The depolarization ratio is a valuable parameter for
lidar-based aerosol categorization. Usually, the aerosol particle depolarization
ratio is determined at relatively short wavelengths of 355 nm and/or 532 nm,
but some multi-wavelength studies including longer wavelengths indicate
strong spectral dependency. Here, we investigate the capabilities of Halo
Photonics StreamLine Doppler lidars to retrieve the particle linear
depolarization ratio at the 1565 nm wavelength. We utilize collocated
measurements with another lidar system, PollyXT at Limassol, Cyprus, and at
Kuopio, Finland, to compare the depolarization ratio observed by the two
systems. For mineral-dust-dominated cases we find typically a slightly lower
depolarization ratio at 1565 nm than at 355 and 532 nm. However, for dust
mixed with other aerosol we find a higher depolarization ratio at 1565 nm. For
polluted marine aerosol we find a marginally lower depolarization ratio at
1565 nm compared to 355 and 532 nm. For mixed spruce and birch pollen we
find a slightly higher depolarization ratio at 1565 nm compared to 532 nm.
Overall, we conclude that Halo Doppler lidars can provide a particle linear
depolarization ratio at the 1565 nm wavelength at least in the lowest 2–3 km
above ground.
Introduction
Aerosols and their interactions with clouds remain the largest source of
uncertainty in the Earth's radiative budget (IPCC, 2013). Remote sensing
measurements with lidars enable continuous long-term observations of the
vertical distribution of aerosol particles and clouds in the atmosphere,
providing valuable information for improving our understanding of the global
climate system (e.g. Illingworth et al., 2015). Information on the vertical
distribution of aerosols is highly important also for the aviation industry
in case of hazardous aerosol emissions from for example volcanic eruptions (Hirtl
et al., 2020).
Lidar measurements of aerosol optical properties at multiple wavelengths can
be used to categorize elevated aerosol layers into different types such as
mineral dust, smoke, marine aerosol or volcanic ash (e.g. Baars et al.,
2017; Papagiannopoulos et al., 2018). One of the most important parameters
for such aerosol typing is the depolarization ratio, which enables
distinguishing spherical and non-spherical particles from each other (e.g.
Burton et al., 2012; Baars et al., 2017). Furthermore, the depolarization
ratio can be used to quantify the contributions of different aerosol types
to elevated layers (Mamouri and Ansmann, 2017). It is essential also for
estimating vertical profiles of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations from remote sensing observations
(Mamouri and Ansmann, 2016).
Currently, the particle linear depolarization ratio is most commonly
measured at relatively short wavelengths of 355 and/or 532 nm (e.g.
Illingworth et al., 2015; Baars et al., 2016), though some lidar systems are
capable of depolarization ratio measurement at longer wavelengths of 710
and 1064 nm (e.g. Freudenthaler et al., 2009; Burton et al., 2012). For
instance, Burton et al. (2012) used the ratio of depolarization ratios at
1064 and 532 nm as part of their aerosol typing procedure. However, to
our knowledge, the aerosol particle depolarization ratio has not been reported
at wavelengths longer than 1064 nm.
Previous studies on the spectral dependency of the depolarization ratio between
355 and 1064 nm have shown a steep decrease in the depolarization ratio from
532 to 1064 nm for elevated biomass burning aerosols (Burton et al.,
2012, 2015; Haarig et al., 2018; Hu et al., 2019). On the contrary, mineral
dust layers present an increasing depolarization ratio with increasing
wavelength (Groß et al., 2011; Burton et al., 2015) or a relatively weak
maximum at 532 nm (Freudenthaler et al., 2009; Burton et al., 2015; Haarig
et al., 2017). For some aerosol types, such as marine aerosol (Groß et al.,
2011) and volcanic ash (Groß et al., 2012), no spectral dependency was
observed. However, volcanic ash mixed with boundary layer aerosol was
observed with a clearly lower depolarization ratio at 355 nm than at 532 nm
(Groß et al., 2012).
The spectral dependency of the depolarization ratio has been attributed largely
to the shape of the size distribution of polarizing aerosol particles. In
smoke layers, the depolarization signal is probably due to non-spherical
soot aggregates, which are in the size range of 100 nm to hundreds of nanometres and
thus do not produce a large depolarization ratio at 1064 nm (Burton et al.,
2015; Haarig et al., 2018; Hu et al., 2019). Recently, Gialitaki et al. (2020) modelled smoke as near-spherical submicron particles and found good
agreement with the observed spectral dependency of the depolarization ratio. On
the other hand, mineral dust contains significant amounts of coarse-mode
particles (>1µm in diameter), which can explain the
large depolarization ratio also observed at the 1064 nm wavelength
(Freudenthaler et al., 2009; Groß et al., 2011; Burton et al., 2015; Haarig
et al., 2017). In aged dust layers, the faster removal of supermicron
particles is thought to result in the depolarization ratio peaking at 532 nm
(Freudenthaler et al., 2009; Groß et al., 2011; Burton et al., 2015; Haarig
et al., 2017). In other words, spectral analysis of the depolarization ratio
could permit more in-depth diagnosis of coarse-mode polarizing aerosol.
Halo StreamLine Doppler lidars are commercially available fibre-optic
systems that operate at a wavelength of 1565 nm and can be equipped with a
cross-polar receiver channel for measuring the depolarization ratio (Pearson et
al., 2009). Over the last few years these lidars have become widely used in
wind and turbulence studies (e.g. Päschke et al., 2015; Vakkari et al.,
2015; Tuononen et al., 2017; Manninen et al., 2018). Additionally,
depolarization ratio measurements by Halo lidars have been used to study
cloud and precipitation phase (e.g. Achtert et al., 2015).
Now, recently developed post-processing (Vakkari et al., 2019) allows the
utilization of significantly weaker signals from Halo Doppler lidars than
previously. Therefore, the main aim of this paper is to assess the
capabilities of Halo Doppler lidars in providing particle linear
depolarization ratio measurements at the 1565 nm wavelength. To do so, we
utilize collocated Halo Doppler lidar and multiwavelength Raman lidar
PollyXT observations during two measurement campaigns, where different
polarizing aerosols were observed. Overall, the comparison indicates that
Halo Doppler lidars can add another wavelength at 1565 nm to studies on the
spectral dependency of the particle linear depolarization ratio, at least in the
lowest 2–3 km above ground.
Materials and methods
Here we use data from two measurement campaigns where a Halo Photonics
Doppler lidar and a PollyXT Raman lidar were collocated: at Kuopio, Finland,
from 9 to 16 May 2016 and at Limassol, Cyprus, from 21 April to 22 May 2017. The campaigns represent quite different environments (Fig. 1) and
enable the comparison of the depolarization ratio at 1565 nm by the Halo
instrument to the depolarization ratio at 355 and 532 nm from PollyXT for a
range of aerosol types. Furthermore, the campaigns were equipped with
different devices of the Halo and PollyXT designs, and thus potential
differences between instrument individuals can be investigated.
Locations of Vehmasmäki and Limassol measurement sites.
The Vehmasmäki site (62.738∘ N, 27.543∘ E; 190 m a.s.l.) in Kuopio is a rural location surrounded by boreal forest (Bohlmann
et al., 2019). The focus of the campaign in May 2016 was to investigate the
capability to characterize the optical properties of airborne pollen with
the multiwavelength Raman lidar PollyXT (Bohlmann et al., 2019). Here, we
utilize 1 week of collocated measurements to compare Halo depolarization
at 1565 nm to PollyXT during a spruce and birch pollination episode.
Limassol (34.675∘ N, 33.043∘ E; 22 m a.s.l.) is located
at the southern shore of Cyprus in the eastern Mediterranean. Measurements
at Limassol were part of the Cyprus Clouds Aerosol and Rain Experiment
(CyCARE; Ansmann et al., 2019) and were performed as a collaboration between the
Cyprus University of Technology (CUT), Limassol, and the Leibniz Institute for
Tropospheric Research (TROPOS), Leipzig. During April–May, several Saharan
dust episodes were observed at Limassol in addition to the regional aerosol.
Halo Doppler lidar
Halo Photonics StreamLine lidars are commercially available 1565 nm pulsed
Doppler lidars equipped with a heterodyne detector (Pearson et al., 2009).
Halo StreamLine lidars emit linearly polarized light, and the optical path
is constructed with fibre-optic components, which can be equipped with a
cross-polar receiver channel. The cross-polar channel is implemented through
a fibre-optic switch between the normal receiver path and path with a
fibre-optic polarizer. Thus, the measurement of the co- and cross-polar
signals is not simultaneous but consecutive in vertically pointing mode.
For instance, if the integration time per ray is set to 7 s, then co-polar
signal is collected for 7 s and then cross-polar signal is collected during
the next 7 s.
For research purposes, the most commonly used variants of StreamLine lidars
are StreamLine, StreamLine Pro and StreamLine XR. The StreamLine and the
more powerful StreamLine XR lidars enable full hemispheric scanning. The
Streamline Pro is designed without moving parts on the outside, which limits
the scanning to a cone of 20∘ from vertical. All StreamLine
variants can be used for depolarization ratio measurements, but an important
difference between XR and other StreamLine versions is that the XR
background noise level cannot be determined as accurately in the near range
as for the non-XR versions (Vakkari et al., 2019). This difference is
attributed to the more sensitive amplifier used in the StreamLine XR
(Vakkari et al., 2019).
In this study we utilize vertically pointing measurements only from two
StreamLine Pro systems. The operating specifications of these systems are
given in Table 1. StreamLine lidars send and receive pulses through a
single lens, which avoids issues with overlap and leads to a minimum range
of 90 m due to impact of the outgoing pulse. At Vehmasmäki, we focused
on boundary layer aerosol and set integration time per ray to 7 s and
telescope focus to 2000 m. At Limassol, we expected to encounter elevated
aerosol layers frequently and set the integration time per ray to 11.5 s and
telescope focus to infinity. The integration time is set to balance between
signal strength and good enough time resolution for retrievals of turbulent
properties.
Specifications of Halo Doppler lidars used in this study.
Wavelength1565 nmPulse repetition rate15 kHzPulse energy20 µJPulse duration0.2 µsNyquist velocity20 m s-1Sampling frequency50 MHzVelocity resolution0.038 m s-1Points per range gate10Range resolution30 mMaximum range9600 mLens diameter8 cmLens divergence33 µradTelescopemonostatic optic-fibre coupled
Halo StreamLine lidars provide three parameters along the beam direction:
radial Doppler velocity, signal-to-noise ratio (SNR) and attenuated
backscatter (β), which is calculated from SNR taking into account the
telescope focus. For a coherent Doppler lidar attenuated backscatter is
obtained as
β(z)=ASNR(z)Tf(z),
where z is the range from the instrument, A incorporates system-specific constants
and Tf(z) is the telescope focus function, which includes range correction
(Frehlich and Kavaya, 1991; Pentikäinen et al., 2020).
A background check to determine range-resolved background noise level is
performed automatically once per hour. The raw signal from atmospheric
measurement is then divided by this noise level in the firmware and returned
as SNR (see Vakkari et al., 2019). We post-processed SNR according to
Vakkari et al. (2019), which ensures that both co- and cross-polar SNR have
an unbiased noise level; i.e. SNR is 0 when there is no signal (cf.
Manninen et al., 2016). Furthermore, the post-processing is essential to be
able to further reduce the instrumental noise floor by averaging the SNR
(Vakkari et al., 2019). After post-processing SNR, β is calculated
with Eq. (1).
Halo depolarization ratio
We estimate the instrumental uncertainty in Halo StreamLine SNR from the
standard deviation of SNR in the cloud- and aerosol-free part of the
profile. Given the long wavelength and low pulse energy, no contribution
from molecular scattering is observed in the signal. At 1565 nm the
molecular backscatter coefficient is about 1.9×10-8 m-1 sr-1 at mean sea level, using mean values for the atmospheric number
density taken from the US Standard Atmosphere, 1976 (COESA 1976). The
two-way atmospheric transmittance at 1565 nm is still 0.9994 at 2 km
altitude above a lidar situated at mean sea level. Hence, the measured
depolarization ratio can be safely assumed to represent the particle linear
depolarization ratio.
In Fig. 2a, consecutive co- and cross-polar SNR profiles are presented,
where aerosol signal is observed up to 800 m above ground level (a.g.l.) and
a liquid cloud base is observed at 840 m a.g.l. In liquid cloud the signal
attenuates quickly, and above 1 km the profiles represent instrumental noise
only. We use the measurements above 1 km to calculate standard deviations of
co-polar SNR (σco) and cross-polar SNR (σcross).
In Fig. 2c, the raw depolarization ratio (δ*) is calculated simply as
the ratio of cross-polar SNR to co-polar SNR, and uncertainty is estimated
from σco and σcross by Gaussian error propagation.
Profiles at Limassol, Cyprus, on 2 May 2017 at 12:08 UTC. (a) Co-
and cross-polar SNR. A liquid cloud at approx. 800 m a.g.l. results in full
attenuation of signal. Below cloud layer aerosol signal is visible. Above 1 km variability in SNR is due to instrumental noise only. (b) The same as
panel (a) but limited to lowest 1 km a.g.l. (c) Ratio of cross-polar SNR
to co-polar SNR up to 1 km a.g.l. calculated from profiles in panel (a).
Error bars represent uncertainty due to instrumental noise estimated from
SNR at >1 km a.g.l. in panel (a).
The construction of Halo StreamLine lidars does not include a calibrator
for depolarization channel, unlike PollyXT lidars for instance (Engelmann et
al., 2016). Furthermore, the user cannot change the optical path to include
a calibrator or check the depolarizing effects of the individual components.
Therefore, we are limited to evaluating the Halo depolarization ratio at
liquid cloud base.
Spherical cloud droplets do not polarize the directly backscattered
radiation, and thus non-zero δ* at liquid cloud base is an indication
of incomplete extinction (or bleed-through) in the lidar internal polarizer.
However, measurement of δ* at cloud base can be biased by signal
saturation or changes in cloud properties between co- and cross-polar
measurement. Furthermore, multiple scattering results in increasing
depolarization signal inside a liquid cloud (e.g. Liou and Schotland, 1971).
This increase in in-cloud δ* is clearly seen in Fig. 2c: at cloud
base δ* is 0.0102, and at the next gate 30 m deeper inside the cloud
δ* has increased to 0.0116.
The magnitude of the multiple scattering effect on depolarization ratio
depends on both cloud and lidar properties (e.g. Donovan et al., 2015). In
Halo StreamLine lidars the instrument telescope design has a matched field
of view and divergence of 33 µrad (Table 1), and consequently the
effect is small: in Fig. 2c δ* increases by 0.0014 in 30 m. For
instance, for the system modelled by Donovan et al. (2015) in-cloud multiple
scattering increases the depolarization ratio from 0 to 0.05 in approx. 50 m.
Nevertheless, to minimize the effect of multiple scattering we only consider
δ* at the cloud base for the determination of the average
bleed-through and use measurements in several clouds.
For low-level clouds, we have observed saturation of the co-polar signal in
the more powerful StreamLine XR instruments. Signal saturation at liquid
cloud base is readily identified as a non-linear relationship between co- and
cross-polar SNR. For the measurement cases analysed here, we did not observe
indications of saturation. Furthermore, we note that δ* at
cloud base should be determined with as high a time resolution as possible to
ensure that both co- and cross-polar measurements represent the same part of
the cloud. In practice, integration time is kept constant during a
measurement campaign and was set as a compromise between temporal
resolution and signal strength. We mitigate the effect of poor time
resolution by choosing cases where cloud base remains at the same altitude
(within lidar resolution) for some tens of minutes and thus one can assume
temporal homogeneity. No vertical smoothing is applied in calculating
δ*, as the signal at cloud base is strong compared to the aerosol signal.
Finally, it should be noted that, especially in higher latitudes, it is not
always trivial to find purely liquid phase clouds. Typically, mixed-phase
clouds can be distinguished by the depolarizing features of ice crystals.
That is, in the histogram of δ* at cloud base a secondary peak with
higher δ* than liquid clouds would occur, which was not the case for
our study.
To characterize the Halo polarizer bleed-through, we determined the δ* at liquid cloud base during both campaigns (Fig. 3). During the campaign
at Limassol, we determined δ* at cloud base on 25 April and on 2 May 2017. From the distribution in Fig. 3a, the bleed-through is 0.011 ± 0.007 (mean ± standard deviation). At Vehmasmäki, we utilized
clouds on 13, 14 and 16 May 2016 as shown in Fig. 3b. At Vehmasmäki, the
estimated bleed-through is 0.016 ± 0.009 (mean ± standard
deviation). The mean cloud base δ* values observed for these two systems in
Fig. 3 are well in line with our experience with these and five other StreamLine and StreamLine XR systems in Finland, where cloud base δ*
typically ranges from 0.01 to 0.02.
Ratio of cross-polar SNR to co-polar SNR at liquid cloud base
measured with Halo Doppler lidar. (a) Distribution of cloud base δ*
at Limassol. (b) Distribution of cloud base δ* at Vehmasmäki.
We attribute the spread in the distributions in Fig. 3 mostly to variability
of the clouds at the measurement sites and to the fact that co- and
cross-polar measurements are consecutive and not simultaneous. Given that
the cross-polar measurement channel is constructed with fibre-optic
technology, we do not expect changes in the performance of the polarizer.
This is also our experience with Halo systems in Finland since 2016, but we
recommend to check the bleed-through monthly or after an instrument is moved
to a new location. Considering the large natural variability of
the depolarization ratio (e.g. Illingworth et al., 2015; Baars et al., 2016) we
find the spread of observations in Fig. 3 tolerable. The standard deviation
in Fig. 3 is included in the uncertainty calculation of the Halo depolarization
ratio.
We account for the observed bleed-through (B) in calculating Halo particle
linear depolarization ratio (δ1565) as
δ=SNRcross-B⋅SNRcoSNRco,
where SNRco and SNRcross are the observed co- and cross-polar SNR,
respectively. Uncertainty in SNRcross corrected for bleed-through (i.e.
numerator in Eq. 2) is estimated as
σcross,B=σcross2+B⋅SNRco2⋅σB2B2+σco2SNRco2,
where σB is standard deviation of the distribution in Fig. 3.
Finally, uncertainty in δ1565 taking into account instrumental
noise and uncertainty in bleed-through is estimated as
σδ=δσcross,B2SNRcross-B⋅SNRco2+σco2SNRco2.
PollyXT
PollyXT is a multiwavelength Raman lidar capable of depolarization ratio
measurement at one or two wavelengths depending on instrument configuration
(Baars et al., 2016; Engelmann et al., 2016). PollyXT emits simultaneously
355, 532 and 1064 nm wavelength pulses at a repetition frequency of 20 Hz.
All PollyXT lidars are built with the same design, but there are small
differences in the number of receiver channels equipped in each individual
system. A detailed description of PollyXT design is given by Baars et al. (2016) and Engelmann et al. (2016).
At Vehmasmäki, PollyXT was configured with elastic backscatter channels
(355, 532 and 1064 nm); Raman-shifted channels at 387, 407 and 607 nm; and a
cross-polar channel at 532 nm (Bohlmann et al., 2019). Due to the biaxial
construction of emission and detection units, complete overlap is reached at
800–900 m a.g.l. (Engelmann, et al., 2016), and thus only measurements
>800 m a.g.l. are utilized for this study (Bohlmann et al.,
2019). The original spatial resolution is 30 m and temporal resolution 30 s
for the Vehmasmäki system (Bohlmann et al., 2019).
At Limassol, PollyXT operated the same receiver channels as the
Vehmasmäki system had and additionally a cross-polar channel at 355 nm,
together with a near-range telescope with 355 and 532 nm receiver channels.
The near-range channels enable retrieval of optical properties down to 150 m a.g.l. (Engelmann et al., 2016). Raw spatial resolution is 7.5 m and
temporal resolution is 30 s.
During night-time, the Raman method (Ansmann et al., 1992) is used to
retrieve aerosol optical properties from the raw signals. For daytime
measurements, the method of Klett (1981) can be utilized. Here, we present
only measurements when the Raman method was applied. The calibration of
the depolarization ratio was performed at both Vehmasmäki and Limassol using
the so-called Δ90∘ method (Freudenthaler, 2016), and the
relative uncertainty in the particle linear depolarization ratio was estimated
to be 10 %.
Auxiliary data
Air mass history was estimated with the Hybrid Single-Particle Lagrangian
Integrated Trajectory model, HYSPLIT (Stein et al., 2015). HYSPLIT was run
through the READY website (Rolph et al., 2017) using the NCEP Global Data
Assimilation System (GDAS) meteorology at 0.5∘ horizontal
resolution. Back trajectories of 96 h arriving at the elevation
of aerosol layers of interest were calculated.
Results
In this section we analyse observations of dust, marine and pollen aerosols
during the Limassol and Vehmasmäki campaigns, where said aerosol types
were observed simultaneously with Halo and PollyXT lidars. Dust and marine
aerosols were observed during the Limassol campaign in the eastern Mediterranean,
and pollen was observed during the Vehmasmäki campaign in a boreal
forest region in Finland. We conclude this section with an overall
comparison of the depolarization ratio measurements with the two instruments.
Elevated dust layersLimassol 21 April 2017
Right at the beginning of Halo measurements at Limassol on 21 April 2017,
several elevated layers were observed as seen in Fig. 4. Although Halo can
observe elevated layers up to 6 km a.g.l. on this day, the signal is too
weak to retrieve their depolarization ratio. This is clearly visible in the
uncertainty in the Halo depolarization ratio in Fig. 4c. At 300 s
integration time (i.e. 10 min of alternating co- and cross-polar
measurement), the depolarization ratio can be determined up to 1–1.5 km a.g.l.
with σδ<0.05 on this day (Fig. 4d). The
depolarization ratio can be retrieved also for the relatively strong
elevated layer at 3 km a.g.l. during the morning hours (Fig. 4d).
Limassol 21 April 2017 measurements with Halo Doppler lidar. (a) Time series of co-polar SNR at 300 s integration time. (b) Time series of
cross-polar SNR at 300 s integration time. (c) Uncertainty in the depolarization
ratio. (d) Particle linear depolarization ratio filtered with σδ<0.05.
Increasing both temporal and spatial averaging enables the utilization of
some of the weaker signals. Figure 5 presents profiles of the Halo and PollyXT
depolarization ratio, where both are averaged over 1.5 h (20:00–21:30 UTC) and smoothed vertically with a 300 m running mean. In the lowest layer
<1 km a.g.l., practically no difference is observed in the
depolarization ratio at the different wavelengths. Back-trajectory
calculations (Fig. 6) indicate this layer to be mostly regional air from
eastern Mediterranean, and the relatively large lidar ratio is in the range
of observations of smoke or smoke mixed with dust (e.g. Groß et al., 2011;
Baars et al., 2016). On the other hand, for the layer from 1.5 km to 2 km a.g.l. a clear increase in δ with increasing wavelength is observed.
For this layer air mass history indicates origins over northern Africa (Fig. 6), and the lidar ratio (42 ± 4 at 355 nm, 47 ± 5 at 532 nm) is in
the range of dust (Ansmann et al., 2011). For this layer the mean (± standard deviation) δ values at 355, 532 and 1565 nm are 0.19 ± 0.008, 0.23 ± 0.008 and 0.29 ± 0.008, respectively. Above 2 km a.g.l., the uncertainty in δ at 1565 nm increases rapidly and is not
used for quantitative analysis here.
Averaged profiles at Limassol on 21 April 2017 at 20:00–21:30 UTC.
All profiles have been smoothed by a 300 m running mean. (a) Backscatter by
PollyXT (wavelengths 355–1064 nm) and attenuated backscatter by Halo
(1565 nm). (b) Particle linear depolarization ratio. Error bars represent
measurement uncertainty. (c) Lidar ratio. For PollyXT β355,
β532, LR355 and LR532, a near-range telescope is used for
data <900 m a.g.l.
Back trajectories of 96 h arriving at Limassol on 21 April 2017 at
21:00 UTC.
Limassol 27 April 2017
Stronger elevated aerosol layers were observed at Limassol on 27 April 2017.
On this day, the depolarization ratio can be retrieved by Halo up to 3 km a.g.l.
(Fig. 7). For an averaging period of 01:25–02:30 UTC, the depolarization ratio
is retrieved for the elevated layer at 1600–2200 m a.g.l. For this layer,
the depolarization ratio at 1565 nm is 0.30 ± 0.005, which is a little
lower than for the shorter wavelengths: 0.36 ± 0.01 at 355 nm and
0.34 ± 0.002 at 532 nm, respectively. For this layer, the air mass
history indicates southerly origins.
On the same day (27 April 2017) at 19:00–20:00 UTC, the depolarization ratio
can be retrieved from the surface up to 2.6 km a.g.l. (Fig. 8). In the
lowest 500 m, the depolarization ratio at 1565 nm is clearly higher than at the
shorter wavelengths, suggesting a mixture of larger mineral dust particles
with smaller particles of a lower depolarization ratio. For the layer at
1500–2500 m a.g.l., practically no wavelength dependency is observed for
the depolarization ratio, indicating that backscatter at all wavelengths is
dominated by the same aerosol. The layer-averaged depolarization ratios are
0.31 ± 0.006, 0.33 ± 0.005 and 0.32 ± 0.008 at 355, 532
and 1565 nm, respectively. The high depolarization ratio and lidar ratio of
47 ± 5 at 355 nm (38 ± 3 at 532 nm) indicate almost pure dust
(Ansmann et al., 2011; Baars et al., 2016). Air mass history, on the other
hand, indicates northerly or north-westerly origins at both 2 km a.g.l. and
at the surface (Fig. 9).
Limassol 27 April 2017 measurements with Halo Doppler lidar. (a) Time series of co-polar SNR at 300 s integration time. (b) Time series of
cross-polar SNR at 300 s integration time. (c) Uncertainty in the depolarization
ratio. (d) Particle linear depolarization ratio filtered with σδ<0.05.
Averaged profiles at Limassol on 27 April 2017 at 19:00–20:00 UTC.
All profiles have been smoothed by a 300 m running mean. (a) Backscatter by
PollyXT (wavelengths 355–1064 nm) and attenuated backscatter by Halo
(1565 nm). (b) Particle linear depolarization ratio. Error bars represent
measurement uncertainty. (c) Lidar ratio. For PollyXT β355 and
β532, a near-range telescope is used for data <900 m a.g.l.
Polluted marine aerosol
On 20 May 2017 at Limassol, a very low aerosol depolarization ratio is
observed throughout the day as seen in Fig. 10. During the morning and
afternoon liquid clouds are observed, but during the evening Raman retrievals
with PollyXT were possible. Figure 11 presents Halo depolarization ratio
profiles averaged for the duration of the PollyXT retrieval at 19:54–21:30 UTC. For the surface layer (up to 1 km a.g.l.), a small decrease in the
depolarization ratio with increasing wavelength is observed. The
layer-averaged depolarization ratios are 0.03 ± 0.01, 0.015 ± 0.002 and 0.009 ± 0.003 at 355, 532 and 1565 nm, respectively.
The layer-averaged lidar ratio at 355 nm is 39 ± 4 sr, whereas the
lidar ratio at 532 nm is very noisy at 47 ± 35 sr. The low
depolarization ratio is typical of marine aerosol, smoke and pollution
(Groß et al., 2011; Illingworth et al., 2015). The 355 nm lidar ratio lies
between the values reported for marine aerosol and smoke (Illingworth et
al., 2015).
Back trajectories of 96 h arriving at Limassol on 27 April.
Back trajectories arriving at 19:00 and 02:00 UTC are included.
Limassol 20 May 2017 measurements with Halo Doppler lidar. (a) Time series of co-polar SNR at 300 s integration time. (b) Time series of
cross-polar SNR at 300 s integration time. (c) Uncertainty in the depolarization
ratio. (d) Particle linear depolarization ratio filtered with σδ<0.05.
Above 1 km a.g.l., an optically thin aerosol layer is observed (Fig. 11).
Halo indicates a higher depolarization ratio for this layer than at the
surface, but the signal is so weak that the uncertainty in the depolarization
ratio at 1565 nm becomes very large (Fig. 11b). Back trajectories arriving
over Limassol at 21:00 UTC indicate different but mostly northerly origins for
the air mass at 500 m and at 2 km a.g.l. (Fig. 12).
Averaged profiles at Limassol on 20 May 2017 at 19:55–21:30 UTC. All
profiles have been smoothed by a 300 m running mean. (a) Backscatter by
PollyXT (wavelengths 355–1064 nm) and attenuated backscatter by Halo
(1565 nm). (b) Particle linear depolarization ratio. Error bars represent
measurement uncertainty. (c) Lidar ratio. For PollyXT β355,
β532, LR355 and LR532, a near-range telescope is used for
data <900 m a.g.l.
Back trajectories of 96 h arriving at Limassol on 20 May 2017 at
21:00 UTC.
Pollen in boreal forest
On 15 May 2016, substantial amounts of spruce and birch pollen were observed
at Vehmasmäki with both an in situ sampler and the PollyXT lidar
(Bohlmann et al., 2019). The presence of more polarizing spruce pollen
(Bohlmann et al., 2019) in the boundary layer is observed also with Halo
lidar as seen in Fig. 13d. However, the backscatter (Fig. 14a) is low
compared to the case studies presented for Limassol, and the low signal
results in significant noise in the lidar ratio (Fig. 14c).
Comparing the depolarization ratios measured with Halo and PollyXT (Fig. 14b) shows a nearly constant depolarization ratio at 1565 nm, while the
depolarization ratio at 532 nm decreases with height. At 1565 nm, the Halo
signal is probably dominated by pollen grains, which are tens of micrometres
in diameter. At the 355 and 532 nm wavelengths, the backscatter is increasing
with height (Fig. 14a), and thus the decreasing depolarization ratio at 532 nm may reflect an increasing fraction of signal from non-pollen aerosol with
increasing height. For the layer from 800 m to 1 km a.g.l. in Fig. 14, the
mean depolarization ratios are 0.236 ± 0.009 and 0.269 ± 0.005 at
532 and 1565 nm, respectively.
Vehmasmäki 15 May 2016 measurements with Halo Doppler lidar.
(a) Time series of co-polar SNR at 350 s integration time. (b) Time series
of cross-polar SNR at 350 s integration time. (c) Uncertainty in
the depolarization ratio. (d) Particle linear depolarization ratio filtered with
σδ<0.05.
Averaged profiles at Vehmasmäki on 15 May 2016 at 19:00–21:00 UTC. All profiles have been smoothed by a 300 m running mean. (a) Backscatter
by PollyXT (wavelengths 355–1064 nm) and attenuated backscatter by Halo
(1565 nm). (b) Particle linear depolarization ratio. Error bars represent
measurement uncertainty. (c) Lidar ratio.
Overview of depolarization ratio wavelength dependency
An overall comparison of the depolarization ratio at different wavelengths
for the Limassol and Vehmasmäki campaigns is presented in Fig. 15, where
the Halo vertical resolution of 30 m has been smoothed with a 300 m running
mean. The original time resolution observations by Halo have been averaged
to match the temporal resolution of PollyXT Raman retrievals (ranging from
45 min to 2 h).
In Fig. 15a, three regions can be observed in the scatterplot. For δ532<0.05, δ1565 matches very closely with the
shorter wavelength. For δ532 ranging from 0.05 to 0.25, δ1565 is systematically larger than δ532. For δ532>0.3, δ1565 is lower than the
depolarization ratio at the shorter wavelength. A very similar pattern is
present in Fig. 15b: for δ355<0.05, δ1565 matches δ355 closely; for δ355
ranging from 0.05 to 0.25, δ1565 is larger than δ355; and for δ355>0.3, δ1565 is
lower than δ355. Similar regions appear even when comparing the two shorter wavelengths
(Fig. 15c): for δ355<0.05,
δ532 is lower than δ355; for δ355
ranging from 0.1 to 0.3, the depolarization ratio is on average equal on both
wavelengths; and for δ355>0.3, δ532 is
lower than δ355.
Comparison of particle linear depolarization ratio at different
wavelengths. Observations represent 30 m vertical resolution and have been
smoothed by a 300 m running mean. To reduce scatter, a stricter uncertainty
threshold is applied, and only data for σδ<0.01
(at the 1565 nm wavelength) are included. The mean is calculated at intervals of
0.025 on the x axis, and error bars indicate standard deviation. (a) Depolarization ratio at 1565 nm (Halo) vs. depolarization ratio at 532 nm
(PollyXT) at Limassol. (b) Depolarization ratio at 1565 nm vs.
depolarization ratio at 355 nm (PollyXT) at Limassol. (c) Depolarization
ratio at 532 nm vs. depolarization ratio at 355 nm at Limassol. (d) Depolarization ratio at 1565 nm vs. depolarization ratio at 532 nm at
Vehmasmäki.
Figure 15a–c also show similar correlations between the depolarization ratios
at different wavelengths. Therefore, bearing in mind the similar patterns in
all three scatterplots in Fig. 15a–c, we consider the scatter to originate
mainly from the atmospheric aerosol properties rather than in instrumental
effects. For instance, any bias in the estimated bleed-through in the Halo
polarizer would show up as bias in Fig. 15a and b. However, such bias is
not present in the cases when δ355 and/or δ532 are
low.
Considering the sources at Limassol during the campaign, the higher δ1565 for intermediate depolarization ratios ranging from 0.1 to 0.25
likely represents mixtures of dust with other aerosol types. A mixture of
coarse, polarizing dust with less polarizing and smaller aerosol would
result in the observed spectral dependency of the depolarization ratio. For aged-dust-dominated cases, lower depolarization ratios at longer wavelength could
be due to the faster removal of coarse particles compared to submicron
aerosol (e.g. Burton et al., 2015). In any case, the observed wavelength
dependency in Fig. 15a–c for large δ suggests that, for
dust-dominated cases, smaller particle sizes have, on average, a higher
depolarization ratio at Limassol.
Another type of polarizing aerosol, i.e. pollen, was observed with a
collocated Halo and PollyXT at Vehmasmäki (Bohlmann et al., 2019).
Comparatively low signal levels, together with 800 m minimum range for the
PollyXT system at Vehmasmäki (Bohlmann et al., 2019), reduce the amount
of data available for comparison of Halo and PollyXT depolarization ratio
during the campaign (Fig. 15d). During this campaign, the depolarization
ratio at 1565 nm is a little larger than at 532 nm, but the difference is
small compared to the scatter observed at Limassol.
A further look into the distribution and spectral dependency of the
depolarization ratio at Limassol is presented in Fig. 16. In Fig. 16a and
b, the 2D histograms of the depolarization ratio show that both the 532 and
1565 nm wavelengths present a bi-modal distribution below 1 km a.g.l. In
other words, there are also less polarizing aerosols frequently present in
the lowest 1 km in addition to dust and dusty mixtures with a depolarization
ratio >0.2. However, above about 1.5–2 km a.g.l., almost all
retrievals indicate dust or dusty mixtures. Note that the vertical extent of
the data is limited by the sensitivity of the Halo instrument, as Fig. 16a
and b are limited to cases when both wavelengths are available.
2D histograms of the particle linear depolarization ratio and height
at Limassol using 30 m vertical resolution smoothed by a 300 m running mean.
Only data for σδ<0.01 (at the 1565 nm wavelength)
are included. (a) Depolarization ratio at 532 nm. (b) Depolarization ratio at
1565 nm. (c) Ratio of depolarization ratios at 1565 and 532 nm. (d) Same
as panel (c) but scaled with number of observations at each height.
In Fig. 16c and d, the ratio of depolarization ratios at 1565 and 532 nm exhibits a clear height dependency. Above about 1.5 km a.g.l., the majority
of the observations present a lower depolarization ratio at 1565 nm than at
532 nm, while below 1.5 km a.g.l. the depolarization ratio is higher at the
longer wavelength. In previous studies (Freudenthaler et al., 2009; Groß et
al., 2011; Burton et al., 2015; Haarig et al., 2017), a lower depolarization
ratio at longer wavelengths has been attributed to faster removal of coarse-mode dust. However, our observations indicate the presence of a small coarse-mode, probably mineral, dust for sub-1.5 km aerosols most of the time at
Limassol.
Discussion
The majority of aerosol depolarization ratio measurements have been carried
out at relatively short wavelengths (355 and 532 nm) with only a few
previous studies investigating the spectral dependency including 710 nm
(Freudenthaler et al., 2009; Groß et al., 2011) and/or 1064 nm
(Freudenthaler et al., 2009; Burton et al., 2012, 2015; Haarig et al., 2017,
2018; Hu et al., 2019). In this study we have for the first time determined
aerosol particle depolarization ratios at a wavelength of 1565 nm.
From an instrumental point of view, the Halo Doppler lidar depolarization
ratio seems to be of comparable quality to the PollyXT depolarization ratio when
the aerosol signal is strong. However, Halo has a much less powerful laser
than PollyXT, which significantly limits the range of usable signal. On the
other hand, Halo Doppler lidars are capable of independent operation for
months and are therefore suitable for operational use in meteorological
measurement networks.
The integration time and range gate length are adjustable in Halo firmware,
and prolonging these parameters would increase the sensitivity of the
system. However, high spatial and temporal resolution are preferable for
utilizing the Doppler capabilities of Halo lidars. Inspecting the internal
polarizer performance at liquid cloud base also requires a higher
resolution. Overall, the configuration of a Halo Doppler lidar needs to be
considered individually for the aims of each measurement campaign.
The spectral dependency that we observed for the 355, 532 and 1565 nm
particle linear depolarization ratio agrees reasonably well with previous
spectral analyses for similar aerosol types as seen in Table 2 and Fig. 17.
For the mineral dust depolarization ratio, both decreasing and increasing trends
with increasing wavelength have been observed previously (Table 2). This is
the case for our observations at Limassol as well, though on average δ1565 tends to be a little lower than δ532 (Fig. 16).
Probably, the spectral dependency of the mineral dust depolarization ratio
depends on both the age of the dust and the origin of the dust. The spectral
dependency of the depolarization ratio modelled with MOPSMAP (Gasteiger and
Wiegner, 2018) for desert dust aerosol from the OPAC database (Koepke et al.,
2015) agrees reasonably well with the Saharan dust case on 21 April 2017 in
this study (Fig. 17). On the other hand, the sun-photometer-based retrieval
by Toledano et al. (2019) for long-range transported Saharan dust over
Barbados indicates a slightly lower depolarization ratio of 0.19 at 1640 nm
compared to this study at 1565 nm (Fig. 17). The lower depolarization ratio
at 1640 nm over Barbados is reasonable considering the much longer transport
compared to this study.
Particle linear depolarization ratio as a function of wavelength
for dust observations in Table 2. Additionally, spectral dependency modelled
with MOPSMAP based on the OPAC database for desert dust (Koepke et al., 2015;
Gasteiger and Wiegner, 2018) and AERONET inversion by Toledano et al. (2019)
are included.
Spectral dependency of depolarization ratio for dust, marine
aerosol and pollen.
Depolarization ratio Time and origin355 nm532 nm710 nm1064 nm1565 nmThis study, Limassol21 Apr 2017 20:00–21:30;Saharan dust0.19 ± 0.0080.23 ± 0.0080.29 ± 0.00827 Apr 2017 01:25–02:33;dust (Egypt)0.36 ± 0.010.34 ± 0.0020.30 ± 0.00527 Apr 2017 at 19:00–20:00;dust (Turkey)0.31 ± 0.0060.33 ± 0.0050.32 ± 0.008Haarig et al. (2017)Barbados 2013, 2014;Saharan dust0.252 ± 0.0300.280 ± 0.0200.225 ± 0.022Burton et al. (2015)US 2014; Saharan dust0.209 ± 0.0150.304 ± 0.0050.270 ± 0.005Mexico Chihuahua 2013;local dust0.225 ± 0.0410.373 ± 0.0140.383 ± 0.006Groß et al. (2011)Cabo Verde 2008;Saharan dust0.24–0.270.29–0.310.36–0.40Freudenthaler et al. (2009)Morocco 2006;Saharan dust0.24–0.280.31 ± 0.030.26–0.300.27 ± 0.04This study, Limassol20 May 2017 at 19:55–21:30;polluted marine0.03 ± 0.010.015 ± 0.0020.009 ± 0.003Groß et al. (2011)Cabo Verde 2008; marine0.02 ± 0.010.02 ± 0.02This study, Vehmasmäki15 May 2016 19:00–21:00; spruce and birch pollen0.236 ± 0.0090.269 ± 0.005
Wavelength-dependent changes in the mineral dust depolarization ratio are small
compared to elevated smoke layers, which can help to distinguish between
these two aerosol types (Burton et al., 2012). For elevated smoke, a strong
decrease in the depolarization ratio has been reported from >0.20 at
short wavelengths to δ<0.05 at 1064 nm (Burton et al.,
2015; Haarig et al., 2018; Hu et al., 2019). Thus, adding a depolarization
ratio measurement at 1565 nm can provide added value to the commonly used
measurements at the 355 and 532 nm wavelengths.
For marine aerosols, the depolarization ratio is small and has practically
no spectral dependency (Groß et al., 2011), which is what we observed at
Limassol. For the mixture of spruce and birch pollen at Vehmasmäki, the
differences in the depolarization ratio at 532 and 1565 nm are small.
Conclusions
In this paper we report for the first time remote sensing measurements of
the atmospheric aerosol particle linear depolarization ratio at a wavelength of
1565 nm. Using observations at liquid cloud base we have been able to
characterize the Halo Doppler lidar polarizer bleed-through with sufficient
accuracy to obtain useful depolarization ratio measurements; uncertainty in
the bleed-through is propagated to the depolarization ratio measurement. A
comparison of two different Halo Doppler lidar systems with two PollyXT
systems during collocated measurements at Limassol, Cyprus, and Kuopio –
Vehmasmäki, Finland, shows good agreement between the lidar systems. The
agreement between the instruments is remarkably good considering the large
wavelength difference: the PollyXT depolarization ratio is retrieved at 355
and/or 532 nm. However, given the much lower laser energy in Halo Doppler
lidars, it is not surprising that the vertical extent of the usable
depolarization ratio is much lower than for PollyXT.
For relatively fresh mineral dust, we find particle linear depolarization
ratios at 1565 nm ranging from 0.29 to 0.32, which is in good agreement with
previous observations, including measurements at the 710 and 1064 nm
wavelengths (Freudenthaler et al., 2009; Groß et al., 2011; Burton et al.,
2015; Haarig et al., 2017). For polluted marine aerosol we observed a very low
depolarization ratio of 0.009 at 1565 nm with a small decrease with
increasing wavelength. The spruce and birch pollen depolarization ratio has been
characterized only recently at 532 nm (Bohlmann et al., 2019). Our
measurements indicate a slightly higher depolarization ratio of 0.27 at 1565 nm compared to 0.24 at 532 nm. Overall, our results indicate that Halo
Doppler lidars can add another wavelength at 1565 nm to studies on the
spectral dependency of the particle linear depolarization ratio, at least in the
lowest 2–3 km above ground.
For aerosol typing, adding a particle linear depolarization ratio at 1565 nm
to shorter wavelengths can help to distinguish biomass burning aerosols from
dust, as a much stronger spectral dependency has been observed for elevated
biomass burning aerosols than for dust (e.g. Haarig et al., 2017, 2018; Hu
et al., 2019). In case there is prior knowledge of prevailing aerosols, such
as transport of volcanic ash, even stand-alone particle linear
depolarization ratio measurements with Halo Doppler lidars can probably
provide useful information for aerosol typing.
Data availability
Processed lidar data are available upon request from the authors. Level 0
PollyXT observations are available at https://polly.tropos.de/ (Polly NET, 2020, last
access: 18 August 2020). Trajectory model HYSPLIT and GDAS meteorological
data are available at https://www.ready.noaa.gov/HYSPLIT.php (ARL, 2020, last access: 18 August 2020).
Author contributions
Conceptualization and formal analysis were carried out by VV, while investigation was carried out by HB, SB, JB, MK, RM and EJO'C. Data curation was carried out by VV, HB, SB, MK and EJO'C. VV wrote the original draft, which was reviewed and edited by VV, HB, SB, MK and EJO'C.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Tropospheric profiling (ISTP11) (AMT/ACP inter-journal SI)”. It is a result of the 11th edition of the International Symposium on Tropospheric Profiling (ISTP), Toulouse, France, 20–24 May 2019.
Acknowledgements
The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (https://www.ready.noaa.gov, last access: 18 August 2020) used in this publication.
Financial support
This research has been supported by the National Emergency Supply Agency of Finland. The Vehmasmäki pollen study and data evaluation have been supported by the Academy of Finland (grant no. 310312). The Limassol, Cyprus, observations have been supported by the SIROCCO project (grant no. EXCELLENCE/1216/0217) co-funded by the Republic of Cyprus and the structural funds of the European Union for Cyprus through the Research and Innovation Foundation and EXCELSIOR project that received funding from the European Union (H2020-WIDESPREAD-04-2017: Teaming Phase 2) project under grant agreement no. 857510 and from the Republic of Cyprus.
Review statement
This paper was edited by Andreas Richter and reviewed by two anonymous referees.
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