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3 + 2 + X: What is the most useful depolarization input for retrieving microphysical properties of non-spherical particles from lidar measurements by assuming spheroidal particle shapes?

TL;DR: In this paper, the effect of including the particle linear depolarization ratio (δ) as a third input parameter to the inversion of lidar data was investigated, and it was shown that δ355 gives a non-spherical fraction that closely resembles the dust ratio obtained from using β532 and δ532 in a methodology applied in aerosol-type separation.
Abstract: . The typical multiwavelength aerosol lidar data set for inversion of optical to microphysical parameters is composed of three backscatter coefficients (β) at 355, 532, and 1064 nm and two extinction coefficients (α) at 355 and 532 nm. This data combination is referred to as 3β + 2α or 3 + 2 data set. This set of data is sufficient for retrieving some important microphysical particle parameters if the particles have spherical shape. Here, we investigate the effect of including the particle linear depolarization ratio (δ) as a third input parameter to the inversion of lidar data. The inversion algorithm is generally not used if measurements show values of δ that exceed 0.10 at 532 nm, i.e. in the presence of non-spherical particles such as desert dust, volcanic ash, and under special circumstances biomass-burning smoke. We use experimental data collected with instruments that are capable of measuring δ at all three lidar wavelengths with an inversion routine that uses the theory of light scattering by randomly oriented spheroids to replicate scattering properties of non-spherical particles. This is the first systematic test of the effect of using all theoretically possible combinations of δ taken at 355, 532, and 1064 nm as input in the lidar data inversion. We find that depolarization information at least at one wavelength already provides useful information in the inversion of optical data that describe light-scattering by non-spherical particles. However, any choice of δλ will give lower values of the single-scattering albedo than the traditional 3 + 2 data set. We find that input data sets that include δ355 give a non-spherical fraction that closely resembles the dust ratio we obtain from using β532 and δ532 in a methodology applied in aerosol-type separation. The use of δ355 in data sets of two or three δλ reduces the fraction of non-spherical particles that is retrieved when using δ532 and δ1064. Use of the latter two without accounting for δ355 generally leads to high fractions of non-spherical particles that we consider not trustworthy. The use of three δλ instead of two δλ including the constraint that one of these is measured at 355 nm does not provide any advantage over using 3 + 2 + δ355. We conclude that — depending on measurement capability — the future standard input for inversion using spheroid kernels might be 3 + 2 + δ355 or 3 + 2 + δ355 + δ532.

Summary (4 min read)

1 Introduction

  • Over the past 2 decades, the inversion of multiwavelength aerosol lidar measurements for the retrieval of aerosol microphysical properties (Müller et al., 1998, 1999a, b, 2001; Veselovskii et al., 2002; Ansmann and Müller, 2005) matured to a stage that allows for automated and unattended data processing (Müller et al., 2014).
  • By definition, this theory cannot be applied to describe light scattering by non-spherical particles.
  • On the one hand, the answer to the question of what inversion input provides the most accurate estimate of dust microphysical parameters requires independent measurements of these parameters.
  • Finally, the latest developments of realizing depolarization-ratio profiling at 1064 nm or multiple wavelengths (Burton et al., 2015; Haarig et al., 2017a) leads to the question of whether these new measurement capabilities might also advance the quality of the inversion of lidar measurements in the presence of non-spherical particles.

2 Spectral δ for mineral dust from measurements and modelling

  • The particle linear depolarization ratio δ, as measured by depolarization lidar, is the ratio of the particle backscatter coefficients measured at planes of polarization perpendicular and parallel to the plane of polarization of the emitted laser light (Gimmestad, 2008).
  • The characteristics of Dubovik’s spheroidal model, though sufficient to reproduce laboratory measurements over a wide range of scattering angles (Dubovik et al., 2006), might hence be a limiting factor when it comes to lidar applications.
  • Mineral dust and volcanic ash were generally considered to be the aerosol types that show the highest values of δ at the wavelengths used in aerosol lidar.

3.1 Lidar data

  • To date, few lidar instruments have the capability of measuring particle linear depolarization ratios at three wavelengths simultaneously, and the authors refer to Burton et al. (2015), Haarig et al. (2017a), and Hu et al. (2019) as examples of such studies.
  • HSRL-2 is the second-generation airborne HSRL developed at NASA Langley Research Center.
  • Tech., 12, 4421–4437, 2019 www.atmos-meas-tech.net/12/4421/2019/ averaging temporally over several minutes of measurements and in the second step by carrying out data-averaging over height layers of 150 m. 3+ 2+ 3 measurements with TROPOS’ BERTHA lidar during SALTRACE are used to assess the performance of the different inversion input data sets in the presence of pure dust conditions.
  • This test under pure dust conditions is needed, as such a scenario was not encountered during DISCOVER-AQ.

3.2 Retrieval of dust fraction from optical data

  • The particle linear depolarization ratio is an intensive aerosol property that can be applied for aerosol classification (Burton et al., 2012; Groß et al., 2013).
  • This approach generally assumes mixtures with a coarse- mode that is composed of mineral dust and a spherical finemode.
  • In principle, these aerosol-type separation techniques can be used to obtain input data sets for the inversion of lidar data that represent the spherical and non-spherical particles in a mixed aerosol plume, respectively.
  • In the discussion of their findings, the authors will consider the dust ratio for the two-component (Tesche et al., 2009b; Burton et al., 2014) and three-component (Mamouri and Ansmann, 2014, 2017) mixtures as the lower and upper limit, respectively, of the likely dust contribution.

3.3 Inversion of lidar data

  • The inversion of multiwavelength lidar data is based on using light-scattering kernels that were computed on the basis of Mie theory (Ansmann and Müller, 2005).
  • The information provided by δ allows for retrieving the spheroid particle fraction as an additional inversion output parameter.
  • The authors investigate if this input is sufficient for retrieving (some of) the microphysical parameters or if improved results can be obtained by adding depolarization information at 355 and/or 1064 nm (Gasteiger and Freudenthaler, 2014).
  • In the analysis of the inversion calculations, the authors have averaged those 140–200 solutions (median value of 160 for the different input data sets) that revealed the smallest discrepancy to the optical input data.
  • In general, median discrepancy increased with increasing number of input data from 1 (no depolarization input), to 7–11 (one depolarization input), to 13–17 (two depolarization inputs), and finally 22 (three depolarization inputs).

4 Results

  • The authors present selected measurement cases that illustrate the effect of the choice of inversion input data sets on the retrieved aerosol microphysical properties.
  • These case studies describe scenarios of varying concentration of non-spherical particles.
  • The authors then discuss the results for the entire data set outlined in Table 1.

4.1 Example: pure dust

  • This case represents nearly pure dust conditions, i.e. a situation dominated by non-spherical particles, and has previously been described by Mamouri and Ansmann (2017).
  • While higher dust fractions would be desirable to properly represent pure-dust conditions (see, e.g. Freudenthaler et al., 2009), the general scarcity of suitable measurement data means that this is the “purest” 3+ 2+ 3 dust case available to us at the time of this study.
  • The authors obtain a much clearer separation between the inversion results for Set I (the traditional 3+ 2 data set) and Sets II to VIII (which include depolarization information) for the SSA, the spheroid fraction, and the imaginary part of the complex refractive index.
  • The unrealistic values of spheroid particles obtained for Set I coincide with SSA values of as low as 0.82 and extremely high imaginary parts of the refractive index of 0.015 to 0.018.
  • SSA (the imaginary part) is slightly lower for input data that include δ355 (Sets II, V, VI, and VIII), compared to those that do not include depolarization information at 355 nm (Sets III, IV, and VII).

4.2 Example: mixed dust

  • This measurement case provides more insight into the sensitivity of data products of optical input data that were taken under mixed dust conditions, i.e. a situation in which mineral dust is mixed with spherical particles and depolarization values are below the ones generally observed for pure dust.
  • The highest values are found for inversions that make use of the full set of δλ, i.e. the 3+ 2+ 3 data set.
  • In fact, the authors find comparably small differences of the values of volume concentration for the different input data sets that are defined by a variable number of depolarization information.
  • The most plausible spheroid fractions of around 40 %, combined with strong vertical homogeneity are found for input data sets that contain δ355, i.e. sets II, V, VI, and VIII.

4.3 General findings

  • Figure 6 presents two cases for which the choice of depolarization input has a profound effect on the retrieved spheroid fraction.
  • The correlation between Sets II and III and between Sets V and VII shows little difference for the effective radius.
  • The strongest effect with regard to the choice of input data is found for the imaginary part of the refractive index and thus the SSA.
  • Data sets that include δ355 generally give larger values than those that exclude δ355, with the traditional 3+2 data set leading to values that are by far the highest.

5 Discussion

  • The authors would like to start the discussion by emphasizing that the results presented here are specific to the application of the spheroid model of Dubovik et al. (2006).
  • In addition, the intensive lidar parameters lidar ratio and particle linear depolarization ratio, which can be calculated from the inferred scattering matrix, do not agree with coincident measurements at the 355 and 532 nm lidar wavelengths (Müller et al., 2013).
  • Other models that employ more realistic geometries of nonsymmetric non-spherical particles (Gasteiger et al., 2011) account for surface roughness (Kemppinen et al., 2015a) and particle inhomogeneities (Kemppinen et al., 2015b) or employ spheroids with wider ranges of the oblate-to-prolate ra- www.atmos-meas-tech.net/12/4421/2019/.
  • The authors study shows that δ532 may not be an ideal input parameter.
  • The lack of spectral depolarization-ratio measurements under dusty conditions allowed neither for investigating how the choice of input parameters affects the quality of inversion results compared to benchmark data nor for testing if the choice is ideal.

6 Summary and conclusions

  • The authors have performed a first systematic relational investigation of the effect of exploiting different combinations of depolarization information as input to the inversion of optical lidar data into aerosol microphysical properties.
  • The authors are comparing the output of the different inversion runs to each other and to the dust ratio obtained from the optical data.
  • The authors find that inversion without depolarization information (i.e. the traditional 3+2 data set) cannot lead to spheroid particle fractions larger than 40 %, even if spheroid kernels, i.e. the spheroid Dubovik model, are used.
  • New models with more realistic particle geometries (Kahnert et al., 2014; Nousiainen and Kandler, 2015) will be needed to accurately link microphysical properties to the optical parameters measured with advanced aerosol lidar (Gasteiger et al., 2011).
  • This paper was edited by Vassilis Amiridis and reviewed by three anonymous referees.

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Atmos. Meas. Tech., 12, 4421–4437, 2019
https://doi.org/10.5194/amt-12-4421-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
3 + 2 + X: what is the most useful depolarization input for retrieving
microphysical properties of non-spherical particles from lidar
measurements using the spheroid model of Dubovik et al. (2006)?
Matthias Tesche
1,a
, Alexei Kolgotin
2
, Moritz Haarig
3
, Sharon P. Burton
4
, Richard A. Ferrare
4
, Chris A. Hostetler
4
,
and Detlef Müller
1
1
School of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, UK
2
A. M. Prokhorov General Physics Institute, Moscow, Russia
3
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
4
NASA Langley Research Center, Hampton, USA
a
now at: Leipzig Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
Correspondence: Matthias Tesche (matthias.tesche@uni-leipzig.de)
Received: 22 February 2019 Discussion started: 20 March 2019
Revised: 12 July 2019 Accepted: 17 July 2019 Published: 19 August 2019
Abstract. The typical multiwavelength aerosol lidar data
set for inversion of optical to microphysical parameters is
composed of three backscatter coefficients (β) at 355, 532,
and 1064 nm and two extinction coefficients (α) at 355 and
532 nm. This data combination is referred to as a 3β + 2α
or 3 + 2 data set. This set of data is sufficient for retrieving
some important microphysical particle parameters if the par-
ticles have spherical shape. Here, we investigate the effect of
including the particle linear depolarization ratio (δ) as a third
input parameter for the inversion of lidar data. The inversion
algorithm is generally not used if measurements show values
of δ that exceed 0.10 at 532 nm, i.e. in the presence of non-
spherical particles such as desert dust, volcanic ash, and, un-
der special circumstances, biomass-burning smoke. We use
experimental data collected with instruments that are capable
of measuring δ at all three lidar wavelengths with an inver-
sion routine that applies the spheroidal light-scattering model
of Dubovik et al. (2006) with a fixed axis-ratio distribution to
replicate scattering properties of non-spherical particles. The
inversion gives the fraction of spheroids required to replicate
the optical data as an additional output parameter. This is the
first systematic test of the effect of using all theoretically pos-
sible combinations of δ taken at 355, 532, and 1064 nm as
input in the lidar data inversion.
We find that depolarization information of at least one
wavelength already provides useful information for the in-
version of optical data that have been collected in the pres-
ence of non-spherical mineral dust particles. However, any
choice of δ
λ
will give lower values of the single-scattering
albedo than the traditional 3 + 2 data set. We find that input
data sets that include δ
355
give a spheroid fraction that closely
resembles the dust ratio we obtain from using β
532
and δ
532
in a methodology applied in aerosol-type separation. The use
of δ
355
in data sets of two or three δ
λ
reduces the spheroid
fraction that is retrieved when using δ
532
and δ
1064
. Use of
the latter two parameters without accounting for δ
355
gen-
erally leads to high spheroid fractions that we consider not
trustworthy. The use of three δ
λ
instead of two δ
λ
, includ-
ing the constraint that one of these is measured at 355 nm
does not provide any advantage over using 3 + 2 + δ
355
for
the observations with varying contributions of mineral dust
considered here. However, additional measurements at wave-
lengths different from 355 nm would be desirable for appli-
cation to a wider range of aerosol scenarios that may include
non-spherical smoke particles, which can have values of δ
355
that are indistinguishable from those found for mineral dust.
We therefore conclude that depending on measurement ca-
pability the future standard input for inversion of lidar data
taken in the presence of mineral dust particles and using the
spheroid model of Dubovik et al. (2006) might be 3+2+δ
355
or 3 + 2 + δ
355
+ δ
532
.
Published by Copernicus Publications on behalf of the European Geosciences Union.

4422 M. Tesche et al.: 3 + 2 + X
1 Introduction
Over the past 2 decades, the inversion of multiwavelength
aerosol lidar measurements for the retrieval of aerosol mi-
crophysical properties (Müller et al., 1998, 1999a, b, 2001;
Veselovskii et al., 2002; Ansmann and Müller, 2005) ma-
tured to a stage that allows for automated and unattended data
processing (Müller et al., 2014). The methodology uses mul-
tiwavelength lidar measurements of aerosol backscatter and
extinction coefficients (i.e. the availability of a 3β +2α input
data set, also referred to as 3+ 2 data set) and the mathemat-
ically correct description of light scattering by small parti-
cles to solve the ill-posed inverse problem at hand (Ansmann
and Müller, 2005). Mie theory is used for the mathemati-
cal description of light scattering by particles. By definition,
this theory cannot be applied to describe light scattering by
non-spherical particles. This constraint causes a problem, as
aerosol types such as mineral dust or volcanic ash are of non-
spherical shape.
The presence of such non-spherical particles in lidar mea-
surements is identified by non-zero values of the particle
linear depolarization ratio (δ, Gimmestad, 2008). Spherical
particles do not depolarize the emitted laser light and thus
show values of δ close to zero. Depolarization-ratio measure-
ments with advanced lidar (Freudenthaler et al., 2009) allow
for the retrieval of the contribution of non-spherical particles
to the measured intensive optical parameters (Tesche et al.,
2009b; Burton et al., 2014) and thus allow for comprehen-
sive aerosol-type characterization (Burton et al., 2012; Groß
et al., 2013).
A database for light scattering by spheroids (Dubovik
model, Dubovik et al., 2006), developed for the inversion
of sun photometer measurements within the framework of
the Aerosol Robotic Network (AERONET, https://aeronet.
gsfc.nasa.gov/, last access: 12 August 2019, Holben et al.,
1998), has been implemented in the lidar data inversion algo-
rithm used here. The first application of the Dubovik model
to lidar measurements of mineral dust has been presented
by Veselovskii et al. (2010), Di Girolamo et al. (2012), Pa-
payannis et al. (2012), and Müller et al. (2013). Veselovskii
et al. (2010) performed inversions with the spheroid light-
scattering database on the basis of the traditional 3 + 2 input
data set as well as for a 3+2+ 1 data set that uses δ
532
as ad-
ditional input. The latter parameter can provide information
on the contribution of mineral dust to the total aerosol op-
tical properties. From the comparison of the inversion runs
with the different input data sets, the authors conclude that
using 3 + 2 + 1 provides no advantage over the conventional
3+2 input run in which the spheroid fraction is set a priori to
100 %. They attribute this insensitivity (with regard to the use
of δ
532
) to the fact that (i) the Dubovik model has not been
specifically designed for lidar applications, i.e. the mathe-
matical description of light scattering at 180
, and (ii) that
high values of δ
532
can only be obtained for values of parti-
cle refractive indices that are below values found from atmo-
spheric observations (Veselovskii et al., 2010). Papayannis
et al. (2012) present results of the inversion of 3 + 2 data in
the presence of mineral dust, while Di Girolamo et al. (2012)
and Müller et al. (2013) used 3 + 2 + 1 data sets with de-
polarization information at 355 nm and 532 nm, respectively.
Veselovskii et al. (2016) present results of the inversion of li-
dar data for mineral dust for the case of the conventional 3+2
input (with spheroid fraction set to 100 %) and the 3 + 2 + 1
input with depolarization information at 532 nm. The authors
conclude that it is currently not possible to come to a defini-
tive conclusion as to which input data set leads to a more
accurate estimation of dust parameters. Instead, they recom-
mend to use the 3 + 2 input for measurements of pure dust,
as these inversions provide more realistic estimations of the
refractive index of dust particles. Scattering kernels based on
Mie theory cannot represent light scattering by non-spherical
particles, i.e. particles that lead to increased δ
λ
in a lidar mea-
surement. A way to circumvent this problem is to split the
optical input according to the information related to spheri-
cal and non-spherical particles. The data obtained in that way
can subsequently be used to run the inversion considering
only spherical scatterers (i.e. Mie kernels) and non-spherical
scatterers (i.e. spheroid kernels), respectively. We provide a
detailed discussion of this aspect in Sect. 3.3. However, the
aim of this work is to gain insight into the performance of
the inversion using Dubovik’s model for mixed dust cases, as
such scenarios have not yet been considered in earlier stud-
ies.
On the one hand, the answer to the question of what inver-
sion input provides the most accurate estimate of dust micro-
physical parameters requires independent measurements of
these parameters. An example for such a study is presented
by Müller et al. (2013). However, the comprehensive data
sets required for such an effort can only be obtained in the
framework of dedicated and extensive experiments. On the
other hand, there has as of yet been no systematic estima-
tion of the effect of using different depolarization inputs for
the inversion of lidar data. Today, depolarization-ratio profil-
ing is most commonly performed at 532 nm. This explains
the use of this wavelength in the studies of Veselovskii et
al. (2010, 2016) and Müller et al. (2013). This wavelength is
also the only one for which comparisons of the algorithm
performance exist with regard to using the 3 + 2 and the
3+2+1 data set. Setting a future standard on depolarization-
ratio profiling requires us to assess which wavelength pro-
vides the best prospects not only for aerosol characterization
but also for using the added information as input to inver-
sion runs. Most inversions that use the Dubovik model fo-
cused on pure-dust conditions. Values of δ
532
were similar
to values observed close to dust source regions (Freuden-
thaler et al., 2009). Such conditions warrant the use of the
3 + 2 data set with the spheroid fraction set to 100 %. It still
needs to be investigated if depolarization information also
allows for the successful retrieval of aerosol microphysical
properties in mixed layers of mineral dust and other spheri-
Atmos. Meas. Tech., 12, 4421–4437, 2019 www.atmos-meas-tech.net/12/4421/2019/

M. Tesche et al.: 3 + 2 + X 4423
cal aerosol types, i.e. aerosol scenarios that are common dur-
ing observations of long-range transport of mineral dust in
the free troposphere. Finally, the latest developments of re-
alizing depolarization-ratio profiling at 1064 nm or multiple
wavelengths (Burton et al., 2015; Haarig et al., 2017a) leads
to the question of whether these new measurement capabil-
ities might also advance the quality of the inversion of lidar
measurements in the presence of non-spherical particles.
In this study, we investigate the effect of using δ at 355,
532, and 1064 nm as additional inversion input to answer the
following question:
What is the optimum choice of δ
λ
in the inversion of lidar
measurements of non-spherical particles using the spheroid
model of Dubovik et al. (2006)?
We address this question by using 3 + 2 + 3 multiwave-
length lidar measurements taken under both pure and mixed-
dust conditions. Specifically, we assume that values of δ
λ
are
accurate within their respective measurement error and that
the findings of our study are primarily related to the light-
scattering model used in the inversion calculations. Section 2
presents an overview of the literature on measurements and
modelling of spectral particle linear depolarization ratios of
mineral dust particles. The data sources and inversion setup
are introduced in Sect. 3. The results are presented and dis-
cussed in Sects. 4 and 5, respectively. We close with a sum-
mary and our conclusions in Sect. 6.
2 Spectral δ for mineral dust from measurements and
modelling
The particle linear depolarization ratio δ, as measured by
depolarization lidar, is the ratio of the particle backscatter
coefficients measured at planes of polarization perpendicu-
lar and parallel to the plane of polarization of the emitted
laser light (Gimmestad, 2008). Like the lidar ratio and the
Ångström exponent, it is an intensive parameter that takes
on characteristic values for different aerosol types (Burton et
al., 2012). It is most sensitive to the shape of the scattering
particles, i.e. δ = 0 for spherical scatterers (or those that ap-
pear spherical with respect to the considered wavelength),
and increases with particle non-sphericity for the particle
shapes commonly found for atmospheric aerosols and ice
crystals. Reliable measurements of δ require careful instru-
ment characterization together with dedicated calibration ef-
forts (Freudenthaler et al., 2009; Freudenthaler, 2016).
Light scattering by non-spherical particles such as min-
eral dust poses a great challenge for applications in atmo-
spheric science as it cannot be described by Mie scattering.
The problem has been addressed by introducing a variety of
non-spherical model particles (Kahnert et al., 2014) that dif-
fer considerably in their capability to properly describe light
depolarization (specifically at the 180
backscatter direction
crucial for lidar). Spheroids were found to be particularly
versatile for this purpose as they allow for addressing differ-
ent degrees of non-sphericity in mathematically simple ex-
pressions. The specific spheroid model is the one introduced
by Dubovik et al. (2006). It has been developed for the anal-
ysis of AERONET sun photometer measurements and thus
is optimized for passive remote sensing applications. It has
been applied in the inversion of lidar data Veselovskii et al.
(2010) and to retrieve lidar-specific parameters (Müller et al.,
2010; Shin et al., 2018), even though it has not been designed
to describe light scattering at a 180
backscatter direction.
Dubovik’s spheroidal model features a defined aspect-ratio
distribution, which separates it from other applications of
spheroidal models used for lidar applications (Wiegner et al.,
2009; Gasteiger and Freudenthaler, 2014).
Figure 1 presents spectral particle linear depolarization ra-
tios obtained from laboratory (Miffre et al., 2016) and field
measurements (Tesche et al., 2009a; Burton et al., 2015;
Groß et al., 2015; Haarig et al., 2017a) with lidar, the anal-
ysis of AERONET observations using the Dubovik model
(Müller et al., 2010; Shin et al., 2018), and modelling us-
ing spheroids (Wiegner et al., 2009) and other irregularly
shaped particles (Gasteiger et al., 2011) used to mimic the
light-scattering properties of mineral dust. The purpose of
the figure is to provide some context on the range of particle
linear depolarization ratios for mineral dust (δ
d
) as inferred
from measurements and modelling. Field measurements per-
formed in Morocco (Tesche et al., 2009a), Barbados (Groß
et al., 2015; Haarig et al., 2017a), or with research aircraft
over the Caribbean and the Chihuahuan Desert (Burton et al.,
2015) show values of δ
d
in the range from 0.22 to 0.38 at the
common lidar wavelengths 355, 532, and 1064 nm. Labora-
tory measurements of Arizona test dust (Miffre et al., 2016)
confirm the field measurements at 532 nm and give a value of
0.35 at 355 nm, which is about 0.1 larger than what most field
measurements show at this wavelength. However, Miffre et
al. (2016) investigated only two size distributions and did
not consider particles with diameters larger than 800 nm for
their study. Figure 1 also gives δ
λ
as obtained from the in-
version of AERONET sun photometer measurements using
Dubovik’s model (Müller et al., 2010; Shin et al., 2018).
AERONET derives values at wavelengths that differ from the
lidar observations, i.e. 440, 670, 870, and 1020 nm. In gen-
eral, AERONET-derived δ are closest to the lidar reference at
the longest wavelength. The values at 440 nm are lower than
the lidar observations at both 355 and 532 nm, though a direct
comparison is impossible due to the mismatch in the wave-
lengths of observations. For comparison, Fig. 1 gives the re-
sults of modelling studies using Mie scattering (δ
λ
= 0), a
spheroid model with aspect ratios and oblate-to-prolate ratios
different from the ones used by the Dubovik model (Wiegner
et al., 2009), and irregularly shaped particles (Gasteiger et
al., 2011). The latter two options are considered here to illus-
trate the range of modelling results (grey areas in Fig. 1) of
the light scattering by non-spherical particles with specific
focus on lidar application. The comparison shows that the
AERONET-derived values using Dubovik’s model are on the
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4424 M. Tesche et al.: 3 + 2 + X
Figure 1. Overview of particle linear depolarization ratios for pure mineral dust from field measurements (Tesche et al., 2009a; Burton et
al., 2015; Groß et al., 2015; Haarig et al., 2017a), laboratory studies (Miffre et al., 2016), AERONET observations (Müller et al., 2010; Shin
et al., 2018), and modelling (Wiegner et al., 2009; Gasteiger et al., 2011). The use of Mie theory (spherical particles) gives values of zero.
The range for modelling results is defined by the studies of Wiegner et al. (2009) (lower boundary, spheroids) and Gasteiger et al. (2011)
(upper boundary, mixtures of irregularly shaped particles). The dashed blue and dashed red lines show that field measurements can reveal
high depolarization ratios for biomass-burning smoke as well (Burton et al., 2015; Haarig et al., 2018).
lower boundary of the values inferred using other particle ge-
ometries at wavelengths smaller than 800 nm. The large val-
ues observed with lidar can only be reproduced by Gasteiger
et al. (2011). The characteristics of Dubovik’s spheroidal
model, though sufficient to reproduce laboratory measure-
ments over a wide range of scattering angles (Dubovik et
al., 2006), might hence be a limiting factor when it comes
to lidar applications.
Mineral dust and volcanic ash were generally considered
to be the aerosol types that show the highest values of δ at
the wavelengths used in aerosol lidar. This would make it
easy to detect their occurrence in a lidar measurement. How-
ever, recent case studies have shown that biomass-burning
smoke can also show values as high as δ = 0.20–0.25 at 355
and 532 nm (Burton et al., 2015; Haarig et al., 2018; Hu
et al., 2019). Two of the observed cases of highly depolar-
izing smoke particles are also presented in Fig. 1 to show the
a contrast to the observed δ spectra for mineral dust. Light-
scattering simulations have shown that such values can be
reproduced using spheroids of nearly spherical shape (with
aspect ratios close to unity, Bi et al., 2018; Gialitaki et al.,
2019) and agglomerates of spheroids (Mishchenko et al.,
2016). In the studies of Bi et al. (2018) and Mishchenko
et al. (2016), the model particles need to contain substan-
tial amounts of non-absorbing or weakly absorbing material
with an imaginary part of the refractive index below 0.01.
3 Data and methods
This section provides an overview of the lidar data used in
this study, different methodologies for estimating the contri-
bution of non-spherical particles to the measured optical data
and a brief description of the inversion procedure.
3.1 Lidar data
To date, few lidar instruments have the capability of measur-
ing particle linear depolarization ratios at three wavelengths
simultaneously, and we refer to Burton et al. (2015), Haarig
et al. (2017a), and Hu et al. (2019) as examples of such stud-
ies. Here, we use data from the NASA Langley Research
Center’s High Spectral Resolution Lidar 2 (HSRL-2) that
has been operated aboard the NASA B-200 King Air aircraft
in the framework of the DISCOVER-AQ project (https://
discover-aq.larc.nasa.gov/, last access: 12 August 2019) and
data taken with the Backscatter Extinction lidar-Ratio Tem-
perature Humidity profiling Apparatus (BERTHA) of the
Leibniz Institute for Tropospheric Research (TROPOS) dur-
ing the Saharan Aerosol Long-range Transport and Aerosol–
Cloud–Interaction Experiment (SALTRACE, Weinzierl et
al., 2017).
HSRL-2 is the second-generation airborne HSRL devel-
oped at NASA Langley Research Center. It builds on the her-
itage of the HSRL-1 system (Hair et al., 2008) but operates at
the laser wavelengths of 355, 532, and 1064 nm. The 3+2+3
data collected with HSRL-2 allow for a comprehensive char-
acterization of different aerosol types (Burton et al., 2012)
and the retrieval of microphysical particle properties (Müller
et al., 2014). Further details on the instrument can be found
in Müller et al. (2014) and Burton et al. (2018).
DISCOVER-AQ measurements with HSRL-2 were
screened for observations that showed elevated levels of
δ
532
. The observations were identified as dusty mix (Burton
et al., 2012) and include flights during DISCOVER-AQ
California 2013 (two cases), DISCOVER-AQ Texas 2013
(four cases), and DISCOVER-AQ Colorado 2014 (three
cases). An overview of the DISCOVER-AQ measurement
days considered here is given in Table 1. The optical input
data for the inversion were obtained in the first step by
Atmos. Meas. Tech., 12, 4421–4437, 2019 www.atmos-meas-tech.net/12/4421/2019/

M. Tesche et al.: 3 + 2 + X 4425
Table 1. Overview of the 3 + 2 + 3 lidar measurements taken with BERTHA and HSRL-2 and used in this study. The HSRL-2 aerosol type
was determined following the procedure outlined in Burton et al. (2012). Note that HSRL-2 measurements include transit flights. The lower
and upper values for the range of dust ratios refer to values obtained from using methods described by Tesche et al. (2009b) and Mamouri
and Ansmann (2014) (sum of fine and coarse dust), respectively.
Date Time (UTC) Height (km) Mean δ
532
Dust ratio (%) Aerosol type
HSRL-2: DISCOVER-AQ California (2013), Texas (2013), Colorado (2014)
20130130 16:56–17:12 0.3–1.0 0.05 ± 0.04 16–65 Urban pollution, fresh smoke
1.0–1.2 0.28 ± 0.04 89–90 Dusty mix
20130208 17:37–18:02 2.0–2.4 0.32 ± 0.04 100–94 Dusty mix
3.8–4.2 0.12 ± 0.01 40–70 Dusty mix, urban pollution
20130925 20:57–21:05 0.3–2.4 0.10 ± 0.01 35–61 Dusty mix, urban pollution
20130926 20:36–20:41 0.3–2.1 0.10 ± 0.01 35–60 Dusty mix, urban pollution
20130928 16:12–16:17 0.3–1.9 0.04 ± 0.01 14–19 Urban pollution
20140713 14:35–14:46 0.4–3.0 0.10 ± 0.03 34–56 Dusty mix, urban pollution
17:13–17:36 0.5–5.1 0.20 ± 0.05 67–83 Dusty mix
20140717 19:17–19:19 2.0–4.0 0.04 ± 0.00 13–18 Urban pollution, polluted marine
20140722 20:09–20:36 2.0–3.0 0.15 ± 0.02 52–76 Dusty mix
3.0–5.5 0.10 ± 0.01 31–53 Urban pollution, dusty mix
BERTHA: SALTRACE, Barbados
20140303 22:30–23:30 1.0–2.8 0.12 ± 0.04 40–61 Dusty mix
20140620 23:10–02:10 1.0–4.0 0.26 ± 0.02 83–88 Mineral dust
averaging temporally over several minutes of measurements
and in the second step by carrying out data-averaging over
height layers of 150 m.
3 + 2 + 3 measurements with TROPOS’ BERTHA lidar
during SALTRACE are used to assess the performance of the
different inversion input data sets in the presence of pure dust
conditions. This test under pure dust conditions is needed, as
such a scenario was not encountered during DISCOVER-AQ.
While BERTHA had been used to characterize the opti-
cal properties of pure dust during the Saharan Mineral Dust
Experiment (SAMUM, Tesche et al., 2009b), the capabil-
ity of carrying out triple-wavelength δ measurements with
BERTHA has only recently been presented in Haarig et al.
(2017a). So far, such measurements have been performed
to characterize mineral dust (Haarig et al., 2017a), marine
aerosols (Haarig et al., 2017b), and biomass-burning smoke
(Haarig et al., 2018). An overview of the SALTRACE mea-
surement days considered here is given in Table 1.
3.2 Retrieval of dust fraction from optical data
The particle linear depolarization ratio is an intensive aerosol
property that can be applied for aerosol classification (Bur-
ton et al., 2012; Groß et al., 2013). Because of its sensitivity
to particle shape, it can also be used to separate the contri-
butions of spherical and non-spherical particles to the optical
parameters measured with aerosol lidar (Tesche et al., 2009b;
Burton et al., 2014) or sun photometers (Shin et al., 2019).
This approach generally assumes mixtures with a coarse-
mode that is composed of mineral dust and a spherical fine-
mode. Tesche et al. (2009b, 2011b) use measurements of δ
532
together with threshold values representative for pure aerosol
types to separate the contribution of dust and biomass-
burning smoke to the optical properties measured with mul-
tiwavelength aerosol Raman lidar at Cabo Verde. Their ap-
proach assumes an external mixture of two aerosol types. A
generalized form of this method that covers a broader vari-
ety of aerosol mixtures has been presented by Burton et al.
(2014) for measurements with HSRL-1. Mamouri and Ans-
mann (2014, 2017) have refined the aerosol-type separation
further using a two-step approach that allows for the separa-
tion of contributions of coarse dust, fine dust, and non-dust
aerosol types, i.e. marine or continental aerosol.
In principle, these aerosol-type separation techniques can
be used to obtain input data sets for the inversion of lidar
data that represent the spherical and non-spherical particles
in a mixed aerosol plume, respectively. The inversion could
then be run with the conventional 3 + 2 input data set and
the spheroid fraction (see Sect. 3.3) set to either 0 % (i.e.
Mie kernels) or 100 %. In this study, however, we aim to test
how the inversion performs if we use different combinations
of additional depolarization-ratio input that allow us to ac-
count for the contribution of non-spherical particles to the
optical input data. We use the dust ratio, i.e. the ratio of dust-
related backscatter to total backscatter coefficient at 532 nm,
(i) as an estimate of the dust contribution and (ii) for compar-
ing to the spheroid fraction inferred from the inversion. Dust
ratios were either taken from the DISCOVER-AQ database
www.atmos-meas-tech.net/12/4421/2019/ Atmos. Meas. Tech., 12, 4421–4437, 2019

Citations
More filters
01 Jan 2019
TL;DR: In this paper, the authors used the discrete dipole approximation (DDA) method to identify key morphological features, which affect the depolarisation ratio of a soot particle.
Abstract: Soot containing aerosol has both adverse impacts on the Earth's climate and on human health. Monitoring soot sources, transport pathways and sinks on global scale requires satellite-borne remote sensing techniques. A detailed understanding of the soot particle's optical properties is important to improve the interpretation of remote sensing data as well as the use of lidar remote sensing data in chemical transport modelling. The calculations of the optical properties were carried out using the discrete dipole approximation (DDA). Aim of this thesis is to identify key morphological features, which affect the depolarisation ratio. As soot particles age in the atmosphere, condensation of other compounds from the gas phase onto the particles results in soot aggregates coated by liquid-phase material. Initially, the soot particles are coated by a thin film (i.e., the coating follows the shape of the aggregate). As more liquid phase material is added, the coating becomes increasingly spherical. It is found that this transition from film coating to radial growth of spherical shells is an important process affecting the linear depolarisation ratio. If this transition occurs first at relatively high amounts of coating, then the depolarisation ratio tends to be high. Conversely, if the coating becomes already spherical at low amounts of coating material, then the depolarisation ratio of the coated soot particles is much lower. The linear depolarisation ratio of thickly coated aggregates was found to be sensitive to changes in the complex refractive index of the coating material, which represents changes in the chemical composition. These differences in the optical properties, even after averaging over a particle size distribution, are large enough to clearly distinguish the coating materials.

1 citations


Cites background from "3 + 2 + X: What is the most useful ..."

  • ...(2019) the impacts of deviations on optical cross sections from an idealised, bare monodisperse fractal aggregate were investigated....

    [...]

Book ChapterDOI
01 Jan 2022
TL;DR: In this paper , Miffre et al. presented some recent advances in the field of light backscattering and light scattering by complex-shaped atmospheric particles at specific backward scattering angle at which lidar instruments operate.
Abstract: Atmospheric particlesAtmospheric particles may somewhat counterbalance the global warming effect of the Earth’s atmosphere due to greenhouse gases by directly contributing to the Earth’s climate through light scattering and absorption processes. According to the IPCCIPCC report (IPCC in Climate change 2013: the physical science basis. New York: Cambridge Univ. Press, 2013), the contribution of such particles to the Earth’s radiative budget however remains difficult to handle and quantify, mainly due to the complexity of these particles, which present a wide range of sizes, shapes and complex refractive indices. To face such a complexity, a major source of global data on these particles is provided by ground-based and satellite-based lidar remote sensingLidar remote sensing instruments, which are based on light backscatteringLight backscattering and extinction by atmospheric particlesAtmospheric particles. In this context, this book chapter proposes to present some recent advances in the field of light backscatteringLight backscattering by complex-shaped atmospheric particlesAtmospheric particles at specific backward scattering angle ( $$\theta =\pi$$ ) at which lidar instruments operate, for the first time to our knowledge in laboratory where a π-polarimeter has been built and operated for aerosolsAerosol (Miffre et al. in J Quant Spectrosc Radiat Transf 169:79–90, 2016; Miffre et al. in J Quant Spectrosc Radiat Transf 222–223:45–59, 2019b; Miffre et al. Atmos Meas Tech, 2022). These papers are the results of a team work in which Prof. Rairoux’s expertise in lidar remote sensingLidar remote sensing and laser spectroscopy played a key role. This work also owes much to former PhD students, G. David and D. Cholleton, who also played a key role. Laboratory experiments at near ( $$\theta <\pi )$$ backscattering angles are likewise proposed in complement as well as cooperative works with ONERA (Paulien et al. in J Quant Spectrosc Radiat Transf 260, 2021) and chemical colleagues from Lyon University (France) and North Carolina University (USA) (Dubois et al. in Phys Chem Chem Phys 23:5927–5935, 2021) to explore light backscatteringLight backscattering by complex-shaped particles. The benefits of this new laboratory approach, in comparison with existing light scattering numerical simulations and lidar field experiments, is discussed. We hope this book chapter will improve our understanding of the complex physical process of light backscatteringLight backscattering by atmospheric particlesAtmospheric particles, to in turn improve our understanding of the radiative properties of complex-shaped atmospheric particlesAtmospheric particles, to provide answer to radiative transfer issues.
References
More filters
Book ChapterDOI
01 Jan 2005
TL;DR: In this paper, a vertically resolved measurement of surface-area concentration, volume and mass concentrations, mean particle size, and thevolum-extinction-coefficientareofgreatinterest.
Abstract: Atmospheric aerosols play an important role in many atmosphericprocesses. Although only a minor constituent of the atmosphere, theyhave appreciable influence on the Earth’s radiation budget, air qual-ity and visibility, clouds, precipitation, and chemical processes inthe troposphere and stratosphere. The occurrence, residence time,physical properties, chemical composition, and corresponding complex-refractive-index characteristics of the particles, as well as the resultingclimate-relevant optical properties are subject to large diversity espe-cially in the troposphere because of widely different sources andmeteorological processes. Therefore, vertically resolved measurementsofphysicalandopticalpropertiesofparticlessuchastheparticlesurface-area concentration, volume and mass concentrations, mean particle size,andthevolumeextinctioncoefficientareofgreatinterest. Routine(long-term), height-resolved observations of these parameters can only becarried out with lidar.Commonly, aerosols are described in terms of aerosol types inclimate models. These aerosol types are defined as internal or externalmixtures of different components, and each component has distinc-tive properties. The

140 citations


"3 + 2 + X: What is the most useful ..." refers background or methods in this paper

  • ...as 3+2 data set) and the mathematically correct description of light scattering by small particles to solve the ill-pose d inverse problem at hand ( Ansmann and Müller , 2005). Mie theory is used for the mathematical description of light scattering by particles. By definition, this theory cannot be applied to describ e light scattering by non-spherical particles. This causes a problem, as aerosol types such as mineral dust or volcanic ash are of no n-spherical shape. The presence of such non-spherical particles in lidar measu r ments is identified by non-zero values of the particle line ar 10 depolarization ratio ( δ, Gimmestad2008). Spherical particles do not depolarize the emitted la ser light, and thus, show values of δ close to zero. Depolarization-ratio measurements with adv anced lidars ( Freudenthaler et al. , 2009) allow for the retrieval of the contribution of non-spherical particles to the measu red intensive optical parameters ( Tesche et al. , 2009b;Burton et al. , 2014), and thus allow for comprehensive aerosol-type chara terization ( Burton et al. , 2012;Groß et al. , 2013). A data base for light-scattering by non-spherical particle s (Dubovik model,Dubovik et al.2006) developed for the inversion 15 of sun-photometer measurements within the framework of the Aerosol Robotic Network (AERONET, https://aeronet.gsfc. nasa.gov/, Holben et al.1998) has been implemented in the lidar data inversion algor ithm used here. The first application of the Dubovik model to lidar measurements of mineral dust has been present ed by Veselovskii et al.(2010), Di Girolamo et al. (2012), Papayannis et al. (2012), andMüller et al....

    [...]

  • ...as 3+2 data set) and the mathematically correct description of light scattering by small particles to solve the ill-pose d inverse problem at hand ( Ansmann and Müller , 2005). Mie theory is used for the mathematical description of light scattering by particles. By definition, this theory cannot be applied to describ e light scattering by non-spherical particles. This causes a problem, as aerosol types such as mineral dust or volcanic ash are of no n-spherical shape. The presence of such non-spherical particles in lidar measu r ments is identified by non-zero values of the particle line ar 10 depolarization ratio ( δ, Gimmestad2008). Spherical particles do not depolarize the emitted la ser light, and thus, show values of δ close to zero. Depolarization-ratio measurements with adv anced lidars ( Freudenthaler et al. , 2009) allow for the retrieval of the contribution of non-spherical particles to the measu red intensive optical parameters ( Tesche et al. , 2009b;Burton et al. , 2014), and thus allow for comprehensive aerosol-type chara terization ( Burton et al. , 2012;Groß et al. , 2013). A data base for light-scattering by non-spherical particle s (Dubovik model,Dubovik et al.2006) developed for the inversion 15 of sun-photometer measurements within the framework of the Aerosol Robotic Network (AERONET, https://aeronet.gsfc. nasa.gov/, Holben et al.1998) has been implemented in the lidar data inversion algor ithm used here. The first application of the Dubovik model to lidar measurements of mineral dust has been present ed by Veselovskii et al.(2010), Di Girolamo et al. (2012), Papayannis et al. (2012), andMüller et al. (2013).Veselovskii et al....

    [...]

  • ...as 3+2 data set) and the mathematically correct description of light scattering by small particles to solve the ill-pose d inverse problem at hand ( Ansmann and Müller , 2005). Mie theory is used for the mathematical description of light scattering by particles. By definition, this theory cannot be applied to describ e light scattering by non-spherical particles. This causes a problem, as aerosol types such as mineral dust or volcanic ash are of no n-spherical shape. The presence of such non-spherical particles in lidar measu r ments is identified by non-zero values of the particle line ar 10 depolarization ratio ( δ, Gimmestad2008). Spherical particles do not depolarize the emitted la ser light, and thus, show values of δ close to zero. Depolarization-ratio measurements with adv anced lidars ( Freudenthaler et al. , 2009) allow for the retrieval of the contribution of non-spherical particles to the measu red intensive optical parameters ( Tesche et al. , 2009b;Burton et al. , 2014), and thus allow for comprehensive aerosol-type chara terization ( Burton et al. , 2012;Groß et al. , 2013). A data base for light-scattering by non-spherical particle s (Dubovik model,Dubovik et al.2006) developed for the inversion 15 of sun-photometer measurements within the framework of the Aerosol Robotic Network (AERONET, https://aeronet.gsfc. nasa.gov/, Holben et al.1998) has been implemented in the lidar data inversion algor ithm used here. The first application of the Dubovik model to lidar measurements of mineral dust has been present ed by Veselovskii et al.(2010), Di Girolamo et al....

    [...]

  • ...as 3+2 data set) and the mathematically correct description of light scattering by small particles to solve the ill-pose d inverse problem at hand ( Ansmann and Müller , 2005). Mie theory is used for the mathematical description of light scattering by particles. By definition, this theory cannot be applied to describ e light scattering by non-spherical particles. This causes a problem, as aerosol types such as mineral dust or volcanic ash are of no n-spherical shape. The presence of such non-spherical particles in lidar measu r ments is identified by non-zero values of the particle line ar 10 depolarization ratio ( δ, Gimmestad2008). Spherical particles do not depolarize the emitted la ser light, and thus, show values of δ close to zero. Depolarization-ratio measurements with adv anced lidars ( Freudenthaler et al. , 2009) allow for the retrieval of the contribution of non-spherical particles to the measu red intensive optical parameters ( Tesche et al. , 2009b;Burton et al. , 2014), and thus allow for comprehensive aerosol-type chara terization ( Burton et al. , 2012;Groß et al. , 2013). A data base for light-scattering by non-spherical particle s (Dubovik model,Dubovik et al.2006) developed for the inversion 15 of sun-photometer measurements within the framework of the Aerosol Robotic Network (AERONET, https://aeronet.gsfc. nasa.gov/, Holben et al.1998) has been implemented in the lidar data inversion algor ithm used here. The first application of the Dubovik model to lidar measurements of mineral dust has been present ed by Veselovskii et al.(2010), Di Girolamo et al. (2012), Papayannis et al. (2012), andMüller et al. (2013).Veselovskii et al. (2010) performed inversions with the non-spherical lightscattering data base on the basis of the traditional 3+2 inpu t data set as well as a 3+2+1 data set that uses δ532 as additional 20 input....

    [...]

  • ...The inversion of multiwavelength lidar data is based on using light-scattering kernels that were computed on the basis of Mie theory (Ansmann and Müller, 2005)....

    [...]

01 Jan 2019
TL;DR: In this paper, the authors used the discrete dipole approximation (DDA) method to identify key morphological features, which affect the depolarisation ratio of a soot particle.
Abstract: Soot containing aerosol has both adverse impacts on the Earth's climate and on human health. Monitoring soot sources, transport pathways and sinks on global scale requires satellite-borne remote sensing techniques. A detailed understanding of the soot particle's optical properties is important to improve the interpretation of remote sensing data as well as the use of lidar remote sensing data in chemical transport modelling. The calculations of the optical properties were carried out using the discrete dipole approximation (DDA). Aim of this thesis is to identify key morphological features, which affect the depolarisation ratio. As soot particles age in the atmosphere, condensation of other compounds from the gas phase onto the particles results in soot aggregates coated by liquid-phase material. Initially, the soot particles are coated by a thin film (i.e., the coating follows the shape of the aggregate). As more liquid phase material is added, the coating becomes increasingly spherical. It is found that this transition from film coating to radial growth of spherical shells is an important process affecting the linear depolarisation ratio. If this transition occurs first at relatively high amounts of coating, then the depolarisation ratio tends to be high. Conversely, if the coating becomes already spherical at low amounts of coating material, then the depolarisation ratio of the coated soot particles is much lower. The linear depolarisation ratio of thickly coated aggregates was found to be sensitive to changes in the complex refractive index of the coating material, which represents changes in the chemical composition. These differences in the optical properties, even after averaging over a particle size distribution, are large enough to clearly distinguish the coating materials.

1 citations

Trending Questions (1)
What is the input?

The input for the inversion of lidar measurements of non-spherical particles using the spheroid model includes three backscatter coefficients (β) at 355, 532, and 1064 nm, two extinction coefficients (α) at 355 and 532 nm, and the particle linear depolarization ratio (δ) at one or more of these wavelengths.