An imbalance of surface energy fluxes using the eddy covariance (EC) method
is observed in global measurement networks although all necessary corrections
and conversions are applied to the raw data. Mainly during nighttime,
advection can occur, resulting in a closing gap that consequently should also
affect the CO
Preliminary results of the comprehensive experiments reveal a mean nighttime
horizontal advection of CO
A closing gap for energy balance measurements which affects the balance
closure of greenhouse gases (GHGs), e.g., CO
That effect causes an uncertainty in the crucial determination of the
CO
The following simplified equation for CO
The first term on the right-hand side describes the rate of change in storage
of CO
An equivalent equation could be derived for the vertical advection.
Advection is a significant error source applying the EC method mainly in complex
terrain or in areas with land use changes (Aubinet, 2008). Marcolla et
al. (2014) measured within the ADVEX advection experiment situations dominated by a
local slope wind system. The authors observed positive horizontal and
vertical advection (typical values around 7 and
3
A typical daily pattern of advection was described by several authors:
advection is maximal after sunset, when higher gradients of CO
Experimental investigation of the advective CO
Furthermore, advection is most likely a scale-overlapping process (Feigenwinter et al., 2010). The lack of knowledge of the variability in scalar gradients in space and time has been identified as one of the most likely reasons inhibiting significant progress in solving the nighttime problem of underestimating carbon dioxide emissions from forested sites (Aubinet et al., 2010; Thomas, 2011). Marcolla et al. (2014) explained that the uncertainty due to the sampling in time and space with classical single point measurements can be two magnitudes larger at low measurement levels (i.e., at 0.5 m) in comparison to the instrumental uncertainty. The higher number of sample points in time and space results in a better temporal and spatial averaging and reduces the impact of local effects (e.g., heterogeneous vegetation structure) on the 30 min averages derived by Siebicke et al. (2012). Horizontal and vertical resolutions of measurements as well as the size of the control volume are two crucial points for the experimental setup of actual sensor networks with multiple point measurements (Feigenwinter et al., 2010).
Another possibility to provide an adapted data sampling in space and time
is the use of line-integrating measurement methods, which are generally able to
determine the required quantities of CO
Consequently, the main objective of the current study is to develop and apply
an adapted line-averaging method to measure wind components using acoustic
tomography (A-TOM) and CO
Acoustic travel-time tomography is a ground-based remote sensing technique
that uses the dependence of sound speed in air on wind velocity and
temperature along the sound path (Wilson and Thomson, 1994). As a result,
approximations are commonly applied to represent the sound speed in a moving
medium considering an effective, motionless medium. The most common of these
assumptions is the effective sound speed approximation (Rayleigh, 1945;
Ostashev and Wilson, 2016):
Effective sound speed can be estimated from travel-time measurements:
Acoustic tomography as a measurement and analysis method has been further developed since the late 1990s (Ziemann et al., 1999; Arnold et al., 2001). This method was used to monitor spatially resolved wind and temperature fields for different environmental conditions, e.g., in rural (Ziemann et al., 2002) or urban environments (Tetzlaff et al., 2002) with heterogeneous surface properties (Raabe et al., 2005), as well as on different spatial scales, from indoor wind tunnel length scales (Barth and Raabe, 2011; Barth et al., 2007) up to outdoor areas with acoustic path lengths of several hundreds of meters (Arnold et al., 2004). As a result, several inversion techniques were developed and validated regarding their potential for special applications (Fischer et al., 2012). First joint investigations using A-TOM and optical spectrometers confirmed the suitability of combined line-integrating measurements of GHG exchange between surface and atmosphere (Barth et al., 2013; Schäfer et al., 2012).
The performance of A-TOM in reconstructing wind and temperature fields mainly
depends on two factors (Ziemann et al., 2007):
the accuracy of travel-time estimates, which is influenced by
the signal characteristics (e.g., frequency, kind of signal) and the method
of data analysis (correlation technique); and the sound path length and
its uncertainty due to sound propagation effects, especially refraction and
reflection of sound waves, as well as positioning accuracy of sound sources
and receivers.
Thus, the setup of the A-TOM measurements (e.g., positioning of loudspeakers and microphones to optimize the signal-to-noise ratio, SNR) determines the accuracy of the wind components for the calculation of advection. A detailed treatment of uncertainties is given in Sect. 4.1.
The open, unobstructed atmosphere can be described as a complex,
multicomponent system controlled by parameters such as wind, temperature
variation, rain, and pressure fluctuations. The driving parameters for the
infrared (IR) transmittance of the atmosphere are the presence and the
concentration of gas molecules and the length of the optical pathways. The
interactions between IR energy and molecules cause characteristic absorption
or emission lines in the measured spectra (Griffiths and de Haseth, 2007).
The concentration of gases along the optical pathway can be retrieved by
using the Beer–Lambert law. Open-path technology concepts are applied to
measure the absorption loss along an optical path in ambient air. For passive
measurements, changes in the main infrared atmospheric window with respect to
absorbing gases are recorded. For active systems, an IR beam is transmitted
through open, unobstructed atmosphere and the measurement obtained represents
an integrated gas concentration along the optical path – so-called “path-integrated concentration values – PIC” (DIN EN 15483, 2009). The
transmissivity of the atmosphere is more or less controlled by the presence
of the GHGs H
The wavenumber-dependent IR intensity after passing through an absorbing
sample
Hence, Eq. (7) can be written as
OP-FTIR spectroscopy has proven to be a powerful technique enabling online
monitoring of fugitive emissions for industrial, environmental and health
applications (e.g., Harig and Matz, 2001; Griffith et al., 2002; DIN EN
15483, 2009). It allows spatial characterization of emissions and can be
applied noninvasively as an automated surveillance method in large and
potentially inaccessible areas (Schütze et al., 2015). Furthermore,
ground-based optical remote sensing methods like OP-FTIR are well suited to
study dynamic atmospheric processes due to their avoidance of any
disturbances upon emission and/or sampling processes (Reiche et al., 2014;
Schütze and Sauer, 2016). Several successful applications of active and
passive OP-FTIR are reported in terms of air quality monitoring, dynamic
atmospheric processes observations, and emission rate estimations in boundary
layer (e.g., Griffith et al., 2002; Allard et al., 2005; Schäfer et al.,
2012; Chen, 2015). The technique is often combined with other
micrometeorological investigations and provides information on several GHG
target gases, such as CO
The determined gas concentrations are based on the retrieval of concentration values from measured IR spectra. The concentration value obtained is associated with an uncertainty that characterizes the dispersion due to random and systematic errors caused by the measurement and the data processing procedures (Schütze and Sauer, 2016). Thus, instrumental characteristics, applied infrared sources, environmental parameters, and retrieval algorithms represent the main sources of uncertainty. The assessment of uncertainties for these influencing factors relating to the Grillenburg experiment will be discussed in Sect. 4.2.
Map of Grillenburg (middle): meadow in light green; Tharandt Forest in dark green; area under investigation is marked by the blue rectangle (mygeo, 2017; openstreetmap, 2017; see references). Scheme of area under investigation with location of several devices and auxiliary equipment: ATOM1–4 (four masts for travel-time tomography A-TOM), dashed lines mark acoustic paths; R72–73 (two Bruker RAPID passive OP-FTIRs); D1–2 and S1–2 (two Bruker EM27 active OP-FTIRs with source and detector); Young1–2 (two masts, each equipped with two ultrasonic anemometers); Black1–5 (five black screens for passive OP-FTIR); EC tower; SC (soil respiration chamber measurements).
A joint experiment with A-TOM and OP-FTIR techniques as well as additional
measurement equipment was carried out within the SQuAd project at the EC
site Grillenburg. The grassland test site (380 m a.s.l.;
50
An eddy flux tower was established there at a meadow which is extensively
managed with two to four hay harvests per year. The mesophytic hay meadow is
dominated by couch grass (
The EC station was equipped with the following measurement technique to
determine turbulent CO
Additionally, air temperature and air humidity, soil temperature and soil heat flux, global and net radiation, and photosynthetically active radiation, as well as precipitation and evaporation (Class A pan) are measured at the station permanently.
The nearby climate station has delivered data since 1862 and has been at the same
location since 1955. The annual mean temperature is 7.8
The special observation period (SOP) was carried out shortly after the harvest of hay in Grillenburg from 8 until 18 July 2016. Two periods were of special interest because of high solar radiation during the day (convective boundary layer) and the building up of a stably stratified boundary layer during nighttime: 9 until 11 and 15 until 18 July.
On the 8th of July, shortly after the setup, the test site was affected by a thunderstorm. Therefore, the measurements started on the next day. At this time the area of investigation was influenced by a high ridge whose axis was directed from north to south across the center of Germany on the 11th. In its northern part the ridge was overrun by strong warm air advection due to an upper air trough which traveled eastward towards Ireland. Within the broad-based warm sector, very warm air masses from the southwest influenced the experimental site especially on the 10th and 11th. The air mass was potentially unstably layered but was also strongly capped due to the low-tropospheric warm air advection. After this, the weather conditions changed to rainy days due to a trough over central Europe which led to a break in the measurements. On 15 July a high ridge from the Bay of Biscay to the North Sea started to influence the weather conditions. Initially, fairly moist and cool air reached the area with a north-westerly wind direction. The following days were characterized by an intermediate high.
In order to obtain statements on advection during the SOP, information on
spatially distributed CO
The total area under investigation, approximately 120 m
The acoustic measuring field was limited by the position of the acoustic devices, which were mounted on telescopic masts at the corners of the field (ATOM1–4). The height difference within the acoustic measuring field (Fig. 1) was about 2.2 m, estimated from our own tachymeter measurements. The terrain rises in the northern direction from the EC station (near ATOM1) to the location of mast ATOM3. Between the masts equipped with ultrasonic anemometers (Young1 and Young2, horizontal distance of 65 m), the difference in terrain height is approximately 0.5 m.
The A-TOM area inside this field extended to about 50 m
For typical sound speeds of 340 m s
The described acoustic system can be enhanced in future experiments with additional sound sources and receivers to increase the spatial resolution of the measurements, which is especially desirable for the application of tomographic data analysis.
The four supplementary ultrasonic anemometers (YOUNG 81000V, R. M. Young Company, Michigan, USA) were mounted side by side at a height of 2.26 m above ground at the EC station Grillenburg for a period of 6 days (10–16 June) shortly before the SOP. The obtained data were compared among each other to guarantee that all devices measured the same value, which is a requirement to calculate vertical or horizontal gradients with high accuracy. Although all anemometers are of the same kind, series, and age, there are differences in acoustic virtual temperature due to the special characteristics of the individual instrument. One anemometer was used as reference. Regressions between the temperature data of the reference and the other devices were calculated. These equations were used during the SOP to correct the measured temperature values of the ultrasonic anemometers. For the wind velocity, which is the quantity of primary interest, such a correction was not necessary.
Successful application of the nonintrusive methods A-TOM and OP-FTIR requires agreement in the investigated air volume and the spatial resolution of trace gas concentration and wind components. Thus, the OP-FTIR technique was built up within and around the A-TOM array (Fig. 1).
For our OP-FTIR investigations (Fig. 3) we used two Bruker EM27 systems (Bruker Optik GmbH, Ettlingen, Germany) in bistatic operation mode including NiCr glowers as field IR source for active measurements and two Bruker RAPID spectrometers (Bruker Daltonik GmbH, Leipzig, Germany) for passive investigations. Both devices include narrow-band MCT (mercury cadmium telluride) detectors. The instrumental parameters which characterize the devices are given in Table 1.
A detailed description of equipment characteristics for both devices is listed by Schütze and Sauer (2016).
OP-FTIR spectrometer device parameters.
The installation of the spectrometers and associated instruments (sources, screens) was undertaken avoiding any influences on micrometeorological and acoustic measurements. Furthermore, the optical pathways had to be aligned without obstructions. The active OP-FTIR measurements were carried out on two perpendicularly aligned optical paths situated in close vicinity to the A-TOM equipment (Fig. 1). The two EM27 spectrometers (at a height of 0.9 m) and their associated IR sources were installed obtaining optical path lengths of 52 and 64 m, respectively. The spectral measurements were carried out in 2 min sampling intervals including a co-addition of 20 spectra to improve the signal-to-noise ratio.
For passive measurements the two RAPID spectrometers were installed at the outer edges of the field of investigation at a distance of 80 m from each other and at a height of 0.9 m above ground. Five black background screens were used as potential targets for the passive measurements. A complete measurement consisted of 12 different single beam acquisitions with 6 different horizontal directions per device aiming at an even distribution of optical pathways inside the field of investigation. The sampling interval was 5.5 min. For each measurement an internal-temperature-controlled black body within the spectrometer device was applied as a defined radiation source to calibrate the instrument.
OP-FTIR spectrometer used for the SOP at the FLUXNET site Grillenburg.
In order to obtain information on ground surface CO
Theoretical acoustic signal consisting of 2
The acoustic measurements are controlled by an in-house-developed software (MATLAB) which comprises generation of sound signals, control of sound transmission and reception, and subsequent real-time signal analysis. The core hardware (analogue–digital conversion) is an acoustic multichannel spectrometer card (Harmonie PCI octav, sample rate: 51.2 kHz; SINUS Messtechnik GmbH, Germany) which offers eight input and four output channels that are synchronized on a common time basis (Holstein et al., 2004; Barth and Raabe, 2011). This, in turn, is a precondition for accurate travel-time measurements.
Acoustic signals with a frequency of 7 kHz and a special signature (sine
signal with 2
Example of a received acoustic signal (normalized signal amplitude,
nSA,
After propagating through the atmosphere, the sound signal was received by
the microphones and was high-pass filtered. The analogue acoustic signals were
sampled by the acoustic spectrometer card with a sample rate of 51.2 kHz,
i.e., with a time resolution of 19.5
To increase the accuracy of the detected maximum, an interpolation with a
sinc function was applied, which led to an increased temporal resolution by a
factor of about 10. Thus, an uncertainty for travel-time estimation of about
2
The A-TOM masts marked the corners of a rectangle at each level above surface
(see Fig. 1). In order to separate the scalar influence of temperature and
the vectorial influence of wind velocity on the speed of sound between a
source and a receiver (Eq. 3), sound propagation was considered in opposing
directions. Similar to the analysis of ultrasonic measurements (e.g.,
Hanafusa et al., 1982), the assumption of reciprocal sound propagation
(straight-ray propagation between two pairs of speakers and microphones) was
applied:
The passive and active IR spectrometer systems were linked with their own controlling laptops using OPUS software (Bruker Optics Inc.). The software provides interfaces to control measurement options such as spectral region for measurement, wavenumber resolution, and parameters for discrete Fourier transform, apodization function, and repeat intervals. Additionally, for passive measurements a user-written macro program is necessary for controlling the instrument. This macro contains the detailed measurement sequence for a whole passive scan including the parameters for the preceding internal blackbody measurements and the acquisition parameters for the different scans (number of scan directions, vertical and horizontal lens angle, repetition rate).
An OP-FTIR spectroscopic measurement results in a single beam spectrum (SBS).
It describes the distribution of signal intensity with respect to the
wavenumber. The active SBS covers a wavenumber region between 600 and
3900 cm
In practice, the spectra obtained by the spectrometer device are controlled by
instrumental line shape
A transmission spectrum TR( active: superposition of non-modulated artificial IR source (wavenumber
region 700–4000 cm passive: only ambient background emissions resulting from black body
radiation according to Planck's law limited to wavenumber region between 700
and 1500 cm
The data processing of active spectra includes the emission correction of
SBSs for lower wavenumber regions, the calculation of transmission spectra
based on reference spectra, and the determination of spectral windows for
CO
The processing of passive spectral data is different compared to active
spectra. Passive OP-FTIR measures radiation from background traversing the
atmosphere between the background and the spectrometer. The black body
radiation
Technical, signal-dependent, and methodological issues influence the travel-time determination leading to uncertainties due to sampling, signal analysis and cross correlation, calculation of sound speed, and recalculation of wind speed and temperature.
Most important of all, the SNR should be as high as possible. Thus, sound
attenuation due to sound propagation effects should be minimized. A point
source generates spherical waves in an unbounded homogeneous atmosphere
(e.g., Salomons, 2001). In this simple case the sound pressure level at a
microphone can be calculated from the sound power of the loudspeaker together
with the effects of spherical spreading, i.e., geometrical sound attenuation,
and attenuation due to air absorption. Atmospheric absorption is primarily
dependent on sound frequency and secondarily on air temperature and humidity.
The attenuation of sound level is about 8–9 dB/100 m for the used sound
frequency of 7 kHz and typical values of meteorological quantities (DIN ISO
9613-1, 1993; temperature: 15
Additionally, disturbing sounds near the microphones should be avoided. The
flow field itself leads to the most important disturbance. With the used
windscreens, a maximum wind speed of about 6 m s
It was explained in Sect. 3.3.1 that the analogue signal is sampled with a
sample rate of 51.2 kHz (time resolution of 19.5
Variability in acoustic travel time (in rounded sample units) due to
changes in temperature and wind speed for a mean temperature of about
8
To decrease the uncertainty due to analysis of CCF, it is possible to use the
maximum of CCF's absolute value. In this way, the neighboring maxima are
separated only by about 3.7 samples. This value for the travel-time accuracy
of 78.125
The influence of a faulty variable
Considering these uncertainties as the standard deviation of a single
measurement, the standard error of mean values decreases by the factor
The uncertainty in line-averaged wind and temperature data is further influenced by additional effects of the sound propagation between a loudspeaker and a microphone: reflection at ground surface and refraction due to wind and temperature gradients.
In practice, the sound source and the receiver are close to the ground, which makes sound propagation more complex. There are not only direct sound waves between the loudspeaker and microphone, but also ground-reflected sound waves (Fig. 6). This wavelet integrates the conditions of the air layer between the ground surface and the receiver. Additionally, the interference between those sound waves can lead to considerable effects which are estimated hereafter.
Scheme of sound wave reflection at the ground surface: direct (solid lines) and reflected (dashed) sound paths, with (red) and without (black) atmospheric refraction due to sound speed gradients.
Relative sound level depending on the sound frequency and on the
distance (red solid line: 50 m, blue dashed line: 70.7 m) from the sound
source to the receiver for a grassland surface. The height of the acoustic
devices above ground is 1.5 m
To estimate the effect of reflection at ground surface, an idealized case is considered (see Ostashev and Wilson, 2016): the air and ground are homogeneous half spaces without any ambient motion. It follows, that the total sound field at a receiver may be assumed as the sum of sound traveling along a direct path from the source plus sound traveling along a path that is reflected by the ground (Fig. 6, black lines). As a result, waves propagating along the air–ground interface are not included. It is reasonable to use this assumption so long as the angle between the ray path and the ground is not too small (nearly grazing sound incidence).
Assuming that the two sound waves are coherent, there is a constructive or destructive interference. The sound level of the received signal increases or decreases compared to the free-field, unbounded sound propagation. Calculations following Salomons (2001) for a spherical sound wave traveling through a homogeneous atmosphere with reflection at a homogeneous ground surface are dependent on the sound propagation geometry (path length differences of the direct and the reflected path), the sound frequency, and the reflection coefficient. The latter is influenced by the impedance of the ground surface which is usually parameterized by the sound frequency and the acoustic flow resistance (Delany and Bazley, 1970).
Commonly, the so-called relative sound level, i.e., the difference between the sound pressure level with and without (i.e., unbounded free-field sound propagation) ground surface, is applied to quantify the ground effect at the receiver (Ostashev and Wilson, 2016). A positive relative sound level marks amplification (maximum of 6 dB); a negative one denotes the attenuation of sound level (in theory, an infinitely high attenuation is possible).
It is essential for a high accuracy of acoustic travel-time measurements to
provide an SNR as large as possible at the receiver. Hence, a positive relative
sound level should be ensured, which can be realized using a suitable
combination of sound frequency, distance between the loudspeaker and microphone,
and heights of the acoustic devices above ground surface. Values of
relative sound level for a grassland site (with acoustic flow resistance of
200 kPa s
Relative sound level depending on the distance and on the sound
frequency for a grassland surface. The height of the acoustic devices above
ground is 1.5 m
For a distance of 50 m between the loudspeaker and microphone and a signal frequency of 7 kHz, the relative sound level is near or greater than 0 dB for both heights (Fig. 7a, b). That means an amplification of received sound level due to the ground effect. Higher or lower frequencies cause a so-called ground dip, i.e., a strong decrease of sound level due to the negative interference phenomenon. The greater the height of acoustic devices above ground surface, the higher the sensitivity of the relative sound level to frequency is (Fig. 7b in comparison to a). An increasing distance from sound source (50 m in comparison to 70.7 m, with the latter corresponding to the diagonals of the A-TOM measurement field) mitigates the risk of a ground dip in the investigated frequency range.
Figure 8a again shows the lower number of ground dips for the lower measurement level of 1.5 m above ground surface. For an increasing height of 3 m above surface (Fig. 8b), the sensitivity of relative sound level on the distance increases due to a growing number of ground dips. Furthermore, the sound level attenuation increases for a growing distance. Thus, sound path lengths of 50 and 70 m together with a signal frequency of 7 kHz are favorable because of an optimized SNR of the received signal. Additionally, Figs. 7 and 8 demonstrate the requirements for the frequency stability of the sound sources. The applied loudspeakers meet these demands.
For outdoor sound propagation, atmospheric turbulence occurs and results in phase and amplitude fluctuations of the sound waves. This effect reduces the coherence between the direct and the reflected sound wave followed by partly attenuated and blurred interference impacts on the measured sound level. The ground dip is especially reduced due to turbulence which increases the SNR at the receiver for special sound frequencies and propagation geometries. In this way, the results of Figs. 7 and 8 show rather extreme values of the ground effect influencing the received sound level without atmospheric turbulence. Very low-turbulence conditions occur, for example, during nighttime with weak or no wind.
Additionally, the finite length of the signal (Fig. 4) has to be considered to evaluate the ground effect. It was examined whether the directly propagating and the reflected sound wave parts could be separated due to their time delay at the receiver. As a result, straight-line sound paths, i.e., a homogeneous atmosphere, were again assumed. The time difference between direct and reflected signal arrivals grow with increasing height above ground of acoustic devices (Table 3).
Time difference (in sample units) between signal arrival of direct
and ground-reflected wave parts for a constant and homogeneous sound speed
(temperature of 8
The greater the distance to the receiver, the smaller the time difference is.
For the sound propagation at the lower level (1.5 m above ground) and a
sound frequency of 7 kHz, i.e., period duration of about 0.14 ms (approximately
7 sample units), the signals of direct and reflected waves cannot be
distinguished because the signal itself has a length of approximately 10 periods
(approximately 1.4 ms
In addition to the effect of reflection at ground surface, refraction due to wind and temperature gradients has to be considered for outdoor sound propagation.
Atmospheric refraction can be described as a changed propagation direction of sound waves (e.g., Salomons, 2001). The resulting curved sound paths lead to a deviation from the straight-line sound propagation. The assumption of reciprocal sound propagation, i.e., along straight lines between transmitter and receiver, allows the simplified separation between the temperature and the wind influence on the acoustic travel time (Eq. 10). However, it is questionable to what extent the refracting effect due to temperature and wind gradients affects this assumption. As a result, vertical gradients of horizontal wind velocity and temperature are especially important because they are usually greater than associated horizontal gradients.
At first, the effect of downward refraction on the travel-time measurements
is estimated because this kind of refraction happens usually during cloudless
nights with a stably stratified atmosphere. Downward refraction occurs due to
positive gradients of effective sound speed (see Eq. 3), for instance during
a temperature inversion and/or for a sound propagation in wind direction
assuming an increasing wind speed with height above ground. If one supposes
that the curved rays can be approximated by circular arcs (strictly speaking
only valid in a motionless medium) depending on a constant vertical sound
speed gradient in a stratified atmosphere (e.g., Attenborough et al., 2007),
then the path length differences d
Here,
Vertical profiles of vertical effective sound speed gradient
(30 min mean) in sound propagation direction simulated by HIRVAC for
homogeneous grassland (vegetation height
Comparison of travel-time uncertainties. Above: travel-time
difference (in sample units), recalculated temperature and wind speed
differences in brackets, between straight-line and curved sound path through
the atmosphere for a maximum vertical gradient of effective sound speed of
0.6 s
At the transmitter height of 1.5 or 3 m, positive vertical gradients of
effective sound speed can be expected for a sound propagation in wind
direction. In general, the vertical gradients increase with decreasing
height. The highest downwind gradients occur at nighttime and reach strong
values of 0.57 s
Outgoing from the simulated vertical sound speed gradients, a travel-time difference between the curved and straight-line direct sound path is calculated according to Eq. (21), including the different sound speed values along the different sound paths (Table 4). Please note that the used sound speed gradients are the maximum values in the simulated diurnal cycle. Therefore, the uncertainty estimation above represents a rather conservative estimation.
These travel-time differences are mostly smaller than the travel-time uncertainty due to the signal analysis (4 sample units, see Sect. 4.1.1). Especially for short distances at a height of 3 m, the difference is negligible. The same magnitude of uncertainties occurs only at longer distances and smaller measurement heights above ground. In this case it has to be proven during the further data and uncertainty analysis that the measured vertical sound speed gradients are similar to the simulated ones. Thus, considering downwind gradients especially for nighttime conditions, the vertical sound speed gradient should be measured, e.g., using accompanying ultrasonic measurements to ensure the applicability of reciprocal sound propagation.
The analysis of measured vertical temperature gradients shows (see Sect. 4.3) that the above-presented estimation of uncertainty mostly reflects a worst case. For further investigations in this study, the data at a height of 1.5 m above ground were used only for the short distance of 50 m. The deviation from the straight-ray approximation leads in this case to an additional travel-time uncertainty of 2 sample units according to Table 4.
Finally, the sound propagation against the wind direction is considered. Only
negative sound speed gradients result from the investigations with the
boundary layer model HIRVAC. Maximum gradients occur at midday (not
shown). This leads to an upward-directed refraction of the sound waves in the
atmosphere. For such conditions, theoretically no signal reaches the
microphone which is located at the same height level as the loudspeaker but
several decameters away from the speaker. Nevertheless, due to a finite
extent of the microphone, its spherical directional pattern, and the
scattering effect of atmospheric turbulence (Salomons, 2001), it is almost
always possible to detect a signal in upwind direction if the wind speed is
smaller than 6 m s
To sum up the outcomes of Sect. 4.1, the following maximum uncertainties result
for measurements at a height of 1.5 m above ground and for distances between
the loudspeaker and microphone of 50 m: (1) 4 sample units due to signal
analysis; (2) 2 sample units due to sound refraction. The resulting
travel-time uncertainty of 6 sample units can be recalculated into an
uncertainty of about 0.4 K and 0.3 m s
Despite the great potential of OP-FTIR spectroscopic measurements, the technology is not commonly used for ground-based micrometeorological atmospheric monitoring due to the uncertainties in obtaining reliable information from the measured spectra (Cieszczyk, 2014). The uncertainties for the retrieval of gas concentration from OP-FTIR spectra can be classified in (1) ambient environmental influences, (2) instrumental influences, and (3) data processing influences.
Infrared spectral data are mainly controlled by the environmental conditions such as pressure and temperature variations. Horrocks et al. (2001) demonstrated that especially temperature has a significant impact on retrieval error and is an important parameter under consideration for subsequent data processing. The challenge in determining gas concentration using passive OP-FTIR under conditions with changing temperatures was described by Cieszczyk (2014).
Following Eq. (18), the main drawback and source for uncertainty in
concentration determination processing from passive spectra obviously result
from the dependency of signal amplitude from the difference between
background temperature
The relative absorbance error
In the case of the Grillenburg experiment the passive radiance spectra were
analyzed in accordance with Harig and Matz (2001) to determine the temperature
difference between background and ambient air. In two spectral regions the
spectra were fitted to the Planck radiation function using a nonlinear
least-squares algorithm. In the spectral range less than 700 cm
For the considered period, more than 90 % of the horizontal sonic
temperature differences at two measurement points are less than 0.4 K.
Furthermore, especially in the nighttime increased absolute values of
temperature differences between background and air (
From the instrumental side, the wavenumber resolution accuracy and the instrumental line shape or apparatus function describe the influence of the spectrometer on the measured spectra. Each spectrometer device convolves the IR intensity due to absorbance effects with this device characteristic function. The ILS is responsible for distortion of spectra caused by the finite detector area and finite optical path difference within the spectrometer. Most of the variation in ILS is driven by the instrumental resolution and the effective FOV (field of view) due to misalignments of optical components inside the spectrometer. These doubts in the true ILS of the applied spectrometer can lead to uncertainties in smoothing of spectral information and later on in concentration determination errors. Horrocks et al. (2001) estimated a concentration retrieval error of about 2 % due to an ILS uncertainty by measuring defined gas concentrations under fixed conditions. However, recent investigations concerning the sensitivity of OP-FTIR retrievals by Smith et al. (2011) point out that using a broader spectral feature for concentration retrieval is suitable for the minimization of the effect of ILS on individual absorption lines.
The applied apodization functions (e.g., boxcar, triangular) and the internal
optical path difference mainly control the influence in terms of spectral
resolution. The manufacturer's maintenance specification concerning a wavenumber
accuracy of 13 % at resolution of 4 cm
Based on the previous data evaluation, the absorbance spectra of the nighttime period from 10 to 11 July showed reasonable absorbance errors smaller than 20 % and were chosen for the subsequent quantitative analysis (Fig. 11b). This period covered an interval of 9.5 h and included 108 spectra for each measured optical path direction. The concentration retrieval is based on chemometric techniques applied to the absorbance spectra deriving spectral properties which are related to quantitative information. It included the usage of least-squares fitting comparing parts of the measured absorbance spectra with simulated reference spectra. The algorithm has been previously well described for instance by Griffiths and de Haseth (2007) and Smith et al. (2011). Reference IR spectra including instrumental line shape were generated by using the HAPI routines (Kochanov et al., 2016). Additional Python routines were designed for the selection of spectral windows and the comparison of measured and simulated spectra based on the classical least-squares approach (CLS) as a straightforward algorithm (Shao et al., 2010). Currently, different retrieval methods to obtain concentration values from measured spectra are available (e.g., CLS and PLS). Smith et al. (2011) observed an increasing underestimation of the CLS-based method at higher path lengths. However, for the Grillenburg experimental setup the optical path lengths and the expected line concentrations were sufficiently low to use a CLS-based retrieval approach neglecting the Beer–Lambert law nonlinearity.
Example of simulated CO
A spectral window ranging from 700 to 760 cm
For the Grillenburg experiment the maximum uncertainty for CO
At this point, the uncertainties of the two methods, A-TOM and OP-FTIR, are
known. Single, instantaneous values of wind components, measured by A-TOM,
can be derived with an uncertainty of 0.3 m s
The wind component in
Figure 13a shows that the wind speed was relatively small during the
investigated nighttime example in July 2016 at the FLUXNET site Grillenburg.
Furthermore, after midnight the wind speed steadily fell to mean
values smaller than 1 m s
Averaged (30 min) data:
These low wind conditions near the surface during a clear night were supported by a stably stratified atmosphere. Figure 13b determines a positive vertical temperature gradient during all nighttime hours.
Between 03:00 and 05:00 CET a noticeably high value of the temperature gradient occurs together with very small wind speed values and a changing wind direction shortly before the onset of this sharp increase in stability. As a result, the A-TOM measurements show a similar behavior in comparison to the measurements using sonic anemometers. Mostly, the spatially averaged data are similar to all point data. However, there are greater differences between the data from sonic anemometers especially during times of high vertical gradients and times of highly variable gradients.
Absolute CO
Averaged (30 min) CO
The temporal and spatial variability in CO
Averaged (30 min) spatial difference in CO
In the next step of the analyses, the horizontal advection and its uncertainty
were calculated. As a result, an adapted form of Eq. (2) was applied according to
the analyzed results so far:
To derive the maximum uncertainty in horizontal advection at a certain height
level above ground, the error propagation law is then applied to Eq. (24)
with
A value of 22.414
Figure 15 shows the resulting estimation of horizontal advection at a height
of 1.5 m above ground, which is representative for the total investigation area of
approximately 120 m
The temporal behavior of advection is generally connected with that of
the spatial concentration difference, but it is modulated by the wind speed.
Mostly, the temporal variability in advection is coupled with the temperature
gradient until 03:00 o'clock. During this first time period, the course of
advection and atmospheric stability is similar: increasing stability occurs
together with increasing advection and vice versa. The turbulent CO
To close the known gap within energy balance which affects the CO
The derived values of mean advection around
10
To demonstrate the applicability of the SQuAd approach, the estimation of
uncertainties of the used measurement and analysis methods was the focus
of attention. As a result, it is important to note that we applied a maximum
error calculation of the used methods A-TOM and passive OP-FTIR to be on the
safe side for further applications. The received values of uncertainties
(0.3 m s
Nevertheless, there are still possibilities to further decrease these
uncertainties. As a result, the data analysis of CO
Further tests to improve the accuracy of the applied OP-FTIR method will focus on an increasing temperature gradient between background and target gas as well as the determination of the influence of FOV on horizontal and vertical resolution. The integral concentration value based on spectral information along the optical path includes a smearing effect caused by the true FOV. Especially for longer pathways and increased horizontal concentration gradients, this effect has to be taken into account. Furthermore, slight misalignments can result in decreased data quality due to an unpredictable uncertainty in effectively considered path lengths and background radiations.
At the expense of temporal resolution and assuming stronger concentration differences between background and the searched air volume, the spatial resolution of the OP-FTIR method can be further enhanced by measuring along a higher number of paths. In a similar way it is possible to increase the number of acoustic paths through the control volume. The results from a high number of optical and acoustic paths can be used to apply a tomographic algorithm and to reconstruct spatially resolved wind and concentration fields.
The presented SQuAd approach offers the possibility to complement previous findings of multilocation, point-like measurements. Thomas (2011) found fundamental differences in the space–time structure of the motions dominating the variability in the wind and temperature fields. This scale mismatch complicates the derivation of meaningful estimates of horizontal advective fluxes without dense spatial information. The SQuAd approach could be applied to provide the necessary spatially representative data. As a result, one advantage of the A-TOM and OP-FTIR method is the combined measurement of wind components and temperature together with several GHGs along similar paths and air volumes.
Although there are remaining tasks concerning the improvement of combined measurement methods within the SQuAd approach, the present study provides first examples of the application of the new method to estimate a spatially representative advection during calm and stably stratified nighttime conditions at a grassland site.
Data are available upon request by contacting the corresponding author.
The data sets will be freely available on servers after finishing
all analyses within the SQuAd project. Please follow the updates on the
project web sites for access information:
Using the assumption of reciprocal sound propagation (see Eqs. 11 and 12), the uncertainty in the acoustic virtual temperature
AZ and MS are responsible for A-TOM and CS for OP-FTIR. All authors designed the SQuAd campaign and carried out the experiment. The overall coordination was carried out by AZ. MS developed and performed the code for controlling A-TOM and analyzing acoustical data. CS developed and performed the code for controlling OP-FTIR and analyzing optical data. AZ prepared the joint data analysis of A-TOM, sonic, and EC data together with the uncertainty calculation of line-averaged wind components (A-TOM). CS prepared the line-averaged concentration data (OP-FTIR) with an uncertainty analysis. AZ prepared the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of interest.
At first we want to thank our project partner Christian Bernhofer (Chair of Meteorology, TU Dresden) for the initial idea for the project, providing staff, and the equipped experimental site Grillenburg. We sincerely thank Armin Raabe (Leipzig Institute for Meteorology, University of Leipzig) for the quick and easy loan of acoustic devices for A-TOM. Many thanks are going to Markus Hehn, Valeri Goldberg, Uwe Eichelmann, Heiko Prasse (Chair of Meteorology, TU Dresden), Andreas Schoßland, and Uta Sauer (Helmholtz Centre for Environmental Research Leipzig) for their support during the preparation and implementation of the experiment.
We are grateful to the referees for their constructive input.
This work was supported by the German Research Foundation (DFG) (grant numbers ZI 623/10-1, SCHU 1428/3-1). Edited by: Dietrich G. Feist Reviewed by: two anonymous referees