed et al., 2009).
The reported bias in the flux due to nonzero $\stackrel{\mathrm{\u203e}}{w}$ varies with different
studies and accumulation methods.
For TEA, Hicks and McMillen (1984) recommended that $\stackrel{\mathrm{\u203e}}{w}$
should not exceed 0.0005 *σ*_{w} if accumulated mass is measured and
0.02 *σ*_{w} when concentrations are measured directly.
Turnipseed et al. (2009) reported that a mean vertical wind bias
of ± 0.25 *σ*_{w} leads to a ± 15 % mean systematic bias in the flux using
the disjunct eddy accumulation method.
Values reported for the REA method show a systematic bias of approximately
5 % of the flux due to a $\stackrel{\mathrm{\u203e}}{w}$ of 0.20 *σ*_{w}
(Pattey et al., 1993), which
agrees with the recommendations of
Businger and Oncley (1990).
The magnitude of the residual mean vertical velocity depends on the
meteorological and topographic features of the measurement site and is larger at
complex sites
(Rannik et al., 2020).

In this paper, we revise the theory of the true eddy accumulation method and
obtain a generalized equation that isolates the error due to nonzero vertical
wind velocity.
The new equation shows that the error in the flux is a function of the
atmospheric transport represented by the transport asymmetry coefficient, *α*_{c}.
We study the value and the interpretation of this coefficient in the framework of
quadrant analysis and define its boundary conditions.
Next, we show analytical and empirical ways to obtain the transport asymmetry
coefficient and explore the implications of these estimates for the flux in a
numerical simulation.
Finally, we show how existing formulations for calculating the TEA flux are
special cases of the new equation.

## 2.1 Eddy covariance

The net ecosystem exchange (NEE) $\stackrel{\mathrm{\u203e}}{{\mathrm{N}}_{c}}$ of a scalar *c* (such as an atmospheric
constituent) is
the total vertical flux $\stackrel{\mathrm{\u203e}}{wc}$ across the measurement plane at a height
*h* and the change in storage below that height
(Gu et al., 2012):

where *w* is the vertical wind velocity (m s^{−1}), and *c* is the molar
density (mol m^{−3}) of the scalar of interest (such as CO_{2}).
The previous equation can be reached either from a holistic mass balance approach
or by averaging the continuity equation for the scalar *c* and integrating from
the surface to measurement height *h*.
In both cases, horizontal advection is ignored as a virtue of the assumption of
horizontal homogeneity, and molecular diffusion is ignored due to its small
magnitude (Gu et al., 2012).
For a full discussion on the equations of surface flux, see, for example
Finnigan et al. (2003) and Foken et al. (2012a).

The storage term measurements and value are beyond the scope of this study, and therefore we ignore them. Consequently, the total vertical flux is represented by the first term on the right-hand side of Eq. (1), which can be further decomposed into turbulent and mean advective parts.

The overlines denote ensemble averages that obey Reynolds averaging rules.
Primes represent departures from the mean.
The ensemble averages are estimated experimentally by time averages. Thus,
for a stationary time series drawn from an ensemble,
the turbulent flux for the averaging period, Δ*t*, can be written as

where *w*(*t*) and *c*(*t*) are realizations of the vertical wind velocity and the
scalar quantity such as CO_{2} concentration, respectively.

## 2.2 True eddy accumulation

The true eddy accumulation method circumvents the need to record the fluctuations
of scalar concentration at a frequency sufficient to represent the individual
flux transport eddies.
Instead, it is sufficient to measure the mean product $\stackrel{\mathrm{\u203e}}{wc}$ for
updraft and downdraft once for each averaging interval, Δ*t* (e.g., 30 min).

The product of *w* and *c* is realized by physically collecting air samples with
a flow rate proportional to the vertical wind velocity, *w*.
The method is formulated assuming ideal conditions in which the mean vertical wind
velocity during the averaging period is assumed to be zero.
When $\stackrel{\mathrm{\u203e}}{w}=\mathrm{0}$, the second term on the right-hand side of Eq. (2)
will be zero and the turbulent flux $\stackrel{\mathrm{\u203e}}{{w}^{\prime}{c}^{\prime}}$ will equal
the total ecosystem flux $\stackrel{\mathrm{\u203e}}{wc}$.
By separating $\stackrel{\mathrm{\u203e}}{wc}$ depending on the direction of the vertical wind velocity
we can write

Hence, by sampling air with a flow rate proportional to the magnitude of
vertical wind velocity and accumulating it according to its direction in updraft
and downdraft reservoirs,
one can measure the quantity $\stackrel{\mathrm{\u203e}}{wc}$ and consequently the flux without
having to measure the high-frequency fluctuations of the scalar, *c*
(Desjardins, 1977; Hicks and McMillen, 1984).

Sampling air proportional to the magnitude of vertical wind velocity requires a
scaling parameter, *A*, that ensures the proportionality of the flow rate to the
magnitude of vertical wind velocity.
The scaling parameter is the product of the pump calibration coefficients and other
coefficients used to adjust the system's dynamic range.
For a short interval of time d*t*, a sample of the volume
${V}_{\mathrm{sample}}=A\left|w\right|\phantom{\rule{0.125em}{0ex}}\mathrm{d}t$ will be collected in the system.
The accumulated sample volume in each of the two reservoirs during a long
enough averaging
period Δ*t* (30 to 60 min) will be

By the end of the averaging period, Δ*t*,
the flux will be equal to the difference
in the scalar accumulated mass between updraft and downdraft reservoirs.

If it is desired to formulate the flux in terms of the accumulated scalar
concentration (mol m^{−3}) instead of the accumulated mass,
the average scalar density of accumulated samples in each of the reservoirs will
equal the accumulated mass of the scalar divided by the accumulated volume:

where *C*_{acc} is the accumulated scalar density and the arrows indicate the reservoir.
The measured concentration in Eq. (7) is the weighted
mean of the scalar concentration and the magnitude of the vertical wind velocity.

When $\stackrel{\mathrm{\u203e}}{w}$ is assumed to be zero, $\stackrel{\mathrm{\u203e}}{\left|{w}^{\uparrow}\right|}=\stackrel{\mathrm{\u203e}}{\left|{w}^{\downarrow}\right|}=\stackrel{\mathrm{\u203e}}{\left|w\right|}/\mathrm{2}$, and we can write the flux in terms of concentrations of accumulated samples, similar to Hicks and McMillen (1984):

where $\stackrel{\mathrm{\u203e}}{\left|w\right|}$ is the mean of the magnitude of the vertical wind velocity.

## 2.3 The problem of nonzero mean vertical wind

Although the total ecosystem flux is defined to be $\stackrel{\mathrm{\u203e}}{wc}$ in
Eq. (2), it is not possible to directly use the measured *w* and
*c* to calculate the total flux.
The reason is the difficulty of obtaining an accurate measurement of *w*.
Any non-turbulent offset (bias) in the mean vertical velocity will lead to a
flux biased with $\stackrel{\mathrm{\u203e}}{w}\stackrel{\mathrm{\u203e}}{c}$.
Several factors contribute to a biased mean vertical wind velocity including
topography at particular in complex sites, non-alignment of the anemometer with
local topography, biases in anemometers, flow perturbations, and meteorological
factors induced by local
circulation or topographical drainage
(Lee et al., 2005; Paw U et al., 2000; Heinesch et al., 2007).
Therefore, the measured biased advective term needs to be discarded and the true
physical term, known as the “Webb term” or Webb–Pearman–Leuning (WPL) term, needs to be
estimated by other means
(Webb et al., 1980; Fuehrer and Friehe, 2002).
The original formulation of the TEA method assumes a zero mean vertical wind
velocity during the flux averaging interval and thus assumes the total ecosystem
flux to be equal to the turbulent flux, $\stackrel{\mathrm{\u203e}}{{w}^{\prime}{c}^{\prime}}$.
However, this assumption is rarely valid under field conditions for the
reasons outlined earlier, and the measured TEA flux will be a biased
total vertical flux, $\stackrel{\mathrm{\u203e}}{wc}$.
If the turbulent flux is to be measured using the TEA method, the biased term
$\stackrel{\mathrm{\u203e}}{w}\stackrel{\mathrm{\u203e}}{c}$ needs to be removed.

Previous efforts have been focused on minimizing $\stackrel{\mathrm{\u203e}}{w}$ to reduce the bias in the TEA flux. However, since the wind information cannot be changed after sampling, any treatments for the wind velocity measurements are final when the air samples have been collected. Thus, there is no way to guarantee a zero mean vertical velocity. A common approach to nullifying mean vertical wind velocity in EC measurements is to rotate the wind coordinates in post-processing to force $\stackrel{\mathrm{\u203e}}{w}$ to zero for each averaging interval. This method – commonly referred to as double rotation – is not feasible in eddy accumulation methods. The planar fit method (Wilczak et al., 2001) and its variants, such as the sector-wise planar fit (Foken et al., 2004), are better suited for online application in the TEA method (Siebicke and Emad, 2019).

The planar fit method aligns the sonic coordinates with the long-term streamline coordinates by aligning the wind vector with the plane that minimizes the sum of squares of the vertical wind velocity means for a long period of time (weeks to months). This approach, while minimizing the vertical wind velocity means of the individual averaging intervals, does not force them to be zero. Considerable spread of $\stackrel{\mathrm{\u203e}}{w}$ values around zero can still be observed after applying the planar fit method (Sun, 2007; Rannik et al., 2020).

## 2.4 TEA equation under nonzero $\stackrel{\mathrm{\u203e}}{w}$ conditions

The goal here is to enable measuring the turbulent flux $\stackrel{\mathrm{\u203e}}{{w}^{\prime}{c}^{\prime}}$ from TEA
measurements when $\stackrel{\mathrm{\u203e}}{w}\ne \mathrm{0}$.
The key to extending the TEA equation to conditions of nonzero $\stackrel{\mathrm{\u203e}}{w}$ is to
obtain an estimate of the scalar mean $\stackrel{\mathrm{\u203e}}{c}$ from TEA measurements and
consequently remove the biased advective term $\stackrel{\mathrm{\u203e}}{w}\phantom{\rule{0.125em}{0ex}}\stackrel{\mathrm{\u203e}}{c}$.
We achieve this by using the weighted mean
of *c* and $\left|w\right|$ as an estimate for *c* after correcting for the correlation
between them.

The weighted mean of the scalar, *c*, and the vertical wind velocity magnitude,
$\left|w\right|$, can be obtained from TEA measurements according to Eq. (7).
By decomposing $\stackrel{\mathrm{\u203e}}{c\left|w\right|}$
into mean and fluctuating parts,

The value of $\stackrel{\mathrm{\u203e}}{c}$ can be found to be

Substituting $\stackrel{\mathrm{\u203e}}{c}$ in Eq. (2), we can write the flux as

We can obtain all the terms in Eq. (11) from our
measurements except for the covariance term $\stackrel{\mathrm{\u203e}}{|w{|}^{\prime}{c}^{\prime}}$.
We define the “transport asymmetry coefficient” for the scalar *c*
(*α*_{c}) as the ratio of the covariance between the wind magnitude and the
scalar to the covariance between the wind and the scalar.

We notice that *α*_{c} is conveniently independent of the scalar standard deviation. It can be written as

where ${\mathit{\rho}}_{c\left|w\right|}$ and *ρ*_{cw} are the correlation coefficients between *c* and $\left|w\right|$ and between *c* and
*w*, respectively.
${\mathit{\sigma}}_{\left|w\right|}$ and *σ*_{w} are the standard deviations of
$\left|w\right|$ and *w*, respectively.
After substitution, we write the turbulent flux as

Finally, we rearrange Eq. (14) and obtain the generalized TEA flux equation that gives a turbulent TEA flux when the mean vertical wind velocity is nonzero:

### 2.4.1 Calculating the corrected TEA flux

The new general equation for TEA (Eq. 15) extends the validity of the method to conditions in which the mean vertical wind velocity is nonzero. We show here how the turbulent TEA flux can be calculated from the measured physical quantities.

The weighted mean over an averaging period Δ*t* can be written as

which in terms of the quantities we are measuring, translates to

Similarly,

After substitution and simplification, we obtain the TEA flux in terms of the measured quantities:

where *F*_{TEA} is the kinematic flux density
(mol m s^{−1}).
${C}_{\mathrm{acc}}^{\uparrow}$ and ${C}_{\mathrm{acc}}^{\downarrow}$ are the mean concentrations
(mol m^{−3}) of the scalar *c* in updraft and downdraft reservoirs at
the end of the averaging period, Δ*t*.
*V*^{↑} and *V*^{↓} are the accumulated sample volumes (m^{3})
in updraft and downdraft reservoirs during the averaging period.
$\stackrel{\mathrm{\u203e}}{\left|w\right|}$ is the mean of the absolute vertical wind velocity
(m s^{−1}) during the averaging period.

## 2.5 Values of the transport asymmetry coefficient *α*_{c}

### 2.5.1 Quadrant analysis of *α*_{c}

The value of *α*_{c} can be analyzed using the framework of quadrant analysis.
Quadrant analysis is commonly used to inspect the
contributions from different quadrants in the $({w}^{\prime},{c}^{\prime})$ plane by sorting the
instantaneous values into four categories (*S*_{1} .. *S*_{4}) according to the sign of the two
fluctuating components (e.g., Katul et al., 1997; Raupach, 1981; Katsouvas et al., 2007).
Here, *S*_{i} is the fraction of the flux transported by contributions in quadrant *i*.
Following the definition of Thomas and Foken (2007), the
pairs *S*_{2} and *S*_{4} are ejections and sweeps for downward-directed net flux
(negative *ρ*_{wc}) and *S*_{1} and *S*_{3} for upward-directed net flux
(positive *ρ*_{wc}).

The total flux is the sum of the contributions from the four quadrants. We similarly find that the covariance term $\stackrel{\mathrm{\u203e}}{\left|{w}^{\prime}\right|{c}^{\prime}}$ can be written as

It follows that *α* can be written in terms of quadrants as

It should be noted that when $\stackrel{\mathrm{\u203e}}{w}\ne \mathrm{0}$, $\stackrel{\mathrm{\u203e}}{\left|{w}^{\prime}\right|{c}^{\prime}}$ is an approximation for $\stackrel{\mathrm{\u203e}}{|w{|}^{\prime}{c}^{\prime}}$, the latter can be found to be

The contributions of $\stackrel{\mathrm{\u203e}}{|w{|}^{\prime}{c}^{\prime}}$ can be accommodated in this analysis by
partitioning the contributions into six categories: the four quadrants and two
additional bands for the contributions when *w*^{′} falls between 0 and
$\stackrel{\mathrm{\u203e}}{w}$.
However, the contribution from the additional bands is small and has little
impact on the interpretation of this analysis.

Consider that the quadrants *S*1 and *S*4 represent the contribution of updrafts to
the flux. Similarly, *S*2 and *S*3 represent the contribution of downdrafts to the flux.
We define

Here, the arrows indicate the direction of the wind and not the sign of the flux;
e.g., flux^{↑} is the portion of the flux transported with
updrafts which can be either positive or negative flux.
If follows that *α*_{c} can be written as

The previous equation indicates that if the flux transported with updrafts,
flux^{↑}, has the same sign as the flux transported with
downdrafts, flux^{↓} , then the value of $\left|{\mathit{\alpha}}_{c}\right|$ will be
smaller than 1.
If $\left|{\mathit{\alpha}}_{c}\right|>\mathrm{1}$ then
flux^{↑} and flux^{↓} have opposing signs,
which indicates that the wind and the scalar are correlated for updrafts and
anticorrelated for downdrafts or vice versa, indicating nonstationary
conditions.

### 2.5.2 Analytical value of *α*_{c}

An analytical expression for the value of *α* can be obtained from
knowledge of the joint probability distribution of the vertical wind velocity
and the scalar.
If the wind and the scalar are assumed to follow a Gaussian joint probability
density function,
we find the analytical value of *α*_{c} in terms of the moments of
the joint probability density function to be

where erf is the error function, and *σ*_{w} is the wind standard deviation.

We can use the analytical value of *α*_{c} and further substitute the
expected value of $\left|w\right|$ with the mean of the folded normal
distribution (Leone et al., 1961) to obtain an
analytical expression for the expectation of the flux error due to a nonzero
vertical wind velocity using $\stackrel{\mathrm{\u203e}}{w}$ and *σ*_{w}.
The analytical expression of the relative error in the flux is found to be

Here, *F*_{err} is the error in the TEA flux when failing to account for
the correlation between the scalar and the magnitude of vertical wind velocity.

### 2.5.3 Scalar similarity to estimate *α*_{c}

The assumption of scalar similarity provides
a potential empirical way to estimate the value of *α*_{c},
i.e., by calculating the value of *α*_{c} from another scalar for which high-frequency
measurements are available, e.g., sonic temperature.
The assumption of scalar similarity is supported by experimental evidence that
has shown that different scalars behave similarly due to a similar transfer
mechanism (Ohtaki, 1985; Wesely, 1988).
However, the assumption of scalar similarity cannot be always guaranteed and
should be used with caution.
Nonetheless, we believe it is a useful assumption to approximate the value of *α*_{c}
given that the value of *α*_{c} is determined by the distribution of turbulent
transport in different quadrants, which is expected to have the same effect on
different scalars under good mixing conditions.

## 3.1 Numerical simulations

We set up a numerical
simulation to test the magnitude of the error due to nonzero $\stackrel{\mathrm{\u203e}}{w}$ on the
flux and investigate the values of the coefficient *α*_{c}.
For this simulation,
we used 10 Hz measurements obtained from a field experiment measuring
vertical wind velocity and scalar concentration using an infrared gas analyzer (IRGA)
and a sonic anemometer.
We used data from a period of 12 d from 15 June 2020 to 26 June 2020.
The data were collected at an ideal flat agricultural site in Braunschweig,
Germany.
A full description of the site and the instrumentation is provided in
the accompanying paper (Emad and Siebicke, 2023).
We added a random $\stackrel{\mathrm{\u203e}}{w}$ offset in the range −0.25 to 0.25 m s^{−1} to each averaging interval but limited $\stackrel{\mathrm{\u203e}}{w}$ to
smaller than 2*σ*_{w}.
We obtained three repetitions and calculated the flux according to different formulas.
In total, there were about 1400 30 min averaging intervals.
The methods compared were
(i) the flux calculated using the concentrations formula of
Hicks and McMillen (1984) shown in
Eq. (8),
(ii) the equation for DEA including the non-equal volume correction of
Turnipseed et al. (2009),
and (iii) the new generalized equation proposed in the current
study (Eq. 14) utilizing
*α*_{θ} values calculated from sonic temperature and the analytical value of *α*_{c}.

We applied minimal quality checks to the resulting fluxes before the comparison.
Tests for stationarity following Foken et al. (2005)
removed 22 % of the averaging intervals.
We limited the values of $\left|{\mathit{\alpha}}_{\mathit{\theta}}\right|$ to less than
1, which removed an additional 4 % of the averaging intervals.
Furthermore, when the sonic temperature was used for calculating
*α*_{θ}, periods with low turbulence intensity ($\left|{\mathit{\rho}}_{w\mathit{\theta}}\right|<\mathrm{0.2}$)
were excluded. The excluded averaging intervals occurred almost exclusively
during nighttime conditions.

We first discuss the newly proposed TEA equation, then compare it to different TEA
formulations. Then, we discuss the interpretation of the transport asymmetry
coefficient *α* and different ways of estimating it.

## 4.1 Nonzero mean vertical wind velocity

The newly proposed TEA equation (Eq. 14) successfully constrained the biased advective term $\stackrel{\mathrm{\u203e}}{w}\stackrel{\mathrm{\u203e}}{c}$. The new equation employs information about the scalar transport to allow the estimation of $\stackrel{\mathrm{\u203e}}{c}$ from available TEA measurements and consequently get an estimate of the biased advective term. Besides the correction of the nonzero $\stackrel{\mathrm{\u203e}}{w}$ bias, the estimation of the scalar mean, $\stackrel{\mathrm{\u203e}}{c}$, is essential for the WPL correction and the calculation of storage fluxes.

The terms of Eq. (14) account for different contributions to the flux. The first term on the right-hand side is equivalent to calculating the flux as the difference in accumulated mass between updraft and downdraft. When $\stackrel{\mathrm{\u203e}}{w}=\mathrm{0}$, the equation is reduced to this term only. The second term accounts for the bias introduced by the biased advective term $\stackrel{\mathrm{\u203e}}{w}\phantom{\rule{0.125em}{0ex}}\stackrel{\mathrm{\u203e}}{c}$ by using the weighted mean of the scalar and the magnitude of wind as an estimate for $\stackrel{\mathrm{\u203e}}{c}$. We show that when $\stackrel{\mathrm{\u203e}}{w}\ne \mathrm{0}$, the first two terms are equivalent to using the concentration formula of Hicks and McMillen (1984) shown in Eq. (8), with the unequal volume correction of Turnipseed et al. (2009) that accounts for the small difference between the weighted mean ${\stackrel{\mathrm{\u203e}}{c}}_{\mathrm{W}}$ and average of concentrations $({C}_{\mathrm{acc}}^{\uparrow}+{C}_{\mathrm{acc}}^{\downarrow})/\mathrm{2}$. Refer to Appendix A for details about this equality. The new third term $\stackrel{\mathrm{\u203e}}{{c}^{\prime}|w{|}^{\prime}}/\stackrel{\mathrm{\u203e}}{\left|w\right|}$ corrects for the correlation between the scalar and the magnitude of the wind. Ignoring the third term will result in a flux biased with the ratio $\stackrel{\mathrm{\u203e}}{w}/\stackrel{\mathrm{\u203e}}{\left|w\right|}{\mathit{\alpha}}_{c}$.

The new TEA equation reveals an important insight. When using the new equation to calculate the flux, the error in the flux when $\stackrel{\mathrm{\u203e}}{w}\ne \mathrm{0}$ is independent of the scalar concentration and is governed by the characteristics of the turbulent transport. This strengthens confidence in using the TEA method for measuring atmospheric constituents with a high background concentration and small flux (low deposition velocity).

Using the new TEA formula with an estimated value for *α*_{c} was effective
in reducing the uncertainty and the systematic error in the calculated fluxes
(Fig. 1).
To quantify the magnitude of the systematic bias and uncertainty resulting from
nonzero $\stackrel{\mathrm{\u203e}}{w}$ on the fluxes, we used the simulation results to obtain the
slope and the coefficient of determination, *R*^{2}, from a linear fit of the
calculated fluxes against the reference EC flux.
The simulation results show an increased bias and uncertainty in the fluxes when
$\stackrel{\mathrm{\u203e}}{w}\ne \mathrm{0}$ and a significant improvement when using an estimate of *α*
for correction (Fig. 1).

We found that using the accumulated mass difference to calculate the TEA flux
(first term in Eq. 14) produced the
largest errors. Values of $\stackrel{\mathrm{\u203e}}{w}$ as small as 0.01 *σ*_{w} were
sufficient to produce more than a 10 % mean bias in the flux magnitude for
CO_{2} in our dataset.

The use of the concentration TEA equation
of Hicks and McMillen (1984) was a considerable improvement
over using the mass difference but still overestimated the TEA flux.
The slope of the linear fit was 1.12 (*R*^{2}=0.94).
Using the DEA equation of Turnipseed et al. (2009), which includes
an additional term to correct for the effect of unequal volume
on the flux, led to underestimating the TEA flux, yielding a slope of 0.84
(*R*^{2}=0.93).
The correction of nonzero $\stackrel{\mathrm{\u203e}}{w}$ using *α*_{θ} utilizing the
assumption of scalar similarity significantly reduced the bias
and the uncertainty and gave a slope of 1.005 (*R*^{2}=0.995).
The use of the analytical value of *α*_{c} using
Eq. (26) similarly reduced the bias but with a smaller
reduction in uncertainty, yielding a slope of 0.991 (*R*^{2}=0.97).
The higher uncertainty when using the analytical value of *α*_{c} is
likely due to the deviation from the assumed Gaussian probability distribution.

These results indicate that the proposed corrections using an estimate of
*α*_{c} are very effective in minimizing or removing the bias from TEA flux
when $\stackrel{\mathrm{\u203e}}{w}\ne \mathrm{0}$ even when using the analytical value of *α*_{c}.

## 4.2 Value and interpretation of the transport asymmetry coefficient *α*

The value of *α*_{c} defined in Eq. (12) indicates
the disparity of the flux transport between updrafts and downdrafts.
Values of *α*_{c} larger than 1 indicate that updraft flux (*S*_{1}+*S*_{4})
and downdraft flux (*S*_{2}+*S*_{3}) have opposing signs.
This pattern indicates that the wind and the scalar are correlated for updraft
flux and anticorrelated for the downdraft flux (or the other way around).
This pattern violates the stationarity conditions.

Therefore, we conclude that for stationary flows the systematic error in the
TEA flux is smaller than $\pm \stackrel{\mathrm{\u203e}}{w}/\stackrel{\mathrm{\u203e}}{\left|w\right|}$.
Observed values of *α* for H_{2}O, CO_{2}, *θ*, and the wind component, *u*, are shown in Fig. 2.
The data confirm that values of $\left|\mathit{\alpha}\right|$ are
consistently below 1 for the four scalars when the stationarity criterion is
met.
However, when the correlation between *w* and the scalar is low during
conditions associated with low developed turbulence, spurious correlations might
lead to values of $\left|\mathit{\alpha}\right|$ larger than 1.

We found that the value of *α* for CO_{2} moderately correlates with
the skewness of the measured scalar (*r*=0.61, data not shown).
The observed mean of *α* for CO_{2} and sonic temperature calculated from
high-frequency measurements for periods with negligible $\stackrel{\mathrm{\u203e}}{w}$ was approximately
0.2 for unstable and good turbulent mixing
conditions ($\left|{\mathit{\rho}}_{wc}\right|>\mathrm{0.25}$) with a standard error of SE=0.01.
For stable stratification (*ζ*>0), the mean of *α* was
approximately equal to −0.18 but with a higher spread around the mean:
SE=0.09.
These values indicate that updrafts have a larger contribution to the flux under unstable
stratification and smaller contribution during stable stratification.
The results generally agree with values found from studies using conditional
sampling (Greenhut and Khalsa, 1982) and large eddy simulations (LESs) (Wyngaard and Moeng, 1992), which found
that updraft contribution to the flux is 2 to 3 times larger than
downdraft contribution under unstable conditions due to the contribution of convective
thermals.

The sign of *α* indicates whether updrafts or downdrafts have a larger
contribution to the flux.
Inspecting Eq. (25), we find that a positive *α*
indicates that the magnitude of updraft contribution to the flux is larger than
the magnitude of the downdraft contribution ($\left|{\mathrm{flux}}^{\uparrow}\right|>\left|{\mathrm{flux}}^{\downarrow}\right|$), while the opposite is true for a negative *α*.

The analytical value of *α* from Eq. (26) was
effective in minimizing the systematic bias as confirmed by the simulation
results.
However, the assumption of a Gaussian distribution,
although used in the literature, e.g.,
Wyngaard and Moeng (1992), is not adequate.
While the wind might be normally distributed for
most stability classes (Chu et al., 1996),
the scalar can significantly depart from normality
(Berg and Stull, 2004).
Other distributions might be more suited for approximating the joint probability
distribution (Frenkiel and Klebanoff, 1973).
For example,
Katsouvas et al. (2007), using experimental data, showed that a
third-order Gram–Charlier distribution was necessary and sufficient in most
cases for describing the quadrant time and flux contributions.
It is worth considering this distribution to find a better analytical
formula to calculate the expectation of *α*.

The hypothesis of scalar similarity was proposed as another source for
estimating the values of *α*.
The similarity was empirically confirmed by investigating the values of
*α*_{θ} and *α*_{c} from high-frequency measurements (Fig. 3).
A linear fit with a slope of 0.98 and *R*^{2} of 0.92 was obtained
during steady-state and well-developed turbulence conditions.
During such conditions, *α*_{θ} can substitute *α*_{c} to calculate
the flux correction ratio.
However, the correction becomes large and unreliable in periods when *σ*_{w}
and *ρ*_{cw} are small, associated with small fluxes during nighttime and
stable conditions.
Additionally, temperature is considered a poor proxy during nearly neutral
conditions due to its contribution to buoyancy
(McBean, 1973; Hicks et al., 1980).
We noticed that the variance in *α* values is higher under weakly developed
turbulence
and experimentally determined the threshold for the optimum use of
*α*_{θ} for the correction as $\left|{\mathit{\rho}}_{cw}\right|=\mathrm{0.2}$.
Below this threshold, values of *α*_{θ} larger than 1 are observed,
making the correction unreliable.
This threshold can be seen as an indicator for the violation of assumptions of
homogeneity and stationarity or other problematic conditions.
Similar uses for the correlation coefficient are common in the literature, e.g.,
Foken and Wichura (1996).

Another use of the formulation using *α* is to find a $\stackrel{\mathrm{\u203e}}{w}$ threshold
above which the TEA flux measurement becomes unreliable.
For example, if we define the bias in the flux as not exceeding 10 % of
the flux,
we can experimentally find that the error in the flux due to nonzero $\stackrel{\mathrm{\u203e}}{w}$
becomes larger than 10 % when $\stackrel{\mathrm{\u203e}}{w}$
exceeds 0.21*σ*_{w} for periods with good turbulent mixing
conditions ($\left|{\mathit{\rho}}_{w,{\mathrm{CO}}_{\mathrm{2}}}\right|>\mathrm{0.2}$).
This threshold is close to the analytical value of 0.323 *σ*_{w}
obtained from the Gaussian joint probability distribution.
To push this threshold further, *α*_{θ}
calculated from sonic temperature can be used during good turbulent mixing
conditions ($\left|{\mathit{\rho}}_{w,{\mathrm{CO}}_{\mathrm{2}}}\right|>\mathrm{0.2}$).
Simulations indicate that the average relative confidence interval for the
predicted value of *α*_{θ} from ${\mathit{\alpha}}_{{\mathrm{CO}}_{\mathrm{2}}}$ is 0.17 %
of the fit value.
In summary for this example, to keep the error in the flux below 10 %,
*α*_{θ} can be safely used to correct for biased $\stackrel{\mathrm{\u203e}}{w}$ as long as
$\stackrel{\mathrm{\u203e}}{w}<\mathrm{0.7}\phantom{\rule{0.125em}{0ex}}{\mathit{\sigma}}_{w}$.
This limit is considered forgiving and easy to achieve with online
coordinate rotation and further rather simple online treatments.
The only time when this limit is expected to be reached is when *σ*_{w} is
very small (e.g., during nighttime conditions) where other problems such as low
turbulent mixing and violations of the assumptions of the EC and TEA methods are
expected to occur.
These periods largely overlap with periods considered to be of low quality and
are usually excluded from the analysis (Foken et al., 2012b).

To summarize, we find that the error in the TEA flux is constrained to
$\stackrel{\mathrm{\u203e}}{w}/\stackrel{\mathrm{\u203e}}{\left|w\right|}$ for $\left|a\right|<\mathrm{1}$, which was shown here to be true for
stationary conditions, which are at the focus of turbulent flux measurements.
If a correction is desired to minimize this error,
two options were presented to estimate *α*_{c}: first, an analytical solution,
and second, an estimate employing scalar similarity.
Finally, with the use of
*α*_{c}, the typically observed systematic flux bias due to nonzero mean
vertical wind velocity could be effectively characterized and minimized.

In this paper, we revised the theory of the true eddy accumulation method and extended its applicability to measure turbulent fluxes under nonideal conditions in which the mean vertical wind velocity during the averaging interval is not zero. The new generalized equation allows estimating the scalar mean during the flux averaging interval and defining conditions in which the error in the flux is significant.

The new formulation allowed constraining the relative systematic error in the TEA flux to the ratio $\stackrel{\mathrm{\u203e}}{w}/\stackrel{\mathrm{\u203e}}{\left|w\right|}$ under stationarity conditions. This systematic error was reduced to be a function of the disparity of atmospheric transport instead of having it scale with the scalar background concentration. This development significantly reduces the systematic bias in TEA fluxes under nonideal conditions, allowing the TEA method to be used indifferently with various atmospheric constituents.

The coefficient *α*_{c}, defined to quantify the atmospheric transport
asymmetry, has proved to be very useful in estimating and removing the error in
measured TEA fluxes.
We showed two methods for estimating *α*_{c} to reduce the flux systematic
bias: (i) an estimate of *α*_{c} based on the assumption of flux variance
similarity and (ii) an analytical expression based on the assumption of a
Gaussian joint probability distribution of the scalar concentration and vertical
wind velocity.
Both of these estimation methods were shown to be effective in
minimizing the systematic error in the flux when compared to conventional TEA
formulas.

In conclusion, the results presented in this paper showed that it is possible to achieve minimum bias in the TEA flux under most atmospheric conditions as well as identify those conditions which are less favorable. We believe that these results increase confidence in using the TEA method for different atmospheric constituents and under a variety of atmospheric conditions.

We show here how the TEA flux formula of Hicks and McMillen (1984), originally formulated under the assumption of $\stackrel{\mathrm{\u203e}}{w}=\mathrm{0}$, is equivalent to using $({C}_{\mathrm{acc}}^{\uparrow}+{C}_{\mathrm{acc}}^{\downarrow})/\mathrm{2}$ as an estimate for $\stackrel{\mathrm{\u203e}}{c}$ in the second term on the right-hand side of Eq. (2).

We write the conditional expectation of $\stackrel{\mathrm{\u203e}}{w}$ as

where sign(*w*) is the sign of vertical wind velocity.
P(*w*^{↑}) and P(*w*^{↓}) are the observed
probabilities of the sign of *w*,
which equals the ratio of the time the wind is positive or negative to the total
integration interval time:

and similarly

By substituting $\stackrel{\mathrm{\u203e}}{\left|w\right|}/\mathrm{2}$ with

we obtain

After rearrangement and simplification we get to

When Eq. (A6) is compared with Eq. (2), it is clear that the term $\frac{{C}_{\mathrm{acc}}^{+}+{C}_{\mathrm{acc}}^{-}}{\mathrm{2}}$ is used as an estimate for $\stackrel{\mathrm{\u203e}}{c}$.

Symbols | ||

c |
mol m^{−3} |
Molar density of a scalar |

w |
m s^{−1} |
Vertical wind velocity |

Δt |
s | Flux averaging interval |

A |
– | TEA sampling scaling factor |

V |
m^{3} |
Volume |

C |
mol m^{−3} |
Mean concentration of |

accumulated samples | ||

α_{c} |
– | Transport asymmetry coefficient |

for scalar c |
||

ρ |
– | Correlation coefficient |

S_{i} |
– | Flux contribution from quadrant i |

Subscripts | ||

acc | Accumulated samples | |

↑ | Updraft buffer volume | |

↓ | Downdraft buffer volume | |

c |
Atmospheric constituent |

Data and scripts needed for producing the results and the figures presented in this paper are provided in Emad and Siebicke (2022).

AE developed the generalized TEA equation, implemented needed software, analyzed the data, and wrote the paper. LS provided supervision and feedback on the results, analysis, and paper.

The contact author has declared that none of the authors has any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We would like to thank the two referees (Christoph Thomas and an anonymous reviewer) and the editor for their constructive, useful, and detailed comments, which have significantly improved the paper. We gratefully acknowledge the support of the Bioclimatology group, led by Alexander Knohl at the University of Göttingen, in particular technical assistance by Justus Presse, Frank Tiedemann, Marek Peksa, Dietmar Fellert, and Edgar Tunsch. We thank Christian Brümmer, Jean-Pierre Delorme from the Thünen Institute for Agricultural Climate Protection, and Mathias Herbst from the Center for Agrometeorological Research of the German Meteorological Service (DWD) for facilitating the fieldwork in Braunschweig. We further acknowledge Christian Markwitz for the fruitful discussions during the preparation of the paper and for reading and commenting on the paper. We thank Alexander Knohl, Nicolò Camarretta, Justus van Ramshorst, and Yannik Wardius for reading the paper and providing useful comments.

This research has been supported by the Niedersächsische Ministerium für Wissenschaft und Kultur (Wissenschaft.Niedersachsen.Weltoffen grant), the H2020 European Research Council (grant no. 682512), and the Deutsche Forschungsgemeinschaft (grant no. INST 186/1118-1 FUGG).

This open-access publication was funded by the University of Göttingen.

This paper was edited by Hartwig Harder and reviewed by Christoph Thomas and one anonymous referee.

Baldocchi, D.: Measuring Fluxes of Trace Gases and Energy between Ecosystems and the Atmosphere – the State and Future of the Eddy Covariance Method, Global Change Biol., 20, 3600–3609, https://doi.org/10.1111/gcb.12649, 2014. a

Baldocchi, D. D., Hicks, B. B., and Meyers, T. P.: Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods, Ecology, 69, 1331–1340, https://doi.org/10.2307/1941631, 1988. a

Berg, L. K. and Stull, R. B.: Parameterization of Joint Frequency Distributions of Potential Temperature and Water Vapor Mixing Ratio in the Daytime Convective Boundary Layer, J. Atmos. Sci., 61, 813–828, https://doi.org/10.1175/1520-0469(2004)061<0813:POJFDO>2.0.CO;2, 2004. a

Bowling, D. R., Turnipseed, A. A., Delany, A. C., Baldocchi, D. D., Greenberg, J. P., and Monson, R. K.: The Use of Relaxed Eddy Accumulation to Measure Biosphere-Atmosphere Exchange of Isoprene and Other Biological Trace Gases, Oecologia, 116, 306–315, https://doi.org/10.1007/s004420050592, 1998. a

Businger, J. A. and Oncley, S. P.: Flux Measurement with Conditional Sampling, J. Atmos. Ocean. Technol., 7, 349–352, https://doi.org/10.1175/1520-0426(1990)007<0349:FMWCS>2.0.CO;2, 1990. a, b, c, d

Chu, C. R., Parlange, M. B., Katul, G. G., and Albertson, J. D.: Probability Density Functions of Turbulent Velocity and Temperature in the Atmospheric Surface Layer, Water Resour. Res., 32, 1681–1688, https://doi.org/10.1029/96WR00287, 1996. a

Desjardins, R. L.: Energy Budget by an Eddy Correlation Method, J. Appl. Meteorol., 16, 248–250, https://doi.org/10.1175/1520-0450(1977)016<0248:EBBAEC>2.0.CO;2, 1977. a, b

Emad, A. and Siebicke, L.: Reproduction Data and Code for the Paper: True Eddy Accumulation – Part 1: Solutions to the Problem of Non-Vanishing Mean Vertical Wind Velocity, Zenodo [data set, code], https://doi.org/10.5281/zenodo.7047610, 2022. a

Emad, A. and Siebicke, L.: True eddy accumulation – Part 2: Theory and experiment of the short-time eddy accumulation method, Atmos. Meas. Tech., 16, 41–55, https://doi.org/10.5194/amt-16-41-2023, 2023. a

Finnigan, J. J., Clement, R., Malhi, Y., Leuning, R., and Cleugh, H.: A Re-Evaluation of Long-Term Flux Measurement Techniques Part I: Averaging and Coordinate Rotation, Bound. Lay. Meteorol., 107, 1–48, https://doi.org/10.1023/A:1021554900225, 2003. a

Foken, T. and Wichura, B.: Tools for Quality Assessment of Surface-Based Flux Measurements, Agr. Forest Meteorol., 78, 83–105, https://doi.org/10.1016/0168-1923(95)02248-1, 1996. a, b

Foken, T., Gockede, M., Mauder, M., Mahrt, L., Amiro, B., and Munger, W.: Post-Field Data Quality Control, Handbook of Micrometeorology, 29, 181–208, https://doi.org/10.1007/1-4020-2265-4_9, 2004. a

Foken, T., Göockede, M., Mauder, M., Mahrt, L., Amiro, B., and Munger, W.: Post-Field Data Quality Control, in: Handbook of Micrometeorology: A Guide for Surface Flux Measurement and Analysis, edited by: Lee, X., Massman, W., and Law, B., Atmospheric and Oceanographic Sciences Library, 181–208, Springer Netherlands, Dordrecht, https://doi.org/10.1007/1-4020-2265-4_9, 2005. a

Foken, T., Aubinet, M., and Leuning, R.: The Eddy Covariance Method, in: Eddy Covariance: A Practical Guide to Measurement and Data Analysis, edited by: Aubinet, M., Vesala, T., and Papale, D., Springer Atmospheric Sciences, 1–19, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-2351-1_1, 2012a. a

Foken, T., Leuning, R., Oncley, S. R., Mauder, M., and Aubinet, M.: Corrections and Data Quality Control, in: Eddy Covariance: A Practical Guide to Measurement and Data Analysis, edited by: Aubinet, M., Vesala, T., and Papale, D., Springer Atmospheric Sciences, 85–131, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-2351-1_4, 2012b. a

Frenkiel, F. N. and Klebanoff, P. S.: Probability Distributions and Correlations in a Turbulent Boundary Layer, The Phys. Fluids, 16, 725–737, https://doi.org/10.1063/1.1694421, 1973. a

Fuehrer, P. L. and Friehe, C. A.: Flux Corrections Revisited, Bound. Lay. Meteorol., 102, 415–458, https://doi.org/10.1023/A:1013826900579, 2002. a

Greenhut, G. K. and Khalsa, S. J. S.: Updraft and Downdraft Events in the Atmospheric Boundary Layer Over the Equatorial Pacific Ocean, J. Atmos. Sci., 39, 1803–1818, https://doi.org/10.1175/1520-0469(1982)039<1803:UADEIT>2.0.CO;2, 1982. a

Gu, L., Massman, W. J., Leuning, R., Pallardy, S. G., Meyers, T., Hanson, P. J., Riggs, J. S., Hosman, K. P., and Yang, B.: The Fundamental Equation of Eddy Covariance and Its Application in Flux Measurements, Agr. Forest Meteorol., 152, 135–148, https://doi.org/10.1016/j.agrformet.2011.09.014, 2012. a, b

Heinesch, B., Yernaux, M., and Aubinet, M.: Some Methodological Questions Concerning Advection Measurements: A Case Study, Bound. Lay. Meteorol., 122, 457–478, https://doi.org/10.1007/s10546-006-9102-4, 2007. a

Hicks, B. B. and Baldocchi, D. D.: Measurement of Fluxes Over Land: Capabilities, Origins, and Remaining Challenges, Bound. Lay. Meteorol., 177, 365–394, https://doi.org/10.1007/s10546-020-00531-y, 2020. a

Hicks, B. B. and McMillen, R. T.: A Simulation of the Eddy Accumulation Method for Measuring Pollutant Fluxes, J. Climate Appl. Meteorol., 23, 637–643, https://doi.org/10.1175/1520-0450(1984)023<0637:ASOTEA>2.0.CO;2, 1984. a, b, c, d, e, f, g, h

Hicks, B. B., Wesely, M. L., and Durham, J. L.: Critique of Methods to Measure Dry Deposition Workshop Summary, Tech. Rep. PB-81126443; EPA-600/9-80-050, Argonne National Lab., IL (USA), Environmental Protection Agency, Research Triangle Park, NC (USA), 1980. a

Katsouvas, G. D., Helmis, C. G., and Wang, Q.: Quadrant Analysis of the Scalar and Momentum Fluxes in the Stable Marine Atmospheric Surface Layer, Bound. Lay. Meteorol., 124, 335–360, https://doi.org/10.1007/s10546-007-9169-6, 2007. a, b

Katul, G., Kuhn, G., Schieldge, J., and Hsieh, C.-I.: The Ejection-Sweep Character of Scalar Fluxes in the Unstable Surface Layer, Bound. Lay. Meteorol., 83, 1–26, https://doi.org/10.1023/A:1000293516830, 1997. a

Lee, X., Finnigan, J., and Paw U, K. T.: Coordinate Systems and Flux Bias Error, in: Handbook of Micrometeorology: A Guide for Surface Flux Measurement and Analysis, edited by: Lee, X., Massman, W., and Law, B., Atmospheric and Oceanographic Sciences Library, 33–66, Springer Netherlands, Dordrecht, https://doi.org/10.1007/1-4020-2265-4_3, 2005. a

Lenschow, D. H., Mann, J., and Kristensen, L.: How Long Is Long Enough When Measuring Fluxes and Other Turbulence Statistics?, J. Atmos. Ocean. Technol., 11, 661–673, https://doi.org/10.1175/1520-0426(1994)011<0661:HLILEW>2.0.CO;2, 1994. a

Leone, F. C., Nelson, L. S., and Nottingham, R. B.: The Folded Normal Distribution, Technometrics, 3, 543–550, https://doi.org/10.1080/00401706.1961.10489974, 1961. a

McBean, G. A.: Comparison of the Turbulent Transfer Processes near the Surface, Bound. Lay. Meteorol., 4, 265–274, https://doi.org/10.1007/BF02265237, 1973. a

Monin, A. S. and Obukhov, A. M.: Basic Laws of Turbulent Mixing in the Ground Layer of the Atmosphere, Translated for Geophysics Research Directorate, AF Cambridge Research Center... by the American meteorological Society, Tr. Akad. Nauk SSSR Geophiz. Inst., Boston, MA, 24, 163–187, 1954. a

Ohtaki, E.: On the Similarity in Atmospheric Fluctuations of Carbon Dioxide, Water Vapor and Temperature over Vegetated Fields, Bound. Lay. Meteorol., 32, 25–37, https://doi.org/10.1007/BF00120712, 1985. a

Pattey, E., Desjardins, R. L., and Rochette, P.: Accuracy of the Relaxed Eddy-Accumulation Technique, Evaluated Using CO2 Flux Measurements, Bound. Lay. Meteorol., 66, 341–355, https://doi.org/10.1007/BF00712728, 1993. a, b

Paw U, K. T., Baldocchi, D. D., Meyers, T. P., and Wilson, K. B.: Correction Of Eddy-Covariance Measurements Incorporating Both Advective Effects And Density Fluxes, Bound. Lay. Meteorol., 97, 487–511, https://doi.org/10.1023/A:1002786702909, 2000. a

Rannik, Ü., Vesala, T., Peltola, O., Novick, K. A., Aurela, M., Järvi, L., Montagnani, L., Mölder, M., Peichl, M., Pilegaard, K., and Mammarella, I.: Impact of Coordinate Rotation on Eddy Covariance Fluxes at Complex Sites, Agr. Forest Meteorol., 287, 107940, https://doi.org/10.1016/j.agrformet.2020.107940, 2020. a, b

Raupach, M. R.: Conditional Statistics of Reynolds Stress in Rough-Wall and Smooth-Wall Turbulent Boundary Layers, J. Fluid Mechan., 108, 363–382, https://doi.org/10.1017/S0022112081002164, 1981. a

Rinne, J. and Ammann, C.: Disjunct Eddy Covariance Method, in: Eddy Covariance: A Practical Guide to Measurement and Data Analysis, edited by: Aubinet, M., Vesala, T., and Papale, D., Springer Atmospheric Sciences, 291–307, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-2351-1_10, 2012. a

Rinne, H. J. I., Delany, A. C., Greenberg, J. P., and Guenther, A. B.: A True Eddy Accumulation System for Trace Gas Fluxes Using Disjunct Eddy Sampling Method, J. Geophys. Res.-Atmos., 105, 24791–24798, https://doi.org/10.1029/2000JD900315, 2000. a

Rinne, J., Tuovinen, J.-P., Laurila, T., Hakola, H., Aurela, M., and Hypén, H.: Measurements of Hydrocarbon Fluxes by a Gradient Method above a Northern Boreal Forest, Agr. Forest Meteorol., 102, 25–37, https://doi.org/10.1016/S0168-1923(00)00088-5, 2000. a

Siebicke, L. and Emad, A.: True eddy accumulation trace gas flux measurements: proof of concept, Atmos. Meas. Tech., 12, 4393–4420, https://doi.org/10.5194/amt-12-4393-2019, 2019. a

Sun, J.: Tilt Corrections over Complex Terrain and Their Implication for
CO_{2} Transport, Bound. Lay. Meteorol., 124, 143–159,
https://doi.org/10.1007/s10546-007-9186-5, 2007. a

Thomas, C. and Foken, T.: Flux Contribution of Coherent Structures and Its Implications for the Exchange of Energy and Matter in a Tall Spruce Canopy, Bound. Lay. Meteorol., 123, 317–337, https://doi.org/10.1007/s10546-006-9144-7, 2007. a

Turnipseed, A. A., Pressley, S. N., Karl, T., Lamb, B., Nemitz, E., Allwine, E., Cooper, W. A., Shertz, S., and Guenther, A. B.: The use of disjunct eddy sampling methods for the determination of ecosystem level fluxes of trace gases, Atmos. Chem. Phys., 9, 981–994, https://doi.org/10.5194/acp-9-981-2009, 2009. a, b, c, d, e, f

Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of Flux Measurements for Density Effects Due to Heat and Water Vapour Transfer, Q. J. Roy. Meteorol. Soc., 106, 85–100, https://doi.org/10.1002/qj.49710644707, 1980. a

Wesely, M. L.: Use of Variance Techniques to Measure Dry Air-Surface Exchange Rates, Bound. Lay. Meteorol., 44, 13–31, https://doi.org/10.1007/BF00117291, 1988. a

Wilczak, J. M., Oncley, S. P., and Stage, S. A.: Sonic Anemometer Tilt Correction Algorithms, Bound. Lay. Meteorol., 99, 127–150, https://doi.org/10.1023/A:1018966204465, 2001. a

Wyngaard, J. C. and Moeng, C.-H.: Parameterizing Turbulent Diffusion through the Joint Probability Density, Bound. Lay. Meteorol., 60, 1–13, https://doi.org/10.1007/BF00122059, 1992. a, b

- Abstract
- Introduction
- Theory
- Methods
- Results and discussion
- Conclusions
- Appendix A: Hicks and McMillen formulation
- Appendix B: Symbols and subscripts with units
- Code and data availability
- Author contributions
- Competing interests
- Disclaimer
- Acknowledgements
- Financial support
- Review statement
- References

- Abstract
- Introduction
- Theory
- Methods
- Results and discussion
- Conclusions
- Appendix A: Hicks and McMillen formulation
- Appendix B: Symbols and subscripts with units
- Code and data availability
- Author contributions
- Competing interests
- Disclaimer
- Acknowledgements
- Financial support
- Review statement
- References