Vertical profiles of the particle backscattering coefficient
at 355, 532 and 1064 nm measured by the University of Basilicata Raman
lidar system (BASIL) have been compared with simulated particle backscatter profiles obtained through a Mie
scattering code based on the use of simultaneous and almost co-located
profiles provided by an airborne optical particle counter. Measurements
were carried out during dedicated flights of the French research aircraft
ATR42 in the framework of the European Facility for Airborne Research (EUFAR)
project “WaLiTemp”, as part of the Hydrological Cycle in the Mediterranean
Experiment – Special Observation Period 1 (HyMeX-SOP1). Results from two
selected case studies are reported and discussed in the paper, and a dedicated analysis approach is illustrated and applied to the
dataset. Results reveal a good agreement between measured and simulated
multi-wavelength particle backscattering profiles. Specifically, simulated
and measured particle backscattering profiles at 355 and 532 nm for the second case study are found to
deviate less than 15 % (mean value =5.9 %) and 50 % (mean
value =25.9 %), respectively, when considering the presence of a
continental–urban aerosol component, while slightly larger deviation values are found for the first study. The reported good agreement between
measured and simulated multi-wavelength particle backscatter profiles
testifies to the ability of multi-wavelength Raman lidar systems to infer
aerosol types at different altitudes.
Introduction
Aerosols are a key atmospheric component, playing a major role in
meteo-climatic processes. Aerosols influence precipitation processes and the
water cycles primary through two effects: the direct effect, as a result of
the scattering/absorption of solar radiation (among others, Haywood and
Boucher, 2000; Takemura et al., 2005), and the indirect effect, as a result
of the interaction with clouds (among others, Sekiguchi et al., 2003; Yang
et al., 2011). A semi-direct effect can also arise in the presence of high
aerosol loading, determining scattering and absorption enhancement,
ultimately leading to an alteration of atmospheric stability (e.g. Mitchell,
1971). Despite the well-recognized aerosol importance in meteorological
processes and climate evolution, only a limited number of remote sensing
techniques can provide vertically resolved measurements of the microphysical
properties of aerosol particles (among others, Bellantone et al., 2008;
Granados-Muñoz et al., 2016; Mhawish et al., 2018). For example, in situ sensors
transported by aerostatic balloons or any other flying vector allow
the vertical profile of aerosol size and microphysical properties to be measured,
with high vertical resolution (of the order of 10 m) but typically with a
limited temporal resolution. Any experiment aimed at characterizing the
temporal evolution of aerosol microphysical properties would require several
consecutive balloon launches or flights, with the time lag between two
consecutive launches/flights unlikely being shorter than 1 h, with a
consequent detriment of the temporal resolution. Additionally, in situ
particle sensors are quite heavy and bulky, which – in the case of
balloon-borne experiments – implies the use of quite large aerostatic
balloons. This makes monitoring by in situ particle sensors very expensive
and logistically difficult to implement.
Remote sensing techniques can overcome these limitations. A variety of
passive optical remote sensors (i.e. spectroradiometers, sun and sky
photometers, etc.) have demonstrated their capability to characterize
aerosol microphysical properties, but they lack in vertical resolution,
which makes them scarcely suited for vertically resolved measurements of
aerosol size and microphysical properties. Low vertical resolution is
combined with a limited temporal resolution when these techniques are
implemented on sun-synchronous orbiting platforms, with a typical “revisit
time” of several hours. Active remote sensing systems may overcome this
limitation. Specifically, lidar systems with aerosol measurement capability
are characterized by high accuracies and temporal/vertical resolutions,
which makes them particularly suited for aerosol typing applications. Lidar
measurements of aerosol optical properties have been reported since the
early 1960s (among others, Fiocco and Grams, 1964; Elterman, 1966).
Originally, measurements were carried out with single-wavelength elastic
backscatter lidars capable of providing vertical profiles of the particle
backscattering coefficient at the laser wavelength. In these systems the
particle backscattering coefficient is determined from the elastic lidar
signals based on the application of the Klett–Fernald–Sasano approach
(Klett, 1981, 1985; Fernald, 1984) or similar derived approaches (Di
Girolamo et al., 1995, 1999). More recently, the acquired capability to measure
roto-vibrational Raman lidar echoes from nitrogen and oxygen molecules has made
the determination of the particle extinction coefficient also possible
(Ansmann et al., 1990, 1992). The possibility of retrieving particle size and
microphysical parameters from multi-wavelength lidar data of particle
backscattering, extinction and depolarization has been recently demonstrated
by a variety of authors (Müller et al., 2001, 2007, 2009; Veselovskii et
al., 2002, 2009, 2010). These measurements can be combined with simultaneous
measurements of the atmospheric thermodynamic profiles (Wulfmeyer et al.,
2005; Di Girolamo et al., 2008, 2018a) to characterize aerosol–cloud
interaction mechanisms. The ground-based University of Basilicata Raman
lidar system (BASIL) has demonstrated the capability to provide
multi-wavelength Raman lidar measurements with high quality and accuracy for
the retrieval of particle size and microphysical parameters (Veselovskii et
al., 2010; Di Girolamo et al., 2012a). The system was deployed in
Candillargues (southern France) in the period from August to November 2012
in the framework of the Hydrological cycle in the Mediterranean Experiment
(HyMeX) Special Observation Period 1 (SOP1). In the present paper,
measurements carried out by BASIL are illustrated with the purpose of
characterizing atmospheric aerosol optical properties. These measurements, in
combination with in situ measurements from an airborne optical particle
counter and the application of a Mie scattering code, are used to infer
aerosol types. Back-trajectory analyses from a Lagrangian model (HYSPLIT)
are used in support of the assessment of aerosol types (Man and Shih, 2001;
Methven et al., 2001; Estellés et al., 2007; Toledano et al., 2009). The
outline of the paper is as follows: Sect. 2 provides a description of
the Raman lidar system BASIL and the airborne optical particle counter;
Sect. 3 illustrates HyMeX-SOP1. The methodology is illustrated in Sect. 4, while measurements and simulations are reported in Sect. 5. Finally,
Sect. 6 summarizes all results and provides some indications for possible
future follow-up activities.
Instrumental setupBASIL
The Raman lidar BASIL has been developed around a pulsed Nd:YAG laser,
emitting pulses at 355, 532 and 1064 nm, with a repetition rate of 20 Hz.
The system includes a large aperture telescope in Newtonian configuration,
with a 400 mm diameter primary mirror, primarily aimed at the collection of
Raman and higher range signals. Two additional smaller telescopes, developed
around two 50 mm diameter 200 mm focal length lenses, are used to collect
the backscatter echoes at 1064 nm and the total and cross-polarized
backscatter echoes at 532 nm. The laser emission at 355 nm (average power of
10 W) is used to stimulate Raman scattering from water vapour and nitrogen and
oxygen molecules (Di Girolamo et al., 2004, 2006, 2009a), which are
ultimately used to measure the vertical profiles of atmospheric temperature,
water vapour mixing ratio and aerosol extinction coefficient at 355 nm.
Elastic backscattering echoes from aerosol and molecular species at 355, 532
and 1064 nm, in combination with the Raman scattering echoes from molecular
nitrogen, are used to measure the vertical profiles of the aerosol
backscattering coefficient at these three wavelengths. More details of the
considered approaches are given in Sect. 4. Raman echoes are very weak and
degraded by solar radiation in daytime. Consequently, high laser powers and
large aperture telescopes are required to measure daytime Raman signals with
a sufficient signal-to-noise ratio throughout a large portion of the
troposphere. The instrumental setup of BASIL has been described in detail in
several previous papers (Di Girolamo et al., 2009a, b, 2012a, b,
2016, 2017; Bhawar et al., 2011). BASIL was deployed in a variety of
international field campaigns (among others, Bhawar et al., 2008; Serio et
al., 2008; Wulfmeyer et al., 2008; Bennett et al., 2011; Ducrocq et al.,
2014; Macke et al., 2017; Di Girolamo et al., 2018b).
Optical particle counter
An optical particle counter (OPC), manufactured by GRIMM Aerosol Technik
GmbH (model Sky-OPC 1.129), is used to measure the size-resolved particle
number concentration dN/dr in the size range 0.25–32 µm. The sensor
includes 31 size bins. The laser beam generated by a 683 nm diode laser
invests the aerosol particles exiting from a pump chamber; the scattered
radiation is deflected by two separate mirrors and detected by a photon
sensor (Heim et al., 2008). By summing up the particle number over all the
size intervals, the total number concentration is derived (Grimm
and Eatough, 2009). The OPC model used in the present effort has a specific
airborne design (McMeeking et al., 2010). The use of a differential pressure
sensor and an external pump allows OPC measurements to be performed
independently of environmental pressure conditions. The OPC was installed
on board the French research aircraft ATR42, operated by the Service des
Avions Instrumentés pour la Recherche en Environnement (SAFIRE), as part
of an ensemble of in situ sensors for the characterization of aerosol and
cloud size and microphysical properties. Dedicated flights by the ATR42 were
performed during HyMeX-SOP 1 in the framework of the European Facility for Airborne Research (EUFAR) project “WaLiTemp”,
with the aircraft looping up and down in the proximity of the Raman lidar
system.
HyMeX and the Special Observation Period 1
The Hydrological cycle in Mediterranean Experiment was conceived with the
overarching goal of collecting a large set of atmospheric and oceanic data
to be used to get a better understanding of the hydrological cycle in the
Mediterranean area. Within this experiment a major field campaign, the
Special Observation Period 1 (SOP1), took place over the north-western
Mediterranean area in the period September–November 2012 (Ducrocq et al.,
2014). During HyMeX-SOP1 the Raman lidar system BASIL was deployed in the
Cévennes-Vivarais atmospheric “supersite”, located in Candillargues
(43∘37′ N, 4∘04′ E; elevation: 1 m). BASIL was operated from 5 September to 5 November 2012, collecting more than 600 h of measurements,
distributed over 51 measurement days, and consisted of 19 Intensive Observation Periods
(IOPs).
The French research aircraft ATR42, hosting the OPC, was stationed at
Montpellier Airport. Its main payload consisted of the airborne DIAL LEANDRE
2, profiling water vapour mixing ratio beneath the aircraft. The ATR42
payload also included in situ sensors for turbulence measurements, as well as
aerosol and cloud microphysics probes, including the OPC. During HyMeX-SOP1,
the ATR42 performed more than 60 flight hours: 8 were supported by the EUFAR
project WaLiTemp, and the remaining hours were supported by the “Mediterranean
Integrated STudies at Regional and Local Scales” (MISTRALS) programme. A
specific flight pattern was defined for the purposes of the WaLiTemp
project (Fig. 1), with the aircraft making spirals (hippodromes) up and
down around a central location, originally aimed to be the atmospheric
supersite in Candillargues. Unfortunately, because of air traffic
restrictions, aircraft sensors' operation was typically started 20 km
eastward of the supersite, and the central location of the hippodromes was also moved 20 km eastward.. Flights hours in the framework of the WaLiTemp
project were carried out on 13 September, 2 and 29 October and 5 November 2012.
ATR42 flight pattern in the framework of the WaLiTemp project (red
line). The light blue dot represents the position of Montpellier Airport,
where the ATR-42 took off and landed, while the red dot represent the
position of the Raman lidar BASIL. The red curve represents the footprint of
the aircraft pattern, including the positions of the spirals (hippodromes)
up and down and the ground track from the airport to the spiraling position.
The distance between the lidar site and the flight pattern is approx. 20 km.
Spiral ascents and descents were carried out with a vertical speed of 150 m min-1.
During each flight, except in the presence of specific logistic issues, a
minimum of two ascent–descent spirals were carried out. For the purposes of
the present comparisons, in order to minimize the effect associated with the
sounding of different air masses, we selected days characterized by
horizontally homogeneous atmospheric conditions.
Methodology
The particle volume backscattering coefficient can be expressed as
βλ0par=∫0∞Qbackrnrdr,
with Qback(r) being the particle backscattering efficiency and
n′(r)=dN/dr being the particle size distribution. Qback(r) can be expressed as
(Grainger et al., 2004)
Qback=2x2∑n=1∞2n+1an2+bn2,
where the terms an and bn represent the Mie scattering amplitudes of
the nth magnetic partial wave (n being the function order).
an and bn are obtained through the following expressions:
3an=ψnxψn′mx-mψn′xψnmxξn1xψn′mx-mξn1′xψnmx4bn=ψn′xψnmx-mψnxψn′mxξn1′xψn′mx-mξn1xψnmx,
where m is the complex refractive index; x=2πr/λ is the particle
size parameter, with λ being the laser wavelength and r being the
particle radius, assumed to be a sphere. ψnx and
ξn1 are Riccati–Bessel functions defined in
terms of the spherical Bessel function of the first kind (Temme, 1996). A
log-normal size distribution is considered in this study, with an analytical
expression for each mode of the form (Grainger et al., 2004):
n′r=N02π1lnS1rexp-lnr-lnrm22ln2S,
where n′(r)=dN/dr is the number of particles within the size interval dr, with
N(r) representing the cumulative particle number distribution for particles
larger than R,rm is the median radius of the distribution, S is the standard
deviation of the distribution and N0 is the particle integral
concentration for the considered mode. S is a measure of the particle
polydispersity, with lnS being equal to 1 for monodisperse particles. The
log-normal distribution is completely described by N0, rm and S.
Three modes are typically considered to describe the different aerosol
components (d'Almeida et al., 1991): a fine or nucleation particle mode, a
large or accumulation particle mode and a giant or coarse particle mode.
For the purposes of this research effort, particle concentration N0 is
obtained by minimizing differences between the size distribution measured by
the OPC and the simulated distribution, while the values of rm and S are
those identified in the following section based on literature results.
Simulated backscatter profiles βλ0parz are obtained through the application Eq. (1) for all
altitudes covered by the OPC, considering different refractive index and
size parameters' values for the three distribution modes, in dependence of
the aerosol type, and integrating the expression over the three distribution
modes. To perform these computations a specific Mie scattering code was
developed by the authors in an IDL environment. The possibility to retrieve the
particle size and microphysical properties from multi-wavelength
measurements of the particle backscattering and extinction coefficient has
been demonstrated by several authors (among others, Müller et al., 2001;
Veselovskii et al., 2002) based on the application of retrieval schemes
employing Tikhonov's inversion with regularization, which apply Mie
scattering theory to an ensemble of particles with spherical shape. However, an
appropriate and effective application of this approach imposes the use of
particle backscatter and extinction profiles with a statistical uncertainty
not exceeding 5 %–10 %. Multi-wavelength Raman lidar measurements of the
particle backscattering and extinction coefficient for the considered case
studies were not characterized by such a low level of uncertainty, this being
especially true for the particle backscatter measurements at 1064 nm.
In order to determine aerosol typology, deviations between measured and
simulated particle backscattering profiles at 355 and 532 nm were
minimized. Initial values in terms of modal radius, r‾, standard deviation, σ, and refractive index for the different
aerosol components were taken from d'Almeida et al. (1991). At each
altitude, the particle size distribution measured by the optical particle
counter is compared with the five aerosol typologies listed in d'Almeida et
al. (1991), which for the sake of clarity are reproduced below:
average continental (continental environment influenced by
anthropogenic pollution);
urban (continental environment heavy influenced by anthropogenic
pollution);
maritime polluted (environment polluted as Mediterranean Sea or
North Atlantic);
clean–polar (Arctic environment during summer period);
clean continental–rural (rural continental environment without
pollution).
Specifically, both urban and continental aerosols include a soot and
pollution fine-mode component (as both aerosol types include the same
aerosol components, they are treated in what follows as a single aerosol
type), a water-soluble accumulation-mode component and a dust-like coarse-mode
component; the maritime polluted aerosol type includes a soot and pollution
fine-mode component, a water-soluble accumulation-mode component and a
sea-salt coarse-mode component; the summertime Arctic aerosol type includes
a sulfate fine-mode component and a sea salt and mineral accumulation-mode
component; the rural aerosol type includes a water-soluble accumulation-mode
component and a dust-like coarse-mode component.
Modal radius, standard deviation and refractive index (real and
imaginary part) for the different considered aerosol components (from
d'Almeida et al., 1991).
r‾σmrmimrmi(µm)(355 nm)(355 nm)(532 nm)(532 nm)Soot0.0122.001.754.65×10-11.754.44×10-1Water-soluble0.0242.241.535.00×10-31.536.00×10-3Dust-like0.4712.511.538.00×10-31.538.00×10-3Sea salt (fine)0.3002.511.391.20×10-71.383.70×10-9Sulfate0.0692.031.451.00×10-81.431.00×10-8Sea salt (acc.)0.4002.031.391.20×10-71.383.7×10-9Mineral0.2702.671.531.70×10-21.535.50×10-3
D'Almeida et al. (1991), Junge and Jaenicke (1971) and Junge (1972)
suggested the use of a tri-modal log-normal size distribution (see
Eq. 5), indicating specific values for the two primary size
distribution parameters, i.e. the modal radius, r‾, and standard deviation, σ. Values of the modal radius, the
standard deviation and the real, nr, and imaginary part, ni, of
refractive index at the three lidar wavelengths (355, 532 and 1064 nm) for
the three different aerosol components considered in the present
computations are inferred from different papers in the literature (d'Almeida et
al., 1991; Shettle and Fenn, 1976, 1979; WCP–112, 1986)
and are listed in Table 1.
The log-normal size distribution has been computed considering the OPC data
in the dimensional range 0.25–2.5 µm, with a 300 m vertical
integration window. Results are illustrated in Fig. 2 (bold black line).
In this same figure the size distribution computed from the OPC data is
compared with the theoretical distributions for the three different modes
(fine mode – red line, accumulation mode – violet line, coarse mode – light
blue line).
Size distribution computed from the OPC data (bold black line),
together with the total theoretical distribution (thin black line) and
theoretical distributions for the three different modes: fine mode (soot and
pollution, red line), accumulation mode (water-soluble aerosols, violet
line) and coarse mode (sea salt, light blue line).
For each of the three modes, the number of particles has been varied in
order for the total theoretical distribution (thin black line) to match
the experimental distribution computed with the OPC data. The matching
between the experimental and theoretical distributions has been optimized
based on the application of a best fit procedure. This approach was applied
to each altitude level. In Fig. 2, we consider experimental and
theoretical distributions at an altitude of 1529 m, this being the lowest
altitude at which aerosols larger than 0.7–0.8 µm were measured by the
OPC.
The vertical profiles of the particle backscattering coefficient at 355, 532
and 1064 nm have been simulated through the above-mentioned Mie scattering
code from the OPC data, considering values of r‾ and σ for the different aerosol components. Measured profiles of
the particle backscattering coefficient profiles at 355 and 532 nm are
obtained from the Raman lidar signals through the application of the Raman
techniques, which relies on the ratio between the 355/532 nm elastic signal
and the corresponding simultaneous molecular nitrogen roto-vibrational Raman signal. The
two signals are characterized by an almost identical overlap function, and
therefore the overlap effect is cancelled out when ratioing the signals.
Conversely, particle backscattering coefficient profiles at 1064 nm are
obtained through the application of a Klett-modified inversion approach (Di
Girolamo et al., 1995, 1999). The specific approach used in the present analysis
considers a height-dependent lidar ratio profile and an iterative procedure
converging to a final particle backscattering profile (Di Girolamo et al. 1995, 1995). Additionally, the elastic backscatter signal at
1064 nm and an additional elastic backscatter signal at 532 nm are collected
with two small telescopes, developed around two 50 mm diameter 200 mm focal
length lenses, with overlap regions not extending above 3–400 m.
A modified version of the approach defined by Di Iorio et al. (2003) was
applied in order to determine the sounded aerosol typology. This approach is
based on the minimization of the relative deviation between the measured and
the simulated particle backscattering coefficient; i.e.
Δ=1Np∑k=1pNβλsimulatedzk-βkmeasuredzkβλmeasured(zk),
where zk is the altitude.
In the attempt to simultaneously minimize deviations between measured and
simulated particle backscattering profiles at 355, 532 and 1064 nm, a total
deviation can be computed as the root sum square of the single deviations at
the two wavelengths, which can be expressed as
Δtot=Δ3552+Δ5322+Δ10642.
ResultsCase study on 13 September 2012
During the first ascending spiral, in situ sensors on board the ATR42 were
operated in the altitude region from 650 to 5700 m above sea level
(hereafter in the paper all altitudes are intended above sea level),
covering the 40 min time interval between 19:55 and 20:35 UTC. BASIL was
operated in the time interval 19:00–23:00 UTC. Figure 3 illustrates the
temporal evolution of the particle backscattering coefficient at 532 nm over
the time interval 19:30–21:30 UTC. The figure is illustrated as a succession
of 5 min vertical profiles with a vertical resolution of 7.5 m. The figure
reveals the presence of a shallow nocturnal boundary layer, which is
testified by the presence of an aerosol layer extending up to 500–600 m and
the presence of a residual layer extending up to 1500–2100 m.
Time evolution of the particle backscattering coefficient at 532 nm over the time interval 19:30–21:30 UTC on 13 September 2012.
Wind direction measurements performed by the on-board flight sensors reveal
a primarily northerly wind, with direction varying in the range ±30∘ depending on altitude. The NOAA HYSPLIT Lagrangian
back-trajectory model (Draxler and Rolph, 1998; Rolph et al., 2017; Stein et
al., 2015) has been used to determine the origin of the sounded air masses.
The HYSPLIT model computes air parcel trajectories, but it can also be used to
simulate complex transport, dispersion, chemical transformation and
deposition mechanisms. A common application of the HYSPLIT model is the
back- and forward-trajectory analysis, which is used to determine the
origin or destination of the investigated air masses and establish
source–receptor relationships.
In the present effort the HYSPLIT model is used to determine air masses
trajectories at specific altitude levels in the days preceding their arrival
on the lidar site in Candillargues. Specifically, Fig. 4 illustrates
back trajectories of the air masses
overpassing the lidar site at 20:00 UTC on 13 September 2012 at an altitude of
600 (red line), 4000 (blue line) and 6000 m (green line). The
trajectories extend back in time for 5 days, thus illustrating the air
masses' path since 20:00 UTC on 8 September 2012.
Air mass back trajectories at 600 (red), 4000 (blue) and
6000 m (green) ending over the lidar site at 20:00 UTC on 13 September 2012.
Air masses reaching the measurement site at altitudes of 600 and 4000 m
originated in the vicinity of Iceland and Greenland and passed at low
altitudes (< 400 m) over the North Atlantic Ocean and over
industrialized areas in France, while air masses at 5826 m originated in the
North Atlantic Ocean in the proximity of the Canadian coasts and persisted in
a marine environment for almost 5 days before reaching France.
Figure 5 compares the vertical profiles of the measured and simulated
particle backscattering coefficient at 355 nm. The measured profile is
obtained from the Raman lidar data integrated over the 40 min time interval
coincident with the airplane ascent time (19:55–20:35 UTC on 13 September 2012), with a vertical resolution of 300 m. Simulated particle backscatter
profiles include all five aerosol components specified above, i.e. the
continental–urban component (red dashed line), the continental (rural)
component (green dashed line), the Arctic summer component (black dashed
line) and the marine (polluted) component (blue dashed line). Figure 5
reveals a good agreement between the measured backscattering coefficient
profile at 355 nm and coefficients simulated at this same wavelength assuming a
continental–urban aerosol component and a marine (polluted) aerosol
component.
Vertical profiles of the measured (black line) and simulated
particle backscattering coefficient at 355 nm over the time interval
19:55–20:35 UTC on 13 September 2012. The error bar in lidar measurements
accounts for the statistical uncertainty.
The same analysis approach was also applied to the data at 532 nm. Figure 6
compares the vertical profiles of the measured (black line) and simulated
(red line) particle backscattering coefficient at 532 nm over the same 40 min time interval on 13 September 2012, again with a vertical resolution of
300 m. Simulated particle backscatter profiles include the five above
specified aerosol components. Lidar data at 532 nm are affected by a larger
statistical uncertainty than those at 355 nm. Also in this case, the
agreement between measured and simulated profiles appears to be quite good up to
3500–4000 m.
Figure 6 reveals that the measured particle backscattering coefficient
profile at 532 nm is well reproduced by the simulated profiles at this same
wavelength, especially the profiles considering a continental–urban
aerosol component and a marine (polluted) aerosol component, with simulated
profiles slightly overestimating the measured profile but being within or
slightly exceeding the measurement error bar. Deviations between measured
and simulated profiles are larger within the aerosol layer centred at 2800 m.
Same as Fig. 5 but for the particle backscattering coefficient at
532 nm.
Figure 7 compares the vertical profiles of the measured and simulated
particle backscattering coefficient at 1064 nm over the same 40 min time
interval considered in Figs. 5 and 6, again with a vertical resolution of
300 m. Particle backscatter measurements at 1064 nm are affected by a
statistical uncertainty larger than the one affecting the measurements at
532 nm. This larger uncertainty is the result of the use of a reduced laser
emission power at 1064 nm because of the restrictions imposed by the air
traffic control authorities. In this case, the agreement between measured
and simulated profiles is poorer but still acceptable up to 2500 m.
Same as Fig. 5 but for the particle backscattering coefficient at
1064 nm.
Figure 8 illustrates the deviations between the measured and the simulated
particle backscattering coefficient profile at 355 nm. The smallest
deviations between the two profiles up to 4500 m are obtained when
considering the presence of a marine polluted aerosol component (smaller
than 53 %, with a mean deviation of 23.2 %). Simulated profiles
obtained considering a continental–urban aerosol component (not exceeding 54 %, with a mean deviation of 24.9 %) deviate less only within the
altitude interval 1200–1300 m, while deviations are very similar above 2600 m. The simulated profile obtained considering the presence of either a
continental rural or an Arctic summer aerosol component largely deviates
from the measured profile (up to 80 % and 92 %, respectively, with a
mean deviation of 50.9 % and 25.9 %). The Arctic component deviates less
only above 4500 m, where the high signal noise level and the limited
particle loading make aerosol type discrimination difficult to accomplish.
Deviation, expressed in percentage, between measured and simulated
particle backscattering coefficient profiles at 355 nm. Simulated profiles
are Arctic summer (black dashed line), continental–urban (red dashed line),
marine (polluted) (blue dashed line) and continental (rural) (green dashed
line).
Figure 9 illustrates the deviations between the measured and the simulated
particle backscattering coefficient profile at 532 nm. Again, the maximum
altitude for aerosol type retrieval is 4340 m. The smallest deviations
between measured and simulated particle backscattering coefficient profiles
are obtained when considering the presence of a continental–urban aerosol
component (not exceeding 105 %, with a mean value of 30.8 %) or a
marine polluted aerosol component (smaller than 106 %, with a mean value
of 30.9 %), while simulated profiles obtained considering the presence of
either a continental rural or an Arctic summer aerosol component largely
deviate from the measured profile (up to 60.6 % and 87 %, respectively,
with a mean deviation of 39.6 % and 79.2 %). The only exception is
given by the interval 2300–3000 m, where the simulated profile obtained
considering a rural aerosol component deviates less.
Same as Fig. 8 but obtained considering particle backscattering
coefficient profiles at 532 nm.
Figure 10 illustrates the deviations between the measured and the simulated
particle backscattering coefficient profile at 1064 nm considering
altitudes up to 2500 m. The smallest deviations between the two profiles over
the considered altitude range are obtained when considering the presence of
a continental–urban aerosol component (not exceeding 61.4 %, with a mean
deviation of 21.2 %). Deviations between measured and simulated profile
obtained considering a marine polluted aerosol component are slightly larger
(smaller than 55 %, with a mean deviation of 28.6 %), while the
simulated profile obtained considering the presence of either a continental
rural or an Arctic summer aerosol component largely deviate from the
measured profile (up to 58 % and 82.7 %, with a mean deviation of
40.9 % and 67.3 %, respectively). Again, the only exception is found in
the interval 1600–1900 m, where the simulated profile obtained considering
the marine polluted aerosol component deviate less.
Same as Fig. 8 but obtained considering particle
backscattering coefficient profiles at 1064 nm up to 2500 m.
The overall deviation was calculated for the five distinct aerosol
components. Figure 11 illustrates the overall deviations between the
measured and the simulated particle backscattering coefficient profiles at
355, 532 and 1064 nm for the different aerosol components. In order to
facilitate the interpretation of results, the overall deviation between
measured and simulated particle backscattering coefficient profiles, for the
different aerosol components, has been plotted together with the measured
particle backscattering profiles at all wavelengths (Fig. 12). In the
lowest portion of the atmosphere up to 1700 m, i.e. inside the planetary
boundary layer, the continental–urban aerosol component is predominant. The
upper layer between 1700 and 2400 m is characterized by the presence of a
maritime aerosol component in the lower part and again an urban aerosol
component in the upper part. Deviations including the particle backscattering coefficient at 1064 nm were computed up to 2500 m because
of the high statistical noise of the 1064 nm lidar signal. Additional layers
are visible in the altitude range 2400–3100 and 3800–4500 m. Above 2400 m
simulations based on the urban and maritime components show similar deviations
from measurements, except in the central part of layer between 2600 and 2900 m and between 4300 and 4500 m, where rural aerosols deviate less. HYSPLIT
back-trajectory analysis confirms that the sounded air masses in the
previous days overpass industrialized areas in France, Belgium and England.
Total deviation, in percentage, between measured and simulated
particle backscattering coefficient profiles at 355, 532 and 1064 nm
(up to 2500 m) for the different aerosol components. Simulated profiles are Arctic summer (black dashed line), continental–urban (red dashed line),
marine (polluted) (blue dashed line) and continental (rural) (green dashed
line).
Case study on 2 October 2012
A second flight took place on 2 October 2012. During the ascending path,
in situ sensors on board the ATR42 were operated in the altitude region from
680 to 5700 m, covering a 44 min time interval between 19:43 and 20:27 UTC. BASIL was operated over the time interval 16:00–24:00 UTC.
Wind direction measurements performed by the on-board flight sensors reveal
a north-westerly wind, with direction varying in the range
220–320∘ depending on altitude. Figure 13 shows the 5-day back trajectories from the NOAA HYSPLIT model at 600, 4000 and 6000 m
(in red, blue and green, respectively), ending on the lidar site at 20:00 UTC on 2 October 2012.
Total deviation, in percentage, between measured and simulated
particle backscattering coefficient profiles for the different aerosol
components (Arctic summer: black dashed line, continental–urban: red dashed
line, marine polluted: blue dashed line; continental rural: green dashed
line) and measured particle backscattering profiles at both 355 (blue line)
and 532 nm (red line). The horizontal blue and red axes refer to the
particle backscattering coefficient at 355 and 532 nm, respectively, while
the horizontal black axis refers to the total deviations. Horizontal orange
lines are also drawn at specific altitudes to identify different aerosol
types in support of the interpretation of the reported results.
Back trajectories at 600 (red), 4000 (blue) and 6000 m
(green) ending on the lidar site at 20:00 UTC on 2 October 2012.
Back-trajectory analysis results reveal that air masses reaching the
measurement site at an altitude of 600 m originated in the North Atlantic Ocean, south of
Iceland, and have passed at low altitudes (500–600 m) over highly anthropogenic continental areas (Ireland, England and northern
France). A different path characterizes air masses at 4000 m. These originated over the North Atlantic Ocean, offshore of the Canadian coast, and
overpassed an area north of the Azores over the northern coast of Spain before
reaching the measurement site. Finally, air masses reaching the measurement
site at 6000 m which originated over the North Pacific Ocean overpassed
Canada, the North Atlantic Ocean, the northern coast of Spain and finally reached the measurement site.
In the analysis of this second case study, we applied the same methodology
considered for the first case study (1991). As for the previous case study,
given the microphysical parameters and aerosol typology for each of the
three given modes, the number of particles has been varied in order for
the theoretical distribution to match the experimental distribution computed
with the OPC data, with the matching between the experimental and
theoretical distributions again obtained through a best fit procedure. The
modal radius, standard deviation and refractive index reported by d'Almeida
et al. (1991) for the different considered aerosol components are listed in
Table 1.
Figure 14 illustrates the vertical profiles of measured (black line) and
simulated particle backscattering coefficient at 355 nm over the 44 min time
interval between 19:43 and 20:27 UTC on 2 October 2012. Simulated particle
backscatter profiles include all five aerosol components specified above,
i.e. the continental–urban component (red dashed line), the continental
(rural) component (green dashed line), the Arctic summer component (black
dashed line) and the marine (polluted) component (blue dashed line). Figure 14 reveals a good agreement between the measured backscattering coefficient
profile at 355 nm and coefficients simulated at this same wavelength assuming a
continental–urban aerosol component and a marine (polluted) aerosol
component.
Vertical profiles of measured (black line) and simulated particle
backscattering coefficient at 355 nm over the time interval 19:43–20:27 UTC
on 2 October 2012. Simulated particle backscatter profiles include five
distinct components: continental–urban (red dashed line), continental
(rural) (green dashed line), Arctic summer (black dashed line) and marine
(polluted) (blue dashed line). The error bar in lidar measurements accounts
for the statistical uncertainty.
Same as Fig. 14 but with particle backscattering coefficient
profiles at 532 nm.
We also applied this same analysis approach to the data at 532 nm, with
Fig. 15 illustrating the vertical profiles of the measured and simulated
particle backscattering coefficient at 532 nm over the same time interval
considered in Fig. 14. Again, simulated particle backscatter profiles
include the five above-specified aerosol components. Figure 15 reveals that
the measured particle backscattering coefficient profile at 532 nm is well
reproduced by the simulated profiles at this same wavelength, especially the profiles considering a continental–urban aerosol component and a
marine (polluted) aerosol component, with simulated profiles slightly
underestimating the measured profile but being within or slightly exceeding
the measurement error bar. Deviations between measured and simulated
profiles are larger within the aerosol layers centred at 3000 and 4000 m.
Due to the limited laser power at 1064 nm for this specific measurement
session, measured profiles of the particle backscattering coefficient at 1064 nm are characterized by high statistical noise, which prevents us from
considering the use of the comparison between measured and simulated particle
backscatter profiles at this wavelength in the present analysis.
Figure 16 illustrates the deviations between the measured and the simulated
particle backscattering coefficient profiles at 355 nm. The smallest
deviations between the measured and the simulated particle backscattering
coefficient profile over the considered altitude range are obtained when
considering the presence of a continental–urban aerosol component (not
exceeding 15 % up to 5000 m, with a mean deviation of 5.9 %).
Deviations between the measured and simulated profile obtained considering a
marine polluted aerosol component slightly exceed these values
(smaller than 20 % up to 5000 m, with a mean deviation of 9.5 %),
while the simulated profile obtained considering the presence of either a
continental rural or an Arctic summer aerosol component largely deviates
from the measured profile (up to 80 %, with a mean deviation of 50.9 %
and 25.9 %, respectively).
Figure 17 illustrates the deviations between measured and simulated particle
backscattering coefficient profiles at 532 nm. Again, the smallest deviations
between the two profiles over the considered altitude range are obtained
when considering a continental–urban aerosol component (not exceeding 50 % up to 5000 m, with a mean deviation of 25.9 %), with the only
exception for the interval 3100–3700 m, where the simulated profile obtained
considering a marine polluted aerosol component deviates less. Above 3700 m
simulated profiles obtained considering a continental–urban and a marine
polluted aerosol component equally deviate from the measured profile.
Deviation, expressed in percentage, between measured and
simulated particle backscattering coefficient profiles at 355 nm. Simulated
profiles are Arctic summer (black dashed line), continental–urban (red
dashed line), marine (polluted) (blue dashed line) and continental (rural)
(green dashed line).
Same as Fig. 16 but obtained considering particle
backscattering coefficient profiles at 532 nm.
In the attempt to simultaneously minimize deviations between measured and
simulated particle backscattering profiles at both 355 and 532 nm, following
Eq. (7), a total deviation can be computed as the root sum square of
the single deviations at the two wavelengths, which can be expressed as
Δtot=Δ3552+Δ5322.
This quantity was calculated for the five distinct aerosol components.
Figure 18 illustrates the total deviations between the measured and the
simulated particle backscattering coefficient profiles at 355 and 532 nm for
the different aerosol components. In order to facilitate the interpretation
of these results, the total deviation between measured and simulated
particle backscattering coefficient profiles for the different aerosol
components has been plotted together with the measured particle
backscattering profiles at both 355 and 532 nm (Fig. 19).
Total deviation, in percentage, between measured and simulated
particle backscattering coefficient profiles at 355 and 532 nm for the
different aerosol components. Simulated profiles are Arctic summer (black
dashed line), continental–urban (red dashed line), marine (polluted) (blue
dashed line) and continental (rural) (green dashed line).
Total deviation, in percentage, between measured and simulated
particle backscattering coefficient profiles for the different aerosol
components (Arctic summer: black dashed line, continental–urban: red dashed
line, marine polluted: blue dashed line; continental rural: green dashed
line) and measured particle backscattering profiles at both 355 (blue line)
and 532 nm (red line). The horizontal blue and red axes refer to the
particle backscattering coefficient at 355 and 532 nm, respectively, while
the horizontal black axis refers to the total deviations. Horizontal orange
lines are also drawn at specific altitudes to identify different aerosol
types in support of the interpretation of the reported results.
Figure 19 allows the following considerations to be made. In the lowest
portion of the atmosphere, up to an altitude of ∼1300 m (altitude 1),
aerosol particles are most likely characterized by a predominant
continental–urban component. This aerosol layer extends up to ∼1600 m,
which is the altitude at which the boundary layer height is located, as also
indicated by the simultaneous radiosonde data (not shown here). In the upper
portion of the boundary layer, in the vertical interval 1300–1600 m,
deviations associated with continental–urban, marine polluted and
continental rural components overlap, which suggests that all three aerosol
components are possible. However, while this upper portion of the boundary
layer is typically characterized by entrainment effects (interfacial
region), which may allow different aerosol components to be ingested, the
continental–urban component is likely to be the predominant component.
Above the top of the boundary layer and up to ∼2700 m (altitude 2),
particle backscatter decreases with altitude. The typology analysis
suggests continental–urban aerosols likely to be the predominant component,
as in fact total deviation between the measured and the simulated particle
backscattering coefficient profile for this aerosol component is far lower
than for all other aerosol components.
In the altitude interval 2700–3600 m (altitudes 2–3, with max. at 3000 m)
the measured particle backscatter profiles reveal the presence of a distinct
aerosol layer. The typology analysis indicates that both the continental–urban
and the marine polluted components are possible. An additional distinct
aerosol layer is found in the altitude interval 3600–4600 m (altitudes 3–4,
with max. at 4000 m). Again, the typology analysis suggests the
continental–urban component is possible. Sounded aerosol particles at
3000 and 4000 m are compatible with continental polluted aerosols, this
possibility being supported by the back-trajectory analysis at 3000 and 4000 m.
A sensitivity study has also been carried out to assess the variability of
the results to changes of specific size and microphysical parameters'
values. The sensitivity study reveals that the considered methodology for
aerosol typing is successfully applicable in the altitude region up to 3900 m, as in fact above this altitude the statistical uncertainty affecting the
lidar signals is high, and this severely reduces the effectiveness of the
aerosol typing methodology. The sensitivity analysis also reveals that in
the lower levels, typically within the boundary layer where aerosol loading
is larger, deviations between measured and simulated particle backscattering
coefficient at the three wavelengths may vary by up to 20 % as a result
of a ±5 % variability of specific size and microphysical
parameters (for example, the real part of the refractive index), which
certainly reduces confidence in the aerosol typing approach but is not
compromising its outcome. Based on the results from this study we may
conclude that the use of particle backscattering measurements at two
wavelengths in combination with OPC measurements allows a sufficiently
reliable assessment of the aerosol types to be obtained, which can be verified and refined
based on the use of back-trajectory analyses.
Summary
During HyMeX-SOP1, the Raman lidar system BASIL was deployed in
Candillargues (southern France) and operated almost continuously over a 2-month period in the time frame October–November 2012. Dedicated flights of
the French research aircraft ATR42 were carried out in the framework of the
EUFAR-WaLiTemp Project. The ATR42 payload included in situ sensors for turbulence
measurements, as well as aerosol and cloud microphysics probes, together with an
optical particle counter (GRIMM Aerosol Technik GmbH, model: Sky-OPC 1.129)
capable of measuring particle number concentration in the size interval 0.25–2.5 µm. A specific flight pattern was considered for the purpose of
this study, with the aircraft making spirals up and down around a central
location approximately 20 km eastward of the lidar site. Vertical profiles
of the particle backscattering coefficient at 355, 532 and 1064 nm have been
simulated through the use of a Mie scattering code, using the data provided
by the optical particle counter. The simulated particle backscatter profiles
have been compared with the profiles measured by the lidar Raman system
BASIL. Results from two selected case studies (on 13 September and on 2 October 2012) are reported and discussed. An analysis approach based on the
application of a Mie scattering code is considered and applied. The approach
ultimately allows the sounded aerosol types to be inferred. The added value of
the reported methodology is represented by the possibility to infer the
presence of different aerosol types based on the use of multi-wavelength
Raman lidar measurement from a ground-based system in combination with an
independent measurement of the particle concentration profile (in our case
we are using the one coming from an optical particle counter mounted on board
an aircraft overpassing the lidar site). This methodology is applicable when
sounded particles are spherical or almost spherical, which allows for the
Mie scattering theory to be applied for the determination of the particle
backscattering coefficient. The HYSPLIT-NOAA back-trajectory model was used to verify the origin of the
sounded aerosol particles.
Five different aerosol typologies are considered,
i.e. continental polluted, clean continental–rural, urban, maritime polluted
and clean–polar, with their size and microphysical properties taken from
literature. The approach leads to an assessment of the predominant aerosol
component based on the application of a minimization approach applied to the
deviations between measured and simulated particle backscattering
profiles at 355 and 532 nm and for the first test case study also at 1064 nm,
considering all five aerosol typologies.
The application of this approach to the case study on 13 September 2012
suggests the presence of urban and maritime aerosols throughout the entire
vertical extent of sounded column, except in the altitude region 2600–2900
and 4300–4500 m ranges, where the presence of a rural component is likely to
be possible. The application of the approach to the case study on 2 October 2012 reveals that continental–urban aerosols are likely to be the
predominant components up to ∼1600 m, while the two distinct aerosol
layers located in the altitude regions 2700–3600 (with max. at 3000 m) and
3600–4600 m (with max. at 4000 m) are identified to likely consist of
continental–urban and/or marine polluted aerosols, respectively. The
correctness of the results has been verified based on the application of the
HYSPLIT-NOAA back-trajectory model, with the analysis extend backing in time
for 5 days allowing the origin of the sounded aerosol
particles to be assessed.
Finally, a sensitivity study has been carried out to assess the variability
of the aerosol typing approach to varying size and microphysical parameters.
The study reveals that the reported approach is successfully applicable in
the altitude region up to
∼4 km, while above this altitude the sensitivity of the
approach is substantially reduced by the high statistical uncertainty
affecting lidar signals. The sensitivity study also reveals that the within-boundary-layer deviations between measured and simulated particle
backscattering coefficients at 355, 532 and 1064 nm may vary up to 20 % as
a result of ±5 % variability of specific size and microphysical
parameters. Such results reveal that the application of the reported approach,
based on the use of particle backscattering measurements at two wavelengths
in combination with OPC measurements, allows a sufficiently reliable
assessment of aerosol typing to be obtained.
Data availability
Data used in this study, together with the related metadata,
are available from the public data repository HyMeX database, which is freely accessible by all users through the
following link: http://mistrals.sedoo.fr/HyMeX/ (Di Girolamo, 2019).
Author contributions
PDG designed and developed the main experiment, and MC designed and developed the additional
receiving unit. PDG, MC, DS, AS and DS carried out the measurements. DS, AS and DS developed the data analysis algorithms and
carried out the data analysis. DS and PDG prepared the manuscript with contributions from DS.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Hydrological cycle in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
This work is a contribution to the HyMeX Program supported by MISTRALS and
ANR IODA-MED grant ANR-11-BS56-0005. This research effort was supported by
the European Commission under the European Facility for Airborne Research of the Seventh Framework Programme (WaLiTemp project). This research
effort was also supported by the project “Smart Cities – Basilicata” by
the Italian Ministry of Education, University and Research. The authors
gratefully acknowledge NOAA Air Resources Laboratory (ARL) for the provision
of the HYSPLIT transport and dispersion model used in this publication.
Review statement
This paper was edited by Domenico Cimini and reviewed by three anonymous referees.
ReferencesAnsmann, A., Riebesell, M., and Weitkamp, C.: Measurement of atmospheric
aerosol extinction profiles with a Raman lidar, Opt. Lett., 15,
746–748, 10.1364/OL.15.000746, 1990.Ansmann, A., Wandinger, U., Riebesell, M., Weitkamp, C., and Michaelis, W.:
Independent measurement of extinction and backscatter profiles in cirrus
clouds by using a combined Raman elastic-backscatter lidar, Appl. Optics,
31, 7113–7131, 10.1364/AO.31.007113, 1992.
Bellantone, V., Carofalo, I., De Tomasi, F., Perrone, M. R., Santese, M.,
Tafuro, A. M., and Turnone, A.: In situ samplings and remote sensing measurements to
characterize aerosol properties over South-East Italy, J. Atmos. Ocean. Tech., 25, 1341–1356, 2008.Bennett, L. J., Blyth, A. M., Burton, R. R., Gadian, A. M., Weckwerth, T.
M., Behrendt, A., Di Girolamo, P., Dorninger, M., Lock, S.-J., Smith, V. H.,
and Mobbs, S. D.: Initiation of convection over the Black Forest mountains
during COPS IOP15a, Q. J. Roy. Meteor. Soc.,
137, 176–189, 10.1002/qj.760, 2011.Bhawar, R., Bianchini, G., Bozzo, A., Cacciani, M., Calvello, M.R.,
Carlotti, M., Castagnoli, F., Cuomo, V., Di Girolamo, P., Di Iorio, T., Di
Liberto, L., di Sarra, A., Esposito, F., Fiocco, G., Fua, D., Grieco, G.,
Maestri, T., Masiello, G., Muscari, G., Palchetti, L., Papandrea, E.,
Pavese, G., Restieri, R., Rizzi, R., Romano, F., Serio, C., Summa, D.,
Todini, G., and Tosi, E.: Spectrally Resolved Observations of Atmospheric
Emitted Radiance in the H2O Rotation Band, Geophys. Res. Lett., 35, L04812, 10.1029/2007GL032207, 2008.Bhawar, R., Di Girolamo, P., Summa, D., Flamant, C., Althausen, D.,
Behrendt, A., Kiemle, C., Bosser, P., Cacciani, M., Champollion, C., Di
Iorio, T., Engelmann, R., Herold, C., Müller, D., Pal, S., Wirth, M., and
Wulfmeyer, V.: The water vapour intercomparison effort in the framework of
the Convective and Orographically-induced Precipitation Study:
airborne-to-ground-based and airborne-to-airborne lidar systems, Q. J. Roy. Meteor. Soc., 137, 325–348,
10.1002/qj.697, 2011.
d'Almeida, G. A., Koepke, P., and Shettle, E. P.: Atmospheric aerosols: Global
climatology and radiative characteristics, A Deepak Publishing, Hampton,
Virginia, 561 pp., 1991.Di Girolamo, P.: Particle backscattering coefficient at 355 mm, Particle backscattering coefficient at 532 mm, Particle backscattering coefficient at
1064 mm, available at: http://mistrals.sedoo.fr/HyMeX/, last access:
3 April 2019.Di Girolamo, P., Gagliardi, R. V., Pappalardo, G., Spinelli, N., Velotta, R.,
and Berardi, V.: Two wavelength lidar analysis of stratospheric aerosol size
distribution, J. Aerosol Sci., 26, 989–1001,
10.1016/0021-8502(95)00025-8, 1995.Di Girolamo, P., Ambrico, P. F., Amodeo, A., Boselli, A., Pappalardo, G., and
Spinelli, N.: Aerosol observations by lidar in the nocturnal boundary layer,
Appl. Optics, 38, 4585–4595, 10.1364/AO.38.004585, 1999.Di Girolamo, P., Marchese, R., Whiteman, D. N., and Demoz, B. B.: Rotational
Raman Lidar measurements of atmospheric temperature in the UV, Geophys. Res. Lett., 31, L01106,
10.1029/2003GL018342, 2004.Di Girolamo, P., Behrendt, A., and Wulfmeyer, V.: Spaceborne profiling of
atmospheric temperature and particle extinction with pure rotational Raman
lidar and of relative humidity in combination with differential absorption
lidar: performance simulations, Appl. Optics, 45, 2474–2494,
10.1364/AO.45.002474, 2006.Di Girolamo, P., Behrendt, A., Kiemle, C., Wulfmeyer, V., Bauer, H., Summa,
D., Dörnbrack, A., and Ehret, G.: Simulation of satellite water vapour
lidar measurements: Performance assessment under real atmospheric
conditions, Remote Sens. Environ., 112, 1552–1568,
10.1016/j.rse.2007.08.008, 2008.Di Girolamo, P., Summa, D., and Ferretti, R.: Multiparameter Raman Lidar
Measurements for the Characterization of a Dry Stratospheric Intrusion
Event, J. Atmos. Ocean. Tech., 26, 1742–1762,
10.1175/2009JTECHA1253.1, 2009a.Di Girolamo, P., Summa, D., Lin, R.-F., Maestri, T., Rizzi, R., and Masiello, G.: UV Raman lidar measurements of relative humidity
for the characterization of cirrus cloud microphysical properties, Atmos. Chem. Phys., 9, 8799–8811, 10.5194/acp-9-8799-2009, 2009b.Di Girolamo, P., Summa, D., Bhawar, R., Di Iorio, T., Cacciani, M.,
Veselovskii, I., Dubovik, O., and Kolgotin, A.: Raman lidar observations of a
Saharan dust outbreak event: Characterization of the dust optical properties
and determination of particle size and microphysical parameters, Atmos.
Environ., 50, 66–78, 10.1016/j.atmosenv.2011.12.061, 2012a.Di Girolamo, P., Summa, D., Cacciani, M., Norton, E. G., Peters, G., and Dufournet, Y.: Lidar and radar measurements of the
melting layer: observations of dark and bright band phenomena, Atmos. Chem. Phys., 12, 4143–4157, 10.5194/acp-12-4143-2012, 2012b.Di Girolamo, P., Flamant, C., Cacciani, M., Richard, E., Ducrocq, V., Summa,
D., Stelitano, D., Fourrié, N., and Saïd, F.: Observation of
low-level wind reversals in the Gulf of Lion area and their impact on the
water vapour variability, Q. J. Roy. Meteor. Soc., 142, 153–172, 10.1002/qj.2767, 2016.Di Girolamo, P., Cacciani, M., Summa, D., Scoccione, A., De Rosa, B., Behrendt, A., and Wulfmeyer, V.: Characterisation
of boundary layer turbulent processes by the Raman lidar BASIL in the frame of HD(CP)2 Observational Prototype Experiment,
Atmos. Chem. Phys., 17, 745–767, 10.5194/acp-17-745-2017, 2017.Di Girolamo, P., Behrendt, A., and Wulfmeyer, V.: Space-borne profiling of
atmospheric thermodynamic variables with Raman lidar: performance
simulations, Opt. Express, 26, 8125–8161, 10.1364/OE.26.008125,
2018a.Di Girolamo, P., Scoccione, A., Cacciani, M., Summa, D., De Rosa, B., and Schween, J. H.: Clear-air lidar dark
band, Atmos. Chem. Phys., 18, 4885–4896, 10.5194/acp-18-4885-2018,
2018b.Di Iorio, T., Di Sarra, A., Junkermann, W., Cacciani, M., Fiocco, G., and
Fuà, D.: Tropospheric aerosols in the Mediterranean: 1. Microphysical
and optical properties, J. Geophys. Res.-Atmos.,
108, 4316, 10.1029/2002JD002815, 2003.
Draxler, R. R. and Hess, G. D.: An overview of the HYSPLIT_4
modeling system for trajectories, dispersion and deposition,
Aust. Meteorol. Mag., 47, 295–308, 1998.Ducrocq, V., Braud, I., Davolio, S., Ferretti, R., Flamant, C., Jansa, A.,
Kalthoff, N., Richard, E., Taupier-Letage, I., Ayral, P., Belamari, S., Berne, A.,
Borga, M., Boudevillain, B., Bock, O., Boichard, J., Bouin, M., Bousquet, O.,
Bouvier, C., Chiggiato, J., Cimini, D., Corsmeier, U., Coppola, L., Cocquerez, P.,
Defer, E., Delanoë, J., Di Girolamo, P., Doerenbecher, A., Drobinski, P.,
Dufournet, Y., Fourrié, N., Gourley, J. J., Labatut, L., Lambert, D., Le
Coz, J.,
Marzano, F. S., Molinié, G., Montani, A., Nord, G., Nuret, M., Ramage, K.,
Rison, W., Roussot, O., Said, F., Schwarzenboeck, A., Testor, P., Van Baelen, J.,
Vincendon, B., Aran, M., and Tamayo, J.: HyMeX-SOP1: The Field Campaign Dedicated
to Heavy Precipitation and Flash Flooding in the Northwestern Mediterranean,
B. Am. Meteor. Soc., 95, 1083–1100, 10.1175/BAMS-D-12-00244.1,
2014.Elterman, L.: Aerosol Measurements in the Troposphere and Stratosphere,
Appl. Optics, 5, 1769–1776, 10.1364/AO.5.001769, 1966.Estellés, V., Martínez-Lozano, J. A., and
Utrillas, M. P.: Influence of air mass history on the columnar aerosol
properties at Valencia, Spain, J. Geophys. Res., 112, D15211, 10.1029/2007JD008593, 2007.Fernald, F. G.: Analysis of atmospheric lidar observations: some comments,
Appl. Optics, 23, 652–653, 10.1364/AO.23.000652, 1984.Fiocco, G. and Grams, G.: Observations of the Aerosol Layer at 20 km by
Optical Radar, J. Atmos. Sci., 21, 323–324,
10.1175/1520-0469(1964)021<0323:OOTALA>2.0.CO;2,
1964.Grainger, R. G., Lucas, J., Thomas, G. E., and Ewen, G. B. L.: Calculation of Mie derivatives, Appl. Opt. 43, 5386–5393,
10.1364/AO.43.005386, 2004.Granados-Muñoz, M. J., Bravo-Aranda, J. A., Baumgardner, D., Guerrero-Rascado, J. L., Pérez-Ramírez, D., Navas-Guzmán, F.,
Veselovskii, I., Lyamani, H., Valenzuela, A., Olmo, F. J., Titos, G., Andrey, J., Chaikovsky, A., Dubovik, O., Gil-Ojeda, M., and
Alados-Arboledas, L.: A comparative study of aerosol microphysical properties retrieved from ground-based remote sensing and
aircraft in situ measurements during a Saharan dust event, Atmos. Meas. Tech., 9, 1113–1133, 10.5194/amt-9-1113-2016, 2016.Grimm, H. and Eatough, D. J.: Aerosol Measurement: The Use of Optical Light
Scattering for the Determination of Particulate Size Distribution, and
Particulate Mass, Including the Semi-Volatile Fraction, J. Air Waste Manage., 59, 101–107,
10.3155/1047-3289.59.1.101, 2009.Haywood, J. and Boucher, O.: Estimates of the direct and indirect radiative
forcing due to tropospheric aerosols: A review, Rev. Geophys.,
38, 513–543, 10.1029/1999RG000078, 2000.Heim, M., Mullins, B. J., Umhauer, H., and Kasper, G.: Performance evaluation
of three optical particle counters with an efficient “multimodal”
calibration method, J. Aerosol Sci., 39, 1019–1031,
10.1016/j.jaerosci.2008.07.006, 2008.Junge, C. and Jaenicke, R.: New results in background aerosols studies from
the Atlantic expedition of the R.V. Meteor, Spring 1969, J. Aerosol Sci., 2, 305–314, 10.1016/0021-8502(71)90055-3, 1971.Junge, C. E.: Our knowledge of the physico-chemistry of aerosols in the
undisturbed marine environment, J. Geophys. Res., 77,
5183–5200, 10.1029/JC077i027p05183, 2012.Klett, J. D.: Stable analytical inversion solution for processing lidar
returns, Appl. Optics, 20, 211–220, 10.1364/AO.20.000211, 1981.Klett, J. D.: Lidar inversion with variable backscatter/extinction ratios,
Appl. Optics, 24, 1638–1643, 10.1364/AO.24.001638, 1985.Macke, A., Seifert, P., Baars, H., Barthlott, C., Beekmans, C., Behrendt, A., Bohn, B., Brueck, M., Bühl, J., Crewell, S., Damian, T., D
eneke, H., Düsing, S., Foth, A., Di Girolamo, P., Hammann, E., Heinze, R., Hirsikko, A., Kalisch, J., Kalthoff, N., Kinne, S., Kohler, M.,
Löhnert, U., Madhavan, B. L., Maurer, V., Muppa, S. K., Schween, J., Serikov, I., Siebert, H., Simmer, C., Späth, F., Steinke, S., Träumner, K.,
Trömel, S., Wehner, B., Wieser, A., Wulfmeyer, V., and Xie, X.: The HD(CP)2 Observational Prototype Experiment (HOPE) – an overview,
Atmos. Chem. Phys., 17, 4887–4914, 10.5194/acp-17-4887-2017, 2017.Man, C. K. and Shih, M. Y.: Identification of sources of PM10 aerosols in
Hong Kong by wind trajectory analysis, J. Aerosol Sci.,
32, 1213–1223,
10.1016/S0021-8502(01)00052-0, 2001.McMeeking, G. R., Hamburger, T., Liu, D., Flynn, M., Morgan, W. T., Northway, M., Highwood, E. J., Krejci, R., Allan, J. D.,
Minikin, A., and Coe, H.: Black carbon measurements in the boundary layer over western and northern Europe,
Atmos. Chem. Phys., 10, 9393–9414, 10.5194/acp-10-9393-2010, 2010.
Methven, J., Evans, M., Simmonds, P., and Spain, G.: Estimating
relationships between air mass origin and chemical composition, J. Geophys.
Res., 106, 5005–5019, 2001.Mhawish, A., Kumar, M., Mishra A. K., Srivastava P. K., and Banerjee, T.:
Chapter 3 – Remote Sensing of Aerosols From Space: Retrieval of Properties
and Applications, Remote Sensing of Aerosols, Clouds, and Precipitation,
Elsevier, 45–83,
10.1016/B978-0-12-810437-8.00003-7, 2018.Mitchell, J. M.: The Effect of Atmospheric Aerosols on Climate with Special
Reference to Temperature near the Earth's Surface, J. Appl. Meteor., 10,
703–714, 10.1175/1520-0450(1971)010<0703:TEOAAO>2.0.CO;2, 1971.Müller, D., Wandinger, U., Althausen, D., and Fiebig, M.: Comprehensive
particle characterization from three-wavelength Raman-lidar observations:
case study, Appl. Optics, 40, 4863–4869, 10.1364/AO.40.004863,
2001.Müller D., Mattis, I., Ansmann, A., Wandinger, U., Ritter, C., and
Kaiser, D.: Multiwavelength Raman lidar observations of particle growth
during long-range transport of forest-fire smoke in the free troposphere,
Geophys. Res. Lett., 34, L05803, 10.1029/2006GL027936, 2007.Müller, T., Schladitz, A., Massling, A., Kaaden, N., Kandler, K., and
Wiedensohler, A.: Spectral absorption coefficients and imaginary parts of
refractive indices of Saharan dust during SAMUM-1, Tellus B, 61, 79–95, 10.1111/j.1600-0889.2008.00399.x,
2009.Rolph, G., Stein, A., and Stunder, B.: Real-time Environmental Applications
and Display sYstem: READY, Environ. Modell. Softw., 95,
210–228, 10.1016/j.envsoft.2017.06.025, 2017.
Shettle, P. and Fenn, R. W.: Models of the Atmospheric Aerosols and Their Optical Properties, in: AGARD Conference Proceedings No. 183,
Optical Propagation in the Atmosphere, Lyngby, Denmark, 1976.
Shettle, E. P. and Fenn, R. W.: Models for the Aerosols of the Lower Atmosphere and the Effects of Humidity Variations on Their Optical
Properties, AFGL-TR-79-0214, 675, p. 94, 1979.Sekiguchi, M., Nakajima, T., Suzuki, K., Kawamoto, K., Higurashi, A.,
Rosenfeld, D., Sano, I., and Mukai, S.: A study of the direct and indirect
effects of aerosols using global satellite data sets of aerosol and cloud
parameters, J. Geophys. Res.-Atmos., 108, 4699,
10.1029/2002JD003359, 2003.Serio, C., Masiello, G., Esposito, F., Di Girolamo, P., Di Iorio, T.,
Palchetti, L., Bianchini, G., Muscari, G., Pavese, G., Rizzi, R., Carli, B.,
and Cuomo, V.: Retrieval of foreign-broadened water vapor continuum coefficients
from emitted spectral radiance in the H2O rotational band from 240 to 590 cm-1, Opt. Express, 16, 15816–15833, 10.1364/OE.16.015816, 2008.Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D.,
and Ngan, F.: NOAA's HYSPLIT Atmospheric Transport and Dispersion Modeling
System, B. Am. Meteor. Soc., 96, 2059–2077,
10.1175/BAMS-D-14-00110.1, 2015.Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y., and Nakajima, T.:
Simulation of climate response to aerosol direct and indirect effects with
aerosol transport-radiation model, J. Geophys. Res.-Atmos., 110, D02202, 10.1029/2004JD005029, 2005.
Temme, N. M.: Special Functions: An introduction to the classical functions
of mathematical physics, (2nd print ed.), New York, Wiley,
228–231, ISBN 0471113131, 1996.Toledano, C., Cachorro, V. E., De Frutos, A. M., Torres, B., Berjon, A.,
Sorribas, M., and Stone, R. S.: Airmass Classification and Analysis of Aerosol
Types at El Arenosillo (Spain), J. Appl. Meteorol. Clim., 8, 962–981, 10.1175/2008jamc2006.1, 2009.
Veselovskii, I., Kolgotin, A., Griaznov, V., Müller, D., Wandinger, U.,
and Whiteman, D. N.: Inversion with regularization for the retrieval of
tropospheric aerosol parameters from multiwavelength lidar sounding, Appl. Optics, 41, 3685–3699, 10.1364/AO.41.003685, 2002.Veselovskii, I., Whiteman, D. N., Kolgotin, A., Andrews, E., and Korenskii,
M.: Demonstration of Aerosol Property Profiling by Multiwavelength Lidar
under Varying Relative Humidity Conditions, J. Atmos. Ocean. Tech.,
26, 1543–1557, 10.1175/2009JTECHA1254.1, 2009.Veselovskii, I., Dubovik, O., Kolgotin, A., Lapyonok, T., Di Girolamo, P.,
Summa, D., Whiteman, D. N., Mishchenko, M., and Tanré, D.: Application of
randomly oriented spheroids for retrieval of dust particle parameters from
multiwavelength lidar measurements, J. Geophys. Res.-Atmos., 115, D21203, 10.1029/2010JD014139, 2010.
WCP-112: A Preliminary Cloudless Standard Atmosphere for Radiation
Computation, WCP/IAMAP Radiation Commission, Geneva, WCP, (ICSU/WMO/WCP/IAMAP) (WCP-112), ii, 53 pp., Call no: WCP 112 TD 241986, 1986.Wulfmeyer, V., Bauer, H., Di Girolamo, P., and Serio, C.: Comparison of
active and passive water vapor remote sensing from space: An analysis based
on the simulated performance of IASI and space borne differential absorption
lidar, Remote Sens. Environ., 95, 211–230,
10.1016/j.rse.2004.12.019, 2005.Wulfmeyer, V., Behrendt, A., Bauer, H. S., Kottmeier, C., Corsmeier, U.,
Blyth, A., Craig, G., Schumann, U., Hagen, M., Crewell, S., Di Girolamo, P.,
Flamant, C., Miller, M., Montani, A., Mobbs, S., Richard, E., Rotach, M. W.,
Arpagaus, M., Russchenberg, H., Schlüssel, P., König, M.,
Gärtner, V., Steinacker, R., Dorninger, M., Turner, D. D., Weckwerth,
T., Hense, A., and Simmer, C.: Research campaign: The convective and
orographically induced precipitation study – A research and development
project of the World Weather Research Program for improving quantitative
precipitation forecasting in low-mountain regions, B. Am.
Meteorol. Soc., 89, 1477–1486, 10.1175/2008BAMS2367.1, 2008.Yang, F., Tan, J., Zhao, Q., Du, Z., He, K., Ma, Y., Duan, F., Chen, G., and Zhao, Q.: Characteristics of PM2.5 speciation in
representative megacities and across China, Atmos. Chem. Phys., 11, 5207–5219, 10.5194/acp-11-5207-2011, 2011.