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Atmos. Chem. Phys., 19, 11651–11668, 2019
https://doi.org/10.5194/acp-19-11651-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Characterization of aerosol hygroscopicity using Raman lidar
measurements at the EARLINET station of Payerne
Francisco Navas-Guzmán1, Giovanni Martucci1, Martine Collaud Coen1, María José Granados-Muñoz2,
Maxime Hervo1, Michael Sicard2,3, and Alexander Haefele1,4
1Federal Office of Meteorology and Climatology MeteoSwiss, Payerne, Switzerland
2Remote Sensing Laboratory/CommSensLab, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
3Ciències i Tecnologies de l’Espai – Centre de Recerca de l’Aeronàutica i de l’Espai/Institut d’Estudis Espacials de Catalunya
(CTE-CRAE/IEEC), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
4Department of Physics and Astronomy, University of Western Ontario, London, Canada
Correspondence: Francisco Navas Guzmán (francisco.navas@meteoswiss.ch)
Received: 26 March 2019 – Discussion started: 15 April 2019
Revised: 8 August 2019 – Accepted: 9 August 2019 – Published: 17 September 2019
Abstract. This study focuses on the analysis of aerosol hy-
groscopicity using remote sensing techniques. Continuous
observations of aerosol backscatter coefficient (βaer), tem-
perature (T) and water vapor mixing ratio (r) have been per-
formed by means of a Raman lidar system at the aerological
station of MeteoSwiss at Payerne (Switzerland) since 2008.
These measurements allow us to monitor in a continuous way
any change in aerosol properties as a function of the rela-
tive humidity (RH). These changes can be observed either in
time at a constant altitude or in altitude at a constant time.
The accuracy and precision of RH measurements from the li-
dar have been evaluated using the radiosonde (RS) technique
as a reference. A total of 172 RS profiles were used in this
intercomparison, which revealed a bias smaller than 4% RH
and a standard deviation smaller than 10 % RH between both
techniques in the whole (in lower) troposphere at nighttime
(at daytime), indicating the good performance of the lidar
for characterizing RH. A methodology to identify situations
favorable to studying aerosol hygroscopicity has been estab-
lished, and the aerosol hygroscopicity has been characterized
by means of the backscatter enhancement factor (fβ). Two
case studies, corresponding to different types of aerosol, are
used to illustrate the potential of this methodology.
The first case corresponds to a mixture of rural aerosol
and smoke particles (smoke mixture), which showed a higher
hygroscopicity (f355
β=2.8 and f1064
β=1.8 in the RH range
73 %–97%) than the second case, in which mineral dust was
present (f355
β=1.2 and f1064
β=1.1 in the RH range 68 %–
84 %).
The higher sensitivity of the shortest wavelength to hygro-
scopic growth was qualitatively reproduced using Mie simu-
lations. In addition, a good agreement was found between the
hygroscopic analysis done in the vertical and in time for Case
I, where the latter also allowed us to observe the hydration
and dehydration of the smoke mixture. Finally, the impact
of aerosol hygroscopicity on the Earth’s radiative balance
has been evaluated using the GAME (Global Atmospheric
Model) radiative transfer model. The model showed an im-
pact with an increase in absolute value of 2.4 W m−2at the
surface with respect to the dry conditions for the hygroscopic
layer of Case I (smoke mixture).
1 Introduction
Atmospheric aerosol particles scatter and absorb solar radi-
ation and therefore have an impact on the Earth’s radiative
budget (direct effect). In addition, aerosol particles can act as
cloud condensation nuclei (CCN) and modify cloud micro-
physical properties, also altering the global radiative budget
(indirect effects) in this way (Haywood and Boucher, 2000).
The uncertainty in assessing total anthropogenic greenhouse
gas and aerosol impacts on climate must be substantially re-
duced from its current level to allow meaningful predictions
of future climate. This uncertainty is currently dominated by
Published by Copernicus Publications on behalf of the European Geosciences Union.
11652 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
the aerosol component (Bindoff et al., 2013). Evaluation of
aerosol effects on climate must take into account high spatial
and temporal variation of aerosol concentrations and prop-
erties as well as the aerosol interactions with clouds and its
influence on precipitation. During the last years a huge ef-
fort was made to characterize vertically resolved profiles of
optical and microphysical properties for different kinds of
particles. Raman lidars (light detection and ranging) have
proven to be an essential tool to obtain profiles of these prop-
erties without modifying the environmental conditions (Ans-
mann et al., 1992; Navas-Guzmán et al., 2013; Granados-
Muñoz et al., 2014). Networks like EARLINET (European
Aerosol Research Lidar Network) have contributed to cre-
ate a quantitative, comprehensive, and statistically signifi-
cant database for the horizontal, vertical, and temporal dis-
tribution of aerosols on a continental scale (Bösenberg et al.,
2003; Pappalardo et al., 2014; Sicard et al., 2015).
However, despite the big effort of the scientific commu-
nity to characterize aerosol effects, there are some processes
that are not well understood yet. For example, an important
factor that can modify the role of aerosols in the global en-
ergy budget is RH. Under high RH conditions, aerosol par-
ticles may uptake water, changing their size and chemical
composition (hygroscopic growth). This hygroscopic growth
affects the direct scattering of radiation and especially the
indirect effects, as the ability of aerosol to act as CCN is di-
rectly related to their affinity for water vapor (Hänel, 1976;
Feingold and Morley, 2003; Zieger et al., 2013). Thus, under-
standing aerosol hygroscopic growth is of high importance
to quantify the influence of atmospheric aerosol in climate
models or for comparisons of remote sensing with in situ
measurements which are often performed under dry condi-
tions (Zieger et al., 2010).
Several studies have addressed the characterization of
aerosol hygroscopicity using in situ measurements over the
last years. Techniques such as with the Humidified Tandem
Differential Mobility Analyzer (HT-DMA) (e.g., Swietlicki
et al., 2008) or humidified tandem nephelometers have been
extensively used to quantify the change in particle diame-
ter or aerosol optical properties due to water uptake (Pilat
and Charlson, 1966; Fierz-Schmidhauser et al., 2010; Zieger
et al., 2013). However, despite the great contribution that
these techniques can provide to the understanding of the
aerosol hygroscopic processes, they also present some short-
comings. For example, most in situ techniques are limited
by the fact that they modify the ambient conditions and are
also subject to particle losses in the sampling lines, thereby
altering the real atmospheric aerosol properties (Bedoya-
Velásquez et al., 2018). In addition, they present larger er-
rors in the characterization of the aerosol hygroscopicity for
high RH (conditions close to saturation), especially when the
aerosol is more hygroscopic (Titos et al., 2016).
In this sense, remote sensing techniques could overcome
these difficulties since they can provide vertically resolved
measurements without modifying the aerosol sample. In ad-
dition, they are also able to measure under RH close to sat-
uration, that is, where particles are more affected by hygro-
scopic growth (Feingold and Morley, 2003). However, the
number of aerosol hygroscopic studies using remote sensing
is modest, and most of them were limited to specific field
campaigns. The main limitation to addressing these studies
comes from the difficulty in obtaining RH profiles with high
vertical and temporal resolution. Most of the studies car-
ried out so far have used sensors to measure RH onboard
RSs or aircrafts or in meteorological towers in combina-
tion with the aerosol measurements from lidars to investigate
the aerosol hygroscopicity (Wulfmeyer and Feingold, 2000;
Veselovskii et al., 2009; Granados-Muñoz et al., 2015; Ha-
effelin et al., 2016; Lv et al., 2017; Zhao et al., 2017; Fer-
nández et al., 2018). Recently, the combination of Tprofiles
from microwave radiometers (MWRs) and rprofiles from
Raman lidar has been used to retrieve RH profiles (Navas-
Guzmán et al., 2014) and applied to aerosol hygroscopic
studies (Bedoya-Velásquez et al., 2018). However, although
this synergy of instrumentation can be a good solution for
many stations, the uncertainties coming from MWR Tpro-
files can be problematic due to their lower spatial resolution
(Navas-Guzmán et al., 2016). In addition, most of the rmea-
surements from the Raman lidar system are limited to night-
time observations, due to the low signal-to-noise ratio (SNR)
of the Raman channels during daytime.
In the present study we show the capability of the RAman
Lidar for Meteorological Observations (RALMO) operated
at the aerological station of MeteoSwiss at Payerne (Switzer-
land) to monitor aerosol hygroscopicity based on its continu-
ous aerosol and RH measurements. The methodology needed
for this characterization is introduced and applied to two case
studies in which different aerosol types were present.
2 Experimental site and instrumentation
Data from collocated remote sensing and in situ sensors were
acquired at the aerological station of MeteoSwiss at Pay-
erne (Switzerland, 46.82◦N, 6.95◦E; 491 m above sea level
(a.s.l.)). The station is located in a rural area on the Swiss
Plateau, between the Jura mountains (25 km to the northwest)
and the Alpine foothills (20 km to the southeast). The site is
not significantly affected by industrial pollution and the re-
gion is characterized by a mid-latitude continental climate.
Aerosol vertical information is mainly obtained from li-
dar systems installed at the station. RALMO is the main
tool of this study and was developed by the Swiss Federal
Institute of Technology (EPFL) in collaboration with Me-
teoSwiss and has been operated at MeteoSwiss Payerne since
August 2008. The instrument is dedicated to operational me-
teorology, model validation, climatological studies as well
as ground truthing of satellite data. The lidar was specially
designed to satisfy the requirements for operational moni-
toring of the atmosphere and to create a durable and ho-
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F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements 11653
mogeneous dataset to be used for climatology studies (Di-
noev et al., 2013). The lidar system uses a Nd:YAG laser
source which emits pulses of 8 ns duration at a wavelength
of 355 nm and with a repetition rate of 30 Hz. The mean
energy per pulse at 355 nm is around 400 mJ. Before be-
ing emitted into the atmosphere the beam is expanded to a
diameter of 14 cm which reduces the beam divergence (to
0.1 mrad) and ensures eye safety. The receiving system con-
sists of four telescopes with 30 cm parabolic mirrors which
are arranged symmetrically around the vertically mounted
beam expander to receive the backscattered photons. The to-
tal aperture of this telescope system is equivalent to a tele-
scope with a single mirror of 60 cm diameter, and it has a
field of view of 0.2 mrad. This narrow field of view com-
bined with the narrow-band receiver and high pulse energy
allows daytime operation. Optical fibers connect the tele-
scope mirrors with two grating polychromators which allow
us to isolate the rotational–vibrational Raman signals of ni-
trogen and water vapor (wavelengths of 386.7 and 407.5 nm,
respectively) as well as the elastic signal and four portions
of the pure-rotational Raman spectrum around 355 nm for T,
aerosol backscatter and extinction measurements. The opti-
cal signals are finally detected by photomultipliers and ac-
quired by a transient recorder (Brocard et al., 2013). A de-
tailed description of the different parts of this lidar system
can be found in Dinoev et al. (2013). RALMO was incorpo-
rated into EARLINET in 2008.
In order to gain more spectral information in the profiles
of aerosol properties, data from a co-located ceilometer have
been used in this study. The CHM15k ceilometer from the
Lufft company operated at Payerne is a lidar cloud height
sensor based on a single wavelength backscattering tech-
nique. Its Nd:YAG narrow-beam microchip laser operates at
1064 nm. Cloud layers can be detected in a range of up to
15 km. In addition to the cloud base information, we derive
βaer at 1064 nm from the elastic signal using the Klett inver-
sion technique (Klett, 1981).
RS measurements were also used in this study to as-
sess the T,rand RH profiles retrieved from RALMO. The
SRS-C50 sondes were flown operationally at Payerne and
launched twice a day at 11:00 and 23:00 UTC. The sondes
were launched and transported through the troposphere and
stratosphere by a balloon inflated with hydrogen and reach-
ing on average 35km. The vertical resolution of the mea-
sured profiles of Tand humidity is about 6 m. The sensors of
these RSs include copper–constantan thermocouples for T, a
full-range water hypsometer for pressure and a sensor with
a hygristor for RH. The accuracy of these three parameters
in the troposphere is 0.1 K for T, 2 hPa (accuracy decreases
with height) for pressure and 5 % to 10 % for RH.
Sun-photometer measurements have also been included
in this study to complement the aerosol information. Me-
teoSwiss operates four Precision Filter Radiometers (PFRs)
that were installed in the framework of the international
GAW-PFR (Global Atmosphere Watch – Precision Filter Ra-
diometer) program of the World Meteorological Organiza-
tion (WMO). They measure the direct solar irradiance at the
four wavelengths (368, 412, 500 and 862nm) used in the
GAW-PFR network as well as nine additional wavelengths
(305, 311, 318, 332, 450, 610, 675, 718, 778, 817, 946 and
1024 nm). Integrated water vapor (IWV) is obtained from the
measurements at 718, 817 and 946 nm. The aerosol optical
depths (AODs) have been calculated based on the solar ir-
radiance at the different wavelengths using the GAW-PFR
algorithms (McArthur et al., 2003). In addition, the AOD
Ångström exponent (AE) is used in this study as a qualita-
tive indicator of the relative dominance of fine- and coarse-
mode aerosols. Values of AE larger than 1.5 are indicative of
a higher fine-mode fraction (Nyeki et al., 2012).
In situ observations at ground level were also used to com-
plement the column aerosol information obtained from re-
mote sensing. These in situ measurements at Payerne are
carried out by EMPA in the framework of the Nabel (Na-
tional Air Pollution Monitoring Network) monitoring pro-
gram. Aerosol absorption coefficients at seven wavelengths
(from 370 to 950 nm) were obtained from aethalometer mea-
surements (AE31 model). Light absorption measurements at
ultraviolet, visible and infrared wavelengths can be used to
quantitatively assess the aerosol source contribution (Col-
laud Coen et al., 2004; Sandradewi et al., 2008; Segura et al.,
2014). In addition, concentrations of PM10 and PM2.5(ambi-
ent air levels of atmospheric particulate matter finer than 10
and 2.5 microns) were obtained from an aerosol spectrometer
(Fidas 200). These measurements were used to characterize
the size of the particles that arrived at our station.
3 Methodology
3.1 Retrievals of RH and aerosol property profiles
As mentioned in the introduction, the main practical limita-
tion to studying the effect of hygroscopicity on optical and
microphysical aerosol properties is the lack of simultane-
ously and continuously available aerosol and RH measure-
ments with a good vertical and temporal resolution. RALMO
can overcome this limitation thanks to its capability to per-
form continuous measurements of T, water vapor and aerosol
profiles during day and night. In this section the methodol-
ogy to retrieve these atmospheric parameters from the lidar
signals is described.
Tmeasurements are made using the pure-rotational Ra-
man (PRR) technique (Vaughan et al., 1993). An atmospheric
Tprofile can be derived from the analysis of the intensities of
lines with low and high quantum numbers that have opposite
Tderivatives. A detailed description of the Tinversion tech-
nique applied to our system can be found in Martucci et al.
(2019).
ris defined as the ratio of the mass of water vapor to
the mass of dry air in a sample of the atmosphere (Gold-
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11654 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
smith et al., 1998). Profiles of rcan be obtained from Ra-
man lidar measurements as the ratio of rotational–vibrational
Raman scattering intensities from water vapor and nitrogen
molecules (Whiteman, 2003a, b; Brocard et al., 2013; Navas-
Guzmán et al., 2014). It can be expressed as follows:
r(z) =CSH2O
SN2
exp
z
Z
0αr,λH2O−αr,λN2dr
,(1)
where Cis the lidar calibration coefficient that takes into ac-
count the fractional volume of nitrogen in the atmosphere
(0.78), the instrumental transmission and detection efficien-
cies at the wavelengths of the Raman returns and the range-
independent Raman backscatter cross section for nitrogen
and water vapor. The calibration coefficient must be deter-
mined for each specific lidar. In the case of RALMO, Cis
obtained by combining absolute calibration with co-located
radiosounding, with relative calibration using the solar back-
ground (Martucci et al., 2018). SH2Oand SN2are the Raman
lidar signals for water vapor and nitrogen, respectively, after
being corrected for saturation and background. The exponen-
tial term is the difference in the atmospheric transmission be-
tween the surface and the altitude zfor nitrogen (386.7 nm)
and water vapor (407.5nm). The molecular extinction is cal-
culated considering the US Standard Atmosphere. For nor-
mal conditions at Payerne the effect of differential extinction
due to aerosols between these two wavelengths is small and
is neglected (Dinoev et al., 2013). However, when this differ-
ential extinction is not negligible, Muñoz-Porcar et al. (2018)
showed that it can accurately be calculated using a radiative
transfer model with a relatively simple parametrization.
RH profiles are obtained by combining Tand r. RH is
defined as the ratio of the actual amount of water vapor in
the air compared to the equilibrium amount (saturation) at
that T(Yau and Rogers, 1996), and it can be calculated as
RH(z) =e(z)
ew(z) ×100,(2)
where e(z) is the water vapor pressure, while ew(z) refers to
the saturation pressure. The water vapor pressure is related
to ras follows:
e(z) =p(z)r (z)
0.622 +r (z) ,(3)
where p(z) is the air pressure profile which can be estimated
from RSs or assuming a US standard atmosphere. In our
case, and thanks to the availability of operational RS mea-
surements in our station, the closest RS in time is used to
calculate the pressure profile. For saturation pressure we use
the following expression:
ew(z) =6.107expMA[T (z) −273]
MB+[T (z) −273],(4)
where the constants MAand MBare 17.84 (17.08) and 245.4
(234.2), respectively, for Tbelow (above) 273K (List, 1951).
Aerosol vertical information is also retrieved from
RALMO measurements. The backscattered radiation from
aerosols and molecules due to Mie and Rayleigh scattering,
respectively, has the same wavelength as the laser and is re-
ferred to as the elastic signal, SEl(z). Stokes and anti-Stokes
portions of the PRR spectrum with opposite Tdependence
are summed, giving in good approximation a T-independent
inelastic signal SPRR(z).βaer is derived from the ratio be-
tween the elastic and inelastic signals as follows:
βaer(z) =βmol (z)kSEl(z)
SPRR(z) −1,(5)
where kis the calibration constant and βmol is the molecular
backscatter coefficient. βmol is calculated using a measured
atmospheric density, while the kconstant is estimated assum-
ing a molecular behavior of the atmosphere in the far range
(upper troposphere).
3.2 Selection of aerosol hygroscopic cases
Continuous measurements of aerosol and RH profiles from
RALMO lidar allow us to monitor any change in aerosol
properties that could occur as a result of the water uptake by
particles under high RH (aerosol hygroscopic growth). How-
ever, to ensure that the changes in the aerosol properties are
due to hygroscopic growth and not to changes in the load
or composition of the aerosol, certain requirements must be
fulfilled.
As a first condition an increase in βaer should occur simul-
taneously with an increase in RH. This condition could be
observed in the vertical for a certain aerosol layer (vertical
hygroscopic growth), but it could also be found as a func-
tion of time at a constant altitude. It is worth remarking that
RALMO is one of the few remote sensing instruments in the
world that is able to monitor those processes as a function of
time and height. Cases fulfilling the previous condition were
selected as potential cases of hygroscopic growth. After that,
we needed to ensure a high degree of homogeneity in the
investigated aerosol layer. For that, a second requirement to
be fulfilled is that the origin of the air masses, in the region
where the presence of hygroscopicity is assessed, is indepen-
dent of altitude (in the case of vertical hygroscopic growth).
To guarantee that, we have used backward trajectory anal-
ysis from the HYSPLIT model (Hybrid Single-Particle La-
grangian Integrated Trajectory) (Draxler and Rolph, 2003).
As a final condition to ensure good mixing within the layer,
we used the profiles of rand potential temperature (θ) ob-
tained from RALMO measurements. Slow-varying or con-
stant values of these two parameters are indicative of well-
mixed conditions across the aerosol layer.
Similar criteria to assess the homogeneity of an aerosol
layer were required in previous studies using remote sens-
ing (Veselovskii et al., 2009; Granados-Muñoz et al., 2015;
Bedoya-Velásquez et al., 2018). However, this is the first time
that the profiles of rand θare obtained from the same instru-
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F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements 11655
ment as the aerosol measurements. It presents a clear advan-
tage since all the parameters are measured for the same atmo-
spheric column, as opposed to studies using RSs or MWR.
Another novelty of this study is the capability to moni-
tor aerosol hygroscopic growth in time at different altitudes
in the troposphere. For this temporal analysis we used the r
measurements to ensure that changes in aerosol properties in
time are only due to hygroscopic processes. In the absence of
condensation and evaporation processes rcan be considered
an atmospheric tracer and we assume that air parcels with
the same rhave in good approximation the same origin and
hence the same aerosol load and composition. In addition,
wind measurements from a collocated wind profiler are used
to corroborate that the wind direction was the same during
the analyzed period.
Once all the previous requirements have been fulfilled, the
aerosol hygroscopicity is characterized by means of the fβ.
This parameter is defined as
fζ(RH)=ζ(RH)
ζ(RHref),(6)
where ζ(RH)represents an aerosol property at a certain RH.
RHref is the so-called reference RH, and it is chosen as the
lowest value of RH in the analyzed layer or time interval. In
this study, fβhas been calculated for βaer.
In order to be able to compare our results with other
studies in which RHref or the RH range could be different,
the humidograms (RH versus the fβ) are parameterized us-
ing fitting equations (e.g., Titos et al., 2016). In this study
we use the one-parameter equation introduced by Hänel
(1976), which has been used in other hygroscopic studies us-
ing remote sensing (Granados-Muñoz et al., 2015; Bedoya-
Velásquez et al., 2018). The general form of the Hänel equa-
tion is expressed as
fζ(RH)=1−RH/100
1−RHref/100 −γ
,(7)
where γis an indicator of the aerosol hygroscopicity. Larger
values of γindicate higher hygroscopicity.
4 Validation of lidar measurements versus operational
RSs
Since data quality is critical, a validation with respect to the
RS technique has been carried out, which we consider the
reference. As was indicated in Sect. 2, operational RSs are
launched twice per day at the aerological station of Payerne.
The availability of simultaneous lidar and RS measurements
at our station allowed us to minimize the differences due to
spatio-temporal inhomogeneities.
Figure 1 shows the r,Tand RH profiles obtained
from RALMO lidar and RS at nighttime (23:00 UTC) on
20 September 2017. A very good agreement can be observed
Figure 1. Mixing ratio (r), temperature (T) and relative humidity
(RH) profiles from lidar (blue lines) and operational RS (red line)
at 23:00 UTC on 20 September 2017.
from this figure for the three atmospheric parameters. We can
see that RALMO provided very accurate results even for alti-
tude ranges where strong gradients were observed, as for the
wet layer located above 2km above ground level (a.g.l.) or
for the Tinversion observed at 1.7km (a.g.l.). The mean and
standard deviation for the whole profile (from ground to 8 km
(a.g.l.)) of the relative differences in rwere −1% ±13 %,
while the mean and standard deviation of Tand RH differ-
ences were −0.1±0.7 K and −1 % ±6% RH, respectively.
This example shows the potential of RALMO to provide ac-
curate measurements with a good spatial resolution. In order
to evaluate the accuracy and the precision of RALMO re-
trievals, a statistical analysis of lidar and RS differences was
carried out. A total of 172 RS were used in this intercompar-
ison during daytime and nighttime for the period from July
to December of 2017. The Tvalidation of RALMO lidar is
discussed in more depth in a separate paper (Martucci et al.,
2019). Here we would only like to discuss the statistics for
this 6-month period.
For nighttime measurements (23:00 UTC), a total of
100 RS were used to compare with lidar and the mean Tde-
viation profile evidenced a small bias of 0.05 ±0.06 K in the
first 5 km and 0.15 ±0.15 K above that altitude. The stan-
dard deviation profile also confirmed the excellent perfor-
mance of RALMO in retrieving T, with standard deviations
below 1K in the full troposphere (mean values of 0.6±0.1 K
below 5km (a.g.l.) and 1.00 ±0.16 K above). For daytime
measurements (11:00 UTC), 72 pairs of profiles were used.
The mean Tdeviations between lidar and RS for daytime
measurements were −0.5±0.2 K in the lower troposphere
(0–5 km a.g.l.) and −0.1±0.6 in the upper troposphere (5–
10 km a.g.l.). The Tstandard deviations were 0.8±0.2 and
2.4±0.8 for the same two altitude ranges. These results also
prove the good performance of RALMO during daytime, al-
though they show larger discrepancies than during nighttime,
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11656 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
Figure 2. RH validation between RALMO lidar and RS at
23:00 UTC for the period from July to December 2017. (a) Pro-
files of RH deviation between lidar and RS. (b) Mean RH deviation
(lidar–RS). (c) Standard RH deviation profile (red line) and number
of profiles used at each altitude for these statistics (green line).
especially in the upper troposphere (from 5 to 10 km). The
reliability and the high quality of the Tprofiles obtained by
RALMO are key aspects for addressing aerosol hygroscopic
studies, since this is the most difficult to obtain of the atmo-
spheric parameters using remote sensing techniques.
Figure 2 presents the same statistics as in the previous
discussion but for RH measurements. As was explained in
Sect. 3, RH profiles from lidar were obtained from the com-
bination of rand Tmeasurements. Figure 2a shows all
RH deviation profiles between lidar and RS, while Fig. 2b
presents the mean RH deviation profile. This plot reveals
a small bias between both instruments that ranges from
positive values (+3 % RH at 1.4 km a.s.l.) to negative ones
(−9 %RH at 5.6km a.s.l.). Above 9 km (a.s.l.) the bias again
becomes positive, reaching a maximum value of +6 % RH.
The mean bias and standard deviation along the region from
ground to 2.1 kma.s.l. is +2.0±0.9 % RH, while it increases
to −4±2 % RH in the range 2.1–9 km. We can affirm that
the shape observed in the RH bias between both instru-
ments is mainly coming from the rmeasurements since
the Tstatistics showed a negligible bias in the whole col-
umn (with almost zero bias, not shown). It is due to larger
inhomogeneities in rin time and in space which produce
larger discrepancies between lidar and RS observations. Re-
garding the standard deviations observed for this parame-
ter (Fig. 2c), we can observe that it increases with alti-
tude in the lower troposphere (from 4.4% RH at ground to
8 % RH at 2 km a.s.l.). Above this altitude the standard de-
viation values oscillate around a quite constant value in al-
titude. The mean RH standard deviation in the lower tropo-
sphere (from ground to 2.1 km) was 6.5%±1.3 % RH, while
it was 8.5%±1.5%RH above this altitude. The RH compar-
ison for daytime measurements (not shown here) also pre-
sented a small bias between lidar and RS, with a mean value
of −0.4%±2.4% RH in the range from ground to 5 kma.s.l.
The mean standard deviations obtained for the same altitude
range were slightly larger than during nighttime, with a mean
value of 9%±3 % RH.
Above 5km (a.s.l.) and during daytime the SNR of
RALMO is smaller compared to nighttime measurements
due to the solar background; for this reason the RH profiles
were not calculated above this altitude in order to avoid large
uncertainties. In any case we would like to highlight the good
quality of RALMO RH information. The high quality of the
RH measurements shown in this intercomparison is a key as-
pect to be able to address the aerosol hygroscopic studies, as
will be shown in the next sections.
5 Study of aerosol hygroscopicity
Two case studies are presented in which the hygroscopicity
of different types of aerosol is characterized. The two case
studies took place during summer 2017 and feature smoke
particles and mineral dust.
5.1 Case I: hygroscopic growth of smoke mixture
Case I corresponds to 7 September 2017. The temporal evo-
lution of r, RH and βaer at 355 nm in the lower troposphere
(0–5 km a.s.l.) is shown in Fig. 3 for this day. According to
the aerosol measurements (lowest panel), low clouds were
present at around 1.6 km (a.g.l.) during the first part of the
day (until 12:00 UTC). After that, two clear aerosol layers
can be identified, the atmospheric boundary layer (ABL) and
a strong lofted aerosol layer located between 2 and 4 km
(a.g.l.) which appeared in the afternoon. A usual convective
boundary layer development was observed during the day,
with maximum height occurring at around 14:00 UTC. It is
interesting to remark how within the ABL the intensity of
the aerosol backscatter signal was stronger at the top of this
layer even when a strong mixing was expected at the cen-
tral hours of the day (between 13:00 and 17:00 UTC). How-
ever, a larger homogeneity was observed for the same layer in
the rmeasurements (Fig. 3, upper panel), indicating that the
convective processes were strong enough to produce a well-
mixed ABL in that time interval. The third element that can
help to understand the observed variations in the backscatter
signals is the RH measurements (central panel). From that
plot, we can observe how the intensification observed in βaer
is well correlated with the values observed for RH, with the
highest values of both properties at the top of the ABL. This
kind of behavior could be due to aerosol hygroscopic pro-
cesses, and a detailed analysis is presented in the following
paragraphs.
According to the NAAPS (Navy Aerosol Analysis and
Prediction System) (Christensen, 1997) model, 7 Septem-
ber 2017 at 18:00 UTC is characterized by the presence of
smoke above the measurement station (Fig. 4a). This model
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F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements 11657
Figure 3. Temporal evolution of vertical profiles of r, RH and βaer at 355nm from RALMO lidar on 7 September 2017.
predicted smoke surface concentrations (blue color map)
between 4 and 8 µg m−3at Payerne. The HYSPLIT back-
trajectory analysis indicated that the air masses above our
station in the lower layers of the troposphere had their ori-
gin in North America (Fig. 4b). The VIIRS (Visible Infrared
Imaging Radiometer Suite) fire and thermal anomalies prod-
uct available from the joint NASA/NOAA Suomi-National
Polar orbiting Partnership (S-NPP) satellite (Fig. 4c) showed
that in the studied period (from 25 August to 3 Septem-
ber 2017) several intensive hotspots were found along the
calculated air mass trajectories, especially in some areas of
the northwest of the United States and in the central part
of Canada. In addition, carbon monoxide (CO) observations
from the Atmospheric Infrared Sounder (AIRS) onboard the
Aqua satellite monitored a plume (not shown here) with a
high concentration of CO that moved from North America to
Europe during that period, reaching the eastern part of Eu-
rope on 6 September 2017. Over Payerne, the total column
CO concentrations observed with AIRS were in the range of
100 and 130 parts per billion by volume (ppbv) during 7 and
8 September, which are considerably higher than the mean
concentration observed in the previous month (70–80ppbv).
CO is considered a good tracer of smoke particles since it is
generated in the incomplete combustion of biomass.
In situ measurements carried out at Payerne station
showed some changes in the aerosol properties that could
be characteristics of smoke particles (Fig. 5). An increase
in the PM2.5mass concentration was observed in the period
from 6 to 8 September, with values changing from 1µg m−3
(12:00 UTC on 6 September) to 9.9 µg m−3(20:00 UTC on
8 September), indicating an increase in the concentration
of small particles at the surface. The PM2.5concentration
reached during that evening was clearly above the mean sum-
mer value (5.2µg m−3). The PM2.5increase occurred at the
same time as an increase in the absorption coefficient at dif-
ferent wavelengths observed by an aethalometer. The absorp-
tion Ångström exponent (AAE), which represents the wave-
length dependence of absorption and depends on the com-
position of absorbing aerosols, showed relatively low val-
ues (between 1.1 and 1.3 for most of the measurements)
which can be characteristics of biomass-burning particles
(Schmeisser et al., 2017). Therefore, model predictions and
satellite and in situ observations agreed and point to a mix-
ture of local aerosol and smoke particles from biomass burn-
ing in the atmospheric column over Payerne.
Vertical information of aerosol, Tand rwas obtained
using the RALMO lidar and ceilometer measurements on
7 September 2017. Figure 6 shows the profiles of βaer at 355
and 1064 nm, RH and the auxiliary information of θand r
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11658 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
Figure 4. (a) NAAPS total optical depth from sulfate (orange/red scale), dust (green/yellow scale) and smoke (blue scale) at 18:00 UTC
on 7 September 2017. (b) Backward trajectories from the NOAA HYSPLIT model ending at 15:00UTC, 7 September 2017, calculated for
the altitudes 2500 and 3500 m a.s.l. (c) Fire map from the VIIRS instrument for the period from 25 August to 3 September 2017 (source:
https://firms.modaps.eosdis.nasa.gov/, last access: 12 October 2018).
for the time interval 15:00–15:30UTC. From this figure, a
marked increase in βaer with altitude was observed for the al-
titude range between 1.7 and 2.3 km (a.s.l.). Simultaneously
to this increase, we observed an increase in RH with values
increasing from 73 % (bottom of the layer) to 97 % (top of
the layer). The profiles of θand r(Fig. 6c) were used as
indicators of a good mixing as explained in Sect. 3.2. These
profiles show quite constant values for both properties within
the layer (300.6±0.5 K and 5.3±0.1 g kg−1, respectively),
indicating that the layer was well mixed.
Following the methodology presented in Sect. 3.2, fβat
355 and 1064 nm were obtained for the investigated layer
from the combination of βaer and RH measurements. Fig-
ure 7 shows the dependency of fβ(RH) with RH, called
a humidogram. The reference RH for this case was 73 %,
which corresponds to the lowest value in the layer. From this
figure, we can observe how β355 increased by a factor of
2.8 (fβ(97%)=2.8), while humidity increased from 73 %
to 97 % (blue points). Lower values of fβare observed at
1064 nm (red points). In the infrared βincreased by a fac-
tor of 1.8 with respect to its value at RHref (73 %), indi-
cating a lower sensitivity of this wavelength to the aerosol
hygroscopic growth. Hänel hygroscopic parameters (γ) ob-
tained using the fitting equation (Eq. 7) were calculated (solid
lines) in order to make our measurements comparable with
other studies. This parameter is proportional to the aerosol
hygroscopicity, and it had values of 0.48 ±0.08 at 355 nm
and 0.29±0.08 at 1064nm. Independent vertical profiles ob-
tained from the measurements of the previous 30min time
interval (from 14:30 to 15:00UTC) were also analyzed in or-
der to check the consistency of our results (figure not shown
here). For that period, a simultaneous increase in βaer and
RH was also observed in the altitude range 1.5–2.2km. Al-
though the RH range observed for this time interval (70%–
93 %) was slightly different from the one shown in Fig. 6,
γshowed consistent results, within the associated uncertain-
ties, for both time intervals (γ355 =0.57 ±0.14 and γ1064 =
0.35±0.14). A similar value of the hygroscopic parameter at
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F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements 11659
Figure 5. (a) Surface PM2.5concentrations at Payerne from 6 to 8 September 2017. (b) Absorption coefficient at seven wavelengths and
(c) absorption Angstrom exponent from aethalometer measurements.
Figure 6. Vertical profiles of (a) βaer at 355 and 1064nm, (b) RH
and (c) θand rfor the time interval 15:00–15:30 UTC on 7 Septem-
ber 2017. The grey shaded area indicates the layer in which aerosol
hygroscopic growth occurred.
355 nm (γ355 =0.40) is reported by Bedoya-Velásquez et al.
(2018) and associated with the presence of smoke particles.
In that study a combination of lidar and MWR measurements
was used for the aerosol hygroscopic analysis. However, the
spectral dependency found in our case is not what has been
reported in other studies. Higher values for longer wave-
lengths (between 355 and 532 nm) were found in γin other
studies for smoke particles and also for anthropogenic par-
ticles using remote sensing (Bedoya-Velásquez et al., 2018;
Lv et al., 2017). Haarig et al. (2017) also reported this higher
hygroscopicity at 1064 nm but for marine particles. In situ
studies also showed higher values for longer wavelengths,
although over a shorter range (450–700nm) and for marine
particles (Kotchenruther et al., 1999; Zieger et al., 2013).
We performed Mie simulations in order to understand
whether the spectral dependency observed in our study could
be realistic or not. βaer at the lidar wavelengths (355, 532
and 1064 nm) as a function of RH were computed using a
Mie code (Bond et al., 2006; Mätzler, 2002). For these sim-
ulations some inputs such as the aerosol growth factor (1.6,
which is typical for hygroscopic aerosol) and the refractive
index (m=1.5+i0.01) were assumed. Figure 8 shows the
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11660 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
Figure 7. fβat 355 and 1064 nm retrieved from the lidar profiles
(layer: 1.7–2.3 km a.s.l.) at the time interval 15:00–15:30 UTC on
7 September 2017.
fβcalculated from the Mie calculation as a function of RH
for different wavelengths and particle sizes. Monomodal dis-
tributions were used assuming different diameters for the dry
particles and a geometric standard deviation of 1.5. From this
figure we can observe that the backscatter is very sensitive
to wavelength and particle size as expected, whose relation-
ship is characterized in scattering theory by the size param-
eter (x=πD/λ), where Dis the particle diameter. A Mie
scattering regime is expected for x≈1. A stronger increase
in the backscattered radiation at the shortest wavelengths is
expected for small particles (Ddry =200 nm) when RH in-
creases according to these simulations (Fig. 8a). These re-
sults indicate that even a decrease in βaer at 1064nm could
be expected when the size particles increase due to hygro-
scopic growth. Figure 8b shows the humidogram for a dry
particle diameter of 400 nm. A different spectral dependency
is observed for this case, with slightly higher fβat 532 nm
than at 355 nm and lower at 1064 nm. These results agree
with our observations and also explain the different spectral
dependency observed in other studies for smoke and anthro-
pogenic particles that could have similar sizes (small par-
ticles) to what is simulated here. Panels c and d in Fig. 8
show that for the biggest dry particle diameters (600 and
800 nm) the spectral dependency of fβis inverted with re-
spect to small particles with larger fβvalues at the longest
wavelengths. This spectral dependency agrees with the ob-
servations in Haarig et al. (2017) for marine particles which
are considered much bigger particles than smoke particles.
However, we must also point out that for many cases, larger
particles in the atmosphere are further away from the ideal
case of a sphere considered in Mie theory. Hence, this type
of comparison should be considered with caution.
Continuous aerosol and RH measurements from RALMO
lidar also allow us to monitor aerosol hygroscopic pro-
cesses occurring in time. Figure 9 shows the evolution of r,
wind direction, βaer at 355 nm and RH at 1.3 km (a.s.l.) on
7 September 2017. As was indicated in Sect. 3.2, water va-
por is considered a good tracer in the atmosphere, and con-
Figure 8. fβcalculated from Mie simulations at 355, 532 and
1064 nm and for different particle diameters. The size parameter
(x) has been indicated for each configuration of wavelength and
size particle.
Figure 9. Evolution of rand wind direction (a) and βaer
355 and RH (b)
at 1.3 km (a.s.l.) on 7 September 2017.
stant values of rmean the air parcels have very similar ori-
gins. In this case, we can observe that rwas quite constant
(6.7±0.3 g kg−1, Fig. 9, top) during the evening (from 16:00
to 23:30 UTC), fulfilling the previous criterion. In addition,
a simultaneous increase in βaer and RH was observed for the
indicated period and altitude (Fig. 9, bottom). RH changed
from 63 % in late afternoon to reach values close to 90 % at
midnight.
In order to quantify the aerosol hygroscopic effect that
took place in time, fβwas calculated for these measurements
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F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements 11661
Figure 10. Humidograms at 355 nm (a) and 1064 nm (b) retrieved from continuous measurements for hydration and dehydration processes
on 7 September 2017.
Figure 11. Same as Fig. 3, on 8 July 2017.
(Fig. 10a, blue filled circles). The initial value of βaer
355 (at
RHref =63 %) increased by a factor of 2 when RH reached
the maximum values of the evening. The Hänel parameter-
ization was used for this dataset, providing a hygroscopic
parameter of 0.54 ±0.17 which is in good agreement with
the values observed in the atmospheric column in the after-
noon of this day. In addition to this hydration process (wa-
ter uptake) we could also observe the dehydration (evapora-
tion) that occurred within this aerosol layer during the after-
noon of this day (from 11:00 to 16:00 UTC, Fig. 9). In this
period, rand the wind direction measurements were stable
(6.0±0.5 g kg−1and 302◦±27◦, respectively), evidence that
the air mass did not change. A decrease in βaer
355 took place at
the same time when RH decreased from 93 % to 59 %. The
humidogram obtained for this dehydration process (Fig. 10a,
blue open circles) also showed fβvalues very close to the
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11662 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
Figure 12. (a) Dust concentration profile from NMMB/BSC model
forecast at 06:00 UTC on 8 July 2017 (adapted from http://www.
bsc.es/ess/bsc-dust-daily-forecast, last access: 15 September 2018).
(b) Vertical profile of βaer at 355 nm from RALMO lidar for the
time interval from 01:00 to 01:30 UTC on 8 July 2017.
ones calculated in the later period and a hygroscopic param-
eter from the Hänel parametrization of 0.40±0.08. The same
behavior was observed for β1064 from the ceilometer mea-
surements along this day, with hygroscopic parameter values
of 0.41±0.16 and 0.34±0.09 for hydration and dehydration
processes, respectively, showing again lower values than at
the ultraviolet channel. We would like to remark on the good
agreement found in the aerosol hygroscopicity of this case
using a vertical analysis and a temporal analysis.
5.2 Case II: hygroscopic growth of mineral dust
particles
The second case took place on 8 July 2017. Figure 11
shows the evolution of r, RH and βaer at 355nm from
RALMO during the morning of this day. From the aerosol
measurements (lowest panel) the presence of particles at
high altitudes is evident, reaching almost 6km (a.s.l.) in
the late morning (around 09:00 UTC). For this day, the
NMMB/BSC (Non-hydrostatic Multiscale Model/Barcelona
Supercomputing Center) Dust model (Pérez et al., 2011)
predicted dust particles over the western part of Europe,
including Switzerland. A dust concentration profile calcu-
lated using this model for the EARLINET station of Payerne
(Fig. 12a) showed higher concentration of mineral dust in
the lower troposphere with a profile very similar to what was
observed with our Raman lidar (Fig. 12b). Back-trajectory
analysis from the HYSPLIT model (not shown here) indi-
cated that the observed air masses had their origin in northern
Africa. Remote and in situ measurements carried out at our
station also confirmed features typical of this kind of particle.
The AOD Angstrom exponent obtained from the PFR sun-
photometer measurements presented low values (between 0.5
and 0.6) throughout the morning, indicating the presence of
coarse particles in the atmospheric column. In situ measure-
ments also showed a strong increase in the PM10 concentra-
tion at the surface during this day, with values ranging from
14 µg m−3at 03:00 UTC to 37.4 µg m−3at 15:00 UTC. The
annual mean PM10 concentration in 2017 was 12 µg m−3,
which is much lower than the values observed during this
event.
Once the aerosol was well identified using models and
measurements, we analyzed the vertical profiles obtained
from the lidar (Fig. 13a–c). We observed a simultaneous in-
crease in βaer from the lidar systems with RH at the alti-
tude range between 1.9 and 2.3 km (a.s.l.). For that range,
rand θshowed quite constant values, evidence of a well-
mixed layer. The dependence of fβat 355 and 1064nm with
RH is shown in the resultant humidogram in Fig. 13d. Al-
though there was an intensification of the backscatter in both
wavelengths (f355
β(84%)=1.2 and f1064
β(84%)=1.1 with
RHref =68%), it was much lower compared to Case I. The
hygroscopic parameter obtained from the Hänel parametriza-
tion confirmed this behavior, with values of 0.20 ±0.18 and
0.12 ±0.19 at 355 and 1064 nm, respectively. These values
are similar to the ones observed by Lv et al. (2017) also under
the presence of mineral dust (γ355 =0.12 and γ532 =0.24).
As in Case I, we found the opposite spectral dependency
compared to Lv et al. (2017). However, we considered a
wider spectral range.
6 Evaluation of the effect of aerosol hygroscopicity on
the Earth’s radiative balance
Because of the changes in aerosol optical and microphysical
properties due to water uptake, aerosol radiative properties
are modified in the case of hygroscopic growth. As stated be-
fore, aerosol backscatter and extinction coefficients increase
under high RH conditions, which in turn leads to an increase
in the AOD. To compute the AOD, the βaer profiles at 355 nm
were converted to extinction using a generic lidar ratio of
50 sr. Although the choice of LR could affect the AOD, the
relative contribution due to aerosol hygroscopicity would re-
main almost constant. In order to calculate 1AOD, the in-
crement of AOD due to hygroscopic growth, we obtain a so-
called “dry” aerosol extinction profile by using the Hänel pa-
rameterization and assuming that RH in the analyzed layer is
equal to RHref for each case. The “dry” profiles obtained are
included in Fig. 14. AODs for the dry and wet cases, as well
as 1AOD, are summarized in Table 1.
For the two cases analyzed here, the increase in AOD at
355 nm related to the hygroscopic growth in the analyzed lay-
ers is 1AOD =0.017 (with 1AOD =AOD −AODdry) for
Case I and 1AOD =0.001 for Case II. In relative terms, this
results in an increase in the total AOD of 4.7% (15.6 % if
we consider only AOD in the ABL where the hygroscopic
layer is located) due to hygroscopic growth for Case I and
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F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements 11663
Figure 13. Lidar vertical profiles of (a) βaer at 355 and 1064nm, (b) RH and (c) θand robtained between 01:00 and 01:30 on 8 July 2017.
(d) fβat 355 and 1064 nm retrieved for those profiles.
Figure 14. Aerosol extinction profiles at 355 and 1064 nm considering wet (ambient) and dry conditions (in the layer with aerosol hygroscop-
icity, colored dashed lines) for Case I (a) and Case II (b). Horizontal dashed black lines indicate the layer for each case in which hygroscopic
growth occurred.
0.6 % for Case II. As expected, 1AOD is much larger in Case
I since smoke particles are more hygroscopic (γ355 =0.48)
than mineral dust (γ355 =0.20). It is worth noting the sig-
nificant effect of the hygroscopic growth on the ABL AOD,
which increases by nearly 16 %.
Simulations with a radiative transfer model can give us an
estimate of the impact that this change in AOD has on the
aerosol radiative effect (ARE). In this case, we use GAME
(Global Atmospheric ModEl, Dubuisson et al., 1996, 2005).
GAME is a modular radiative transfer model that allows cal-
culation of upward and downward radiative fluxes at differ-
ent vertical levels with high resolution by using the discrete
ordinates method (Stamnes et al., 1988). Details about the
model parameterization can be found in Sicard et al. (2014)
and Granados-Muñoz et al. (2019). In our case, the varia-
tions in the ARE (1ARE) in the shortwave spectral range
are exclusively related to the variations in AOD and the AE
due to hygroscopic growth, whereas the other aerosol prop-
erties are assumed to remain constant. The calculation was
done for a solar zenith of 30◦corresponding to Case I, which
presented stronger hygroscopicity and occurred at daytime
(15:00 UTC). The changes in AOD produce a net increase
(in absolute and relative values) in the aerosol radiative ef-
fect at the surface with respect to the “dry” profiles equal to
2.4 W m−2(5.2 %) and 0.1 W m−2(0.4 %) in Case I and Case
II, respectively. The interpretation of these results needs to be
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11664 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
Table 1. Column AOD and ARE for both dry and wet conditions and their difference. All relative values are given with respect to the dry
conditions.
Layer ARE AREdry 1ARE
γ355 depth (m) AOD355 AODdry
355 1AOD355 (W m−2) (W m−2) (W m−2)
Case I 0.48 600 0.379 0.362 0.017 (4.7 %) −48.8 −46.4 −2.4 (5.2 %)
(smoke) 0.126∗0.109∗0.017∗(15.6 %)∗
Case II 0.20 400 0.157 0.156 0.001 (0.6 %) −27.3 −27.2 −0.1 (0.4 %)
(dust)
∗Indicates values within the ABL (between the surface and 2.5km).
done carefully. Even though radiative transfer models do not
provide an estimation of the ARE uncertainties, a sensitivity
test performed in Granados-Muñoz et al. (2019) showed that
an uncertainty in the AOD of 0.05 can lead to uncertainties
in the ARE of up to 30 %. The values of 1ARE obtained
here are certainly within these uncertainty limits, and an ac-
curate quantitative estimation of the hygroscopicity contribu-
tion to the ARE is quite complex. However, it is necessary to
highlight that our focus here is on the relative contribution
of hygroscopic aerosol to the ARE when compared to dry
conditions. As expected, the effect observed in our simula-
tions is more noticeable in the case of particles with stronger
hygroscopic properties, such as the smoke mixture in Case I.
We also note that the relative increases in AOD (4.7 %)
and ARE (5.2 %) are similar. For the mineral dust event the
effect is almost negligible (1ARE =0.4%). Variations of the
ARE observed in previous studies can reach up to 7W m−2
(Stock et al., 2011); however, a comparison with our data is
not straightforward since these variations are highly depen-
dent on the aerosol load and the aerosol type present in the at-
mosphere. In Case I, although the hygroscopic growth affects
only a thin layer (only 600 m width) and the γvalues are
relatively low (0.48), the aerosol hygroscopic growth effect
on the ARE is still quite noticeable. These results show that
in more favorable conditions, namely thicker layers where
the hygroscopic effect occurs and particles with stronger hy-
groscopic properties, the aerosol hygroscopic effect on the
optical and radiative properties could be quite considerable.
Therefore, we can conclude that including aerosol hygro-
scopic properties in climate model calculations is key for im-
proving the accuracy of aerosol forcing estimates.
7 Conclusions
The present study demonstrates the capability of a Raman
lidar to detect aerosol hygroscopic processes. Continuous
measurements of water vapor, Tand aerosol profiles have
been performed by RALMO lidar almost continuously since
2008 at the aerological station of MeteoSwiss in Payerne
(Switzerland). These measurements allow us to monitor any
change in aerosol properties that could occur as a result of
water uptake by particles under high RH (aerosol hygro-
scopic growth). To ensure that the changes in aerosol are only
due to hygroscopic growth, several criteria were established.
As a first condition an increase in βaer should occur simul-
taneously with an increase in RH. In addition, a high degree
of homogeneity is required in the investigated layer. For that,
back-trajectory analysis is used to verify that the origin of
the air mass is independent of the altitude and low-varying or
constant values of rand θare required as a proxy for well-
mixed conditions throughout the aerosol layer.
The accuracy and the precision of RALMO Tand RH pro-
files were assessed using collocated RS profiles. A total of
172 profiles were used in this intercomparison during day-
time and nighttime for the period from July to December
of 2017. The mean Tdeviations calculated from nighttime
(daytime) measurements revealed almost no bias between
both techniques in the whole troposphere, with mean Tde-
viations of 0.05±0.06 K (−0.5±0.2 K) in the first 5 km and
0.15 ±0.15 K (−0.1±0.6 K) above that altitude. The stan-
dard deviations also confirmed the excellent performance of
RALMO, with values below 1K throughout the troposphere
during nighttime and slightly larger values during daytime
(0.8±0.2 K from ground to 5 km and 2.4±0.8 K from 5 to
10 km).
Lidar profiles of RH were obtained by combining rand
Tmeasurements. Small RH biases were observed between
both techniques during nighttime in the troposphere (from
ground to 9 km a.s.l.), with values ranging between 3% RH
at 1.4 km (a.s.l.) and −9 %RH at 5.6 km (a.s.l.). The standard
deviation of RH deviations also showed the good precision
of the lidar measurements, with values always lower than
9 % RH. The performance of RALMO in terms of RH dur-
ing daytime in the lower troposphere (from ground to 5km)
was very similar to the ones obtained during nighttime. How-
ever, above 5 km the errors were much larger due to the solar
background radiation, and RH profiles were not calculated to
avoid these large uncertainties. The good quality of the RH
measurements found in this intercomparison is a key aspect
to be able to address the aerosol hygroscopic studies.
The methodology presented here was applied to two case
studies. In situ and satellite measurements in addition to
models indicate that Case I (7 September 2017) was char-
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F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements 11665
acterized by a mixture of local aerosol and smoke parti-
cles from fires in North America. fβwas found to be 2.8 at
355 nm and 1.8 at 1064nm when RH increased from 73% to
97 % in the investigated layer. The Hänel hygroscopic param-
eter which is proportional to the aerosol hygroscopicity took
values of 0.48 ±0.08 at 355 nm and 0.29 ±0.08 at 1064 nm
in this case. Independent vertical profiles obtained for a pre-
vious time interval showed the consistency of our results.
Other remote sensing studies have shown a larger sensitivity
to hygroscopic growth for longer wavelengths for this type
of particle, in contrast to our results. However, those studies
were carried out in a shorter wavelength range (355–532 nm).
Haarig et al. (2017) also observed a higher hygroscopicity
at 1064 nm than at 355 nm but for marine particles which
are larger and much more hygroscopic than in our case. Mie
simulations carried out in this study revealed that the spectral
dependency of fβcan change strongly depending on the par-
ticle size and the wavelength of the incident radiation, sup-
porting our results as well as the study of Haarig et al. (2017).
Continuous aerosol and RH measurements from RALMO
also allowed monitoring of aerosol hygroscopic processes as
a function of time for this first case. The evolution of βand
RH at 1.3 km (a.s.l.) on 7 September 2017 showed two pe-
riods in which there was a simultaneous decrease in both
parameters (dehydration process) followed by a simultane-
ous increase (hydration process). The Hänel hygroscopic pa-
rameters calculated from the humidogram (fβvs. RH) for
both periods took values of 0.54 ±0.17 (0.43 ±0.16) and
0.40 ±0.08 (0.34 ±0.09) at 355 nm (1064 nm) for the hy-
dration and dehydration processes, respectively, which are in
good agreement with the results obtained in the spatial (ver-
tical) analysis.
The aerosol hygroscopicity of a second case (8 July 2017),
characterized by the presence of dust particles, was also an-
alyzed in this study. The spatial analysis of the lidar mea-
surements also revealed hygroscopic growth for these parti-
cles but with a very different behavior. A much lower hy-
groscopicity than for the previous case (smoke mixture) was
observed with βincreasing only 1.2 and 1.1 times at 355
and 1064 nm, respectively, when RH increased from 68% to
84 %. The hygroscopic parameters obtained from the humi-
dogram were 0.20±0.18 and 0.12±0.19 at 355 and 1064 nm,
respectively, showing a good agreement with values observed
in other studies for mineral dust. The lower spectral sensitiv-
ity to the aerosol hygroscopicity found for this type of parti-
cle was also remarkable.
Finally, the impact of aerosol hygroscopicity on the
Earth’s radiative balance was evaluated for the two presented
cases using a radiative transfer model (GAME). The aerosol
hygroscopic growth in the investigated layers produced an
increase in AOD at 355nm of 0.017 (4.7 %) for the case with
presence of smoke particles and 0.001 (0.6 %) for the case
with mineral dust. These changes in AOD produced a net in-
crease in absolute (and relative) values of the radiative effect
at the surface of 2.4 Wm−2(5.2 %) and 0.1 W m−2(0.4 %) in
Case I and Case II, respectively. The results were significant
for the case with presence of smoke particles (more hygro-
scopic) despite the aerosol load of the investigated layer not
having been very high. Therefore, we conclude that the effect
of aerosol hygroscopicity on optical and radiative properties
is important and has to be considered in climate model cal-
culations to improve aerosol forcing estimates.
In future work we want to exploit the large dataset
(10 years) of simultaneous aerosol and RH profiles from this
Raman lidar to carry out a statistical analysis of aerosol hy-
groscopic properties.
Data availability. Data used in this paper are avail-
able upon request from the corresponding author (fran-
cisco.navas@meteoswiss.ch).
Author contributions. FNG designed the experiment, analyzed the
data and wrote the manuscript, GM worked on the Tretrievals, MH
and MCC performed the Mie simulations, AH is responsible for the
lidar measurements and participated in the numerous scientific dis-
cussions, and MJGR and MS performed the calculations with the
radiative transfer model (GAME). All the authors provided com-
ments on the manuscript.
Competing interests. The authors declare that they have no conflict
of interest.
Special issue statement. This article is part of the special issue
“EARLINET aerosol profiling: contributions to atmospheric and
climate research”. It is not associated with a conference.
Acknowledgements. We thank the EMPA and the Swiss Federal Of-
fice for the Environment (FOEN) for providing the data of the in
situ measurements carried out at Payerne within the Nabel moni-
toring program. We also acknowledge the financial support by the
European Union’s Horizon 2020 research and innovation program
through project ACTRIS-2 (grant agreement no. 654109). The ra-
diative transfer simulations performed with GAME are supported
by the European Union GRASP-ACE MSCA-RISE Action (grant
agreement no. 778349); the Spanish Ministry of Economy and
Competitiveness (ref. TEC2015-63832-P) and EFRD (European
Fund for Regional Development); the Spanish Ministry of Science,
Innovation and Universities (ref. CGL2017-90884-REDT); and the
Unity of Excellence Maria de Maeztu (ref. MDM-2016-0600) fi-
nanced by the Spanish Agencia Estatal de Investigación. This work
was also supported by the Juan de la Cierva-Formación program
(grant FJCI-2015-23904). The authors also gratefully acknowledge
Philippe Dubuisson (Laboratoire d’Optique Atmosphérique, Uni-
versité de Lille, France) for the use of the GAME model.
www.atmos-chem-phys.net/19/11651/2019/ Atmos. Chem. Phys., 19, 11651–11668, 2019
11666 F. Navas-Guzmán et al.: Aerosol hygroscopicity by Raman lidar measurements
Financial support. This work has been supported by the Swiss Na-
tional Science Foundation (project no. PZ00P2 168114).
Review statement. This paper was edited by Eduardo Landulfo and
reviewed by two anonymous referees.
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