AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-2897-2018Vertical profiles of aerosol mass concentration derived by unmanned airborne in situ and remote sensing instruments during dust eventsVertical profiles of aerosol mass concentrationMamaliDimitrad.mamali@tudelft.nlMarinouElenihttps://orcid.org/0000-0003-2631-6057SciareJeanPikridasMichaelhttps://orcid.org/0000-0002-8131-2369KokkalisPanagiotisKottasMichaelBinietoglouIoannishttps://orcid.org/0000-0002-0065-9791TsekeriAlexandraKeleshisChristosEngelmannRonnyBaarsHolgerhttps://orcid.org/0000-0002-2316-8960AnsmannAlbertAmiridisVassilisRusschenbergHermanBiskosGeorgeg.biskos@tudelft.nlg.biskos@cyi.ac.cyDepartment of Geoscience and Remote Sensing, Delft University of Technology, Delft, the NetherlandsInstitute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, GreeceDepartment of Physics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyThe Cyprus Institute, Energy, Environment and Water Research Centre, Nicosia, CyprusPhysics Department, Faculty of Science, Kuwait University, KuwaitNational Institute for Research and Development in Optoelectronics, Ilfov, RomaniaLeibniz Institute for Tropospheric Research, Leipzig, Germany Dimitra Mamali (d.mamali@tudelft.nl) and George Biskos (g.biskos@tudelft.nl, g.biskos@cyi.ac.cy)17May20181152897291023November201715January201827March20185April2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/2897/2018/amt-11-2897-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/2897/2018/amt-11-2897-2018.pdf
In situ measurements using unmanned aerial vehicles (UAVs) and
remote sensing observations can independently provide dense
vertically resolved measurements of atmospheric aerosols, information which
is strongly required in climate models. In both cases, inverting the recorded
signals to useful information requires assumptions and constraints, and this
can make the comparison of the results difficult. Here we compare, for the
first time, vertical profiles of the aerosol mass concentration derived from
light detection and ranging (lidar) observations and in situ measurements
using an optical particle counter on board a UAV during moderate and
weak Saharan dust episodes. Agreement between the two measurement methods was
within experimental uncertainty for the coarse mode (i.e. particles having
radii >0.5µm), where the properties of dust particles can be assumed
with good accuracy. This result proves that the two techniques can be used
interchangeably for determining the vertical profiles of aerosol
concentrations, bringing them a step closer towards their systematic
exploitation in climate models.
Introduction
Aerosol particles affect the atmospheric energy balance directly by
interacting with solar radiation, and indirectly through the formation of
clouds . Determining the radiative forcing of the
atmospheric aerosol particles is highly uncertain, partly because of the
significant spatial (both vertically and horizontally) and temporal
variability of their concentration, size and chemical composition
. The vertical variability in the properties of the
atmospheric aerosol can be independently determined by modern in situ
measurements using airborne platforms and remote sensing observations.
Comparison of the measurements obtained by these two types of techniques,
however, is fundamental for improving the accuracy of the resulting
observational data for use in climate models.
Light detection and ranging (lidar) instruments are among the most powerful
tools for probing vertically resolved properties of the atmospheric aerosol.
A number of retrieval algorithms that have been developed over the years can
be used to obtain aerosol optical parameters from raw lidar signals,
including the aerosol backscatter coefficient βaer, the aerosol extinction
coefficient αaer and the particle depolarization ratio
δp. Under certain
assumptions, recently developed algorithms can now be used to retrieve other
vertically resolved aerosol properties such as particle absorption and mass
concentration using the synergy of lidar and sun photometer
. To check
the validity of these assumptions and to assure the quality of the final
data, certain aerosol properties retrieved from lidar observations have been
compared with vertical in situ observations using research aircraft
.
Map of Cyprus showing the locations of the observation sites used
for the measurements reported in this paper. The inset in the upper-left
corner shows the greater area of southern Europe, northern Africa and the Middle
East, with the white square indicating the location of Cyprus. The maps were
generated by Google Earth Pro
(https://www.google.com/earth/download/gep/agree.html, last access: 6 April 2017).
Airborne in situ measurements using research aircraft are complex and costly,
and therefore their availability is scarce and time restricted, limiting
comparability with remote sensing observations. What is more, manned aircraft
cannot cover the lowermost part of the atmosphere due to safety restrictions,
posing another major limitation. Recent efforts in aerosol instrumentation
have provided lightweight and miniaturized instruments that can measure the
size and concentration of aerosol particles on board UAVs in a much
simpler and cost-effective manner. As a result, vertical profiling of key
aerosol parameters can now be performed over long periods of time on a
routine basis, and at much lower altitudes compared to measurements with
manned research aircraft. Considering, however, that in
many cases these advantages come at the expense of the quality of the recorded data, measurements
of aerosol properties using miniaturized instruments on board UAVs need to be
validated before using them to bridge the long-lasting gap between in situ
measurements and remote sensing observations.
Photograph of the UAV of the Cyprus Institute used for the measurements reported in this work.
Here we compare, for the first time to our knowledge, vertical profiles of
the aerosol mass concentration, derived from lidar measurements using the
polarization lidar photometer networking technique (POLIPHON), and in situ
measurements with an optical particle counter (OPC) on board a UAV (hereafter referred to as OPCa). It
should be noted that the two techniques do not measure the mass concentration
directly, but this is estimated from the recorded signals of the two
instruments. The measurements were recorded during the BACCHUS-INUIT-ACTRIS
(Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate:
towards a Holistic UnderStanding – Ice Nuclei Research Unit – European Research
Infrastructure for the observation of Aerosol, Clouds and Trace gases
Research InfraSctructure network) campaign that took place in Cyprus during
April 2016.
Instrumentation and methodsSite description
Cyprus is located in the eastern Mediterranean (see Fig. inset)
and receives air masses from Europe, the Middle East and northern Africa
. Therefore, it is an ideal location for
characterizing different aerosol types and investigating the role of
particles in various atmospheric processes.
The measurements reported here were conducted at three different locations.
Aerial measurements using a UAV were carried out at Orounda
(35∘09′ N, 33∘07′ E; 310 m a.s.l.), providing highly resolved spatially and temporally
distributed data up to ca. 2 km above ground level (a.g.l.). Concurrent ground-based
in situ aerosol measurements were taken at the Cyprus Atmospheric
Observatory (CAO) at Agia Marina Xyliatou (35∘04′ N,
33∘06′ E; 535 m a.s.l.), located 6.5 km south of Orounda. A
PollyXT Raman lidar instrument was located in the suburbs of Nicosia
(35∘14′ N, 33∘38′ E; 190 m a.s.l.), ca. 35 km
east of Orounda, providing round-the-clock measurements of the atmospheric
conditions up to 12 km a.g.l. The exact locations of the measuring points
are shown in Fig. and detailed descriptions of the instruments
are given below.
Column-integrated volume size distribution measured with the sun
photometer over Nicosia at 06:57 UTC on 15 April 2016. The ranges of
particle sizes measured by AERONET sun photometers and by the
OPCa are also indicated in the figure.
Unmanned aerial vehicle (UAV)
The UAV employed during the campaign (see Fig. ) has a fixed
wingspan of 3.8 m and is powered by a two-stroke internal combustion engine.
It has a take-off weight of 35 kg that results in a payload capacity of
approximately 12 kg. The payload bay is
1.3 m × 0.23 m × 0.34 m (length–width–height) and can
fit multiple instruments. When loaded, the UAV can fly for up to 4 h with an
air speed velocity of 25 ± 10 ms-1 and can reach altitudes
of up to 4 km a.g.l. (due to
airspace limitations; however, only
flights up to 2 km were permitted). An autopilot system allowed
predetermined flight plans that involved spiral rectangular-shaped ascending
and descending patterns (see Fig. S1 of the Supplement) preventing
contamination of the sampling system from the engine's exhaust. For
consistency, the results shown in the rest of the paper correspond to
measurements during ascent.
UAV-based optical particle counter measurements
Vertical profiles of the particle size distributions of the atmospheric
aerosol were measured using an OPC (i.e. the OPCa; MetOne, model 212-2)
on board the UAV, which reported particle size distributions ranging from
0.15 to 5 µm in radius, in eight size bins. The sampled aerosol was
dried to below 50 % relative humidity (RH) by gently heating the sampling
tube of the OPCa. Assuming spherical shape and constant mass density for the
particles, the size distributions were converted into aerosol mass
concentrations (see Sect. ). In addition to
the OPCa, a single-wavelength aethalometer (AethLabs – model AE51) with a
sampling time resolution of 1 s and a flow rate of 0.2 Lpm, was on board the
UAV to verify that no contamination of the sampled air by the engine exhaust
took place. Each instrument was equipped with an individual sampling inlet
that extended 5 cm from the UAV nose to ensure representative sampling.
Particle mass concentration calculation from the OPCa measurements
The mass concentration profiles of the coarse particles were calculated from
the size distribution measurements recorded by the OPCa. Before converting the
aerosol number concentrations to mass concentrations, the OPC measurements
were averaged over 30 s (original time resolution 1 s). This was found to be
optimal for suppressing high-frequency noise of the OPC raw data and at the
same time maintaining a relatively high spatial resolution of ∼ 80 m in
the vertical direction. The number concentration (dN) of each size bin was
converted to volume concentration according to dV(r)=dN(r)43πr3, where r is the mean radius of each size bin in the recorded
measurements. The volume concentration of particles with radii larger that
0.5 µm were summed and multiplied by ρd, yielding the coarse-mode
mass concentration. The variability in the number size distributions averaged
every 30 s propagated an uncertainty of the order of 10 % in the estimated
volume size distributions and the mass concentrations of the particles
.
(a) AOT500 and (b)Å440-870 as measured with the
sun photometers at CAO (blue circles) and Nicosia (red circles) from 13 to 24 April 2016.
Lidar measurements
A depolarization Raman lidar PollyXT was used in the measurements reported here. This lidar emits
laser pulses simultaneously at three wavelengths: 1064, 532 and 355 nm. The
laser beam interacts with the atmospheric molecules and particles, and a part
of it (backscattered light) is collected by the receiver unit which consists
of two telescopes (near-field and far-field). The elastically backscattered
signals are used as input to the Fernald–Klett algorithm
to retrieve
the vertical profile of the particle backscatter coefficient
βaer. This method assumes a linear relationship between the
aerosol extinction and backscatter (i.e. the fixed lidar ratio S)
throughout the entire atmospheric column, a critical assumption that can
induce uncertainties up to 20–30 % of the retrieved property from statistical
and systematic errors .
In addition to the elastically backscattered signal, PollyXT receives
the nitrogen Raman-shifted signal at wavelengths 387 and 607 nm and the
water vapour Raman signal at 407 nm wavelength. The Raman technique
utilizes the elastic and
inelastic signals to retrieve the particle extinction αaer and
scattering βaer profiles independently, without any critical
assumptions. The range-resolved aerosol lidar ratio can then be directly
estimated as the ratio αaer/βaer. In our
analysis, we used the Raman technique to retrieve the αaer and
βaer profiles at night-time and the Fernald–Klett method
during the daytime when the Raman signal is strongly affected by the background
noise induced by the scattered sunlight. The PollyXT system also
provides information on volume depolarization ratio δv, from which
the particle depolarization ratio δp can be estimated
. This allows
discrimination between spherical particles (e.g. water droplets) and
non-spherical particles such as dust.
Range-corrected lidar signal at 1064 nm (a) and volume linear
depolarization ratio (b) reflecting the atmospheric conditions over Nicosia
on 15 April 2016. Blue colour indicates weak backscattering, yellow–red colours
in the range-corrected lidar signal indicate backscattering mainly from
fine aerosols and dust, whereas the dotted line shows the PBL top. The lidar
observations used for the comparison with the UAV measurements were those
recorded between 07:00 and 07:50 UTC.
Sun/sky photometer measurements
A lunar/sun sky photometer of the AErosol RObotic NEtwork (AERONET;
) was collocated with the lidar at Nicosia,
whereas an additional sun photometer was situated at CAO. Both instruments
provided measurements of the aerosol optical thickness (AOT) at seven
wavelengths (i.e. 340, 380, 440, 500, 675, 871 and 1020 nm). The AERONET
products include parameters corresponding to the total
atmospheric column such as the Ångström exponent Å (at several
wavelength pairs), the particle volume size distributions in the size range
0.05 to 15 µm (particle radius), the fine- and coarse-mode AOT
(τf and τc, respectively) at 440, 675, 871, 1020 nm
and the fine- and coarse-mode volume concentrations
(vf and vc, respectively;
). According to
the retrieval of
the particle volume size distribution was demonstrated to be adequate in
practically all situations with AOT >0.05, which was also the case for the
observations reported here. Cloud-screened and quality-assured level 2.0 data
products were used in this work. The uncertainties for the AOT were <0.02
for UV wavelengths and <0.01 for wavelengths above 440 nm
.
Particle mass concentration profiles derived by the lidar measurements and the POLIPHON method
The mass concentration profiles from the lidar measurements were calculated
using the POLIPHON method as stated above. In the
first step of the method, the contribution of the fine-mode and coarse-mode
particles to the total backscatter coefficient (βt) is calculated
based on depolarization measurements . Here we
assumed an externally mixed aerosol consisting of a fine component with low
depolarization (5 ± 1 %; ) and a coarse
component that induces light depolarization of 31 ± 4 %
, corresponding to dust particles. The
dust-related backscatter coefficient was determined as
βd=βt(δt-δnd)(1+δd)(δd-δnd)(1+δt),
where βt, δt, δnd and δd are
respectively the observed total backscatter coefficient, the observed total depolarization ratio, the assumed non-dust
depolarization ratio and the measured depolarization ratio of dust particles.
Once βd was determined, the non-dust backscatter coefficient
was calculated by βnd = βt-βd. In the calculations presented here we used β and
δp values corresponding to 532 nm wavelength.
In the second step of the method, the mass concentrations of the fine
(non-dust; mnd) and coarse (dust; md) aerosol fractions are
calculated according to :
md=ρd(vc/τc‾)βdSdmnd=ρnd(vf/τf‾)βndSnd,
where ρ is the mass density, whereas the product of the backscattering
coefficient and the lidar ratio βS is the extinction coefficient
of the particles, with subscripts d and nd denoting dust (coarse) and
non-dust (fine) particles. It should be noted that the factors
vc/τc‾ and vf/τf‾ are used to convert the extinction measurements to
particle volume concentration for the coarse and the fine fraction,
respectively. In this work these factors were determined from the daily mean
data of the sun photometer that was collocated with the lidar. The volume
concentrations vf and vc were obtained from the
AERONET data, whereas the fine- and coarse-mode AOTs, τf and
τc, at 532 nm wavelength were calculated using Å
(determined in the 440–675 wavelength range) according to
τf,c(532)=τf,c(440)×(440532)Åf,c(440-675).
Another assumption we made was that the lidar-derived dust and non-dust
fractions are identical to the photometer-derived coarse- and fine-particle
fractions. The inflection point of the AERONET data was adopted as the limit
between the fine- and the coarse-mode particles. As a result, the fine mode
ranged between 0.05 and 0.5 µm (particle radius) and the coarse mode
between 0.5 and 15 µm as shown in Fig. . The calculated
values of vf/τf‾ and
vc/τc‾ (see Table ) are in
line with the conversion factors mentioned by , who performed an extensive analysis of the conversion
factors of dust over Cyprus.
Values of the input parameters used in the POLIPHON algorithm.
ParameterSymbolValuesSource/referenceDust depolarization ratioδd31 ± 4 %Non-dust depolarization ratioδnd5 ± 1 %Dust lidar ratioSd47± 10 srRaman measurements, this studyNon-dust lidar ratioSnd60± 10 srDust particle densityρd2.6 ± 0.6 g cm-3Dust conversion factor (15 April 2016)vc/τc‾0.67 ± 0.05 ×10-6Sun photometer, this studyNon-dust conversion factor (15 April 2016)vf/τf‾0.24 ± 0.018 × 10-6Sun photometer, this studyDust conversion factor (22 April 2016)vc/τc‾0.81 ± 0.04 × 10-6Sun photometer, this studyNon-dust conversion factor (22 April 2016)vf/τf‾0.14 ± 0.019 × 10-6Sun photometer, this study
Back-trajectories of the air masses arriving at various altitudes
over Cyprus on 15 April, 07:00 (UTC). The back-trajectories were calculated
for a duration of 5 days using the HYSPLIT transport and dispersion model
with GDAS 1∘ meteorological data
through the Real-time Environmental Applications and Display sYstem (READY;
http://ready.arl.noaa.gov/index.php, last access: 26 June 2017).
Daytime profiles of β (355, 532 and 1064 nm wavelength)
(a), δp (355 and 532 nm wavelength)
(b), βd and βnd(c) determined by POLIPHON, as
well as RH profiles from in situ measurements on board the UAV and from
WRF-ARW model simulations over Nicosia at 08:00 UTC (d).
Range-corrected lidar signal at 1064 nm (a) and volume
linear depolarization ratio (b) reflecting the atmospheric
conditions over Nicosia on 22 April 2016. Blue colour indicates weak
backscattering, yellow–red colours in the range-corrected lidar signal indicate backscattering mainly from fine aerosols and dust,
whereas the dotted line shows the PBL top. The lidar observations used for
the comparison with the UAV measurements were those recorded between
04:22 and 05:00 UTC.
Back-trajectories of the air masses arriving at 500, 1000 and
1500 m over Cyprus at 04:00 UTC on 22 April. The back-trajectories were
calculated for a duration of 6 days. The black circles indicate the locations
where the air mass was below 100 m altitude.
Daytime profiles of β (532 and 1064 nm wavelength)
(a), δp (355 and 532 nm wavelength)
(b), βd and βnd(c) determined by POLIPHON, as well as RH profiles from WRF-ARW
model over Nicosia at 04:00 UTC (d). The 355 nm channel of the lidar was
discarded due to misalignment.
Aerosol mass concentration profiles for case studies I and II
(a, c) and the respective biases (b, d). In plots
(a) and (c), the blue solid lines represent the mass concentration
derived by the POLIPHON. The mass concentration measured by the OPCa is
plotted in red with the red shaded area representing the uncertainties in the
in situ measurement. The green lines show the mass concentration from the
OPCa corrected for the particles losses. In plots (b) and (d),
the red dots show the biases between the values measured by the OPCa and
the lidar (OPCa–lidar) before applying the corrections, while the
green dots are the biases after corrections. The red and green solid lines
show the mean biases before and after correction, respectively.
Apart from v/τ, the other parameters required for determining the
aerosol mass concentration from the lidar measurements are ρ, β
and S. Regarding ρ, we used a density of 2.6 ± 0.6 g cm-3
for the coarse-mode particles (corresponding to dust according to
). Chemical analysis of filter samples collected
during the measurements showed that the dust density assumed here is valid
(data not shown). Values for Snd (60 ± 10 sr) were taken
from the literature and actual measurements were
used for Sd. Sd measurements were only possible
at night-time when the Raman channels were operating. We measured the
same Sd values (47 ± 10 sr) for both events analysed here
(see Fig. S2 in the Supplement). All the values of the parameters that are
required as input for the calculations are summarized in Table .
POLIPHON method – error estimation
The uncertainties of βd and βnd in Eq. (1) were
determined by Monte Carlo calculations . For each
input parameter, we generated 100 normally distributed random numbers. The
values provided in Table 1 were used as the mean parameter and the standard
deviation of the normal distributions. Then, 100 βd and
βnd values were calculated for each point in the atmospheric
column and from these the mean values and the standard deviations (errors) of
βd and βnd were estimated to be 22 and
28 %, respectively. For Eqs. (2) and (3) the uncertainties were
calculated analytically using the error propagation law.
Results and discussionHomogeneity of aerosol properties over the measurement sites
Given the proximity (6.5 km) of the ground (at CAO) and the airborne in situ
observations (at Orounda), as well as the absence of any strong pollution
sources in the region, the measurements were considered to correspond to the
same air parcel in terms of atmospheric composition. The third measurement
location (Nicosia) was situated 35 km away from the airfield. As suggested by
the comparison of sun photometer measurements at Nicosia and CAO, however, all
locations were affected by the same air masses with a minor influence from
local emissions that were mostly trapped within the planetary boundary layer
(PBL).
Figure shows the AOT500 and the Å440-870 measured by
the sun photometers in Nicosia and at CAO from 13 to 24 April 2016 when
concurrent measurements were taken at the two locations. Overall, the
temporal variability of these two parameters observed at Nicosia was very
similar to the respective measurements at CAO, exhibiting correlations
coefficients of 0.89 and 0.87 for AOT500 and Å440-870,
respectively. This good correlation was further enhanced during the dust
event cases (e.g. on 15 April 2016) when the relative contribution of the
aerosol fine mode was minimized, suggesting that a comparison of aerosol
measurements at these locations is meaningful. In terms of absolute values,
AOT500 was 15–50 % higher at Nicosia compared to CAO, even during the
cases with the dust events, when coarse particles dominated. The higher
values at Nicosia are mainly due to the altitudinal difference between the
sites (Nicosia is at an altitude of 190 m, whereas CAO at 535 m above sea
level) and the contribution of the local aerosol sources to the total aerosol
burden. This was further justified by the higher Å440-870
measurements at Nicosia, which signify the presence of small aerosol particles
from anthropogenic sources.
Comparison of the mass concentration measurements
A total of six UAV flights with an OPCa on board were launched during the
entire campaign. However, only two fulfilled all the necessary requirements for
comparison with the lidar observations. Those requirements are that (1) there
are simultaneous measurements of lidar and OPCa, (2) there are cloud-free
conditions or clouds are above 7–8 km altitude so that the lidar retrievals
can be made, (3) there is enough dust loading, (4) AERONET data are available, and (5) the airborne in situ measurements were taken before
the full development of the PBL. All these requirements were fulfilled during
the measurements on 15 and 22 April 2016, which are analysed below.
Case study I: 15 April 2016
The atmospheric situation over south-eastern Europe on 15 April 2016 was
dominated by a high-pressure system resulting in mostly cloud-free conditions
over Cyprus. A dust event of moderate intensity was observed, resulting in an
average AOT500 value of 0.4 over Nisosia and CAO (see discussion in
Sect. 3.1 and Fig. ). Figure shows the lidar
time–height display for that day, with the upper panel showing the
range-corrected signal of the 1064 nm channel, which provides information
about the aerosol loading and the presence of clouds, and the lower panel the
linear volume depolarization ratio δv at 532 nm, which is used to
discriminate particles of different shapes that can be indicative of
different sources. Throughout the day, high concentrations of aerosol
particles were observed even up to ca. 7 km altitude (Fig. a), with a persistent aerosol layer extending from 2.5 to ca.
7 km. Back-trajectory analysis (see Fig. ) confirmed that
this layer resulted from a Saharan dust event that originated in Algeria and
travelled over Italy, Greece and Turkey before reaching Cyprus. Despite
passing over polluted areas, the core of the dust layer remained pure (see
analysis below) due to its high elevation (> 2 km) all along the path. The
δv plot (Fig. b) also shows the temporal
evolution of this dust layer. From 00:00–03:00 UTC the dust extends from 2 to
7 km altitude, but later (until 14:00 UTC) it becomes shallower. From the
early morning hours (07:00 UTC) to the early afternoon (14:00 UTC) when the
boundary layer develops, the dust layer is confined above it, reaching up to
5 km altitude. After the collapse of the boundary layer, the dust layer
starts to descend and finally reaches the ground at 18:00 UTC.
The cloud-free and time-homogeneous atmospheric scene between 07:00 and 07:50 UTC, which overlapped with the time window of the UAV flight, was selected
for calculating the parameters of the atmospheric aerosol using the POLIPHON
method. As mentioned earlier, the Sd values used as input in POLIPHON were
not measured at the same time window (07:00 and 07:50 UTC), but they were
derived from night-time measurements. However, back-trajectory analysis
verified that the lidar was measuring the same air mass type during these
time spans (Figs. and S3). The lidar profiles of β
(retrieved with the Fernald–Klett method; Sect. )
and δp, that were used as input in POLIPHON (532 nm only), are shown
in Fig. a–b. The backscatter signal increased gradually from
1 Mm-1 sr -1 at 1 km (532 nm), reaching a maximum of ca. 2.3 Mm-1 sr -1
at 3 km where the dust layer core was. The pure dust layer
spanned from ca. 2.5 to 3.8 km (δp∼30±2 %) while below 2 km
the dust was mixed with near-spherical particles, probably from the
residual layer, as indicated by the relatively low δp values
ranging between 12 and 30 %. Figure c shows the
POLIPHON-derived dust and non-dust-related backscatter coefficients βd
and βnd from Eq. (), and respective uncertainties
determined by Monte Carlo calculations (see Sect. ). The backscatter coefficient of the fine-mode
particles βnd decreased with altitude, while the dust particles were
present even down to 0.7 km. As discussed in Sect. , the
lidar ratio value used in the Fernald–Klett retrieval and the lidar ratio
corresponding to the dust particles Sd that is required as input in the
POLIPHON algorithm, were estimated from Raman lidar measurements taken
between 00:00 and 01:40 UTC (UTC+3 local time), just before sunrise. It should be
noted here that Raman measurements are only possible at night as during the
day scattered sunlight induces high background noise signal. The fact that
the dust layer observed during the Raman measurements had the same origin and
followed the same atmospheric path before reaching the measurement site
between 07:00 and 07:50 UTC was confirmed by back-trajectory analysis (Figs. and S3).
Vertical profiles of the RH measured on board the UAV and predicted by the
WRF-ARW atmospheric model showed that the
atmosphere was dry enough (RH ≲ 50 %) at the ground level and up to
4 km altitude (Fig. d). As a result we could safely assume
that the aerosol particles were dry and thus changes in the mass density and
backscatter coefficient due to water uptake were negligible.
Case study II: 22 April 2016
Contrary to case study I, a low-intensity dust event (AOT500=0.1) was
recorded over Cyprus on 22 April 2016. The evolution of the boundary layer
dominating the atmospheric situation that day is depicted in the lidar
time–height plots shown in Fig. . From 00:00 to 10:00 UTC
a sparse dust layer extended between 1 and 2 km a.g.l. while after the PBL
decay a shallower dust plume was observed between 1 and 1.5 km altitude.
According to the back-trajectory analysis (Fig. ) the dust air
mass at 1.5 km originated from Egypt at the ground level, then it was
elevated and passed over Libya, the Mediterranean and Turkey before reaching
Cyprus.
The UAV flight on that day was launched between 04:22 and 05:16 UTC. The
atmospheric scene between 04:20 and 05:00 UTC (Fig. ) was
selected for the comparison due to its stable conditions above 0.8 km. Also
in this case, the same procedure as in case I was followed to retrieve the
lidar profiles that were used as input in the POLIPHON algorithm. The
backscatter coefficient, the particle depolarization ratio, the
POLIPHON-derived dust and non-dust related backscatter coefficients as well
as the RH profiles of this atmospheric scene are shown in
Fig. . In contrast to the estimated δp values
determined from the measurements on 15 April, here δ532p
decreases gradually with height from 0.8 to 2 km
exhibiting values between 10 and 17 %.
These relatively low δ532p values indicate a mixture of
Saharan dust with spherical continental/anthropogenic particles. This is
supported by the paths that the air mass follow between 1 and 2 km, which
originated from north-eastern Africa, close to Cairo and Alexandria. The
lidar ratio of 40 ± 7 Sr, measured during the previous night (at a
height where the signal is mostly free of noise; i.e. 1.2–1.4 km) agrees
with the findings of and
, who respectively reported S532 values of 40± 5 Sr and 47 Sr for dust originating from eastern
Sahara.
Particle mass concentration profiles
Figure a and c shows the mass concentration
profiles for the coarse particles (particles larger than 0.5 µm in
radius) derived by the lidar observations using the POLIPHON method for the
inversion and the OPCa measurements. The lidar profiles, were calculated
by Eqs. () and () using the measured βd and
βnd, profiles and the dust density values from the literature
(see Table ). The respective OPCa profiles were determined by
the recorded particle number size distributions assuming the same dust
particle density (see Sect. for details).
To ensure that the lidar observations are representative of the atmospheric
aerosol over Orounda and over CAO we compare the data for altitudes higher
than 0.8 km a.s.l. during morning hours when the PBL was shallow.
The mass concentration profiles from the lidar and the OPCa observed on
15 April 2016 (Fig. a) show a good correlation, with R=0.9. In terms of absolute values, the mass concentrations measured by the
OPCa (red curve) lie within the uncertainty limits (38 %) of the lidar
observations, with the former being equal to or lower for the entire range of
altitudes, exhibiting a bias ranging from -23.0 to -2.4µg m-3 with
a mean of -12.0µg m-3 (Fig. b). The discrepancies
between the two methods can be partly attributed to the assumptions used in
POLIPHON: (1) constant S throughout the atmospheric column, (2) contribution in
the coarse mode only from depolarizing particles and (3) the assumption of an
externally mixed aerosol. Assumptions used for the manipulation of the OPC
measurements that can explain differences between the two methods are mainly
related to the refractive index and the shape of the particles. The
refractive index can notably influence the size distribution measured by the
OPC, inducing sizing uncertainties of up to 30 %
. The refractive index
used for calibrating OPCa, however, has a value of 1.59, which is
very close to literature values for Saharan dust (n=1.56;
). The difference between the refractive index
values used for the calibration of OPCa and those used for the retrieval
of the lidar measurements is estimated to introduce a bias of 2 % to the
calculated mass concentration values. Regarding particle shape, the effect of
non-sphericity on the particle sizing by light-scattering instruments with
a similar scattering angle range to OPCa (90∘± 60∘)
is less than 20 %, with a tendency towards undersizing
.
Another source of discrepancy between the mass concentrations determined by
OPCa and the lidar is the limitation of the former to measure particles
larger than a few tens of microns due to aerodynamic inlet loses
(sedimentation and inertial deposition), resulting in an underestimation of
20 % of the coarse-mode volume concentration (see yellow–green hatched area
in Fig. ). To account for that, we corrected the OPCa
measurements using the formula mOPC=mPOLIPHON∫OPCadV/dlnr∫POLIPHONdV/dlnr.
This correction significantly improved the agreement between the OPCa
(green curve in Fig. a) and the lidar measurements,
constraining the bias range between -11.1 and 8.8 µg m-3 which
results in a decreased mean bias of -1.1 µg m-3. Further
statistical analysis between the lidar and the corrected OPCa measurements
showed that our hypothesis that the two observations refer to the same
aerosol population is valid. To be more specific, the two-tailed t test
yielded a p value of 0.70 (assuming equal variances), indicating that the
differences between the mean values of the two types of observations are not
statistically significant.
The mass concentration profiles determined by the lidar and the OPCa
measurements on 22 April 2016 (Fig. c) also show a good
correlation, with R=0.9. In terms of absolute values, the mass
concentrations determined by the OPCa measurements (green line) are
lower compared to those determined by the lidar observations for the entire
range of altitudes, exhibiting biases in the range from -14.7 to
0.6 µg m-3 with a mean value of -8.7 µg m-3 (Fig. d). The integrated volume size distribution
measured by the sun photometer in Nicosia (see Fig. S4) showed that in this case
the OPCa underestimates the coarse volume fraction by 48 %. Upon
correction, the mean bias decreases to -1.6 µg m-3 and,
with the exception of one point at 1.8 km altitude, the mass concentration
values from the OPCa lie within the calculated uncertainty resulting from
the POLIPHON algorithm used to invert the lidar data (32 %). At higher
altitudes the mass concentration decreases drastically and OPCa
measurements drop below the POLIPHON uncertainty limits. Also in this case,
the two-tailed t test (assuming equal variances) yielded a p value of
0.05 indicating marginal statistically insignificant differences between the
mean value derived from two types of measurements.
Overall, the airborne in situ and lidar observations are in good agreement
both during the observation of a moderate and weak dust event, after
the necessary corrections for the OPCa measurements. In the case of the
moderate dust event the volume concentration fraction that is not captured by
the OPC range is small and so is the corresponding correction. In contrast,
during the weak dust event, the OPC misses almost 50 % of the volume size
distribution, which introduces large measurement ambiguities.
Summary and conclusions
In this study we compare, for the first
time to our knowledge, vertical profiles of the aerosol mass concentrations
determined independently by an OPC on board a UAV and by remote sensing
observations using data from a lidar and a sun photometer. The measurements
were taken during two cases of dust events that occurred in the region of
the eastern Mediterranean on 15 and 22 April 2016. During those days, the UAV
flew up to ca. 2 km altitude with the OPC measuring the size distributions
of sampled aerosol particles with radii in the range 0.15–5 µm,
from which the aerosol mass concentration was calculated. The same
information was retrieved by the concurrent lidar and sun photometer
measurements that were inverted using the POLIPHON method.
During the measurements on 15 April 2016 the dense dust layer extended from 2
to 4 km, while a mixture of dust and near-spherical particles was observed
below 2 km. The mass concentration of the coarse-mode particles increased
from ca. 30 µg m-3 at 0.8 km to ca. 70 µg m-3 at 1.8 km. Agreement between the in situ measurements and
the lidar observations retrieved with the POLIPHON method was very good (R=0.9), with the in situ measurements lying within the POLIPHON uncertainty
limits (38 %), exhibiting a mean bias of -12.0 µg m-3 that
can be mainly attributed to the difference in the cut-off diameters measured
by the two techniques. Corrections applied to account for this difference in
the cut-off diameters further enhanced the agreement, decreasing the mean
bias to -1.1 µg m-3.
In the measurements carried out on 22 April 2016, a sparse dust layer was
observed between 0.8 and 2 km altitude during the morning hours. Information
from the lidar measurements and the back-trajectory analysis suggests that
this layer was a mixture of desert dust with continental/pollution particles.
Despite that, agreement between the airborne in situ and remote
sensing measurements in this case was also very good (R=0.9). In terms of
absolute values, the corrected mass concentrations measured by the airborne
OPC were equal or lower than those derived from the lidar measurements for
the entire range of altitudes and exhibited a mean bias of -1.6 µg m-3. The concentrations measured by the airborne OPC were within the
calculated uncertainty for POLIPHON.
The measurements reported here indicate that unmanned airborne OPC
measurements and lidar observations can provide reliable ways to determine
coarse-mode aerosol mass concentration profiles in the atmospheric column,
thereby bridging the gap between in situ and remote sensing observations.
Considering that both methods can provide dense data sets in a cost-effective
manner and on a regular basis, this finding paves the way towards their
systematic exploitation in climate models.
The sun photometer data sets used in this study are
available at https://aeronet.gsfc.nasa.gov (last access: 6 March 2017, Holben et al., 1998). The OPC
data sets analysed during the current study are available from the co-author
M.Pikridas (m.pikridas@cyi.ac.cy) upon reasonable request. The PollyXT
lidar (http://polly.tropos.de, last access: 10 May 2017, Baars et al., 2016) data sets analysed
during the current study are available from the co-authors Albert Ansmann
(albert@tropos.de) and Eleni Marinou (elmarinou@noa.gr).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-2897-2018-supplement.
JS, AA conceived the experiment, EM, MP, MK, CK, RE conducted
the experiment(s), DM analysed the data, prepared the figures and wrote the
manuscript. HB applied corrections to lidar profiles. GB supervised the
work and contributed to the writing of the manuscript. PK, IB, AT, EM
and VA supported the data analysis procedure. All authors reviewed the
manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
This project received funding from the European Union's Seventh Framework
Program (FP7) project BACCHUS (Impact of Biogenic versus Anthropogenic
emissions on Clouds and Climate: towards a Holistic UnderStanding) under
grant agreement no. 603445, and the European Union's Horizon 2020 research
and innovation program ACTRIS-2 (Aerosols, Clouds and Trace gases Research
InfraStructure Network) under grant agreement no. 654109. The authors extend
special thanks to Robin Lewis Modini from the Paul Scherrer Institute for
assisting in the Mie calculations. Panagiotis Kokkalis acknowledges the funding of the Greek
State Scholarship Foundation: IKY. Part of this project is implemented within
the framework of the Action “Reinforcement of Postdoctoral Researchers” of the Operational Program “Human Resource Development, Education and
Lifelong Learning”, and is co-financed by the European Social Fund (ESF)
and the Greek government (NSRF, 2014–2020).
Edited by: Szymon Malinowski
Reviewed by: two anonymous referees
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