Introduction
Turbulent atmospheric mixing is a key process in the lower troposphere for
climate, weather and air quality. Turbulent mixing is regarded as a
significant player in aerosol microphysical processes (e.g. Nilsson et al.,
2001; Wehner et al., 2010; Hirsikko et al., 2013) and in cloud microphysics
(e.g. Pinsky et al., 2008). Representing turbulent mixing in numerical
models requires an understanding of the variability in space and time, the
length scales involved and the processes that are responsible: friction,
surface heating, shear (Baklanov et al., 2011). Stably stratified layers and
diurnal cycles require particular attention (Holtslag et al., 2013). From an
air quality perspective, the height of the layer that is in constant contact
with the surface, i.e. mixing layer height (MLH) is a critical parameter
governing the dispersion of air pollutants (e.g. White et al., 2009). The
continuous monitoring of the atmospheric mixing profile covering the lowest
few kilometres is not a straightforward task and there are only a few
long-term data sets (e.g. Harvey et al., 2013; Schween et al., 2014). In
many studies the mixed layer is characterised indirectly, often in terms of
MLH inferred from aerosol backscatter profiles (e.g. Baars et al., 2008;
Korhonen et al., 2014) or temperature profiles (e.g. Beyrich and Leps,
2012). However, indirect methods in MLH estimation may potentially suffer
from erroneous interpretation, especially for stably stratified layers and
during the initial early morning phase or afternoon collapse of a convective
boundary layer (e.g. Pearson et al., 2010; Schween et al., 2014).
In situ measurements of turbulent mixing in the lower troposphere have been
conducted successfully on a variety of platforms. For instance, turbulent
mixing measurements have been carried out with radiosondes (Harrison and
Hogan, 2006), tethered balloons (Siebert et al., 2003), various aircraft
(Muschinski and Wode, 1998; Khelif et al., 1999) and recently small
unmanned aerial vehicles (Martin et al., 2011). Deployed in-aircraft in situ
sensors can yield mixing information with an unsurpassed resolution both
spatially and temporally (Muschinski and Wode, 1998; Martin et al., 2014).
The drawback with these methods is that they are restricted to short-term
campaigns. Only radiosonde measurements are possible routinely, but
turbulent sensors are not yet part of the standard operational package.
Furthermore, the temporal resolution for routine operational launches is
rather coarse; typically a maximum of four per day. Mast-based sonic
anemometers provide excellent turbulent measurements with high temporal
resolution, but masts taller than 100 m are rare; therefore remote
sensing techniques are currently the only viable option for long-term
continuous monitoring through the entire lower troposphere.
Several remote sensing techniques, such as Doppler sodar (e.g. Beyrich,
1997; Seibert et al., 2000; Emeis et al., 2008), Doppler lidar (e.g. Harvey
et al., 2013; Schween et al., 2014) and radar wind profiler (e.g. Bianco et
al., 2008; Emeis et al., 2008), enable continuous measurements of the
vertical wind velocity (w) profile with high time resolution. Subsequently,
these measurements can be processed to provide vertical profiles of vertical
velocity variance, σw2 (e.g. Pearson et al., 2010), or
turbulent kinetic energy (TKE) dissipation rate (e.g. O'Connor et al., 2010) with
resolutions better than a few minutes. However, no single remote sensing
instrument has been able to cover the full range of MLHs from instrument
level up to the top of the atmospheric boundary layer. Doppler sodars can
cover the low range from 10 m up to a few hundred metres, possibly
reaching 1 km in good conditions (e.g. Emeis et al., 2008), but, in many
environments, the daytime convective MLH exceeds the range of sodars. On the other hand, vertically pointing Doppler lidars and radar wind profilers are
usually sensitive enough to reach the top of the atmospheric boundary layer
and beyond but cannot see closer than an instrument-specific range,
typically 100–200 m (Bianco et al., 2008; Pearson et al., 2009;
Srinivasulu et al., 2012), which hampers the detection of low-level only
mixing.
Scanning Doppler lidars can partially overcome a minimum height limitation
by retrieving radial wind velocity measurements at a low elevation angle
(Banta et al., 2006). In this paper we present a method whereby scanning
Doppler lidars can identify the presence of turbulent mixing from the
instrument level up, making it possible to cover the full range of potential
MLHs with an appropriate selection of scan types from one instrument. From
the various possible low-elevation angle scanning patterns, we selected
vertical azimuth display (VAD) scans as the basis for low-level MLH
detection. The main reason for choosing VAD scans is that it simultaneously
provides the horizontal wind profile (e.g. Browning and Wexler, 1968) and
can be utilised in studying the surface effects on the wind field in the
vicinity of the Doppler lidar. The method for identifying turbulent mixing
and obtaining MLH is described in Sect. 2. By applying the VAD-based MLH
detection to two data sets from very different environments, i.e. summertime
Mediterranean coast and wintertime Baltic Sea coast, in Sect. 3, we show
that this method compares well with MLH inferred from vertically pointing
measurements. With this method we frequently identified MLHs below the
lowest usable range gate of the Doppler lidar operating in vertical mode at
both locations.
Location of Loviisa and Limassol measurement sites. In the 20 km × 20 km topographic map inserts for Loviisa (National Land Survey of Finland,
2014) and Limassol (United States Geological Survey, 2014) the location of
the lidar is indicated by a red dot.
Doppler lidar specifications.
Wavelength
1.5 µm
Pulse repetition rate
15 kHz
Nyquist velocity
20 m s-1
Sampling frequency
50 MHz
Velocity resolution
0.038 m s-1
Points per range gate
10
Range resolution
30 m
Pulse duration
0.2 µs
Lens diameter
8 cm
Lens divergence
33 µrad
Telescope
monostatic optic-fibre
coupled
Methodology
Measurements
In this study we utilise Doppler lidar measurements from two locations:
Limassol, Cyprus, and Loviisa, Finland (Fig. 1). Measurements
were carried out with a Halo Photonics Streamline scanning Doppler lidar
(Pearson et al., 2009). The Halo Photonics Streamline is a 1.5 µm
pulsed Doppler lidar with a heterodyne detector that can switch between co-
and cross-polar channels (Pearson et al., 2009). Standard operating
specifications are given in Table 1, and the minimum range of the instrument
is 90 m. The accumulation time per ray varies according to scan type and
location.
At Limassol, measurements were performed at the Cyprus University of
Technology campus (34.6756∘ N, 33.0403∘ E;
15 m a.s.l.)
from 22 August to 15 October 2013. Solar noon at Limassol is 09:40 UTC. The
measurement site was on a rooftop 600 m NE from the Mediterranean Sea
shoreline (Fig. 1). The Limassol campaign took place in typical
Mediterranean summer conditions with surface temperatures ranging from +15
to +35 ∘C, very low cloud cover and three rain showers.
At Loviisa, the measurement campaign took place on the Fortum Power and Heat
Oy nuclear power plant site on Hästholmen island (60.3660∘ N,
26.3500∘ E; 24 m a.s.l.) from 10 December 2013 to 17 March 2014.
Solar noon at Loviisa is 10:20 UTC. Hästholmen island is approximately
2000 m long in the SE–NW direction and 500 m wide in the SW–NE direction
(Fig. 1). From the measurement platform at the centre of the island, the
distance to shoreline in the SW direction was 300 m and in the NE
direction was 200 m. The Loviisa measurements were representative of winter
conditions in the Baltic Sea region, characterised by surface temperatures
ranging from -25 to +10 ∘C, frequent bursts of rain and snow
and few cloud-free conditions.
Scan settings at Limassol and Loviisa. At Loviisa the 15∘ elevation angle VAD integration time was decreased from 10 to 7 s on 11
February 2014.
Site
Limassol
Loviisa
VAD elevation angle (∘)
10
30
4
15
Number of azimuthal angles
23
24
72
24
Integration time (s)
3
3
7
10/7
Repeat interval (min)
20
20
30
15
At both locations, a fixed scanning routine was operated continuously
throughout the campaign. At Limassol the scanning schedule consisted of two
VAD scans (e.g. Browning and Wexler, 1968) at 30∘ and
10∘ elevation angle every 20 min (Table 2), a three-beam
Doppler beam swing every 20 min as an alternative method for
retrieving the vertical profile of horizontal wind (e.g. Lane et al., 2013)
and a range height indicator scan every 10 min. Besides scanning, 15
out of every 20 min were available for vertically pointing measurements.
Vertically pointing data were recorded at 3 s integration time and every
second beam was measured with the cross-polar receiver (Pearson et al.,
2009).
At Loviisa, the terrain allowed VAD scans to be operated at a lower
elevation angle than at Limassol and thus the scan schedule at Loviisa
consisted of a 24-azimuthal-direction VAD scan at 15∘ elevation
angle and a 72-azimuthal-direction VAD scan at 4∘ elevation angle
(Table 2). At Loviisa, vertically pointing measurements were initially
operated at 8 s integration time, but on 11 February 2014 the integration
time was increased to 16 s. Again, every second vertical beam was measured
with the cross-polar receiver as at Limassol. Due to the longer integration
time for each beam when scanning, there were 13 min free per 30 min for
vertically pointing measurements. At both locations the focus of the Doppler
lidar telescope was set to 2000 m. In this study we utilise only the VAD
scans and co-polar vertically pointing measurements.
Velocity measurement uncertainty is directly related to the instrument
sensitivity, so there is a potential trade-off between achieving the high
temporal resolution suitable for investigating turbulent conditions at the
expense of measurement sensitivity and uncertainty. The integration times
for each particular scan at each location were selected to achieve the
highest temporal resolution possible while retaining sufficient sensitivity
for each individual measurement; at Loviisa, a much lower atmospheric
aerosol loading required a longer integration time per individual ray.
Additional optimisation was required when implementing the scan schedule so
that the relevant scans were acquired while still providing enough
vertically pointing coverage.
(a) Radial wind speed and sinusoidal fit on 24 August 2013 at
03:57 (UTC) for two consecutive altitudes from a 10∘ elevation
angle VAD scan. (b) Scatterplot of the residuals of the fit presented in
panel (a). (c) Histogram of the difference of residuals at 148 and 153 m a.s.l. altitudes from a 10∘ elevation angle VAD scan for a calm
period (03:57 UTC) and a turbulent period (11:59 UTC) with corresponding
variances indicated in the legend. The συ2 (Eq. 6)
is 0.03 at 03:57 UTC and 0.04 at 11:59 UTC.
Estimating turbulent mixing from a VAD
In a smooth homogeneous wind flow field, radial velocities in a VAD follow a
sinusoidal curve. A vertical profile of the horizontal wind vector is
obtained from the sinusoidal fit (Fig. 2a) to the VAD at each elevation
level (Browning and Wexler, 1968). Deviations from this ideal shape (cf.
Fig. 2a) can originate from several processes such as wind field divergence
or deformation (Browning and Wexler, 1968), instrumental noise and
turbulence. Considering one radial measurement in a VAD, the observed radial
velocity (VR) at a single range gate can be expressed analogous to the
Reynolds formulation in statistical turbulence theory:
VR=Vwind+R,
where Vwind is the radial component of homogeneous horizontal wind and
R is the deviation from the homogenous horizontal wind. The horizontal wind
component Vwind is then derived by fitting a sinusoidal curve to
VR as a function of the azimuthal angle (Fig. 2a) for each elevation
level in a VAD, with R being the residuals of these fits.
Contrary to the Reynolds formulation, however, the deviation term
R in a VAD contains more components than the purely turbulent fluctuations. These
additional components arise from fact that, over the relatively large volume
of atmosphere covered by the VAD scan, there are often non-turbulent changes
in the horizontal wind speed and direction on scales of hundreds of metres
to kilometres that are also contained in R in Eq. (1) when Vwind is
estimated from a fit over the full VAD. In quiescent conditions, the
non-turbulent contributions may even form most of the residual. This is
evident when correlating the residuals of two consecutive range gates as in
Fig. 2b; a high correlation indicates that the residuals are dominated by
flow patterns with length scales that are large compared to the 30 m radial
resolution of the instrument.
The contribution of some non-turbulent processes (such as translation,
divergence, relative vorticity, stretching and shearing deformation) to R can
be estimated through a Taylor expansion (Browning and Wexler, 1968).
However, within the first 100 m above the surface, the focus of
this study, R can also contain significant contributions from local
distortions. Typical surface-induced changes would be wind flows around a
building or wind channelling around an island. In most environments
quantifying this kind of local effects with reasonable accuracy would
require such detailed knowledge of the surface at the measurement location
that determining turbulent contribution directly
from Eq. (1) is impractical. Therefore, we consider the change in VR from one range
gate at distance r to the next range gate at (r+30 m) for one radial
measurement:
VR(r)-VR(r+30m)=ΔVR=ΔVwind+ΔR.
In this way, the problem of determining the non-turbulent changes in the
wind field within the VAD volume has been reduced to the question of whether
these changes are significant on the scale of the range resolution of the
Doppler lidar.
The changes in the wind field that can be represented with the Taylor
expansion occur over length scales that are significantly larger than the
Doppler lidar range resolution of 30 m (e.g. Browning and Wexler, 1968) and
thus their contribution to ΔR can be considered negligible. Similarly,
if the localised effects in the wind field can be considered smooth at the
Doppler lidar range resolution scale (here 30 m), i.e. the surface is
reasonably homogeneous or the measurement is not close to the surface, then
we can assume also their contribution to ΔR to be very small.
With these assumptions we can now estimate
ΔR≈ΔVturb+υ,
where Vturb is the contribution from turbulent mixing that we are
interested in and
υ=δ2(r)+δ2(r+30m),
i.e. we have assumed that the instrumental measurement uncertainty for the
two range gates is uncorrelated.
Thus the proxy variable for identifying turbulent mixing is the variance of
the difference of residuals from two consecutive elevation levels in a VAD
subtracted by the variance of the corresponding measurement uncertainty
(cf. Fig. 2c):
σVAD2(r+15m)=1n∑i=1nΔRi-1n∑i=1n(ΔRi)2-συ2,
where the subscript i refers to individual radials and n is the number of
radials within a VAD. The measurement uncertainty variance συ2 is estimated from the δi2 calculated
for every ith radial:
συ2=median(δ2(r))+median(δ2(r+30m)),
where median is taken over all radials at each range gate.
The instrument uncertainty in velocity δ is primarily a function of
the signal-to-noise ratio (SNR) (Pearson et al., 2009) and is calculated for
every individual radial velocity measurement in a VAD. Based on pointing
accuracy tests performed during both campaigns towards hard targets at known
direction and distance, we consider the pointing accuracy error negligible
especially when compared to the uncertainty arising from the measurement
itself. The choice of median in Eq. (6) is to avoid outliers skewing the
distribution. In general, most of the points in a particular VAD display
similar sensitivity, but the presence of cloud or precipitation can increase
SNR, whereas certain radials may be completely or partially obscured by
buildings or trees. Measurements in the presence of precipitation have to be
carefully evaluated as the signal can be dominated by the terminal fall
velocity of the drop, and large drops do not necessarily track the turbulent
motion of the air. Here, we only calculate σVAD2 when SNR > 0.0025 for at least 20 out of 24 points at any given
elevation level. In terms of συ2 the SNR limit of
0.0025 is equivalent to a threshold of 1.58 m2 s-2 using the
instrument specifications given in Table 1. This is derived by calculating
δ from SNR according to O'Connor et al. (2010) and then applying
Eq. (6). Note that this assumes that the turbulence is isotropic in nature.
Abrupt changes in surface roughness also have an impact on the turbulent
properties of the wind field (Garrat, 1990). For instance, the 4∘ elevation angle VAD at Loviisa is so close to the surface that the radial
wind field is clearly affected by the change of surface roughness moving
from sea to land. To minimise the effect of surface roughness changes at
Loviisa we considered only a 55∘ wide sector (i.e. 12 azimuthal
directions) upwind of the island to derive MLH from the 4∘ elevation angle VAD. For 10∘ and higher elevation angle VADs we
used the full 360∘ to determine σVAD2 and
MLH.
Turbulent kinetic energy dissipation rate
The dissipation rate of TKE was determined from
the vertical velocity measurements according to the method described by
O'Connor et al. (2010). This method utilises the velocity variance over a
specific number of samples, from which the dissipation rate is derived using
appropriate advective length scales obtained from the vertical profiles of
horizontal wind. The implicit constraint in this method is that the
advective length scales for calculating the variance should remain within
the inertial subrange. Typically, this means that the total time available
for collecting samples for one variance profile should not exceed about
3 min and that the number of samples available per dissipation rate
profile therefore depends on the integration time for an individual ray. For
vertical profiles with 3 min resolution at Limassol, optimal
operating conditions provided 60 vertical velocity samples per dissipation
rate profile. At Loviisa, a much lower atmospheric aerosol loading required
a longer integration time per individual ray to obtain sufficient
sensitivity; therefore only 11 vertical velocity samples per 3 min
resolution dissipation rate profile were available. Vertical resolution was
30 m, and dissipation rate values were determined only when the relative
uncertainty in the variance was less than 1, i.e. observed variance at least
twice the theoretical contribution from noise.
Frequency plots of (a) σVAD2 vs. σw2 for 30∘ elevation angle VAD at Limassol, (b)
σVAD2 from 10∘ elevation angle VAD vs. σVAD2 from 30∘ elevation angle VAD at Limassol, (c)
σVAD2 vs. σw2 for 15∘ elevation angle VAD at Loviisa and (d) σVAD2 from
4∘ elevation angle VAD vs. σVAD2 from
15∘ elevation angle VAD at Loviisa. Only VAD measurements for
radius < 500 m are included. The συ2
contribution (cf. Eq. 6) has been subtracted from σw2.
Correlation coefficient for logarithmic data and 1:1 line are included in
all plots.
Diurnal variation at
Limassol on 24 August 2013 of (a) TKE dissipation rate, (b) σVAD2 from 30∘ elevation angle VAD, (c) σVAD2 from 10∘ elevation angle VAD and (d) MLH.
Diurnal variation at
Limassol on 17 September 2013 of (a) TKE dissipation rate, (b) σVAD2 from 30∘ elevation angle VAD, (c) σVAD2 from 10∘ elevation angle VAD and (d) MLH.
Diurnal variation at
Loviisa on 26 December 2013 of (a) TKE dissipation rate, (b) σVAD2 from 15∘ elevation angle VAD, (c) σVAD2 from 4∘ elevation angle VAD and (d) MLH.
Diurnal variation at
Loviisa on 27 December 2013 of (a) TKE dissipation rate, (b) σVAD2 from 15∘ elevation angle VAD, (c) σVAD2 from 4∘ elevation angle VAD and (d) MLH.
Results and discussion
Comparison to vertical wind speed variance
We first investigate how σVAD2 correlates with the
vertical velocity variance σw2 calculated directly from
the vertically pointing time series. Note that we do not expect σVAD2 to be equivalent to σw2 since the
effective measurement volumes can encompass very different length scales.
Figure 3 shows that, at Limassol, σVAD2 from both
30 and 10∘ elevation VADs correlates reasonably well
with σw2. At Loviisa the correlation is not quite as good
for the 15∘ VAD (Fig. 3), which we partly attribute to the narrow
range of observed values available; note that for high-latitude Finland in
winter there is minimal diurnal influence on the turbulent mixing. In
addition, the reduction in instrument sensitivity due to the low aerosol
loading results in fewer data points at Loviisa and very few low σw2 values during the campaign. At Loviisa, the correlation
between the 15 and 4∘ elevation VADs is rather poor
(Fig. 3d), but the data are close to the 1:1 line and show a similar scatter
to data from Limassol. Altogether, the relationship between σVAD2 and σw2 is reasonably linear especially
for σw2 < 0.1 m2 s-2 (Fig. 3), which is
the most important range for determining the MLH. The reasonably close
agreement with σVAD2 values at different elevation angles
(Fig. 3b and d) suggests that the relationship between σVAD2 and σw2 is independent of the VAD
elevation angle (Fig. 3) and thus the VAD elevation angle is not a critical
parameter for considering σVAD2 as a proxy for turbulent
mixing.
Comparison of VAD and vertically pointing MLH estimate. For
Limassol data the 25th, 50th and 75th percentile of the
VAD-based MLH are presented for the cases when the nearest-neighbour
TKE-based MLH is 120 m a.s.l. For Loviisa data, the 25th, 50th
and 75th percentile of the VAD-based MLH from the 15∘ elevation angle VAD are presented for the cases when the nearest-neighbour
TKE-based MLH is 159 m a.s.l. The 25th, 50th and 75th
percentile of the VAD-based MLH from the 4∘ elevation angle VAD at
Loviisa are presented for the cases when the nearest-neighbour 15∘ elevation angle VAD-based MLH is 55 m a.s.l.
VAD elevation
VAD MLH
N
VAD
Vertically pointing
angle (∘)
(m a.s.l.) percentile
radius (m)
MLH (m a.s.l.)
25th
50th
75th
30
105
150
195
95
182
120
10
94
145
171
119
595
120
15
170
187
230
33
504
159
4
47
50
53
26
444
55
Comparing MLH from a VAD and vertically pointing measurements
The diurnal variation of TKE dissipation rate calculated from
vertically pointing measurements is plotted together with σVAD2 for Limassol in Figs. 4 and 5 and for Loviisa in Figs. 6 and 7. In a qualitative sense the diurnal dissipation rate and σVAD2 profiles agree. The greatest difference is the instrument
sensitivity for VADs below 15∘ in elevation: the signal clearly
peters out at a lower altitude than for the vertically pointing data. This
is due to two reasons: firstly, at low elevation angles the range, or path length,
to a given altitude is obviously much further than when vertically pointing
and the relative rate of attenuation in the vertical plane is
correspondingly higher; secondly, the integration time for vertically pointing rays
can be increased without a major impact on the scan schedule.
We used the simplest possible MLH detection scheme – a constant threshold
value – to assess the usefulness of σVAD2 as a measure of
the MLH. For σVAD2, the MLH was diagnosed as the altitude
where σVAD2 first drops below 0.05 m2 s-2. A
threshold dissipation rate of 10-4 m2 s-3 (cf. O'Connor et
al., 2010) was selected for the vertically pointing data. In Figs. 4–7, a
MLH estimate is given only if there are more data above, i.e. no MLH estimate
is given if the highest data point, dissipation rate or σVAD2, is still above the respective threshold. However, in such
cases (e.g. Fig. 4c) the highest data point can be used as a lower bound for
the MLH.
MLH obtained in this manner from the dissipation rate and σVAD2 profiles are in good agreement as seen in Figs. 4–7. What
is more, the σVAD2 profile can be used to check whether
the mixed regions observed in the vertically pointing data are indeed
connected to the surface. For instance, Limassol vertically pointing data
indicate a mixed layer up to 500 m at 00:30 UTC on 24 August 2013, but
σVAD2 from the 10∘ VAD shows that this turbulent
region is not connected to the surface. In fact, based on the horizontal
wind profile obtained from the VADs, this turbulent region lies at the lower
edge of a low-level jet.
A comparison of MLH obtained from TKE dissipation rate and 30∘ elevation angle σVAD2 for the full length of the campaign
at Limassol (Fig. 8) shows a reasonable agreement between the two methods.
However, as the VAD radius increases up to 400 m and beyond, the comparison
becomes poorer. This behaviour can be expected considering that the
correlation length scale of turbulence typically scales with MLH (e.g.
Lothon et al., 2009). In Fig. 8 the comparison is plotted for the nearest-neighbour TKE dissipation rate for each VAD-based MLH estimate.
The connection of correlation length scale and MLH renders a comparison of
MLHs from vertically pointing measurements and lower elevation angle VADs
unfeasible as the radii of 10 and 15∘ elevation angle
VADs already exceed 500 m at the lowest usable range gate for
vertically pointing operation at Limassol and Loviisa (Table 3). However,
when the vertically pointing measurements indicate MLH at the lowest usable
range gate (i.e. a MLH of 120 m a.s.l. at Limassol and a MLH of 159 m a.s.l.
at Loviisa), the 10 and 15∘ elevation angle VADs also
indicate a MLH in the same range, as indicated in Table 3.
The 4∘ elevation angle VAD at Loviisa is so close to the surface
that the change of surface roughness from land to sea can have a major
effect on σVAD2. To minimise the effects of local
topography we have only calculated σVAD2 from the VAD at
4∘ elevation angle for a 55∘ wide sector (i.e. 12
azimuthal angles) upwind of the lidar. Then, the σVAD2
from the 4∘ elevation angle VAD agrees reasonably well with
σVAD2 from the 15∘ elevation angle VAD at
Loviisa (Figs. 6 and 7). MLH from 4 and 15∘ elevation
angle VADs also compare well when the 15∘ elevation angle VAD
indicates MLH of 55 m a.s.l. (Table 3). The width of the 55∘
azimuthal sector for the 4∘ elevation angle VAD MLH calculation is
410 m at the elevation of the lowest range gate of the 15∘ elevation angle VAD.
Sensitivity of VAD-based MLH threshold
To check the sensitivity of the MLH determination to the choice of
threshold, we varied the VAD-based MLH detection threshold by ±0.01 m2 s-2. Increasing the σVAD2 threshold value
decreases the MLH estimate; at Limassol, applying a threshold of 0.06 m2 s-2 leads to, on average (mean), MLHs that are 16 m lower in
the 30∘ elevation angle VAD data. For the 10∘ elevation
angle VAD at Limassol, the higher threshold gives a 9 m lower MLH on average.
Additionally, at Limassol the increased σVAD2 threshold
indicates MLH to be below the lowest usable range gate on 10 % of the
cases when the 0.05 m2 s-2 threshold still indicates a non-zero
MLH.
In the Loviisa data set, increasing the VAD-based MLH threshold has a smaller
effect: for the 15∘ elevation angle VAD the MLH estimate decreases
by 11 m on average, and for the 4∘ elevation angle the MLH
estimate decreases by 3 m on average. There are fewer cases at Loviisa when
the increased σVAD2 threshold indicates MLH below the
lowest usable range gate: 4 % of cases in the 15∘ elevation
angle VAD, when the 0.05 m2 s-2 threshold gives a non-zero MLH;
and 1 % of cases for the 4∘ elevation angle VAD when the 0.05 m2 s-2 threshold gives a non-zero MLH.
Comparison of vertical TKE dissipation-rate-based MLH estimate and
σVAD2-based MLH for 30∘ elevation angle VAD at
Limassol. Red line indicates median, blue rectangle indicates upper and
lower quartiles and whiskers indicate the 10th and 90th
percentile. Black line indicates 1:1 line. The right-hand side axis
indicates the radius of the VAD corresponding to the elevation on left-hand
side axis.
Decreasing the σVAD2 threshold increases the MLH estimate.
At Limassol, decreasing the σVAD2 threshold from 0.05 m2 s-2 to 0.04 m2 s-2
increases the 30∘ elevation angle VAD-based MLH estimate on average by 29 m. For the
10∘ elevation angle VAD-based MLH estimate the mean increase is 17 m. At Loviisa, the respective mean increase in the 15∘ elevation
angle VAD-based MLH estimate is 15 m and in the 4∘ elevation angle
VAD-based MLH estimate 4 m.
Compared to the scatter between the 30∘ elevation angle VAD-based
MLH and the TKE-based MLH in Fig. 8, the changes in the VAD-based MLH due to
±0.01 m2 s-2 changes in the σVAD2
threshold are small. For applications where accurate MLH detection is of
critical importance, a more sophisticated MLH detection scheme than the flat
threshold used here would be appropriate. The flat threshold is clearly a
reasonable and robust initial estimate, and the selected threshold of 0.05 m2 s-2 gives the best agreement with the MLH inferred from the TKE
profile with a threshold of 10-4 m2 s-3.
Histogram of MLH derived from VADs for the cases when vertical measurements
indicate MLH to be below the lowest usable range gate in vertically pointing measurement mode
for Limassol (a) and Loviisa (c). The minimum range for MLH calculation from vertical
measurements was 120 m a.s.l. at Limassol and 159 m a.s.l. at Loviisa. (b) Diurnal frequency
of low MLH at Limassol. (d) Diurnal frequency of low MLH at Loviisa. The minimum range for MLH
calculation from VADs was 30 m a.s.l. at both Limassol and Loviisa.
Frequency of low mixing level heights at Limassol and Loviisa
The vertically pointing TKE dissipation rate data close to surface indicate no
significant mixing, implying MLH below the lowest vertical range gate at Limassol 42 % of the time and at Loviisa 62 % of the time. The
VAD-based MLH estimates show that, at Limassol, in 58 % of the cases when
MLH must be below the vertically pointing altitude limit, there is a shallow
mixed layer at the surface (Fig. 9). At Loviisa the VADs indicate a shallow
mixed layer at the surface on 87 % of the cases when the MLH must be
below the vertically pointing altitude limit (Fig. 9).
At Limassol the MLH exhibits a clear diurnal cycle with low-altitude mixing
levels occurring almost exclusively during night time (Fig. 9b). This agrees
with radiosonde observations of mixing level heights at coastal
Mediterranean locations during summer (e.g. Seidel et al., 2012). At
Loviisa, however, very low MLHs are also common during daytime
(Fig. 9d), typical for cold conditions with a stably stratified atmosphere
and minimal surface heating (e.g. Liu and Liang, 2010).
Conclusions
We have shown for two very different environments that a low-elevation-angle
Doppler lidar VAD scan can be used to identify the presence of turbulent
mixing in the atmosphere. Although the method was developed with
measurements at coastal locations, the method should be applicable in any
environment where VADs can be performed. Furthermore, the VAD-based proxy
for turbulence can be used to identify the MLH. If scanning at a very low
elevation angle is feasible at the measurement location, the VAD-based MLH
can detect the presence or absence of mixing from the instrument level up.
However, at elevation angles lower than 10∘, the impact of
surface roughness changes across the VAD volume must be taken into
consideration.
Comparison of MLHs from vertically pointing data and VADs shows reasonably
good agreement, especially considering the simplicity of the MLH detection
scheme used here. However, the rapid increase in radius for VADs at low
elevation angles limits the altitude range of MLH retrievals from VADs.
Therefore, to cover the full range of MLHs from ground level up, a
combination of vertically pointing and VAD measurements are most suitable.
In this manner, turbulent mixing can be identified from the surface up to
heights of 1 km or more continuously with good time resolution. At
the same time, VADs can be used to retrieve the wind profile, which in turn
can be used to e.g. identify wind shear generated mixing.
Finally we have demonstrated that very shallow MLHs can be present during
the majority of the time when vertically pointing measurements indicate no
mixing; i.e. MLH is below the lowest usable range gate in
vertically pointing measurement mode. At Limassol, representing
Mediterranean summer time conditions, such low MLHs occurred only during the
night; at Loviisa, in Baltic Sea wintertime conditions, very low MLHs were
also common during the day.