AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-949-2018Characterization of smoke and dust episode over West Africa: comparison of MERRA-2 modeling with multiwavelength Mie–Raman lidar observationsVeselovskiiIgoriveselov@hotmail.comGoloubPhilippePodvinThierryTanreDidierda SilvaArlindoColarcoPeterhttps://orcid.org/0000-0003-3525-1662CastellanosPatriciaKorenskiyMikhailhttps://orcid.org/0000-0002-7374-6896HuQiaoyunWhitemanDavid N.Pérez-RamírezDanielhttps://orcid.org/0000-0002-7679-6135AugustinPatrickhttps://orcid.org/0000-0003-0966-2468FourmentinMarchttps://orcid.org/0000-0003-1385-2547KolgotinAlexeiPhysics Instrumentation Center of GPI, Troitsk, Moscow, RussiaJoint Center for Earth Systems Technology, UMBC, Baltimore, USANASA Goddard Space Flight Center, Greenbelt, USALaboratoire d'Optique Atmosphérie, Université de Lille-CNRS, Villeneuve d'Ascq, FranceUniversities Space Research Association, Columbia, Maryland, USAApplied Physics Department, University of Granada, SpainLaboratoire de Physico-Chimie de l'Atmosphère, Université du Littoral Côte d'Opale, FranceIgor Veselovskii (iveselov@hotmail.com)16February201811294996921September201716January201813January20185October2017This 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/11/949/2018/amt-11-949-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/949/2018/amt-11-949-2018.pdf
Observations of multiwavelength Mie–Raman lidar taken during the SHADOW
field campaign are used to analyze a smoke–dust episode over West Africa on
24–27 December 2015. For the case considered, the dust layer extended from
the ground up to approximately 2000 m while the elevated smoke layer
occurred in the 2500–4000 m range. The profiles of lidar measured
backscattering, extinction coefficients, and depolarization ratios are
compared with the vertical distribution of aerosol parameters provided by the
Modern-Era Retrospective Analysis for Research and Applications, version 2
(MERRA-2). The MERRA-2 model simulated the correct location of the
near-surface dust and elevated smoke layers. The values of modeled and
observed aerosol extinction coefficients at both 355 and 532 nm are
also rather close. In particular, for the episode reported, the mean value of
difference between the measured and modeled extinction coefficients at
355 nm is 0.01 km-1 with SD of 0.042 km-1. The
model predicts significant concentration of dust particles inside the
elevated smoke layer, which is supported by an increased depolarization ratio
of 15 % observed in the center of this layer. The modeled at
355 nm the lidar ratio of 65 sr in the near-surface dust
layer is close to the observed value (70 ± 10) sr. At
532 nm, however, the simulated lidar ratio (about 40 sr) is
lower than measurements (55 ± 8 sr). The results presented
demonstrate that the lidar and model data are complimentary and the synergy
of observations and models is a key to improve the aerosols characterization.
Introduction
Atmospheric aerosols are an important factor influencing the Earth's
radiative budget, though its impact is still highly uncertain due largely to
the complicated mechanisms of aerosol–cloud interaction. Aerosol particles
serve as cloud condensation nuclei and ice-nucleating particles, creating a
strong impact on cloud and precipitation formation. However, different aerosol
types differ significantly in their ability to initiate drop and ice crystal
nucleation. There is thus a clear need for a better knowledge on vertically
resolved optical, physical, and chemical aerosol properties. Lidar is
a recognized instrument for vertical profiling of aerosol properties, and the
possibility to invert lidar observations at several wavelengths to aerosol
microphysical properties has been extensively studied both theoretically and
experimentally over the past two decades (e.g., Müller et al., 1999, 2016;
Veselovskii et al., 2002; Böckman et al., 2005). These studies revealed
the importance of using Raman or HSRL (high spectral resolution lidar)
systems, which allow independent measurements of aerosol extinction and
backscattering coefficients to be made. At present, the most practical
configuration of Raman (HSRL) lidar is based on a triple wavelength
Nd:YAG laser. Such a lidar provides the so-called
3β+ 2α set of observations, including three backscattering
(355, 532, 1064 nm) and two extinction (355, 532 nm)
coefficients.
However, the problem of inversion of 3β+ 2α observations is
underdetermined (Chemyakin et al., 2016; Alexandrov and Mishchenko, 2017). As
a result, instead of a unique solution, a family of solutions should be
considered, leading to an increase in retrieval uncertainties. Still the
estimation of volume density (V) and effective radius (reff)
with uncertainty below 30 % is possible, especially when the fine mode in
the particle size distribution (PSD) is predominant (e.g., Veselovskii et al.,
2004; Müller et al., 2005, 2016; Pérez-Ramírez et al., 2013).
The refractive index (RI) can be also estimated from the measurements,
although the uncertainty of such estimation is significant: for the real part
(mR) of RI the uncertainty is normally about ± 0.05 and for
the imaginary part (mI) it is about 50 % when
mI> 0.01 (Veselovskii et al., 2004; Müller et al.,
2016). Proposed improvements of inversion schemes were considered in recent
publications (Chemyakin et al., 2014; Kolgotin et al., 2016), but these
improvements are not able to resolve the fundamental issue: the information
content of 3β+ 2α observations is insufficient to support
exact solution of the problem and additional information should be used in
retrievals to improve the accuracy of the retrieved products (Veselovskii
et al., 2005; Burton et al., 2016; Alexandrov and Mishchenko, 2017; Kahnert
and Andersson, 2017).
We should recall also that in the inversion schemes considered, the
RI is normally assumed to be spectrally and size independent,
which is generally not the case in the atmosphere. The irregularity of the
particles shape can be also a significant error source. Moreover, the volume
density and effective radius obtained from 3β+ 2α
observations are attributed to the whole size distribution, which is of
limited practical use because of the importance of characterizing the
particle properties separately for the fine and coarse modes. Considering
these issues makes the inverse problem even more underdetermined, emphasizing
the need for additional input information.
One opportunity to get this additional information is by combining the lidar
observations with aerosol transport models (Kahnert and Andersson, 2017).
Models provide the vertical distribution of mass mixing ratios of chemical
aerosol components, which can be used as “initial guess” in the inversion
scheme. Modern-Era Retrospective Analysis for Research and Applications, version 2
(MERRA-2), offers a unique opportunity to provide such an “initial
guesses” of the vertical structure of aerosol chemical composition. MERRA-2
is produced with NASA's global Earth system model, GEOS-5 (Goddard Earth
Observing System version 5) (Gelaro et al., 2017) and includes an online
coupling with the Goddard Chemistry, Aerosol, Radiation and Transport model
(GOCART), which allows for assimilation of aerosol optical depth (AOD) from
spaceborne and surface instruments such as MODIS, AVHRR, MISR, and AERONET
(Randles et al., 2017). The fundamental data that MERRA-2 provides are
vertical profiles of the mass mixing ratios of five aerosol components: dust,
sea salt (SS), black and organic carbon (BC and OC), and sulfate (SU) aerosols. The main optical
parameters related to lidar measurements, such as aerosol extinction and
backscattering coefficients can be calculated basing on these data. The
principal question arising, however, is how well the reanalysis reproduces
independent observations and thus can provide a realistic initial guess for
a lidar inversion scheme. Buchard et al. (2017) and Randles et al. (2017)
extensively validated MERRA-2 with independent surface and aircraft
observations of particulate matter (PM2.5) and AOD, as well as
space-based observations of absorption aerosol optical depth and aerosol
index. The extinction profiles derived from airborne HSRL measurements were
also compared with modeling, finding generally good agreement between the
observations and MERRA-2.
For global validation of the aerosol vertical distribution, the modeled
profiles of attenuated backscatter were compared to spaceborne Cloud–Aerosol
Lidar with Orthogonal Polarization (CALIOP) observations (Winker et al.,
2009), and a good consistency between simulations and observations was
reported (Nowottnick et al., 2015; Buchard et al., 2017). Additional
opportunities for model validation are provided by ground-based
multiwavelength Raman or HSRL systems. Such lidars by their nature have
limited spatial coverage but are well suited for the characterization of the
vertical distribution of particle properties at a chosen location.
In our paper, we consider Raman lidar observations taken during a smoke–dust
episode over West Africa in December 2015 during the SHADOW (SaHAran Dust Over West Africa) campaign
(Veselovskii et al., 2016) and compare the vertical profiles of particle
parameters with MERRA-2. The simultaneous presence of dust and smoke layers
in the atmosphere provides an opportunity to test the ability of the model to
reproduce the vertical structure of aerosol properties over the observation
site.
Measurement setup and data analysisObservation site
The observation site is located at the Institute for Research and Development
Center (IRDC), Mbour, Senegal (14∘ N, 17∘ W). Information
about the SHADOW campaign and
instruments at the IRDC site can be found in the recent publication by
Veselovskii et al. (2016). During the SHADOW campaign data from three lidar
instruments were available:
Cimel CE-370 micropulse lidar (www.cimel.fr) operated 24 hday-1 at 532 nm, allowing
real-time monitoring of aerosol and cloud layers.
Doppler lidar Windcube WLS 100 (www.leosphere.com) provided continuous monitoring of the wind field in the
range from 100 m to 5 km with 50 m range resolution at 1543 nm wavelength.
Multiwavelength Mie–Raman polarization lidar LILAS (LIlle Lidar AtmosphereS) allowed simultaneous detection of
elastic and Raman backscatter signals and thus provided 3β+ 2α observations along with depolarization
ratio at 532 nm.
LILAS measurements were performed from inside a laboratory building through
a window at an angle of 47∘ with respect to the horizon. Acquiring
Raman backscatter at 408 nm also permits profiling of the water vapor
mixing ratio (WVMR) (Whiteman et al., 1992). For calibration of the water
vapor channel, radiosonde launches from Dakar (about 70 km away from
Mbour) were used. The large separation between the lidar and radiosonde
locations prevented an accurate calibration, so the WVMR data were used
mainly to monitor the relative change of the water vapor content. The
temporal resolution of the measurements was approximately 3 min. The
backscattering coefficients and depolarization ratio were calculated with
range resolution 7.5 m (corresponding to a vertical spatial
resolution of 5.5. m). The height resolution of the extinction
coefficient measurements varied with height from 50 m (at
1000 m) to 125 m (at 7000 m). The measurements were
performed mainly in the nighttime. In the daytime, the Raman measurements
at 532 nm were possible only up to 2–3 km height, so
continuous night- and daytime Raman measurements were performed only for
selected episodes.
The particle extinction (α) and backscattering (β) coefficients
at 355 and 532 nm are calculated from elastic and Raman backscatter
signals, as described in Ansmann et al. (1992). Backscattering coefficients
at 1064 nm (β1064) were calculated by the Klett method
(Klett, 1981).
In the data analysis both volume (δv) and particle
(δ) depolarization ratios are considered. These ratios are defined as
δv=β⊥p+β⊥mβIIp+βIIm=CP⊥PII,δ=β⊥pβIIp.
Here P is the power of the elastic backscatter signal. Superscripts “p” and “m” indicate particle and molecule
backscattering, while subscripts “⊥” and “II” indicate cross- and co-polarized components, and C is the
calibration constant. Particle depolarization is calculated as suggested by Freudenthaler et al.
(2009):
δ=(1+δm)δvR-(1+δv)δm(1+δm)R-(1+δv).
Here δm is the molecular depolarization ratio and R is the aerosol scattering ratio:
R=βp+βmβm.
For further convenience we will use the notations β=βIIp+β⊥p and
α=αp. To characterize the spectral dependence of β and α, the backscattering and
extinction Ångström exponents (BAE and EAE) for wavelengths λ1 and λ2 are calculated as
Aβ=lnβλ1βλ2lnλ2λ1,Aα=lnαλ1αλ2lnλ2λ1.
The lidar-derived backscattering and extinction coefficients can be inverted
to the particle microphysical properties, as described at Veselovskii
et al. (2002). The only constraints on the permitted RI and the
PSD are that the RI is considered to be
wavelength independent and that the concentration of the particles with radii
below some rmin and above some rmax is zero, where
the values of these radii are found in the process of inversion.
MERRA-2 aerosol reanalysis
The MERRA-2 simulations of aerosol properties over the observation site were
made using the GOCART model (Chin et al., 2002) integrated within GEOS-5. The
model includes representations of dust, SS, BC, OC, and SU aerosols. The
aerosol components are assumed to be externally mixed. The optical properties
of these aerosol components are summarized in Appendix A. Sulfate and
carbonaceous aerosols are both assumed to be in the fine mode. Sea salt and
dust are both represented by five size bins spanning 0.1–10 µm
radius for dust and 0.03–10 µm dry radius for sea salt, allowing
for the simulation of both the fine and coarse fractions of each. A more
complete description of how GOCART is implemented in GEOS-5 is provided in
Colarco et al. (2010), which also includes a detailed evaluation of the model
with respect to MODIS, MISR, and AERONET aerosol optical depth observations.
The aerosol optical properties are primarily based on Mie calculations using
the particle properties as in Colarco et al. (2010) and Chin et al. (2002),
with spectral refractive indices from the Optical Properties of Aerosols and
Clouds (OPAC; Hess et al., 1998) database. However, for dust, non-spherical
optical properties derived from an offline database are used (Colarco
et al., 2014). For SS, SU, and the hydrophilic portion of
carbonaceous aerosol, hygroscopic growth is considered following Chin
et al. (2002), with growth factors from OPAC (Gerber, 1985). The RI for organic carbon is based on the 100 % brown carbon case from
Hammer et al. (2016) and it is implemented as described in Colarco
et al. (2017).
The sources of aerosols in the model include wind-speed-based emissions of
dust and sea salt, fossil fuel combustion, biomass burning, biofuel
consumption, biogenic particulate organic matter, and oxidation of dimethyl
sulfide and SO2, which includes volcanic sources. Aerosol sinks
include convective scavenging, dry deposition, and wet removal, where aerosol
hygroscopic growth is considered in the calculation of particle fall velocity
and deposition velocity. The model resolution is
0.5∘× 0.625∘ latitude by longitude with 72
hybrid-eta layers from the surface to 0.01 hPa. Additional details of
the simulation can be found in Randles et al. (2017) and Buchard
et al. (2017).
In MERRA-2, aerosol and meteorological observations are jointly assimilated
within GEOS-5. Aerosols are assimilated by means of analysis splitting and
the local displacement ensemble methodology (Buchard et al., 2015, 2016). The system assimilated MODIS, AVHRR, MISR, and AERONET
550 nm AOD. AERONET measurements are interpolated to 550 nm
using the Ångström relationship and the closest available channels,
generally 500 and 675 nm. The assimilation determines an AOD
increment, which corrects the model AOD in a way that minimizes the
differences between the model and observations. The AOD increment both
corrects for misplaced aerosol plumes and scales the aerosol mass mixing
ratio to match the observations. The 2-D AOD increment does not contain
enough information to correct either the vertical distribution of aerosols or
the aerosol composition. Thus, the model determines the aerosol speciation,
optical properties, and vertical distributions, while the AOD increments
modulate the aerosol mass. Thus, the assimilated aerosol distributions and
physical and optical properties arise from the forecast model assumptions and
the formulation of the aerosol data assimilation algorithm.
Five-day backward trajectories for the air mass in Mbour at
altitudes 750, 1500, and 3500 m, on 25 December 2015 at 04:00 UTC,
together with the map of forest fires on 20 December 2015.
Experimental results
The smoke layers from forest fires near the Equator were regularly observed
over the instrumentation site during the wintertime measurement sessions made
in December 2015–January 2016. In our study we will focus on a strong smoke
episode that occurred on 24–27 December 2015. Air mass back trajectories
over Mbour on 25 December 2015 at 04:00 UTC are shown in Fig. 1 together
with map of fires on 20 December 2015
(https://worldview.earthdata.nasa.gov).
Range-corrected lidar signal (in arbitrary units) of Cimel MPL for
24–27 December 2015.
The air masses below 1000 m (red line) are transported over the
desert and are strongly loaded by dust, while air masses at 3000 m
(green line) arrive from the south and pass over the regions of forest fires
and thus can transport smoke particles. The Cimel MPL operated continuously
through the period of 24–27 December and thus monitored the arrival and
evolution of the smoke layer, as shown in Fig. 2. An elevated smoke layer
appears on 24 December around 00:00 UTC. The aerosol layer becomes thicker
during the day but remains confined to the height interval of
2.5–4.0 km and stays well separated from the dust layer, which
extends from the ground to approximately 2.0 km. This structure of
the layers is preserved throughout 25 December, as well. Cirrus clouds appear
at 08:00 UTC on 24 December at a height of 8 km, soon after the
smoke layer arrival (Fig. 2a) and persist throughout the smoke episode.
After 12:00 UTC on 24 December the clouds start descending and by 07:00 UTC
on 25 December the cloud base is below 6 km (Fig. 2b). On 26 December
strong precipitation of ice particles occurs (Fig. 2c) and, finally, on
27 December the cloud is located at the top of the smoke–dust layer
(Fig. 2d).
Height–temporal distributions of the backscattering coefficient and
particle depolarization ratio at 532 nm together with the water vapor
mixing ratio derived from the Raman lidar measurements on the nights of
23–24 (a) and 24–25 December 2015 (b).
Multiwavelength Raman lidar observations are available for the 23–25
December period only. The height–temporal evolution of the particle
backscattering coefficient β532, depolarization ratio
δ532,
and water vapor mixing ratio w measured by Raman lidar on the nights 23–24
and 24–25 December 2015 are shown in Fig. 3. Due to the geometrical overlap
factor the extinction data can be processed starting from approximately
750 m, and thus plots of all parameters start at this height. The
depolarization ratios of pure dust observed during SHADOW are in the
30–35 % range (Veselovskii et al., 2016), while the depolarization ratio
of smoke at 532 nm normally is below 10 % (e.g., Tesche et al.,
2011; Burton et al., 2015). Hence depolarization measurements provide
a convenient way to separate the aerosols into dust and smoke components.
On the night of 23–24 December the dust layer extends up to 2500 m,
but with a high depolarization ratio (> 30 %), which is usually associated
with pure dust, is observed only below 1000 m, meaning that in
1000–2500 m range the dust is probably mixed with smoke. The optical
depth of the elevated smoke layer is rather small on 23–24 December (0.1 at
05:00 UTC), but on 24–25 December it increases up to 0.25, making possible
the calculation of extinction coefficients from the Raman lidar signals. For
analyzing the vertical distribution of smoke and dust particle parameters, we
focus on the nighttime measurements of 24–25 December 2015.
Time–height section of horizontal wind direction (arrows) and wind
speed (color map) deduced from Doppler lidar during 24–25 December 2015.
Leftward and downward arrows represent, respectively, easterly wind and
northerly wind.
Time–height section of the logarithmic range-corrected lidar signal
(in arbitrary units) deduced from the Doppler lidar measurements during the
night of
24–25 December 2015.
Figure 4 shows the horizontal wind direction and speed measured by the wind
lidar on 24–25 December. The range-corrected signal of the wind lidar can be
evaluated starting from 100 m height, and the corresponding height–temporal image is shown in Fig. 5. The wind speed was measured in the dust
layer (< 1500 m) for the whole period, but inside the smoke
layer the backscatter signal is lower, so the measurements were possible only
in the period of 16:00–22:00 UTC on 24 December. During 24–25 December
2015, the wind in the low troposphere (< 1500 m) is mainly
dominated by the easterly Harmattan continental trades. Deceleration and
acceleration of the lower part of the Harmattan (< 1000 m) are
observed, respectively, in the beginning of the afternoon and during the
night. The vertical profile of the wind speed demonstrates the presence of
a low-level jet (LLJ), where the maximum wind speed (jet speed) is located at
a height of 350 m (LLJ height) at 01:00 UTC. LLJs are known to
contribute to regional horizontal aerosol transport and to increase vertical
mixing. Indeed, the LLJ occurrence at 01:00 UTC increases the aerosol
loading by transporting desert dust. The corresponding increase of
backscattering due to the LLJ at 01:00 UTC on 25 December can also be seen
in Fig. 3.
Vertical profiles of (a) temperature T, potential
temperature Θ, (b) wind direction and speed, and
(c) relative humidity (RH) and water vapor mixing ratio (WVMR)
measured by the radiosonde in Dakar at 00:00 UTC on 25 December 2015. Solid
line in plot (a) shows the aerosol backscattering coefficient at
532 nm in arbitrary units measured by the Raman lidar at 21:00 UTC
on 24 December.
The vertical profiles of temperature T, potential temperature Θ,
wind direction and speed, together with relative humidity (RH) and WVMR from radiosonde launched from Dakar at 00:00 UTC on
25 December 2015, are shown in Fig. 6. The profile of wind speed and wind
direction obtained from the sonde confirms that the LLJ observed with lidar
at Mbour is not a local phenomenon, because it is also observed at Dakar. The
vertical profile of the potential temperature suggests that the nocturnal
boundary layer top corresponds to the LLJ height. Above 3000 m,
the lidar and sonde depict southerly winds which transport the smoke plume.
The water vapor mixing ratio increases above 2500 m; as a result the
RH in the smoke layer reaches 75 % while in the dust layer RH is below
30 %.
Vertical profiles of the aerosol backscattering (β355,
β532, β1064) and extinction (α355,
α532) coefficients together with the particle depolarization ratio
(δ532) for three temporal intervals: 19:00–23:00, 01:00–04:00, and
04:00–07:00 UTC on 24–25 December 2015. The values of δ532 are
multiplied by factor 0.02.
Extinction (A355/532α) and backscattering
(A355/532β, A532/1064β) Ångström exponents
together with backscattering coefficient β532 for the same three
temporal intervals as in Fig. 7.
To quantify the vertical distribution of particle parameters, Fig. 7 shows
the profiles of backscattering (β355, β532,
β1064), extinction (α355, α532) coefficients and
the particle depolarization ratio (δ532) derived from Raman lidar
measurements for three temporal intervals on the night of 24–25 December:
19:00–23:00, 01:00–04:00, and 04:00–07:00 UTC. For the profiles presented,
the uncertainty of both β and α computations is estimated to be
below 10 % for the Raman technique and to be below 20 % for
β1064 computation by the Klett method. The relative uncertainty of
depolarization measurements is below 15 %. The extinction and
backscattering Ångström exponents A355/532α,
A355/532β, and A532/1064β are given by Fig. 8.
For the first temporal interval (Fig. 7a) dust and smoke layers are well
separated. Extinction coefficients α355 and α532 differ
in the smoke layer (α355>α532), but inside the
near-surface dust layer (below 1750 m) the extinction values are
nearly the same. The depolarization ratio is
δ532= 35 ± 5 % at 750 m and it gradually
decreases with height to 27 ± 4 % at 1750 m. Above that
height δ532 decreases quickly, indicating an increase in the
contribution of smoke particles. For the second and third temporal intervals
the dust and smoke layers appear to mix, leading to layering in the
backscattering coefficient in the 1000–2000 m range. The EAE in this
range is increased up to 0.5 (Fig. 8c), indicating that these layers may
contain significant amounts of smoke.
The EAE of pure dust observed during SHADOW is slightly negative
A355/532α≈-0.1 (Veselovskii et al., 2016). In
Fig. 8a the EAE below 1500 m is about 0.2 ± 0.2, so the dust
likely contains some amount of smoke. Values of EAE close to zero are
observed in Fig. 8b and c below 1000, where the depolarization ratio increases
up to 35 ± 5 %. Inside the dust layer
β355<β532, so the corresponding backscattering
Ångström exponent is negative. The negative values of
A355/532β have been already reported by Veselovskii
et al. (2016), where negative BAE was attributed to an increase of the
imaginary part of the complex RI at 355 nm compared to
532 nm. In the center of the elevated layer at 3100 mδ532= 14 ± 3 %, while at the top of this layer
δ532 decreases to 6 ± 1.5 % (Fig. 8a), indicating
a possible presence of dust particles in the center of the elevated layer. The
loading of elevated layer with dust particles is supported also by the
profiles of A355/532β: for all three temporal intervals
A355/532β demonstrates the dip in the center of the elevated layer,
while A355/532α and A532/1064β do not decrease in
the 2500–4000 m range. As mentioned, for pure dust
A355/532β is negative, so presence of dust in the center of smoke
layer should decrease the backscattering Ångström exponent. The
presence of dust in the smoke layer is not surprising, because upwelling
airflows in forest fires region can lift a significant amount of dust
together with biomass burning products (Nisantzi et al., 2014). We should
also
mention that the spectral dependence of the imaginary part (and thus
A355/532β) depends on the dust origin. In particular, no negative
values of A355/532β of dust were reported during the SAMUM
campaign, so the lidar ratios at 532 nm and 355 nm were
close (Tesche et al., 2011).
Lidar ratios at 355 nm (solid lines) and 532 nm
(dash lines) for three temporal intervals from Fig. 7.
Lidar ratio profiles at 355 and 532 nm, for the same temporal
intervals as in Fig. 7, are shown in Fig. 9. The lidar ratios in the dust
layer at 532 and 355 nm for 19:00–23:00 UTC period are
LR532= 55 ± 8 sr and
LR355= 70 ± 10 sr, respectively. At the top of
the elevated layer, where the smoke particles are predominant, the lidar
ratios for the same period are higher:
LR532= 65 ± 10 sr and
LR355= 75 ± 11 sr. Due to the presence of dust
in the center of the elevated layer, the height dependence of lidar ratios
shows a decrease, with a minimum at approximately 3000 m for all
three temporal intervals. The decrease is more pronounced at 532 nm
because the difference between smoke and dust lidar ratios is larger at this
wavelength. The lidar ratios below 2000 m at 01:00–04:00 and
04:00–07:00 UTC become strongly oscillating because of high gradients of
backscattering and extinction coefficients at low altitudes and are not shown
due to high uncertainties.
Particle depolarization ratio as a function of the extinction
Ångström exponent derived from data shown in Figs. 7 and 8.
Figure 10 shows the dependence of the particle depolarization ratio
δ532 on the extinction Ångström exponent derived from data
in Figs. 7 and 8. The depolarization ratio monotonically decreases while EAE
rises from 0 to 0.9. Thus observed high values of the depolarization ratio
are attributed to big dust particles with EAE close to zero, while small
smoke particles are characterized by low depolarization (below 10 %). If
depolarization ratios of smoke δs and dust
δd are known, the contributions of smoke and dust
particles to the total backscattering can be separated
β=βs+βd (Sugimoto and Lee,
2006; Tesche et al., 2009; Miffre et al., 2012; David et al., 2013; Burton
et al., 2014). Assuming that the depolarization ratios of dust and smoke
particles do not change with height the contributions βd
and βs can be calculated as suggested by Tesche
et al. (2009):
βd=β(δ-δs)(δd-δs)(1+δd)(1+δ)andβs=β-βd.
In our computations we used values δd=35% and δs=7%.
Contributions of dust and smoke to the total backscattering
coefficient β532 together with particle depolarization ratio
δ532 for three temporal intervals on 24–25 December 2015. Magenta
and grey regions correspond to dust and smoke contribution to total
scattering β532=β532d+β532s.
The results of the decomposition of β532 for
β532d and β532s components for the
same three temporal intervals as in Fig. 7 are shown in Fig. 11. This figure
presents the total backscattering coefficient β532 together with the
particle depolarization ratio δ532. The dust contribution to
backscattering is marked with magenta, while the residual backscattering
β532-β532d is attributed to the smoke and is
marked with grey. For the height regions with low backscattering the
uncertainty of β532 is high, so the decomposition for these regions
is not shown. The dust is predominant below 1700 m for
19:00–23:00 UTC period, but even the elevated layer contains
a significant amount of dust: at 3100 mβ532d≈0.3β532. After 01:00 UTC the
smoke layers descend (Fig. 3) and their contribution to backscattering
becomes significant down to 1000 m height.
Vertical profiles of extinction coefficients at
(a) 03:00 UTC, (b) 21:00 UTC and
(c) backscattering coefficients at 21:00 UTC on 24 December 2015
from MERRA-2 model at 532 nm. Profiles are given for five aerosol
components: dust, black carbon (BC), organic carbon (OC), sea salt (SS),
and sulfates (SU), together with total extinction α532 and
backscattering β532.
Comparison of lidar measurements with MERRA-2
MERRA-2 provides the vertical distribution of mass mixing ratios of five
aerosol components, so for each of these components the extinction,
backscattering coefficients, and depolarization ratios can be calculated. The
vertical profiles of extinction coefficient of dust, BC, OC, SS, and SU, together with total
extinction α532, are shown in Fig. 12 for 03:00 UTC and 21:00 UTC
on 24 December 2015. At 03:00 UTC the aerosol is localized below
3000 m. Dust extinction is predominant, but contribution of OC to the
total extinction coefficient rises with height,, reaching maxima at
2250 m. The presence of a significant amount of OC agrees with the
low values of depolarization ratio above 1500 m for this temporal
interval in Fig. 3.
At 21:00 UTC an elevated layer with a maximum of extinction at
3150 m is observed (Fig. 12b). In this layer OC and dust provide
similar contributions to extinction (about 40 % at 3150 m
height). From the results shown in Fig. 11a we can estimate the contribution
of dust to α532 in the center of the elevated layer as 30 % (by
assuming the dust lidar ratio LR532= 55 sr), so the
measured and simulated dust contributions are in good agreement. Below
1750 m the dust is the main contributor to the extinction coefficient
providing 88 % of α532 at 1000 m (Fig. 12b). The
observed dust contribution to α532 at the same height is about
90 % (Fig. 11a), which again shows good agreement between the model and
measurements. Total contribution of BC and SU to extinction is below 20 %
in the elevated smoke layer, and in the near-surface dust layer their
contribution is negligible. The extinction coefficients can be recalculated
to the backscattering using model lidar ratios of the aerosol components.
Figure 12c shows the profiles of backscattering coefficients at
532 nm computed for the same temporal interval as in Fig. 12b. The
simulation of the backscattering coefficient is more challenging than that of
extinction, because backscatter depends more strongly on the particle
morphology and RI. A detailed comparison of measured and
modeled profiles of backscattering coefficients will be performed later in
this section.
Comparison of extinction profiles at (a) 355 nm and
(b) 532 nm derived from Raman lidar measurements (line) and
modeled by MERRA-2 (line + symbols) on the night of 24–25 December 2015.
Model profiles are provided at 18:00, 21:00, 00:00, 03:00, and 06:00 UTC. The
lidar measurements are given for temporal intervals centered at 19:00,
21:00, 00:00, 03:00, and 06:00 UTC. For each profile, 2 h of measurements are
averaged. The profiles are shifted relatively to each other by
0.2 km-1.
As mentioned, the comparison of model and observed values is more
straightforward for extinction coefficients. Figure 13 shows the time series
of extinction profiles at 355 and 532 nm modeled for the night of
24–25 December 2015 at 18:00, 21:00, 00:00, 03:00, and 06:00 UTC. The profiles
are shifted relative to each other by 0.2 km-1. For comparison,
the same figure presents the profiles of extinction coefficients derived from
Raman lidar measurements. The model reproduces well the location of the
elevated smoke layer as well as the top of the near-surface dust layer.
However, the model does not resolve the oscillations of extinction profile
below 2000 m at 03:00 and 06:00 UTC on 25 January.
To quantify the difference between the measured (αmeas) and
modeled (α⟂) extinction coefficients the difference Δα=αmeas-α⟂ was calculated. The
statistical analysis of the frequency distribution of Δα for all
five profiles in Fig. 13 shows that at 355 nm the mean value of
Δα is -0.01 km-1 and SD of 0.042 km-1.
With typical values of extinction coefficient in elevated smoke layer and
near-surface dust layer being on the order of 0.2 km-1, the
relative difference of modeled and measured extinction is estimated to be
below 25 % for the time period considered. The results of statistical
analysis for α532 nm are similar.
Aerosol optical depth at 355 nm on 23–24 December 2015
obtained from MERRA-2 (line + symbols) and from the Raman lidar
measurements (solid lines). The results are given for two height intervals:
750–2000 m (red) and 2500–4500 m (black). Zero of timescale corresponds to 00:00 UTC on 24 December.
To analyze how well the model reproduces the temporal variations of aerosol
optical depth, Fig. 14 presents AODs at 355 nm on 23–24 December
2015 for two height intervals: 750–2000 and 2500–4500 m. The first
interval corresponds to the near-surface dust layer, while the second
interval corresponds to the elevated smoke layer. The AOD is calculated from
the Raman backscatter channel, and in the daytime measurements could be
processed only in the dust layer due to enhanced background noise. Thus daytime measurements in the elevated smoke layer are not plotted. The time of
the appearance of the smoke layer is well represented in the model results
(about 00:00 UTC on 24 December), but the lidar-derived AOD of this
layer increases rapidly from the first appearance of the layer, while in the
model the rapid increase in AOD growth starts approximately 5 h later. The
model predicts that the maximum value of AOD in the smoke layer (0.27) is
reached at 20:00–24:00 UTC interval, which reasonably agrees with
observations: mean value of measured AOD for this interval is
0.23 ± 0.02. After midnight the modeled AOD of the smoke layer
decreases quickly, while lidar measured AOD stays about 0.25. The measured
AOD of the near-surface layer agrees with the model. The observed AOD exceeds
the model values in the beginning (at 00:00 UTC on 24 December measured and
modeled AODs are 0.24 and 0.175, respectively), but after 10:00 UTC the
values are in better agreement. Thus, we can conclude that the model
reproduces the temporal variability of AOD in the dust and smoke layers.
The agreement between modeled and observed extinction profiles provides an
opportunity to test how well the backscattering coefficients can be modeled.
Simulation of backscattering coefficients is especially challenging for dust
for several reasons. First of all, we are not confident in the accuracy of
the presumed scattering phase function in the backward direction. Second, the
backscattering coefficient strongly depends on the particle RI,
in particular on the imaginary part, which may vary over a wide range
depending on dust origin. The in situ ground measurements in West Africa,
performed during the SAMUM field campaign, demonstrate that the mean value of
mI for dust episodes is about 0.003 at 532 nm and 0.02
at 355 nm. However, deviation from these mean values for every
individual measurement can be significant (Müller et al., 2009; Kandler
et al., 2011; Ansmann et al., 2011). The imaginary part of RI of dust in the
model is assumed to be 0.007 at 355 nm, following previous OMI data
analysis (Torres et al., 2007), and 0.0025 at 532 nm.
Backscattering coefficients at (a) 355 nm and
(b) 532 nm measured by Raman lidar (solid line) and modeled
by MERRA-2 (line + symbols) on the night of 24–24 December 2015. Profiles
are shifted relatively to each other by 0.0025 km-1sr-1. The
temporal intervals are the same as in Fig. 13.
Extinction (A355/532α) and backscattering
(A355/532β) Ångström exponents together with the particle
depolarization ratio δ532 obtained from lidar measurements (line)
and from MERRA-2 modeling (line + symbols). Lidar data are averaged over
the
19:00–23:00 UTC period while model data are given for 21:00 UTC.
Figure 15 shows measured and modeled backscattering coefficients at 355 and
532 nm for the same five temporal intervals as in Fig. 13. At
355 nm the modeled and measured values agree for both the smoke and
dust layers. However, at 532 nm the aerosol backscattering
coefficients agree only inside elevated layer, while below 1750 m the
modeled β532 significantly exceeds the measured values. As
mentioned, the modeled lidar ratio LR532 for the mixture is close
to 40 sr at 1000 m, while the measured lidar ratio in the
near-surface dust layer is 55 ± 8 sr. The reason for this
disagreement could be that the assumed imaginary part of the RI
for dust (0.0025 at 532 nm) is too low. Recall, however, that we
cannot determine the imaginary part of the RI for dust by
simply adjusting the modeled lidar ratio to the measured one, because the
lidar ratio depends on several factors besides mI, such as the
PSD and the aspect ratio of the ellipsoids used in the
model. It is possible that the PSD in the model is weighted too
much toward fine-mode dust. The modeled and measured particle
intensive parameters, such as extinction A355-532α and
backscattering A355-532β Ångström exponents and
the particle depolarization ratio δ532, are shown in Fig. 16. The
measurements are averaged over 19:00–23:00 UTC interval while modeled
values are given for 21:00 UTC. The model reproduces well the observed
vertical distribution of A355-532α in both the dust and the
elevated layer. As follows from Fig. 7a, inside the dust layer
β355<β532, so the corresponding A355-532β is
negative with a minimum value of about -0.4. The model predicts values of
A355-532β as low as -1.4. The modeled BAE is sensitive to the
choice of the imaginary part of RI at 355 and 532 nm and, as
mentioned, the chosen mI(532) = 0.0025 may be too low for
this episode. In the elevated layer the modeled A355-532β is
close to the observed one. The modeled BAE has no minimum in the center of
the
elevated layer, because the modeled ratio of dust and OC aerosol
concentrations shows only a small variation throughout the elevated layer.
The model reproduces reasonably well the depolarization in the elevated
layer, but inside the dust layer the modeled δ532 is significantly
lower than what is observed (22 % compared to 35 %). This problem is
well known: the spheroidal model underestimates the depolarization ratio when
typical dust PSD and complex RI are used (Veselovskii et al.,
2010; Wiegner et al., 2009; Müller et al., 2013; Nowottnick et al.,
2015).
Water vapor mixing ratio derived from Raman lidar measurements
(solid line) and obtained from the model (line + symbols) on the night
of
24–25 December 2015. Temporal intervals are the same as in Fig. 13. The
profiles are shifted relatively each other by 5 gkg-1.
One of the MERRA-2 data products is WVMR, which helps to identify atmospheric
parcels, is critically important for determining atmospheric stability, and
serves as the source of water for aerosol hygroscopic growth. Figure 17 shows
five model profiles of WVMR together with the results of Raman lidar
measurements for the same temporal intervals as in Fig. 13. The model
reproduces rather well the WVMR profile inside the elevated layer
(2500–4500 m) on 24 December, though on 25 December the modeled
values in this range are lower than the observations. In the near-surface
dust layer, the deviation of modeled values from the measurements is larger.
Statistical analysis of the deviation of modeled values from lidar
measurements for all five profiles shows that mean difference is
0.04 gkg-1 with SD of differences of 1.6 gkg-1.
Thus in the elevated layer, where WVMR is approximately 8 gkg-1,
the agreement is quite good, but in the dust layer, which is characterized by
low water vapor content (below 4 gkg-1), the difference may be
up to 40 %.
Inversion of lidar measurements to particle microphysical properties
In the previous section, as validation of the model output we compared the
modeled aerosol optical parameters, such as extinction, backscattering
coefficients, and depolarization ratio with the values derived from lidar
measurements in a straightforward way. The comparison of particle
microphysical properties such as volume, effective radius, and complex
RI, however, is not straightforward, since it needs inversion
of the measurements and requires additional assumptions. In the case of dust
particles the inversion becomes especially challenging for the following
reasons:
The size distribution of dust contains a strong coarse mode with particle
radii extending up to ∼ 15 µm, and the estimation of
properties for such big particles is difficult since measurements are only
performed in the wavelength range 0.355–1.064 µm.
The inversions have to consider the RI as spectrally
independent. In fact, the imaginary part of the dust RI is spectrally
dependent with a strong enhancement at 355 nm compared to
532 nm.
The dust particles are not spherical and so the application of Mie formulas
for the forward modeling results in errors in computing the scattering phase
function.
Regarding the shape issue, one of the ways to mimic the scattering properties
of dust particles is to use the model of randomly oriented spheroids
(Mishchenko et al., 1997; Dubovik et al., 2006). The implementation of this
model for inversion of dust lidar measurements is described in Veselovskii
et al. (2010, 2016) and Müller et al. (2013). This algorithm was used
also for inversion of our 3β+ 2α observations. The range of
particle radius in the inversion has been set to a minimum and maximum of
0.075 and 15 µm, respectively. The real part of RI was allowed to
vary in the range 1.35–1.65, while the imaginary part varied in the range
0–0.02. The RI was assumed to be spectrally independent. The
effects of a possible spectral dependence of the imaginary part of RI were
considered in Veselovskii et al. (2016).
Profiles of (a) effective radius, (b) particle
volume, and (c) real part of the refractive index on 24 December 2015
retrieved from 3β+2α lidar measurements shown in Fig. 7a (solid
symbols) and provided by MERRA-2 for 21:00 UTC (open symbols). Inversion of
lidar measurements was performed in assumption of spherical particles (s) and
using the model of spheroids (ns).
Profiles of the effective radius, volume density, and real part of the
RI retrieved from optical measurements in Fig. 7a are shown in
Fig. 18. The inversion was performed for two cases, with the assumption of
all spherical particles or all spheroids. A realistic solution (for the
mixture of spherical and non-spherical particles) should be closer to
spheroids in the dust layer, while in the elevated layer (where
depolarization ratio is below 15 %) it should be closer to the results
obtained with spheres. The model results provided by MERRA-2 are shown on the
same plot. The effective radius and volume density obtained in assumption of
spherical particles are always higher than the values obtained with
spheroids. The modeled effective radius at 1100 m height is
1.1 µm, which is close to
reff= 0.95 ± 0.3 µm obtained from lidar
measurements using the spheroids model. Comparing the lidar retrievals with
model in the dust layer, we should keep in mind that inside
1500–2000 m height range the dust particles are mixed with biomass
burning products; thus the use of only spheroids in retrieval underestimates
the effective radius and volume. Moreover, accounting for the spectral
dependence of the imaginary part of the dust may additionally increase the
retrieved values of V and reff by factor 1.2–1.3 (Veselovskii
et al., 2016).
Lidar-derived effective radius in the elevated layer at 3000 m is
approximately 0.4 and 0.5 µm when spheroids and spheres are used,
respectively, while the modeled value is 0.3 µm. The reason for
the lower value of modeled effective radius is the contribution of black
carbon, which is characterized by small size and relatively low hygroscopic
growth. Recall that in the inversion of lidar measurements, the smallest
radius considered is 0.075 µm. Modeled values of the volume
density agree well with lidar retrievals in both dust and elevated layers.
The estimation of the real part of RI from lidar measurements is sensitive to
the type of kernel functions chosen for retrieval. In the regularization
algorithm the treatment of dust particles as spheres strongly underestimates
mR (Veselovskii et al., 2010), so results obtained with spheres
in the dust layer are not shown in Fig. 18c. At 1000 m the
mR retrieved with spheroids is 1.52 ± 0.05, which agrees
well with the modeled value. Inside the elevated smoke layer, where fine-mode
particles predominate, the application of spheroids overestimates
mR. The lidar-derived real part of RI at 3000 m is
1.43 ± 0.05 for spheres and 1.51 ± 0.05 for spheroids, so we
expect that the true value would lie within this. The simulated value of
mR= 1.50 in the elevated layer is quite high, which is
again the result of BC contribution.
The single scattering albedo at 355 nm (blue),
532 nm (green), and 1064 nm (red) on 24 December 2015
retrieved from 3β+ 2α lidar measurements shown in Fig. 7a
(solid symbols) and provided by the MERRA-2 model for 21:00 UTC
(line + open symbols). For inversion of lidar data the spheroids (ns)
were used below 2000 m and spheres (s) above 2000 m.
The single scattering albedo (SSA) is one of the key parameters to be
retrieved and conclusions about the potential of the multiwavelength lidar
method strongly rely on its ability to profile SSA. Figure 19 shows SSA at
355, 532, and 1064 nm. As mentioned, the spectral dependence of
mI was not accounted for and the algorithm retrieves an average
value of the imaginary part over the interval of 355–1064 nm. In
particular, for dust and OC the imaginary part is underestimated at
355 nm and overestimated at 532 and 1064 nm. As a result, in
the dust layer the retrieved SSA exceeds the model values at 355 nm,
while at 532 nm and 1064 the situation is opposite. Still at a height
of 1000 m, the difference between modeled and lidar-derived SSAs is
below 0.04 for all wavelengths. In the elevated layer, where the spectral
dependence of mI is less pronounced, the simulated and
retrieved SSAs agree well with a corresponding difference of less than 0.02.
Summary and conclusion
The synergy of lidar observations with the aerosol transport model has
a great potential to improve the characterization of aerosols properties, and
as a first step in such synergy one has to demonstrate how well observations
and models agree and describe the same aerosol scenario. For that we have
considered a smoke–dust episode over West Africa to compare the vertical
profiles of particle parameters modeled by MERRA-2 and retrieved from Raman
lidar measurements. In the case selected, the simultaneous presence of the
dust and smoke layers resulted in significant height variation of particle
parameters, providing a good opportunity to test the models' capability to
reproduce complicated vertical structure. Modeled and observed vertical
profiles of α355 and α532 show good similarity: MERRA-2
provides the correct location of both the near-surface and elevated layers.
The modeling of the dust lidar ratio is challenging due to irregularity of
the particles shape and due to the spectral dependence of the imaginary part
of the RI. The mI can change significantly for
dust of different origin and this variability may be accounted for in future
model developments. The modeled at 355 nm the lidar ratio of
65 sr in the near-surface dust layer is close to the observed value
(70 ± 10 sr). At 532 nm, however, the simulated dust
lidar ratio (about 40 sr) is lower than measurements
(55 ± 7 sr). This discrepancy may be an indication that
mI of dust during the episode considered is higher than the
value assumed in the model. Another possible explanation is that the model
PSD is too much weighted toward fine-mode dust. The
measured lidar ratios at the top of the elevated layer, where smoke particles
are predominant, are LR355= 75 ± 11 sr and
LR532= 70 ± 10 sr, which is close to the
corresponding model values for organic carbon of 71 and 66 sr,
respectively.
MERRA-2 predicts the existence of a significant amount of dust in the
elevated smoke layer, and the high values of observed depolarization ratio
agree with this prediction. The existence of minima of A355/532β
in the center of the elevated layer, characterized by the highest
δ532,
also supports this finding. Moreover, the lidar ratios at both 355 and
532 nm also have a minima in the center of the layer because the
lidar ratio of dust is lower than that of smoke. The contributions of dust
and smoke particles to the aerosol backscattering and extinction coefficient
at 532 nm evaluated from particle depolarization ratio agree with the
values provided by the model. Of course an analysis of only one episode is
not sufficient for broad conclusions regarding how well the model reproduces
the vertical distribution of particle properties. More measurements at
different locations are needed. However, the results presented here
demonstrate that observations and the MERRA-2 model contribute in
a complementary way, allowing the separation of the contributions of different
chemical component of the aerosol mixture.
The motivation for this work is to show that the aerosol transport model has
sufficient skill to serve as an additional constraint in inversion of
3β+ 2α lidar observations and development of such
constrained inversion is in progress. Assimilation of lidar measured
parameters in the model is the subject of our future efforts.
MPL observations for SHADOW campaign are available at
http://loa-ptfi.univ-lille1.fr/lidar/calendars/cal_2015_mbour.php (LOA, 2018).
Optical properties of aerosol components in MERRA-2 model
Dependence of the growth factor of organic carbon, black carbon,
sulfates, and sea salt on relative humidity (RH) used in MERRA-2. For the sea
salt the results are given for five size bins from Table A1. The growth
factor increases with increase of bin number.
Table A1 summarizes the main characteristics of five aerosol components:
dust, sea salt, black carbon, organic carbon, and sulfates used in MERRA-2
model. For dust and sea salt five size bins are considered. All values are
given for the relative humidity of 0. Thus OC, BC, and SU with the
effective radii of 0.09, 0.04, and 0.157 µm, respectively, are presented by the fine
fraction only, while dust and sea salt contribute to both fine and coarse
fractions.
Parameters of the aerosol components, such as minimal radius
(rmin), maximal radius (rmax), effective radius
(reff), and real (mR) and imaginary (mI)
part of the refractive index at 355, 532, and 1064 nm used in MERRA-2
model. For dust and sea salt, five size bins are considered. All values are
given for RH = 0.
Dependence of the real and imaginary part of the refractive index of
organic carbon (OC), black carbon (BC), sulfates (SU), and sea salt (SS) on relative humidity (RH)
used in the MERRA-2 model. For the sea salt the results are given for five
size bins from Table 1. Both mR and mI decrease
with bin number increasing.
The dust particles are assumed to be hydrophobic, but other aerosol
components may present significant hygroscopic growth. To account for the
effect of RH, the growth factor g, which is the ratio of
particle radius at current RH to the dry particle radius, is introduced.
Figure A1 shows dependence of the growth factor of different aerosol
components on RH. For sea salt the results are given for
five size bins from Table 1. Each bin has a different growth factor: g
increases with increase of particle radius. Relative humidity modifies also
the particle complex refractive index. Dependence of the real and the
imaginary part of particle components on RH is shown in
Fig. A2. For dry sea salt particles RI is supposed to be the same for all
size bins. However, in the process of hygroscopic growth the RI of different
bins behaves differently: both mR and mI decrease
with bin number (radius) increasing.
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors are very grateful to IRD-Dakar (Institut de Recherche pour le
Développement) for their welcome and efficient support and also thank
Labex CaPPA for supporting this campaign. The CaPPA project (Chemical and
Physical Properties of the Atmosphere) is funded by the French National
Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir)
under contract “ANR-11-LABX-0005-01” and by the Regional Council “Nord-Pas
de Calais” and the “European Funds for Regional Economic Development”
(FEDER). Development of lidar retrieval algorithms was supported by Russian
Science Foundation (project 16-17-10241).
Edited by: Omar Torres Reviewed by: three anonymous referees
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