AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-6013-2018A multiwavelength numerical model in support of quantitative retrievals of
aerosol properties from automated lidar ceilometers and test applications for
AOT and PM10 estimationA numerical model in support of aerosol property retrievals from ALCDionisiDavided.dionisi@isac.cnr.ithttps://orcid.org/0000-0003-3854-521XBarnabaFrancescahttps://orcid.org/0000-0002-1927-6926DiémozHenrihttps://orcid.org/0000-0001-7189-4134Di LibertoLucaGobbiGian PaoloIstituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale
delle Ricerche (ISAC-CNR), Rome, ItalyAosta
Valley Regional Environmental Protection Agency (ARPA Valle d'Aosta), Saint-Christophe (Aosta), Italynow at: Istituto di Scienze Marine,
Consiglio Nazionale delle Ricerche (ISMAR-CNR), Rome, ItalyDavide Dionisi (d.dionisi@isac.cnr.it)8November201811116013604215March20185April20186September201812September2018This 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/11/6013/2018/amt-11-6013-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/6013/2018/amt-11-6013-2018.pdf
The use of automated lidar ceilometer (ALC) systems for the
aerosol vertically resolved characterization has increased in recent
years thanks to their low construction and operation costs and their
capability of providing continuous unattended measurements. At the same time
there is a need to convert the ALC signals into usable geophysical
quantities. In fact, the quantitative assessment of the aerosol properties
from ALC measurements and the relevant assimilation in meteorological
forecast models is amongst the main objectives of the EU COST Action TOPROF
(“Towards operational ground-based profiling with ALCs, Doppler lidars and
microwave radiometers for improving weather forecasts”). Concurrently, the E-PROFILE program of the European
Meteorological Services Network (EUMETNET) focuses on the harmonization of
ALC measurements and data provision across Europe. Within these frameworks,
we implemented a model-assisted methodology to retrieve key aerosol
properties (extinction coefficient, surface area, and volume) from elastic
lidar and/or ALC measurements. The method is based on results from a large
set of aerosol scattering simulations (Mie theory) performed at UV, visible,
and near-IR wavelengths using a Monte Carlo approach to select the input
aerosol microphysical properties. An average “continental aerosol type”
(i.e., clean to moderately polluted continental aerosol conditions) is
addressed in this study. Based on the simulation results, we derive mean
functional relationships linking the aerosol backscatter coefficients to the
abovementioned variables. Applied in the data inversion of single-wavelength
lidars and/or ALCs, these relationships allow quantitative determination of
the vertically resolved aerosol backscatter, extinction, volume, and surface
area and, in turn, of the extinction-to-backscatter ratios (i.e., the
lidar ratios, LRs) and extinction-to-volume conversion factor
(cv) at 355, 532, and 1064 nm. These variables provide valuable
information for visibility, radiative transfer, and air quality applications.
This study also includes (1) validation of the model simulations with real
measurements and (2) test applications of the proposed model-based ALC
inversion methodology. In particular, our model simulations were compared to
backscatter and extinction coefficients independently retrieved by Raman
lidar systems operating at different continental sites within the European
Aerosol Research Lidar Network (EARLINET). This comparison shows good
model–measurement agreement, with LR discrepancies below 20 %. The
model-assisted quantitative retrieval of both aerosol extinction and volume
was then tested using raw data from three different ALCs systems
(CHM 15k Nimbus), operating within the Italian Automated LIdar-CEilometer
network (ALICEnet). For this purpose, a 1-year record of the ALC-derived
aerosol optical thickness (AOT) at each site was compared to direct AOT
measurements performed by colocated sun–sky photometers. This comparison
shows an overall AOT agreement within 30 % at all sites. At one site, the
model-assisted ALC estimation of the aerosol volume and mass (i.e.,
PM10) in the lowermost levels was compared to values measured at
the surface level by colocated in situ instrumentation. Within this
exercise, the ALC-derived daily-mean mass concentration was found to
reproduce the corresponding (EU regulated) PM10 values measured by
the local air quality agency well in terms of both temporal variability and
absolute values. Although limited in space and time, the good performances of
the proposed approach suggest it could possibly
represent a valid option to extend the capabilities of ALCs to provide
quantitative information for operational air quality and meteorological
monitoring.
Introduction
Due to the impact of atmospheric aerosols on both air quality and climate,
substantial efforts have been made to expand our knowledge of their sources,
properties, and fate. Aerosol particles affect the Earth's radiation budget
mainly by two different processes: (1) by scattering and absorbing both solar
and terrestrial radiation (aerosol direct effect; Haywood and Boucher, 2000,
and aerosol semi-direct effect; Johnson et al., 2004) and (2) by serving as cloud and ice
condensation nuclei (aerosol indirect effect; Lohmann and Feichter, 2005;
Stevens and Feingold, 2009; Feingold et al., 2016). The complexity of these
processes and the extreme spatial and temporal variability in the aerosol
sources, physical and chemical properties, and atmospheric processing make the
quantification of their impacts very difficult. Aerosols were also proven to
have
detrimental effects on human health (e.g., D'Amato et al., 2013; World Health
Organization, 2013; Lelieveld et al., 2015). In fact, their concentration
(often evaluated in terms of particulate matter mass, or PM) is regulated by
specific air quality legislation worldwide. In Europe, the Air Quality
Directive 2008/50 defines the “objectives for ambient air quality designed
to avoid, prevent or reduce harmful effects on human health and the
environment as a whole” (EC, 2008).
Among aerosol observational systems, the lidar technique was proven
to be the optimal tool to provide range-resolved, accurate aerosol data
necessary in radiative transfer computations (e.g., Koetz et al., 2006; Tosca
et al., 2017) and is often usefully employed in supporting air quality
studies (e.g., Menut et al., 1997; He et al., 2012). With a spectrum of
different system types (elastic backscatter, Raman, high spectral resolution,
and multiwavelength lidars), each with specific pro and cons (Lolli et al.,
2018), this technique allows retrievals of aerosol and cloud optical
properties and relevant distribution within the atmospheric column at several
ground-based observational sites (Fernald et al., 1972; Klett, 1981; Shipley
et al., 1983; Kovalev and Eichinger, 2004; Heese and Wiegner, 2008; Ansmann
et al., 2012; Dionisi et al., 2013; Perrone et al., 2014). Since 2006, the Cloud Aerosol Lidar and Infrared Pathfinder
Satellite Observation (CALIPSO) platform (Winker et al., 2003) also provides
a unique global view of aerosol and cloud vertical distributions through
space-based observations (at the operating wavelengths of 532 and 1064 nm).
Recently, within the Cloud-Aerosol Transport System (CATS) mission, a lidar
was also installed at the International Space Station (ISS; McGill et al.,
2015; York et al., 2016). Spaceborne lidar observations are
however affected by some drawbacks, such as (1) limited temporal resolution and
spatial coverage (the CALIPSO spatial distance between two consecutive ground
tracks is about 1000 km and each track has a footprint of 70 m), (2) the
contamination of unscreened clouds, and (3) difficulties in quantitatively
characterizing the aerosol properties in the lowermost troposphere
(Pappalardo et al., 2010). Ground-based lidar networks thus still represent
key tools in integrating spaceborne observations to study aerosol properties
and their 4-D distribution. An example of these networks is the European
Aerosol Research Lidar Network (EARLINET,
http://www.earlinet.org/, last access: 29 October 2018), which, since 2000, provides an extensive collection of
ground-based data for aerosol vertical distribution over Europe
(Bösenberg et al., 2003; Pappalardo et al., 2014). The advanced
multiwavelength elastic and Raman lidars employed in this network allow
independent retrieval of aerosol extinction (αa) and
backscattering coefficient (βa) profiles. Yet, despite their
unsurpassed potential in data accuracy, advanced lidar networks such as
EARLINET have the unsolved problems of sparse spatial and temporal
sampling and of complexity of operations. In fact, the typical distance
between the EARLINET stations is of the order of several hundreds of
kilometers and regular measurements of EARLINET are only performed on
selected days of the week (Mondays and Thursdays) and for a few hours (mainly
at nighttime, due to low signal-to-noise ratio (SNR) of the Raman signal in
daylight). Furthermore, these systems are complicated to operate, require
specific expertise, and are therefore unsuitable for operational applications.
Today, hundreds of single-channel automated lidar ceilometers (ALCs) are
in operation over Europe and worldwide. Although such simple lidar-type
instruments were originally designed for cloud base detection only, the
recent technological advancements now make these systems reliable and
affordable for aerosol measurements, increasing the interest in using this
technology in different aerosol-related sectors (e.g., air quality, aviation
security, meteorology). Recent studies showed that the ALC technology
is now mature enough to be used for a quantitative evaluation of the aerosol
physical properties in the lower atmosphere (Wiegner and Geiß,
2012; Wiegner et al., 2014), and the exploitation of the full potential of
ALCs in the aerosol remote sensing is a current matter of discussion in the
lidar community (e.g., Madonna et al., 2015, 2018). The evaluation of ALC
capabilities of providing quantitative aerosol information is among the main
objectives of the EU COST Action ES1303, TOPROF (“Towards operational
ground-based profiling with ALCs, Doppler lidars and microwave radiometers for improving weather forecasts”).
An effort in this direction is also underway in the framework of E-PROFILE,
one of the observation programs of the European Meteorological Services
Network (EUMETNET). In fact, several ALC stations are progressively joining
E-PROFILE to develop an operational network to produce and exchange
ALC-derived profiles of attenuated backscatter. A recent project funded by
the EU LIFE+ program (DIAPASON, Desert-dust Impact on Air quality through
model-Predictions and Advanced Sensors ObservatioNs, LIFE+2010 ENV/IT/391)
also prototyped and tested an ALC system with an additional depolarization
channel, capable of discriminating nonspherical aerosol types, such as
desert dust (Gobbi et al., 2018). Such upgraded ALC systems could further
improve the capabilities of operational aerosol profiling in a near
future.
Given the necessity to couple advancement in instrumental technology with
tools capable of translating raw data into robust, quantitative, and usable
information, we propose and characterize here a methodology to be applied to
elastic backscatter lidars and/or ALC measurements to retrieve, in a
quasi-automatic way, vertically resolved profiles of some key aerosol optical
and microphysical properties. This effort is intended to contribute to better
exploiting these systems' potential in integrating data collected by more
advanced lidar systems/networks. In particular, the ALC-derived aerosol
properties addressed in this study are aerosol backscatter (βa,
km-1 sr-1), extinction (αa, km-1), surface
area (Sa, cm2 cm-3), and volume (Va, cm3 cm-3), the last being convertible into aerosol mass concentration
(µg m-3) via assumption of particle density. For this purpose, we
developed a numerical aerosol model to perform a large set of aerosol
scattering simulations. Based on results from this numerical model, we derive
mean functional relationships linking βa to
αa, Sa, and Va. These
relationships are then applied in the ALC data inversion and analysis. A
similar approach was applied in past studies for lidar-based investigations
of stratospheric (Gobbi, 1995) and tropospheric aerosols (maritime, desert
dust, and continental type) at visible and UV lidar wavelengths (Barnaba and
Gobbi, 2001, 2004a; hereafter BG01, BG04a, respectively;
Barnaba et al., 2004). Here we extend this approach to all the Nd:YAG laser
harmonics commonly used by both advanced lidars and ALC systems (i.e., 355,
532, 1064 nm wavelengths) and specifically address an
“average continental”
aerosol type, intended to represent clean to moderately polluted continental
aerosol conditions (see Sect. 2.1). In fact, despite the known differences
that can be encountered across the continent in both the short- and the
long-term (e.g., Putaud et al., 2010), this aerosol type is expected to
climatologically dominate over most of Europe.
Overall, this investigation is organized as follows: in Sect. 2 we describe
the aerosol model set up to reproduce clean to moderately polluted
continental conditions and the Monte Carlo methodology followed to compute
the corresponding bulk optical and physical properties. Section 3 shows and
discusses the results of the numerical model and presents the model-based,
mean functional relationships linking the different variables at 355, 532, and
1064 nm. In Sect. 4 we evaluate both the model simulations' capability to
reproduce real measurements in continental aerosol conditions and the
capability of the model-based ALC inversion approach to derive quantitative
geophysical information. The EARLINET database was used for the first task
while tests on the accuracy of the model-based ALC inversion were performed
evaluating both the ALC-derived aerosol volume and optical thickness (AOT,
i.e., the vertically integrated aerosol extinction). To this purpose we
applied the proposed methodology to three ALC systems operating within the
Italian Automated LIdar-CEilometer network (ALICEnet, http://www.alice-net.eu/, last access: 29 October 2018). In
particular, the ALC-derived AOT and aerosol volume (plus mass) were compared to reference measurements performed by ground-based sun
photometers and in situ aerosol instruments (optical counters and
PM10 samplers).
Section 5 summarizes the developed approach and main results, critically
examining strengths and weaknesses. It also includes discussion on the
perspectives of the application of this (or similar) methodology to
operational ALC networks.
The aerosol model
A numerical aerosol model was set up to calculate mean functional
relationships between the aerosol backscatter (βa) and some
relevant aerosol properties such as αa, Sa, and Va. This
is carried out in a two-step procedure (Fig. 1), following an approach similar to
that developed by BG01 and BG04a.
Schematic of the two-step model structure developed to obtain, as a
result, functional relationships between the aerosol backscatter
(βa) and the aerosol extinction, surface area, and volume
(αa, Sa, and Va, respectively).
Generate a large set (here 20 000) of aerosol optical and physical
properties by randomly varying, within appropriate ranges, the microphysical
parameters describing the aerosol size distribution and composition (blue
box in Fig. 1).
Based on results at point 1, determine mean functional
relationships linking such key variables (grey box in Fig. 1).
The following section describes rationale and setup of the first step; the
second step is thoroughly discussed in Sect. 3.
Selection of the aerosol microphysical parameters
As anticipated, an average continental aerosol type (i.e., describing clean
to moderately polluted continental conditions; e.g., Hess et al., 1998) was
targeted in this study, this being the aerosol type expected to dominate
over Europe. Based on a scheme originally proposed by d'Almeida et al. (1991) and a large set of observational evidence (e.g., Van
Dingenen et al., 2004), in this work the size distribution is described as
an external mixture of three size modes. These are (in order of increasing
size range) (1) a first ultrafine mode; (2) a second fine mode, mainly
composed of water-soluble particles; and (3) a third mode of coarse particles.
A three-mode lognormal size distribution described by Eq. (1) is employed for
this purpose:
nr=dNdlogr=∑i=13Ni2πlogσiexp-(logr-logri)2logσi2.
In Eq. (1), ri, σi, and Ni are respectively the modal
radius, the width, and the particle number density of the ith
aerosol mode (i= 1, 2, 3). At each computation, ri and σi
are randomly chosen within a relevant variability range. Values of Ni
are conversely obtained by firstly randomly choosing the total number of
particles, Ntot, to be included in the whole size distribution
(Ntot=N1+N2+N3) and then by applying specific
rules for the number mixing ratio, xi=Ni/Ntot, of each
component to this total. To reproduce clean to moderately polluted
continental conditions, the value of Ntot is made variable
between 500 and 1×10-4 cm-3 (e.g., Hess et al., 1998; Van
Dingenen et al., 2004). As the result of different sources and processes, the
three modes are also assumed to have a different composition, which impacts
the optical computations through the relevant particle refractive index
(mi), with both its real and imaginary components (mi=mr_i-i×mim_i). The Mie theory for spherical
particles of radius ri and refractive index mi is then used to
compute the extinction and backscatter coefficients (see below).
Aerosol parameter values as reported in literature for
continental-type aerosols.
Referencer1(µm)r2(µm)r3(µm)N1/NtotN2/NtotN3/Ntotmr_1,mr_2mr_3NtotAerosol typeσ1σ2σ3(%)(%)(%)mim_1mim_2mim_3(cm-3)Whitby (1978)a0.0080.0340.460.560.444×10-4–––1800Clean1.62.12.2continentalD'Almeida et al. (1991)b0.0120.0290.4710.060.942×10-61.751.531.5320 000Average2.02.242.510.440.0120.008continentalHess et al. (1998)b0.0120.0210.4710.560.440.3×10-41.751.531.5315 300Average2.02.242.510.440.0120.008continentalBarnaba and Gobbi (2004a)a0.007–0.0120.021–0.0770.403–0.56.1–54.245.8–93.9(2–26.1)1.25–2.001.531.53103–1041.7–2.02.03–2.242.11–2.24×10-40.07–1.006×10-38×10-3Omar et al. (2009)a–0.093–0.100.68–0.76–0.999–1(0.02–3)–1.38–1.401.40–1.46–Clean and1.53–1.611.9–2.1×10-4(0.1–6.3)×10-3(3.4–6.3)×10-3pollutedcontinentalLevy et al. (2007)b0.0180.0050.511×10-71×10-131.751.531.53–2.02.972.970.446×10-38×10-3Barnaba et al. (2007)a–0.05–0.10.4–0.5–0.98–0.990.01–0.02–1.35–1.551.53–1.6(1-3)×103Continental–1.35–1.701.5–2.0(2.5–20)(1.0–80)coastal×10-3×10-4Amiridis et al. (2015)a–0.03–0.90.47–0.69–1(4–8)–1.42–1.451.45–1.53–Clean and1.6–2.21.9–2.5×10-7(2.3–6)×10-3(2.3–6)×10-3pollutedcontinental
aThe refractive index is at λ=532 nm.
bThe refractive index is at λ=550 nm.
Variability ranges used in this study. Values refer to
ground and dry conditions (see text for details).
A description of the assumptions made for each mode and relevant parameter,
mostly based on literature data (Table 1), is given hereafter; the summary
of the relevant variability chosen for each parameter is provided in
Table 2.
First mode.
This ultrafine mode is the one more directly simulating fresh anthropogenic
emissions. The number mixing ratio xi=1 (Ni=1/Ntot) of this
mode is let variable between 10 % (rural conditions; Van Dingenen et al.,
2004) and 60 % (more polluted conditions; Hess et al., 1998). The
variability in its modal radius (r1=0.005–0.03 µm) is
chosen to include nucleation-mode particles to Aitken-mode particles. To
take into account the wide variability in species within this ultrafine mode,
from non-absorbing (e.g., inorganic particles) to highly absorbing materials
(e.g., black carbon), wide ranges of variability have been set for its
refractive indexes (at λ=355 nm: mr_1 in the
range 1.40—1.8, and mim_1 in the range 0.01–0.47;
see Table 2 for the corresponding values at λ=532 and 1064 nm).
Second mode.
The second aerosol mode accounts for 40 %–90 % of Ntot, with (dry)
r2 between 0.03 and 0.1 µm. Its composition (mr_2, and mim_2) is also made highly variable so as to
include water-soluble inorganic and organic particles (Hess et al., 1998;
BG04a; Dinar et al., 2008). In this case, at λ=355 nm,
mr_2 is in the range 1.40–1.7 and mim_2 is in the range 0.0001–0.01 (Table 2).
Third mode.
This coarser aerosol mode (modal radius r3 in the range
0.3–0.5 µm) is mainly intended to account for soil-derived
(dust-like) particles that are a primary continental emission. A quite narrow
variability is thus fixed for its mr_3 and mim_3
values (1.5–1.6 and 0.0001–0.02, respectively, at 355 nm). The relevant
number mixing ratio x3 (N3/Ntot) is set as variable between
0.01 % and 0.5 %, with this mode contributing mostly to the total aerosol
volume (thus mass) rather than to the total number of particles.
As mentioned, refractive indexes were also made wavelength dependent, as this
feature is also typically observed as linked to the different particle
composition. In particular, for the second mode (water-soluble particles) we
include an increase with the wavelength of the upper boundary values of
mim_2 and a decrease in mr_2 at
λ=1064 nm (d'Almeida et al., 1991). For the (dust-like)
third-mode particles, the upper boundary values of mim_3
are set to decrease with increasing wavelengths (Gasteiger et al., 2011;
Wagner et al., 2012).
For convenience, the aerosol parameter boundaries summarized in Table 2
refer to dry particles and to ground level. However, the effect of a
variable relative humidity (RH), its variability with altitude, and the generally observed
decrease in particle number with altitude is also considered in the model.
More specifically, the number of particles in each mode, Ni, and RH are
both made altitude dependent through the following equations (Patterson et
al., 1980; BG01):
Ni(z)=Ni(0)×exp-zHi,RHz=70×exp-z5.5km×1+dRH.
The altitude z is variable here between 0 and 5 km. Ni(0) and
Hi in Eq. (2) are the number of particles at the ground and the scale
height for each mode, respectively.
To describe the altitude effect, in Eq. (2) an exponential decrease
with height of the particle number density is assumed. To rescale the
particle number density of the different modes, Hi=1,2 is set equal to
5.5 km (Barnaba et al., 2007) while Hi=3 (coarse particles) is set to
0.8 km (Barnaba et al., 2007). In Eq. (3), the additional term
(1 + dRH) allows a further variability with respect to the mean RH(z) profile
assumed; here dRH is randomly chosen between -60 and +60. Values of RH
greater than 95 % are discarded to avoid divergence.
Additionally, while the first and third modes are assumed to be water insoluble,
the second mode (i=2) is fully hygroscopic. Aerosol humidification is thus
considered to act on both particle size and refractive indices of the second
aerosol mode (e.g., BG01) as
r2_RH=r2_02-0.01RH2(1-0.01RH),m2_RH=mw+(m2_0-mw)r2_0r2_RH3.
In Eqs. (4) and (5), r2_RH and m2_RH are
the RH-corrected modal radius and refractive index for the second mode,
respectively; r2_0 and m2_0 are the
particle dry modal radius and refractive index, respectively; mw is the
water refractive index (assumed to be equal to 1.34-i7×10-9, and 1.33-i1.3×10-9, 1.33-i2.9×10-6 at 355, 532, and 1064 nm, respectively).
Finally, following Barnaba et al. (2007), an increase in the width of the
size distribution with altitude (Eq. 6) has been introduced for the first
and second aerosol modes:
σ1,2z=σ1,2(z0)×expz30.
In fact, Barnaba et al. (2007) showed that this was necessary to better
reproduce the observed decrease in the lidar ratio (LR) with altitude, and
is likely related to a broadening of the particle size distribution with aging.
Once the value of each microphysical parameter is randomly selected within
its relevant variability range, and once corrections are applied following
Eqs. (2)–(6), each resulting aerosol size- and composition-resolved
distribution is used to compute the aerosol Sa and Va, as well as
to feed a Mie code (assumption of spherical particles; Bohren and Huffman,
1983) to compute βa and αa (BG01; see also Fig. 1).
Overall, the equations used are as follows.
βa=∫Qbscr,λ,mπr2dNdlogr1rln10drαa=∫Qextr,λ,mπr2dNdlogr1rln10drSa=4π∫r2dNdlogr1rln10drVa=43π∫r3dNdlogr1rln10dr.
Here Qbsc (ri, λ, mi) and Qext (ri,
λ, mi) are, respectively, the backscatter and the extinction
efficiencies. As mentioned, the optical computations are made at the three
different wavelengths: 355, 532, and 1064 nm (i.e., those of Nd:YAG laser
harmonics, the most common wavelengths used by ground-based and spaceborne
aerosol lidars).
Since in our simulations the third aerosol mode is intended to represent
dust-like particles, an empirical correction for non-sphericity is also
applied to the Mie-derived optical properties of this mode. This procedure
is based on BG01, which uses the results of Mishchenko et al. (1997)
obtained for surface-equivalent mixtures of prolate and oblate spheroids.
Model simulation results
In Fig. 2 we show the results of 20 000 simulations of continental aerosol
optical and physical properties derived randomly, varying the relevant aerosol
size distributions and compositions as described in the previous section. In
particular, the results for αa, Sa, and
Va are shown as a function of βa in Fig. 2a, b, c (blue
crosses) referring to λ=1064 nm. For each variable (A), the average
value per bin of βa and relevant standard deviations
(〈A〉±dA) are shown as red dots and vertical bars,
respectively. Note that 10 equally spaced bins per decade of β have
been considered and that 〈A〉±dA values are only shown for bins
containing at least 1 % of the total number of pairs. Corresponding
relative errors (dA/〈A〉) are depicted in Fig. 2d, e, f. Some
sensitivity tests of these model outputs to the variability in the input
microphysical parameters employed are provided in Appendix A.
Scatter plots of (a)αa (km-1),
(b)Sa (cm2 cm-3), and
(c)Va (cm3 cm-3) vs. backscatter
βa (km-1 sr-1) and relevant relative errors
(d, e, f) as derived from 20 000 model computations (blue points)
at λ=1064 nm. Red dots and error bars are the average values per
decade of β and their standard deviations; green lines are the
seventh-order polynomial fit curve of the 20 000 points.
Based on these results, at step two of the procedure (see scheme in Fig. 1), we derive aerosol-specific mean relationships linking aerosol extinction,
surface area, and volume (αa, Sa and Va) to its
backscatter (βa). For this purpose, we used a seventh-order
polynomial fit in log–log coordinates. The choice of a seventh-order
polynomial fit was made for homogeneity with BG01 and BG04a. These
relationships are shown as green lines in Fig. 2a, b, c while the relevant
fit parameters are reported in Table 3 referring to λ=1064 nm (fit parameters related to computations at
λ=355 and 532 nm are given in Table B1 and Table B2, Appendix B).
Parameters of the seventh-order polynomial fits
(y=a0+a1x+a2x2+a3x3+a4x4+a5x5+a6x6+a7x7) for λ=1064 nm, with x=log(βa)
(in km-1 sr-1) and
y=log(αa,Sa,orVa) in km-1,
cm2 cm-3, and
cm3 cm-3, respectively.
The red vertical bars of Fig. 2 also highlight the ranges of αa,
Sa, and Va, which are statistically significant, i.e., those in which,
at λ=1064 nm, the model provides at least 1 % of the
total points per corresponding bin of βa. These are
10-4–10-1 km-1, 10-7–10-5 cm2 cm-3, and 10-13–10-10 cm3 cm-3, for αa, Sa, and
Va,
respectively, corresponding to the backscatter range 9×10-5≤βa≤4×10-3 km-1 sr-1.
In terms of aerosol property variability, the relative errors associated with
αa and Va show almost no dependence on βa, with
values between 30 % and 40 %. Conversely, the modeled aerosol surface
area exhibits a larger dispersion, with relative error values spanning the
range 40 %–70 %, and decreasing as βa increases.
(a, b, c) Scatter plots of LR (sr) versus βa
(km-1 sr-1) at (a) 355 nm, (b) 532 nm, and
(c) 1064 nm (blue points). The seventh-order polynomial fit curve
(green lines) and the average values per decade of β together with
their standard deviations (red points and red vertical bars, respectively)
are also reported. Horizontal black lines are mean values of the
weighted-LR and ±1 SD (solid and dashed lines, respectively).
(d, e, f) Relative errors associated with the model-derived LR at
(d) 355 nm, (e) 532 nm, and (f) 1064 nm.
A key parameter for the inversion of lidar signals is the LR, i.e., the ratio between αa and
βa (Ansmann et al., 1992). In Fig. 3 we thus show the results
of our simulations in terms of LR vs. βa at the three
λ (355, 532, and 1064 nm, Fig. 3a, b, c, respectively) and relevant
dLR / LR values (Fig. 3d, e, f, respectively). The color code is the same
as in
Fig. 2. Additional horizontal black lines have been inserted representing
values (solid central lines) of the weighted-LR ± 1 standard
deviation (dotted side lines), i.e., the LR weighted by the number of
simulated points in each considered backscatter bin. The weighted-LR
values derived at 355, 532, and 1064 nm are 50.1±17.9, 49.6±16.0,
and 37.7±12.6 sr, respectively. Figure 3 also allows showing that the
statistically significant regions of simulated backscatter values shift
towards smaller values with increasing λ (e.g., at λ=355,
the βa extending region is 4×10-5–2×10-2 km-1 sr-1, whereas, at 532 nm, it ranges between
2×10-5 and 1×10-2 km-1 sr-1). Furthermore,
Fig. 3 reveals a quite different shape of the LR vs. βa
functional relationships (green curves) at different wavelengths. At 355 and
532 nm the curve is concave, with quite similar LR maxima of the fitting
curve (54.3 and 53.8 sr at approximately βa=4×10-4
and 2×10-3 km-1 sr-1, respectively). At 1064 nm the
curve is conversely monotonic, with a flex point at βa=3–4×10-4 km-1 sr-1. A larger data dispersion also
characterizes the results at λ=355 and 532 nm (LR values from 10
to 90 sr) in comparison to λ=1064 nm (LR in the range 18–80 sr,
except for a minor number of outliers). This translates into different LR
relative errors at UV, VIS, and infrared (IR) wavelengths. At 1064,
dLR/LR slightly decreases for increasing backscatter,
with values of around 35 %. At the shorter wavelengths, dLR/LR increases as a
function of βa, with a large (>40 %) relative error
for values of βa>2×10-3 km-1 sr-1.
Mean weighted LR at 355, 532, and 532 nm derived in this
work and comparison to the corresponding aerosol subtypes (clean continental,
CC, and polluted continental, PC) from relevant literature.
LR (sr)λ=355 nmλ=532 nmλ=1064 nmOmar et al. (2009)–70±25 (PC)30 (PC)(CALIPSO aerosol model)–35±16 (CC)30 (CC)Amiridis et al. (2015)59.5a (PC)64 (PC)–(LIVAS database)56.5a (CC)54 (CC)–Papagiannopoulos et al. (2016)–62±10 (PC)–(EARLINET measurements)–47±4 (CC)–Düsing et al. (2018)555530; 15b(in situ and lidar measurements)This work50.1±17.949.6±16.037.7±12.6
a Derived using the extinction-related and
backscatter-related Ångström exponents given by Amiridis et
al. (2015).
b See the explanation in the text for the two different values.
To insert our results into a more general context, we compared the derived,
model-based weighted-LR values to some LR data reported in the literature
(Table 4). In particular, we selected some of the works using the aerosol
model developed to invert the CALIPSO lidar data. For example, Omar et al. (2009) consider six different aerosol subtypes: clean continental (CC),
clean marine (CM), dust (D), polluted continental (PC), polluted dust (PD),
and smoke (S). Our model-derived LR at 532 nm falls in the middle of the
range (35–70 sr) fixed by the CALIPSO CC and PC aerosol classes. The work by
Papaggianopoulos et al. (2016), in which the LR values are adjusted
according to EARLINET observations, reports a LR range at 532 nm of 47–62 sr. At the same wavelength, the aerosol range defined by the LIVAS
climatology (LIdar climatology of Vertical Aerosol Structure for space-based
lidar simulation studies; Amiridis et al., 2015) is 54–64 sr. In both cases,
our model seems to be closer to the LR values of the CC aerosol type, which is
compatible with our intention to simulate the clean to moderately polluted
continental aerosol type. At 532 nm, our LR value is also reasonably in
between the CC and PC LR values derived by Omar et al. (2009), but again
closer to the CC LR value. The very small decrease in LR values between 532
and 355 nm estimated by LIVAS for the CC aerosol is also consistent with our
results. Similarly, our model predicts a lower mean LR in the near IR with
respect to the green, in agreement with results of Amiridis et al. (2015) in
CC conditions and not with those in polluted conditions. Table 4 also includes
the continental aerosol LR values estimated in the work of Düsing et al. (2018) through comparison between airborne in situ and ground-based lidar
measurements. Our model is in good agreement with their LR values at 355 and
532 nm. At 1064 nm, the algorithm developed by Düsing et al. (2018)
provided a value of LR of around 15 sr. Conversely, in the same study the
authors found that, rather, a value of LR = 30 sr gives the better
agreement
between their Mie and lidar-based αa, this value being closer to
our model-derived one at 1064 nm (LR = 37.7). The difference between these
two values is explained by the authors to be probably due to the estimation
of the aerosol particle number size distribution, a critical parameter for a
reliable modeling of aerosol particle backscattering.
Extinction-to-volume conversion factors,
cv=Va/αa (and corresponding mass-to-extinction efficiency
values, MEE =αa/(Va⋅ρa),
computed employing
ρa=2 g cm-3) of continental particles as
derived from our model at different wavelengths.
Referencecv10-6m (corresponding MEE, m2g-1) NotesWavelength [nm]3555321064Hess et al. (1998)–0.35 (1.43)–OPAC, clean continental modelHess et al. (1998)–0.28 (1.79)–OPAC, polluted continental modelBarnaba and Gobbi (2004b)–0.18 (2.78)–Continental modelAnsmann et al. (2011b)–0.18 (2.78)–Germany, fine aerosol fractionLewandoski et al. (2010)––0.77–2Mexico City basin(0.25–0.65)Sicard et al. (2012)–0.26 (1.92)–AERONET, SpainMamouri and Ansmann (2016)0.17 (2.94)0.30 (1.67)0.96 (0.52)Germany, continental anthropogenic pollutionMamouri and Ansmann (2016)0.23 (2.17)0.41 (1.22)1.41 (0.35)Cyprus, continental anthropogenic pollutionMamali et al. (2018)0.14, 0.24Cyprus, fine non-dust aerosol fraction(3.57, 2.03)This work0.12 (4.17)0.19 (2.63)0.60 (0.83)Continental (clean to moderately polluted)
As a last added value of the outcome from our model-based results, we derive
here and provide in Table 5 extinction-to-volume conversion factors,
cv=Va/αa (e.g., Ansmann et al.,
2011) at three
different wavelengths (355, 532, 1064 nm) and compare these to similar
outcomes from other studies. To our knowledge, values of continental
particles cv at three wavelengths are only available in Mamouri
and Ansmann (2016). Note that cv is also proportional,
through the particle density ρa, to the inverse of the
so-called mass-to-extinction efficiency (MEE, i.e.,
αa/(Va⋅ρa)), a parameter important in
several aerosol-related applications (e.g., the estimation of PM from satellite AOT or in modules of global circulation and
chemical transport models to compute aerosol radiative forcing effects; Hand
and Malm, 2007). For convenience, model-derived MEE values are also included
in Table 5.
Evaluation of the model performances and potential of its
application
In this section, we evaluate the capability of the model results to
reproduce “real” aerosol conditions and explore the potential of the
proposed model-based ALC inversion in producing quantitative geophysical
information.
In Sect. 3.1 we compare our simulations to real observations of
independent backscatter and extinction coefficients made by different
EARLINET Raman lidars (Bösenberg et al., 2001; Pappalardo et al., 2014).
In Sect. 3.2, our model results are used to invert measurements acquired
by some ALC systems operating within ALICEnet, which networks several ALC
systems (Nimbus CHM 15k by Lufft) located across Italy and run by Italian
research institutions and environmental agencies. Here we use data from some
of these systems to derive the aerosol optical and physical properties (e.g.,
the AOT and the aerosol volume and mass).
Comparison of the modeled aerosol optical properties to EARLINET
measurements
As mentioned, EARLINET Raman stations perform coordinated measurements 2
days per week following a schedule established in 2000 (Bösenberg et al.,
2003). Overall, the EARLINET database includes the following categories:
climatology, CALIPSO, Saharan dust, volcanic eruptions, diurnal
cycles, cirrus, and others (forest fires, photo smog, rural or urban,
and stratosphere). To be comparable to our results, we used EARLINET
βa and αa coefficients at 355 and 532 nm within
the quality-assured (QA) climatology category (Pappalardo et al., 2014).
However, note that additional data filtering was necessary to screen out
residual, likely unreliable values within this QA climatology category. In
particular, we only selected those EARLINET QA data further satisfying the
following criteria:
βa and αa coefficients evaluated independently,
i.e.,
only obtained using the Raman method (Ansmann et al., 1992);
βa and αa>0;
LR < 100;
relative errors on βa and αa<30 %.
Then, we selected those sites in Europe expected to be mostly impacted by
continental aerosols and having the largest datasets (e.g., at least 100
points) at 355 and 532 nm. Overall, five sites satisfied these conditions
(Table 6), namely Madrid (Spain), Potenza and Lecce (Italy), and Leipzig and
Hamburg (Germany). Finally, being interested in continental conditions here,
we filtered out those measurement dates affected by desert dust at the
measuring sites, i.e., we removed from our “model–measurement comparison dataset” all the dates within the EARLINET climatology category also belonging
to the EARLINET Saharan dust category.
Main characteristics of the dataset of the EARLINET
continental sites considered in this study. The listed dataset refers to the
data downloaded from the EARLINET site (last access on the
11 January 2018). NA – not available.
StationNumber of points atAltitude rangePeriod355 and at 532 nm(Δz, in km)Lecce (LC)1012–1091–4Aug 2007–Oct 201340.33∘ N, 18.10∘ E, 30 m a.s.l.Leipzig (LE)5186–45491.5–4Aug 2008–Sept 201651.35∘ N, 12.43∘ E, 90 m a.s.l.Potenza (PO)1244–2191.5–4May 2000–Aug 200940.6∘ N, 15.72∘ E, 760 m a.s.l.Hamburg (HH)243–NA0.5–4Apr 2001–Oct 200253.57∘ N, 9.97∘ E, 25 m a.s.l.Madrid (MA)NA–4920.5–4Jun 2006–Jun 200840.45∘ N, 3.73∘ E, 669 m a.s.l.
Figure 4 depicts the results of the model–measurement comparison at the
sites fulfilling our requirements in terms of LR vs. βa at λ=355 nm (the corresponding results at λ=532 nm, including Madrid
in place of Hamburg, are given in Appendix C, Fig. C1). The colored area
represents the model-simulated data range, while the color code indicates the
absolute number of simulated values (i.e., counts) in each βa–LR
pair. The EARLINET-measured values are reported as open black circles. Note
that, since the model simulations are performed over an altitude range of 0–5 km (see
Sect. 2.1),
only those simulations corresponding to the altitude range
(Δz) covered by the measurements at each EARLINET station were taken
into account here.
Figure 4 shows that the model results encompass the
measured LR vs. βa data well, with a few measurements outside the modeled
range (most of the exceptions are found for Potenza). Statistically, the
highest number density of simulated data fits the observations well, with the
exception of Hamburg (Fig. 4a), which has the lowest number of
measured data (it is not an EARLINET station any longer; see Table 6).
Scatter plots of LR (sr) versus βa
(km-1 sr-1) at 355 nm as simulated by our model (colored region)
and measured by EARLINET lidars (open black circles) in Hamburg
(Germany) (a), Lecce (Italy) (b), Leipzig
(Germany) (c), and Potenza (Italy) (d). The colored area is
the region of simulated values, the color code indicating the number of
simulated values in each βa–LR pair (see legend). In
particular, the color 2-D histogram is computed using a semilogarithmic box
consisting of 10 equally spaced bins per decade of βa on the
x axis and five spaced LR values on the y axis.
Mean LR discrepancies between our model results and EARLINET
measurements and weighted LR at 355 and 532 nm for the considered EARLINET
stations. LC: Lecce; LE: Leipzig; PO: Potenza; HH: Hamburg; MA: Madrid.
In Fig. 5 the previous results at λ=355 nm are converted in terms
of mean LR per bin of βa for both model (blue) and
observations (red, again, only βa bins containing at least
1 % of the total modeled data were considered). This view shows that
there is a general good agreement between the modeled and the measured LR
values, and in their variation with βa. Some major deviations
are found for Potenza and are further discussed in the following. The
model–measurement agreement shown in Fig. 5 was evaluated in quantitative
terms by computing mean LR relative differences at both λ=355 and
532 nm; i.e., we derived ([(LRmod-LRmeas)/LRmeas]⋅100) values, for which LRmod and
LRmeas are the LR values computed by the model and derived
using lidar measurements, respectively. These values are reported in Table 7 for
each considered EARLINET station, together with the measurement-based mean
LR in each observational site (computed weighting the number of observations
per βa-spaced bins).
Model-simulated (blue) and lidar-measured (red) LR vs.
βa mean curves at 355 nm calculated per 10 equally spaced
bins per decade of βa at the (a) Hamburg (HH),
(b) Lecce (LC), (c) Leipzig (LE), and (d) Potenza (PO) EARLINET
lidar stations. Vertical bars are the associated standard deviations.
Results in Fig. 5 and Table 7 also give some hints on the capability of
the aerosol type assumed (and its admitted ranges of variability) to
reproduce real continental aerosol conditions at different sites across
Europe. In fact, the four continental sites selected with our criteria are
still expected to be partially impacted by different aerosol types.
A good agreement between the model and the observations in terms of LR mean
values is found for Hamburg (Fig. 5a), with mean LR differences of the order
of 5 % (Table 7). Still, the measured LR values have a high variability
and their distribution is positioned towards high values of
βa (1×10-3 to 4×10-3 km-1 sr-1). This could be due to the presence of
different aerosol types as slightly polluted marine and polluted aerosol
(Matthias and Bösenberg, 2002).
A good accord for Leipzig (Fig. 5c) also indicates that this site is mostly
dominated by pure continental particles. In fact, the distribution of
observed LR points in Fig. 4, which covers βa values ranging
from 2×10-4 to 3×10-3 km-1 sr-1, is well
centered to the modeled simulations' highest density (counts >40). Table 7
shows that at both wavelengths mean discrepancies with LR measurements stay
well below 10 %.
The highest differences in Fig. 5 are found at some southern Europe EARLINET
sites.
In Lecce (Fig. 5b), the best agreement between model and observations is
found for the lowest values of βa (between 9×10-4
and 1×10-3 km-1 sr-1; see Table 7). Also, the increase
from 10 % to 18 % in the discrepancies at 355 and 532 nm indicates
some model problems in correctly reproducing the spectral variability in the
optical properties, suggesting some mismatch between modeled and real aerosol
sizes at this site (see discussion below).
In Potenza (Fig. 5d), a significant difference between the mean LR curves
emerges for βa values >6×10-4 km-1 sr-1 , with observed LR values lower than those
simulated here.
These discrepancies could be due to the influence of marine aerosols at both
stations (De Tomasi et al., 2006; Mona et al., 2006; Madonna et al., 2011),
which is expected to produce lower LR values for high values of
βa (e.g., BG01). In fact, Madrid shows better performances,
with dLR/LR values comparable to those in Leipzig.
To provide some insight into the reasons of the model–measurement
differences at the Lecce and Potenza sites, some specific model sensitivity tests have
been performed and are reported in Appendix D. In particular, for Lecce, we
found that better agreement between the observed and simulated LR vs. βa behavior at 355 nm is obtained by reducing the variability range of
Ntot (from 500–10 000 to 500–5000 cm-3 at ground).
This indicates that Lecce is likely affected by cleaner continental aerosol-type
conditions. The sensitivity simulations performed for understanding the mismatches
with Potenza measurements show that an extension of the variability range of
the coarse-mode radius is needed to reproduce the observed decrease in LR for
increasing backscatter (Fig. 5d). This suggests a contribution of coarse
particles larger than that assumed (Appendix D). This is compatible with the suspect of marine air
contamination, although at this stage we are not able to exclude additional
contamination of coarser particles of soil origin.
Overall, mean LR differences between our average continental model and data
at selected continental sites in Europe remain lower than 20 % (Table 7), indicating the model reasonably reproduces the clean-to-moderately
polluted continental aerosol conditions we intended to simulate well.
Application of model results to Nimbus CHM 15k ALC measurements
To test and validate the model-based inversion methodology, we used the
derived functional relationships (Sect. 2.2) to invert and analyze the
measurements of some ALICEnet ALCs (Lufft Nimbus CHM 15k systems). These instruments
are biaxial ceilometers that emit laser pulses at 1064 nm (Nd:YAG laser,
class M1) with a typical pulse energy of 8 µJ and a pulse repetition
rate of about 6500 Hz. The instruments have a specified range of 15 km and
fully overlap at around 1500 m (Heese et al., 2010). The manufacturer provides
the overlap correction functions (O(z)) for each system. As shown recently
by Wiegner and Geiß (2012) and Wiegner et al. (2014), a promising
strategy to retrieve the aerosol backscatter coefficient from ALC measurement
is adopting the forward solution of the Klett inversion algorithm (Klett,
1985). This solution requires a known calibration constant of the system
(i.e., absolute calibration, cL) and an assumption of the LR. The
advantage with respect to the backward solution is that calibration is not
affected by the low SNR in the upper troposphere and it is needed
occasionally. Furthermore, starting close to the surface, the data retrieval
allows us to resolve aerosol layers in the boundary layer even if their optical
depth is high. The forward solution of the Klett inversion algorithm is thus
adopted here. For convenience, we report here the equations used within our
procedure to obtain βa from ALC measurements, which are also
described in Wiegner and Geiß (2012, Eqs. 1–3):
βaz=ZzLRNz-βmz
with
Zz=LRz2Pzexp-2∫0zLRβm-αmdz′
and
Nz=cL-2∫0zZ(z′)dz′.
Here, βm and αm are the molecular backscatter and
extinction coefficients calculated from climatological monthly air density
profiles and z2P(z) is the ALC range-corrected (z) signal (P) (also
referred to as RCS), which are the raw data obtained by the considered ALCs. As
anticipated, knowledge of the calibration constant cL is needed to solve
Eq. (13) (and thus 11, forward solution). In our analysis of ALC daily records,
the constant cL has been obtained using the “backward approach” (Rayleigh
calibration) applied to nighttime cloud-free ALC signals averaged over 1 or
2 h at 75 m height resolution. This allows for using the best cL
retrieval (that is the nighttime lowest noise one) in the forward solution
of the lidar equation, which guarantees operating over the best signal-to-noise range of the ALC signal.
Model-based retrieval of aerosol optical properties
Operatively, inversion of the aerosol properties, αa(z) and
βa(z), is performed using an iterative technique since we
need to correct the backscatter signal at each altitude z for extinction
losses. The iterative procedure is stopped when convergence in the integrated
aerosol backscatter (IAB =Σ0zcalβa(z))
is reached (e.g., BG01). At each step, aerosol extinction is derived using the
functional relationship αa=αa
(βa) of Table 3.
Time–height cross section of the aerosol extinction coefficients
αa (km-1), as derived at 1064 nm on 26 June 2016 by
the ALICEnet ALC of Saint-Christophe in the Aosta Valley (northern Italy). The orange circle
points and the pink line are the AOT values (right y axis) measured by a
colocated POM-02L radiometer and estimated from the ALC following our
approach.
(a) Geographical map of the ALC network ALICEnet. The red
circles highlight the selected sites for this study: Saint-Christophe in the
Aosta Valley
(ASC), San Pietro Capofiume (SPC), and Rome Tor Vergata (ASC).
(b)–(d) Histograms of the differences between the
hourly-mean coincident AOTs at 1064 nm as derived by ALCs and measured by
photometers at ASC, SPC, and RTV, respectively. The different colors (red,
blue, and black) depict the different inversion schemes: model-based inversion
scheme, LR = 38 sr and LR = 52 sr. In each panel the
values of the average measured AOT (and its associated standard deviation)
and of the number of considered pairs are also reported.
An example of the outcome of this retrieval methodology is depicted in Fig. 6. It shows the time–height (24 h, 0–6 km) contour plot of αa
retrieved at 1064 nm during a whole day of measurements (26 June 2016)
performed by the ALICEnet system of Saint-Christophe in the Aosta Valley (ASC,
45.7∘ N, 7.4∘ E 560 m a.s.l., northern Italy; Fig. 7a).
Time and altitude resolutions are 1 min and 15 m, respectively. Note that ALC
data are cloud-screened using the cloud mask of the Lufft firmware.
The AOT is obtained by vertically integrating the
ALC-derived αa(z) from the surface up to a fixed height zAOT,
above which the aerosol contribution is assumed to be negligible. In Fig. 6, the ALC-derived AOT values at 1064 nm (pink curve, with a temporal
resolution of 5 min) are superimposed on the extinction contour. Reference AOT
values from a colocated sun–sky radiometer (a Prede POM-02 system) are shown
by orange circles. These were extrapolated at 1064 nm from the instrument
1020 nm channel using the Ångström exponent derived fitting AOT values
at all the radiometer wavelengths. This example illustrates the very good
performances of our model-assisted inversion scheme and the capability of
this approach to extend the (daylight-only) radiometer
observations to nighttime.
Main characteristics of the ALC and colocated sun–sky
radiometer equipment located at the considered ALICEnet sites.
Site typeALC modelALC firmwareSun photometer modelASCalpineNimbus CHM1501040.743POM-02SPCruralNimbus CHM1101150.556POM-02LRTVsemiruralNimbus CHM0700520.720CIMEL CE-318
To evaluate the performances of our model-assisted retrieval of αa(z) over a more statistically significant dataset, the same approach
illustrated in Fig. 6 was applied to a longer record at the ASC site, plus
Nimbus CHM-15k ALC datasets from two additional ALICEnet sites: San Pietro
Capofiume (SPC, 44∘39 N, 11∘37 E, 10 m a.s.l.) and Rome
Tor Vergata (RTV, 41.88∘ N, 12.68∘ E, 100 m a.s.l.). The
location of the instruments is shown in Fig. 7a (red circles), while some
information on system types and site characteristics is given in Table 8.
The data analyzed here were collected during the following periods: April 2015–June 2017, June 2012–June 2013, and February 2014–September 2015 for ASC, SPC, and RTV, respectively.
At those sites, reference AOTs were collected by three colocated sun–sky
radiometers, namely using two SKYNET Prede sun–sky radiometers at ASC and
SPC (POM-02L and POM-02, respectively,
http://www.euroskyrad.net/, last access: 29 October 2018) and an AERONET (AErosol Robotic Networ; Holben et al., 1998)
Cimel CE 318-2 instrument operational at RTV
(https://aeronet.gsfc.nasa.gov, last access: 29 October 2018, Rome Tor Vergata station, data level 2.0). Only AOT values
between 0.01 and 0.2 at 1064 nm were considered. This range allows for
excluding the data points with a 1064 nm AOT lower than the sun photometer
expected accuracy (dAOT = 0.01) and those for which we found aerosol
extinction to cause significant deterioration of our ALC signal. Overall a
total of 1237, 268, and 850 AOT pairs were analyzed at ASC, SPC, and RTV,
respectively.
Also note that, although CHM-15k data are already corrected for the O(z)
function provided by the manufacturer, the variation in the ALC internal
temperature was shown to lead to O(z) differences of up to 45 % in the
first 300 m above ground (Hervo et al., 2016). For this reason, in our
analyses the lowest valid altitude of the CHM-15k for both the SPC and RTV
systems was fixed to be about 400 m. A linear fit of the first two valid ALC
points is then used to extrapolate αa(z) down to the ground
(z0). Conversely, due to the optimal characterization down to the ground
of O(z) provided by Lufft for the CHM-15k system installed at ASC, values
at z0 at this site are not those extrapolated but actually those
measured. The maximum altitude of aerosol extinction vertical integration to
derive the AOT, zAOT, was selected as the first height above 4000 m
at which the range-corrected signal (RCS) has a SNR < 1.
Results of the comparison between the AOT measured by
sun photometers and the one derived by ALCs (model-based and fixed-LR
inversion schemes) at three ALICEnet stations. Mean differences (expressed
in terms of <dAOT>=<(AOTceil-AOTphot)>, <|dAOT|> (module), <dAOT/AOT>, and <|dAOTI/AOT|> )
are reported with their standard deviations.
ALICEnet sites<dAOT><|dAOT|><dAOT/AOT><|dAOT/AOT|>ASCVariable LR from our model-0.004±0.0150.010±0.013-0.25±0.570.31±0.35LR = 52 sr0.002±0.0210.009±0.0150.31±0.580.33±0.35LR = 38 sr-0.004±0.0140.009±0.012-0.23±0.430.30±0.32SPCVariable LR from our model-0.001±0.0200.013±0.016-0.005±0.280.19±0.20LR = 52 sr0.021±0.0260.026±0.020.33±0.350.38±0.26LR = 38 sr-0.003±0.0190.011±0.014-0.043±0.240.16±0.18RTVVariable LR from our model0.004±0.0200.014±0.0140.11±0.490.33±0.30LR = 52 sr0.016±0.0230.021±0.0180.44±0.590.49±0.45LR = 38 sr0.003±0.0190.013±0.0130.088±0.4600.31±0.27
Results of the long-term AOT comparison are summarized in Fig. 7 and Table 9.
For each site under investigation, Fig. 7 shows the histograms of the AOT
differences between the hourly-mean coincident AOTs as derived by the ALCs
and measured by the sun photometers (red curve; corresponding AOT vs. AOT
scatter plots at the three considered sites are given in Appendix E). To
evaluate the advantage of our approach with respect to more standard lidar
inversions, we also computed AOT differences using two fixed-LR values. In
particular, we used LR = 52 sr (i.e., the value suggested by the
E-PROFILE network, black lines) and LR = 38 sr (i.e., the weighted mean
LR value derived from our model; see Sect. 3, blue lines). Figure 7 shows
that the best agreement is found at ASC. The distribution of AOT difference
has a maximum of around 0 for each of the three inversion schemes, with very
low dispersion. The full width at half maximum, FWHM, is in fact around
0.015, and approximately 55 % of the data are included in the interval
-0.01–0.01, which is even within the expected error of photometric
measurement. For SPC and RTV, the red and blue histograms are peaked around
0, whereas the black ones are shifted, with maxima around 0.01–0.02 and
0.02–0.03 for SPC and RTV, respectively. These two sites have higher
dispersion (FWHM = 0.03), and approximately 30 % of the data are
included in the interval -0.01–0.01 for the red and blue histograms at
both sites, which is probably due to the different aerosol load affecting the
different ALICEnet stations. As pointed out by the low value of the average
AOT computed at ASC for the analyzed dataset (〈AOT〉=0.027), low pollution levels generally characterize this site, with some
exceptions due to wind-driven aerosol transport from the nearby Po Valley
(Diémoz et al., 2014, 2018a, b). Conversely, RTV (〈AOT〉=0.044) and especially SPC in the Po Valley (〈AOT〉=0.076) are
characterized by higher aerosol content and pollution levels, which explain
the larger histogram dispersions. Note that the high frequency of fog events
in winter markedly reduces the number of analyzed AOT pairs at the SPC site,
while some desert-dust-affected days at both SPC (e.g., Bucci et al., 2018)
and RTV (e.g., Barnaba et al., 2017) were removed from our datasets (no
desert-dust-affected dates in ASC).
Table 9 summarizes the long-term performances of the model-based procedure
in deriving quantitative AOT from the ALC systems at the three investigated
sites. It includes values of the average differences between the ALC-derived
and sun-photometer-measured AOT (both bias 〈dAOT〉,
and absolute difference 〈|dAOT|〉, with
associated standard deviations) obtained using both the proposed model-based
approach and the fixed-LR inversions. For the SPC and RTV sites, these numbers
show that the best ALC–photometer accordance is reached when employing
either the model-based or the fixed-LR = 38 sr inversion scheme. In fact,
these two approaches have similar performances in terms of mean dAOT values
(〈|dAOT|〉=0.011, 0.013 and 0.013,
0.014 for SPC and RTV, respectively), mean percent error (〈|dAOT|〉/〈AOT〉=0.16, 0.19 and
0.31, 0.33), and a very low mean relative bias (〈dAOT〉/〈AOT〉=-0.043, 0.005 and 0.088, 0.11). Conversely, the fixed-LR = 52 sr retrieval produces an overestimation of AOT in
both SPC and RTV (〈dAOT〉/〈AOT〉=0.33 and 0.44), with larger discrepancies between retrieved and observed AOTs
(〈|dAOT|〉=0.021 and 0.026, 〈|dAOT|〉/〈AOT〉=0.38 and
0.49). For the ASC site, due to the low aerosol content, the differences
among the inversion schemes are almost negligible.
Overall, for the three sites, the statistics over the long-term datasets
employed showed good results of the model-based approach with similar
behavior of the retrievals with a fixed LR of 38 sr, while a fixed LR value
of 52 sr produces an overestimation of the AOT at SPC and RTV. As different
sites have different (and not known a priori) characteristic LR values,
these results highlight the potential of the model-based approach to derive
quite accurate βa and αa coefficients without the
need to choose and fix an arbitrary LR value.
Model-based retrieval of aerosol volume (and mass)
In this section we provide examples of the applicability of the proposed
approach to derive air-quality-relevant parameters. In particular, we use the
ALC, βa-retrieved data, and seventh-order polynomial fit linking
βa (at λ=1064 nm) to Va (see also Table 3 and
Fig. 2c) to derive the aerosol volume (and mass).
The ALC Va estimates were first compared to aerosol volume derived in situ at
the ASC site by two different optical particle counters (OPCs) on
29 December 2016 and 5 September 2017. For the case on 29 December 2016, a TSI optical particle sizer (OPS) 3330 was employed. This instrument
has 16 channels that can be programmed to provide the number concentration at
different (and logarithmically spaced) diameter size ranges within the
interval 0.3–10 µm. Further details can be found in the TSI
manual (2011). For the case on 5 September 2017, the
Fidas® 200s OPC was used. This spectrometer is able to retrieve
high-resolution particle spectra (size measurements between 0.18 and
18 µm, with 32 channels per decade; Pletscher et al., 2016). For both dates,
Fig. 8 shows the time (x axis, 24 h) vs. height (left y axis) contour plots of the
ALC-based retrieval of the aerosol volume concentration (cm3 cm-3).
The OPC-derived aerosol volume concentration measured at ground level is
reported as a function of time (x axis) on the right y axis (grey curve). The
corresponding ALC-derived volume concentration (integrating the ALC data
between 0 and 75 m) is shown by a pink curve (same right y axis). Daily mean
volume concentration values derived by OPCs and by ALC are also plotted (grey
cross and pink triangle, respectively). The horizontal bar in the upper part
of the figure indicates the ranges of RH measured at ground level during the analyzed
cases.
Time–height cross section of the aerosol volume concentration at
Saint-Christophe in the Aosta Valley for 29 December 2016 (a) and 5 September 2017
(b). The right y axis reports the volume concentration measured at
the surface through TSI and Fidas® 200s OPCs
(a, b, grey curves) and the ALC-derived volume
concentration at 75 m (pink curves). The grey crosses and the pink triangles
refer to the daily mean aerosol volume value derived by OPC and ALC
measurements, respectively. The horizontal bars in the upper part of the
panels indicate the ranges (RH<60%, 60%<RH<90%, and RH>90%) of the measured in situ
RH during the analyzed days.
The OPC-to-ALC comparison is certainly affected by intrinsic factors, such as
differences in the atmospheric layer sampled (at ground and integrated
between 0 and 75 m, for OPC and ALC, respectively) and in the probing
methods (in situ and remote sensing, dried air sampled by OPC and ambient
conditions sampled by the ALC). Furthermore, as mentioned in Sect. 4.2.1,
a major critical issue of ALC retrievals at low levels is the correction for
the overlap function, which needs to be experimentally characterized and
verified for each instrument.
These issues are visible in the given examples of Fig. 8. In fact, in Fig. 8a, the agreement between the ALC-derived and the TSI OPC aerosol
Va values is good between 00:00 and 07:00 UTC. In the following hours both
instruments register an increase in the aerosol volume, although with some
discrepancies in absolute values. Starting from 18:00 UTC, the ALC derives an
aerosol volume concentration higher than the OPC concentration by a factor of 3–3.5.
This disagreement could be related to both the presence/arrival of fine
particles (<0.3 m) not measured by the optical counter (see for
example Diémoz et al., 2018a), or to aerosol hygroscopic effects
(increase in volume associated with hygroscopic growth seen by the ALC but not by
the OPC that dries the air samples). This latter effect is confirmed by the
large RH values (RH > 90 %) measured after 18:00 UTC. Figure 8b shows a good agreement between the ALC-derived and Fidas OPC
Va values, in particular until 04:00 UTC and after 16:00 UTC. Some differences
emerge around 07:00 UTC and between 11:00 and 15:00 UTC, when the ALC volume is lower
by a factor of 2 compared to the in situ Fidas Va values. The smaller
minimum detectable size of the Fidas OPC instrument with respect to the OPS
is likely the reason for the better agreement between ALC and OPC Va values
on this test date. In this case, the effect of RH seems to be less important,
and indeed RH values remain lower than 90 %.
In general, high RH values (RH ≥90 %) are known to
markedly affect the aerosol mass estimation from remote-sensing techniques
and its relationship with reference PM2.5 or PM10 measurement
methods, usually performed in dried conditions (e. g. Barnaba et al., 2010;
Adam et al., 2012; Li et al., 2016, 2017). This theme is also
discussed in Diémoz et al. (2018a) for the ALC measurement site of Fig. 8.
Nevertheless, even with the mentioned limitations, results in Fig. 8
show the potential of the developed method in providing sound values of
aerosol volume, and hence mass, in average-RH regimes well, giving support to
more standard PM10 air quality monitoring.
Daily-resolved aerosol mass concentration at SPC, for the period
June–July 2012, estimated from ALC-derived aerosol volume data at
225 m a.s.l. converted into mass using a fixed particle density
ρa=2 g cm-3 (blue dotted line) and a
variable ρa between 1.5 and 2.5 g cm-3 (shaded
blue area). The red solid line is the daily PM10 concentration as
measured by the local air quality agency (ARPA). Vertical yellow shaded
stripes indicate the presence of dust events.
To give a further example in this direction, the model-assisted retrievals
of aerosol mass over a longer time period were used to derive daily-mean
aerosol mass concentrations (PM10, a measurement typical of air
quality stations). For this purpose, for the 2-month period June–July 2012,
we derived daily mean values of aerosol volume at the SPC site using the
functional relationships Va=Va(βa) and then converted
these into mass (PM10) using typical values of aerosol densities
(ρa). Results are shown in Fig. 9. It compares the daily average
PM10 concentration measured in situ at SPC by the Italian Regional
Environmental Protection Agency (ARPA, red solid curve) and the
model-assisted, ALC-derived daily mass concentration obtained assuming both a
fixed particle density ρa=2 g cm-3 (blue dotted curve)
and a range of particle density (1.5–2.5 g cm-3, shaded area), this range
covering approximately the typical ρa values at the SPC site. Yellow
shaded areas indicate the presence of dust events (e.g., Bucci et al., 2018)
that are excluded from the results reported in the next paragraph.
More in detail, the daily-mean, ALC-derived mass concentrations were
estimated in two steps: (1) estimation of hourly mass values for the selected
height and (2) computation of the daily values through the median of the hourly
values. To guarantee a good daily representativeness, the second step is
applied only to those days on which at least 50 % of the hourly values
are
available in all the following temporal ranges: 00:00–05:00, 06:00–11:00,
12:00–17:00, and 18:00–23:00 UTC. Note that, due to the uncertainties associated with the
O(z) in the first hundreds of meters (as previously mentioned, the ALC
system at SPC has an old firmware, and its overlap function is not optimally
characterized), we used the 225 m level as more trustworthy to estimate ALC
mass concentration. Conversely, during the considered period of the
year (i.e., June and July), the comparison to ground-level PM10 at
SPC is expected to be only slightly affected by this height difference,
particularly in daytime, due to the strong convection within the mixing
layer. A possible exception could be in nocturnal conditions when vertical
gradients in the lowermost hundreds of meters can occur. However, our
statistical (3 year) ALC records show the mixing layer height at SPC to
descend below 250 m only 4–5 h per day in July (usually between 22:00 and
03:00 UTC, i.e., when emissions are at a minimum). Overall, Fig. 9 confirms a
good agreement between the ALC-derived and the ARPA reference PM10
values, with a correlation coefficient (R) of 0.64. In fact, mean, absolute
mean, and relative differences between the two series are 〈dPM10〉=2.3±6.0 g cm-3,
〈|dPM10|〉=4.8±4.3 g cm-3,
and 〈(dPM10/PM10)〉=0.14±0.27. This agreement attests that the SPC site can indeed be considered an
average continental site and suggests the potential of this approach to
derive information on aerosol volume and mass. Still, due to the specificity
of each site and to the limited period considered here, these results cannot
be taken as representative of all continental sites at all times. Further
studies at different places and over longer time periods would be necessary
to better assess the uncertainty of the proposed retrieval, including
uncertainties due to the variability in continental conditions (in terms of
particle size distribution, compositions, hygroscopic effects,
etc.) but also in the instrument-dependent performances (e.g.,
overlap corrections).
Summary and discussion
Thanks to their low construction and operation costs and to their capability
of
providing continuous, unattended measurements, the use of
automated lidar ceilometers (ALCs) for aerosol characterization has increased
in recent years. Several numerical approaches were recently proposed to
estimate the aerosol vertical profile either using ceilometer measurements
only or coupling these with ancillary measurements (e.g., Flentje
et al., 2010; Wiegner and Geiß,
2012, 2014; Cazorla et al., 2017; Román et al., 2018).
This work proposes a methodology to retrieve key aerosol properties (such as
extinction coefficient, surface area and volume, and thus mass) from lidar and ALC
measurements using the results from a specifically developed
aerosol numerical model to drive the retrievals. In particular, the numerical
model uses a Monte Carlo approach to simulate a large set (20 000) of
aerosol microphysical properties intended to reproduce the variability in
average (clean to moderately polluted) continental conditions, i.e.,
those expected to dominate over Europe. Based on the assumption of particle
sphericity, relevant computations of aerosol physical (surface area and
volume, Sa and Va) and optical (backscattering and
extinction coefficients, βa and αa, through
Mie scattering theory) properties were performed at three commonly used lidar
wavelengths (i.e., at the Nd:YAG laser harmonics 355, 532, and 1064 nm). Fitting
procedures of this large set (20 000) of βa vs.
αa, Sa, and Va data pairs were then
used to derive mean functional relationships linking βa to
αa, Sa, and Va, respectively. The
model's statistical uncertainties (i.e., those related to the variability in
the microphysical parameters used as input to the computations of the bulk
physical–optical properties) associated with these so-derived mean
relationships were found to be within 30 % and 40 % for
βa vs. αa and βa vs.
Va, respectively, while βa vs. Sa
exhibits a larger dispersion (relative standard uncertainty of
40 %–70 %, depending on βa). It is worth mentioning
that these are higher than those associated with the retrievals of aerosol bulk
parameters using the complete set of Raman lidar observations (three aerosol
backscattering and two extinction coefficients, i.e., the so-called 3+2
approach), assuming, as in our case, no random uncertainty in the lidar input
data. For example, Veselovskii et
al. (2012) found a maximum uncertainty of 15 % for particle volume and
surface area estimation, in the case of 0 % random uncertainty in the
lidar input data. Note, however, that such multiwavelength lidar systems are
10 to 20 times more expensive than ALC systems, need to be operated by highly
trained operators, and are rarely run all day.
The model results also allowed exploration of the expected dependence of the
(continental aerosol) lidar ratio (LR) on βa at 355, 532, and 1064 nm and, in turn, of the dependence of mean weighted-LR value at each wavelength (found to
be 50.1±17.9, 49.6±16.0, and 37.7±12.6 sr, at 355, 532, and 1064 nm, respectively).
LR values at 1064 nm in literature are scarce and their
monotonic increase with βa found in this work (Fig. 3) suggests
that the use of a fixed LR value for the inversion of ALC signals should be
carried out with caution and carefully evaluated case by case. A similar,
non-monotonic behavior characterizes the shapes of LR vs. the βa curve at
355 and 532 nm.
We tested the reliability of our model results in two ways: (1) the model
numerical computations were compared to real lidar measurements
(specifically selected within the EARLINET database), and (2) the
model-assisted retrievals of aerosol optical (AOT) and physical (Va,
PM10) properties by real operational ALC systems were compared to
corresponding reference measurements performed by colocated independent
instrumentation.
In particular, in task (1) our simulations were compared to backscatter and
extinction coefficients at 532 and 355 nm independently retrieved by
advanced Raman lidar systems operating at different EARLINET sites in Europe
(namely Hamburg and Leipzig in Germany, Madrid in Spain, and Lecce and Potenza in
Italy). The model simulations were found to statistically match the
observations well (Figs. 4, 5, and C1). Mean discrepancies between model and
measurement-based LR were found to be lower than 20 %, suggesting a good
capability of the assumed aerosol model (and admitted range of variability)
to represent real average continental aerosol conditions at different
sites across Europe. Some differences emerged for southern Italian EARLINET
sites, possibly affected by the influence of marine aerosols, leading to
lower LR values for high values of βa.
For task (2) we applied the proposed model-based inversion to different ALC
systems (Lufft CHM-15k), part of the Italian ALICEnet network. We first
tested the ability of the proposed approach to derive aerosol extinction by
comparing hourly-mean, vertically integrated αa (i.e.,
hourly mean AOT) derived by three ALC systems to corresponding AOT
measurements from colocated sun photometers (ALICEnet sites of Aosta San
Cristophe (ASC), San Pietro Capofiume (SPC), and Rome Tor Vergata (RTV),
Fig. 7). ALC–sun photometer agreement was found to be within 30 %. Tests
on the use of fixed LR were also performed to investigate the advantage of
the proposed approach with respect to more standard ones. For this purpose, we
used the (1064 nm) fixed-LR value suggested by the E-PROFILE EUMETSAT
program and the weighted mean derived from our model (52 and 38 sr,
respectively). While for the ASC site negligible differences were found among
the three retrieval schemes, for both the SPC and RTV sites the best ALC–sun
photometer agreement in AOT is reached when employing the model-based or the
fixed-LR = 38 sr inversion schemes, with a mean error of around
16 %–19 % and 31 %–33 % for SPC and RTV, respectively.
Applying the fixed LR value of 52 sr produces an overestimation of the AOT,
with a mean relative bias equal to 33 % and 44 % at SPC and RTV,
respectively. This suggests that, at 1064 nm, the LR value for continental
aerosol is lower than the one assumed by the E-PROFILE procedure and, more in
general, this highlights the advantage of a procedure not requiring an
a priori, and to some extent arbitrary, choice of the LR value.
As a second test in task (2), values of aerosol volume (and mass) derived using
the model-assisted ALC retrieval were compared to in situ aerosol
measurements performed by OPCs and PM10 analyzers. A continuous,
2-month comparison (June–July 2012) between daily average aerosol mass
concentration as measured in situ and derived by ALC (in the lowest
altitudes) at SPC, showed a mean relative difference of around 15 %
(Fig. 9).
Overall, the good results obtained in our validation efforts are encouraging
but necessarily related to the specific conditions at the measuring sites
considered and to the characteristics of the instruments employed. They are
therefore not necessarily representative of results obtainable at all
European continental sites, and at all times. Further tests using wider
datasets covering a variety of sites and ALC instrumentation would be
desirable to better understand potential and limits of the applicability of
the proposed method over the larger scale. An obvious intrinsic limitation
is that the method is dependent on the considered aerosol type, which in this
study was tuned to reproduce average continental aerosol conditions. Errors
associated with the application of the derived functional relationship might
be larger if more specific aerosol conditions (e.g., contamination by sea
salt or desert dust particles) affect a given site. In the future, the
information coming from ALC systems with an additional depolarization
channel (as tested in the DIAPASON project; Gobbi et al., 2018) could be
used to force the retrieval to different model schemes (e.g., switching from
“no dust” to “dust” scheme conditions) in the same vertical profile. This
will enhance the capabilities of ALCs to operatively estimate and
characterize the aerosol optical properties (e.g., Gasteiger and
Freudenthaler, 2014).
Additionally, although our validation exercises returned results well within
the uncertainties related to the model statistical variability alone (i.e.,
the relative errors associated with the mean functional relationships), the
expected total uncertainty to be associated to the method should include
terms that have not been specifically addressed in this work, as for example
the instrumental error itself.
Conversely, the proposed approach has the main advantage of allowing
the operational (i.e., 24/7) retrieval of fairly reliable remote-sensing
profiles of aerosol optical (βa, αa) and physical
(Sa, Va) properties (with associated uncertainties and
limitations) by means of relatively simple and robust instruments. This
could temporally and spatially complement the information coming from more
advanced lidar networks (for example, the Raman channel of a multiwavelength
system cannot be used in daylight conditions) and, more in general, could
represent a valid option to deliver, in quasi-real time, the 3-D aerosol
fields useful for operational air quality (e.g., integration of the in situ
surface measurements) and for meteorological and climate monitoring (e.g.,
aerosol–cloud interaction and aerosol transport and dispersion processes).
AERONET Rome Tor Vergata sun photometer AOT data were downloaded from the
AERONET web page (AERONET, 2018). SKYNET sun photometer AOT data were
downloaded from the SKYNET web page (SKYNET, 2018). EARLINET backscattering
and extinction coefficients were downloaded from the EARLINET web page
(EARLINET, 2018). ALICEnet ALC raw data are available upon request at
alicenet@isac.cnr.it.
Model sensitivity tests
To evaluate the proposed continental model configuration (hereafter CM0) and
discuss its sensitivity to the variability in the employed parameters, an
overview of the impact on the model results produced by changing the limit of
the variability ranges of these parameters (i.e., using different model
configuration, CMX) is given in this section.
The varied model (CMX-CM0) mean difference on the considered optical property
(OP) has been quantified through the following equation:
〈dOPOP〉=1Nbin⋅∑i=1Nbin〈OPCMX〉i-〈OPCM0〉i/〈OPCM0〉i,
where Nbin is the total number of defined bins of βa.
The results of the mean differences of αa and LR for different
ranges of βa and for the whole βa interval are reported
in
Table A1, in which relevant sensitivity cases (i.e., relative mean difference
greater than 1 %) at λ=355 nm have been taken into account.
CM1 refers to a model configuration without the first aerosol mode
(N1% =0). The overall decrease in the values of αa and LR
(around 3 %–4 %) is due to the sum of significant and opposite effects for
low and high values of βa for which 〈dαa/αa〉 and 〈dLR/LR〉 are of the
order of -6 % and 8 %, respectively. Removing the coarser aerosol mode
(N3% =0) causes positive mean values for 〈dαa/αa〉 and 〈dLR/LR〉 of the order
of 5 % (sensitivity case CM2). In this case, the largest impact is observed
for the βa range between 2×10-4 and
2×10-3 km-1 sr-1.
An opposite result is obtained by decreasing the upper bound of the r2
variability range (r2=0.03–0.05 µm, CM3). In fact this
model configuration also leads to lower αa and LR values (〈dαa/αa〉 and
〈dLR/LR〉 are
equal to -6 %, approximately). In this case, the variation in the r2 parameter affects the higher ranges of βa
(βa=2×10-4–2×10-2 km-1 sr-1).
Higher modal radii for the coarse-mode particle (r3=1–1.2 µm)
in the CM4 configuration leads to the increase in the contribution of
model-generated points with higher βa and causes lower values of
αa and LR (〈dαa/αa〉 and
〈dLR/LR〉 are equal to -5 %, approximately) only for
high values of βa (βa=2×10-3–2×10-2 km-1 sr-1), whereas the effect over the whole βa
range is around -1 %.
The CM5 configuration accounts for the presence of more absorbing particles
in the first aerosol mode, in which the lower bound of m1im has been
increased by a factor of 10 (m1im=0.1-0.47). This produces a
significant effect only for the lower values of βa (βa=2×10-5–2×10-4 km-1 sr-1), with
an increase in αa and LR of approximately 4 %. Conversely,
increasing the lower bound of the real part of the second aerosol mode
refractive index (m2r=1.55-1.70) has a large impact on the
considered parameters. In fact, the CM6 configuration largely underestimates
both αa and LR (around -15 % for both parameters) for all
βa ranges.
The CM7 configuration refers to the impact of the total number of particles
at the ground (Ntot). In this case, decreasing the upper bound of
the variability range of Ntot by a factor of 2
(Ntot=500–5000 cm-3) lowers the mean values of
αa and LR of around 5 %. Nevertheless, this effect is
totally due to the contribution of the βa values between
2×10-3 and 2×10-2 km-1 sr-1, where
〈dαa/αa> and <dLR/LR〉 are around -10 %. Assuming no increase with
altitude for σ1,2 (sensitivity case CM8) produces relevant
differences in the mean values of αa and LR. In CM8, the
overall overestimation of these two parameters is quite limited
(〈dαa/αa〉=6.3 and 〈dLR/LR〉=6.4), whereas a large and opposite impact is
observed for low and high values of βa. In fact,
〈dαa/αa〉 (〈dLR/LR〉) is
equal to -14.1 (-13.9) and 18.5 (19.0) for βa=2×10-5–2×10-4 and βa=2×10-5–2×10-4 km-1 sr-1, respectively. As explained by Barnaba et
al. (2007), the dependence of σ1,2 on the altitude can be
associated with the fact that, when increasing the distance from the main
aerosol sources, the particle processing is more efficient.
Mean differences of αa and LR among different model
sensitivity cases and the proposed continental model configuration.
The parameters of the seventh-order polynomial fit used to derive the
functional relationships between log(x) and log(y) (where x=βa
and y=αa, Sa or Va) at λ=355 and 532 nm
are reported in Tables B1 and B2, respectively.
Parameters of the seventh-order polynomial fits
(y=a0+a1x+a2x2+a3x3+a4x4+a5x5+a6x6+a7x7) for
λ=355 nm, with x=log(βa)
(in km-1 sr-1 unit) and y=log(αa,Sa, or Va) in
km-1, cm2 cm-3, and cm3 cm-3, respectively.
Parameters of the functional relationshipExtinction coefficientSurface areaVolumeat 355 nma03.79783750765189812.019452592845141-5.314834128998254a13.29403254138978130.8259662793685472.500484347793244a20.96260333686767524.518531616019207-1.196109537503000a30.24179662987067510.625241994796593-1.583236058579546a40.0646091458046882.634051072085453-0.681801883947768a50.0177217521502330.373150843707711-0.145232662646142a60.0027225516258620.027971628176431-0.015471229968392a70.0001572454097830.000854381337164-0.000658925756875
Parameters of the seventh-order polynomial fits
(y=a0+a1x+a2x2+a3x3+a4x4+a5x5+a6x6+a7x7) for
λ=532 nm, with x=log(βa)
(in km-1 sr-1 unit) and y=log(αa,Sa, or Va) in
km-1, cm2 cm-3, and cm3 cm-3, respectively.
Parameters of the functional relationshipExtinction coefficientSurface areaVolumeat 532 nma03.79783750765189812.019452592845141-5.314834128998254a13.29403254138978130.8259662793685472.500484347793244a20.96260333686767524.518531616019207-1.196109537503000a30.24179662987067510.625241994796593-1.583236058579546a40.0646091458046882.634051072085453-0.681801883947768a50.0177217521502330.373150843707711-0.145232662646142a60.0027225516258620.027971628176431-0.015471229968392a70.0001572454097830.000854381337164-0.000658925756875Model – EARLINET comparison at 532 nm
Figure C1 depicts the result of the comparison between EARLINET stations and
our developed model (red and blue curves, respectively) in terms of mean LR
per bin of βa at λ=532 nm. Note that only βa bins
containing at least 1 % of the total modeled data were considered.
Similar to the results at 355 nm shown in Sect. 4.1, a general good
agreement between the modeled and the measured LR values is found. As
attested by the low value of the mean discrepancy of Table 6, the modeled
curve fits with Madrid observations well. Some major deviations are found for
Lecce, which, however, at 532 nm, has a very low number of considered points
(i.e., 109).
Model-simulated (blue) and lidar-measured (red) LR vs.
βa mean curves at 532 nm calculated per 10 equally spaced
bins per decade of βa at the (a) Madrid (MA),
(b) Lecce (LC), (c) Leipzig (LE), and (d) Potenza (PO) EARLINET
lidar stations. Vertical bars are the associated standard deviations.
Model sensitivity tests for optimal configurations at the Lecce and
Potenza
sites
According to the results reported in Table A1, two model configurations (CM0a
and CM0b) have been set up to better reproduce the EARLINET observations of
LR vs. βa at the Lecce and Potenza sites. The comparison
among
these two configurations, the EARLINET measurements, and the CM0 setup is
illustrated in Fig. D1 (panels a and b for Lecce and Potenza, respectively) in terms
of LR mean value curves per 10 equally spaced bins per decade of βa.
Blue and red colors have the same meaning as in Fig. 5 (i.e., CM0 model and
observation curves, respectively); black curves refer to the LR vs. βa estimated through the CM0a and CM0b model versions for Lecce and
Potenza
stations, respectively. Vertical bars are the associated standard deviations.
The only difference between the CM0a and CM0 configurations consists in the upper
bound of the variability range of Ntot (5000 vs. 10 000 cm-3 at
ground, respectively). This modification seems to fit the observed LR vs. βa behavior at 355 nm. The upper bound Ntot value is similar to
the one (i.e., Ntot upper bound = 3000 cm-3 at ground) used in the
work of Barnaba et al. (2007) to characterize the optical properties of the
continental aerosol present over southeastern Italy. The computed mean
model–measurement LR relative difference between CM0a configuration and Lecce
EARLINET measurements is around 5 %.
Similarly, the CM0b configuration uses the same value for the upper bound of
Ntot variability range and, in addition, higher values of the r3 variability range of 1.0–1.2 vs. 0.3–0.5 µm,
respectively. As highlighted by the panel b of Fig. D1, this model
configuration allows a good reproduction of the LR vs. βa behavior derived
by EARLINET lidar Raman measurements at 355 nm. This result seems to indicate
the presence of coarser aerosols in a clean continental environment. In
comparison to the CM0 model, the mean model–measurement LR relative
difference decreases from 17 % to 6 %.
Model-simulated (blue and black lines) and lidar-measured (red
lines) LR vs. βa mean curves at 355 nm calculated per 10
equally spaced bins per decade of βa for the Lecce and
Potenza
EARLINET lidar stations (a, b, respectively). Blue
refers to the CM0 model configuration and black to the CM0a and CM0b model
configurations adapted to the Lecce and Potenza sites, respectively.
ALC vs. sun photometer AOTs
To gain a sense of both absolute and relative errors of AOT, in this section we
report the scatter plots between the hourly-mean coincident
AOTs at 1064 nm as derived with the ALC model-based approach and those measured at
1020 nm by the sun photometers installed at RTV, SPC, and ASC
(Figs. E1, E2, and E3). The corresponding linear fit y=bx (red
line), where x= sun photometer AOT and y= Nimbus CHM 15k
AOT, is also shown in the plots. The values of the correlation coefficients
for the three sites (R=0.77, R=0.72, and
R=0.73 for RTV, SPC, and ASC, respectively) attest to a relatively
good agreement between the two AOT measurements.
Scatter plot between the hourly-mean coincident AOTs at 1064 nm as
derived with the ALC model-based approach and measured at 1020 nm by the
AERONET photometer at RTV. The red line represents the linear fit y=bx
between the two datasets, where x is sun photometer AOT and y is Nimbus
CHM 15k AOT.
Scatter plot between the hourly-mean coincident AOTs at 1064 nm as
derived by the ALC model-based approach and measured at 1020 nm by the
Skyrad photometer at SPC. The red line represents the linear fit y=bx
between the two datasets, where x is sun photometer AOT and y is Nimbus
CHM 15k AOT.
Scatter plot between the hourly-mean coincident AOTs at 1064 nm as
derived by the ALC model-based approach and measured at 1020 nm by the
SKYRAD photometer at ASC. The red line represents the linear fit y=bx
between the two datasets, where x is sun photometer AOT and y is Nimbus
CHM 15k AOT.
DD, FB, and GPG conceived and designed the study. DD adapted the numerical model and performed
the simulations. HD and LDL performed the ALC measurements in ASC and SPC, respectively. DD and HD implemented ALC
inversion codes. DD performed most of the data analysis with contributions and advice from all co-authors. DD, FB,
and GPG wrote the paper with input from all other co-authors.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “SKYNET – the
international network for aerosol, clouds, and solar radiation studies and
their applications (AMT/ACP inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
This work was partly supported by the European Commission LIFE+ project
“DIAPASON” (LIFE+2010 ENV/IT/391). The study also contributes to the
activities of the EU COST Action TOPROF (ES1303). The authors acknowledge
EARLINET for providing aerosol lidar profiles through its website (http://access.earlinet.org/, last access: 29 October 2018) and the EARLINET publishing group 2000–2010
(10.1594/WDCC/EN_all_measurements_2000-2010). We thank the
EARLINET PIs Ulla Wandinger (Leibniz Institute for Tropospheric Research,
Leipzig, Germany), Manuel Pujadas (Centro de Investigaciones Energéticas,
Medioambientales y Tecnológicas, Department of Environment, Madrid,
Spain), Maria Rita Perrone (Department of Mathematics and Physics,
Universita' del Salento, Italy), and Aldo Amodeo (Istituto di Metodologie per
l'Analisi Ambientale, CNR-IMAA, Italy) and their staff for establishing,
maintaining, and running the EARLINET instruments at Leipzig (LE), Madrid
(MA), Lecce (LE), and Potenza (PO), respectively. ALC measurements at San
Pietro Capofiume (SPC) were partly funded by the Supersito project of the
Italian Emilia-Romagna region (DRG no. 428/10). The authors also thank Angelo
Lupi, Mauro Mazzola, and Vito Vitale (ISAC-CNR) for the management of the
PREDE POM-02L sun–sky radiometer measurements at SPC. AOT data analysis for
San Pietro Capofiume and Saint-Christophe in the Aosta Valley was performed as part of a
cooperative activity with the SKYNET network. We also acknowledge the AERONET
team for the processing of the Rome Tor Vergata data used in this research
effort. Edited by: M.
Campanelli
Reviewed by: three anonymous referees
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