This study focuses on the analysis of aerosol hygroscopic growth during the
Sierra Nevada Lidar AerOsol Profiling Experiment (SLOPE I) campaign by using
the synergy of active and passive remote sensors at the ACTRIS Granada
station and in situ instrumentation at a mountain station (Sierra Nevada,
SNS). To this end, a methodology based on simultaneous measurements of
aerosol profiles from an EARLINET multi-wavelength Raman lidar (RL) and
relative humidity (RH) profiles obtained from a multi-instrumental approach
is used. This approach is based on the combination of calibrated water vapor
mixing ratio (r) profiles from RL and continuous temperature profiles from
a microwave radiometer (MWR) for obtaining RH profiles with a reasonable
vertical and temporal resolution. This methodology is validated against the
traditional one that uses RH from co-located radiosounding (RS) measurements,
obtaining differences in the hygroscopic growth parameter (γ) lower
than 5 % between the methodology based on RS and the one presented here.
Additionally, during the SLOPE I campaign the remote sensing methodology used
for aerosol hygroscopic growth studies has been checked against Mie
calculations of aerosol hygroscopic growth using in situ measurements of
particle number size distribution and submicron chemical composition measured
at SNS. The hygroscopic case observed during SLOPE I showed an increase in
the particle backscatter coefficient at 355 and 532 nm with relative
humidity (RH ranged between 78 and 98 %), but also a decrease in the
backscatter-related Ångström exponent (AE) and particle linear
depolarization ratio (PLDR), indicating that the particles became larger and
more spherical due to hygroscopic processes. Vertical and horizontal wind
analysis is performed by means of a co-located Doppler lidar system, in order
to evaluate the horizontal and vertical dynamics of the air masses. Finally,
the Hänel parameterization is applied to experimental data for both
stations, and we found good agreement on γ measured with remote
sensing (γ532=0.48±0.01 and γ355=0.40±0.01) with respect to the values calculated using Mie theory
(γ532=0.53±0.02 and γ355=0.45±0.02),
with relative differences between measurements and simulations lower than
9 % at 532 nm and 11 % at 355 nm.
Introduction
Atmospheric aerosol particles play a crucial role in the
Earth's climate, principally by means of the radiative effect due to
aerosol–radiation and aerosol–cloud interactions, affecting the
Earth–atmosphere energy balance and, hence, the Earth's climate.
Furthermore, aerosol might also modify optical and microphysical cloud
properties, such as albedo and cloud droplet size distribution, that
influence cloud lifetime, since the particles could act as cloud condensation
nuclei (CCN) and ice nuclei (IN) (Twomey, 1977; Albrecht, 1989; Boucher et al., 2013).
Water vapor plays a major role in the aerosol–radiation interaction due to
the ability of some atmospheric aerosol particles to take up water from the
environment. In this sense, hygroscopic growth is the process by which
aerosol particles uptake water and increase their size under high relative
humidity (RH) conditions (Hänel, 1976). Consequently, this process is
also related to changes in the optical and microphysical properties of the
aerosol particles and, hence, it becomes a crucial factor that modifies the
role of aerosols in atmospheric processes and radiative forcing.
Several studies have been carried out over the past years in order to
evaluate how water uptake affects aerosol properties. One parameter used to
quantify these changes is the so-called aerosol hygroscopic enhancement
factor: f (λ, RH), where λ is the wavelength, defined as
the ratio between aerosol optical/microphysical properties at wet atmospheric
conditions and the corresponding reference value at dry conditions
(Hänel, 1976; Ferrare et al., 1998; Feingold et al., 2003; Veselovskii et
al., 2009; Granados-Muñoz et al., 2015; Titos et al., 2014, 2016, and
references therein). Most of the previous studies investigating aerosol
hygroscopicity are based on in situ measurements. One of the most commonly
used in situ instruments for measuring aerosol hygroscopicity is the
Humidified Tandem Differential Mobility Analyzer (HTDMA) (e.g., Swietlicki et
al., 2008) that measures the hygroscopic growth factor, g (RH), that
quantifies the change in particle diameter due to water uptake. Humidified
tandem nephelometers have been extensively used as well to quantify the
effect of the hygroscopic growth in the aerosol optical properties like
scattering, backscattering and extinction coefficients (e.g., Pilat and
Charlson, 1966; Titos et al., 2016). There are other in situ instruments such
as the white-light humidified optical particle spectrometer (WHOPS) (Rosatti
et al., 2015) or the Differential Aerosol Sizing and Hygroscopicity Spectrometer
Probe (DASH-SP) (Sorooshian et al., 2008) that have been used to determine the
impact of enhanced RH on the aerosol properties from airborne platforms.
The effect of RH on the aerosol optical properties can be also determined
with Mie model calculations (e.g., Adam et al., 2012; Fierz-Schmidhauser et
al., 2010; Zieger et al., 2013) using the measured size distribution and
chemical composition as inputs. For this calculation, information on g (RH)
is needed a priori. This factor can be determined experimentally (using HTDMA
measurements for example) or it can be inferred from the individual growth
factors of the different chemical compounds. The assumption of some aerosol
properties such as the refractive index or the growth factor based on the
chemical composition is the main drawback of this method.
In general terms, 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.
Remote sensing systems such as lidars have also been used in recent decades
for aerosol hygroscopic growth studies performed with co-located
radiosounding (RS) measurements (e.g., Ferrare et al., 1998; Feingold et al.,
2003; Veselovskii et al., 2009; Granados-Muñoz et al., 2015;
Fernández et al., 2015; Lv et al., 2017). These systems have been shown
to be robust, with high vertical and temporal resolutions that allow for
study of the aerosol hygroscopic growth under unmodified ambient conditions.
Recent studies presented by Zieger et al. (2011) and Rosati et al. (2016)
show good agreement between in situ and RL extinction coefficients after
taking into account the RH effect on the in situ measured extinction
coefficient. Also, it is possible to use aerosol extinction coefficient to
compare with in situ airborne measures and elastic lidar to study hygroscopic
growth in unmodified ambient conditions. In addition, good results were
obtained by using automatic lidar and ceilometers (ALCs) to investigate
hygroscopic growth and fog formation, mostly for fog event forecasting
purposes (Haeffelin et al., 2016).
Up to now, most hygroscopic growth studies using lidar systems have combined
lidar measurements with RH data from RS (Granados-Muñoz et al., 2015).
The main inconveniences are that RS measurements have low temporal sampling
and they could be drifted away from the vertical atmosphere probed by the
lidar systems. These inconveniences can be easily overcome by combining
calibrated water vapor mixing ratio profiles, r (z) from Raman lidar
(RL), with temperature profiles from ancillary instrumentation for obtaining
collocated RH and aerosol backscatter profiles, using them simultaneously for
hygroscopic growth studies (e.g., Whiteman, 2003; Navas-Guzmán et al.,
2014; Barrera-Verdejo et al., 2016). Navas-Guzmán et al. (2014) proposed
a methodology for retrieving RH profiles by the combination of calibrated
r (z) profiles from a Raman lidar water vapor channel with temperature
profiles obtained from microwave radiometer (MWR) measurements. RH profiles
obtained using this multi-instrumental approach and aerosol profiles from the
lidar are used in this work to study aerosol hygroscopic growth. This
methodology allows us to obtain a larger database of potential hygroscopic
cases since some of the limitations associated with RS are overcome.
Additionally, water vapor and aerosol measurements are performed with the
same system and, thus, the same air volume is probed, avoiding the possible
radiosonde drift and temporal sampling mismatch.
The main goal of this study is to apply the methodology proposed by
Navas-Guzmán et al. (2014), based on the application of the synergy
between RL and MWR, for aerosol hygroscopic growth studies. First, this
methodology for hygroscopic growth studies is compared with the approach
presented in Granados-Muñoz et al. (2015) that uses RS and lidar data.
Once the technique is evaluated, an analysis of the aerosol hygroscopic
growth case observed during the SLOPE I (Sierra Nevada Lidar AerOsol
Profiling Experiment I) campaign is presented. In addition, the results
obtained with the remote sensing data are compared with Mie simulations
performed using in situ measurements from a high-mountain station located at
2500 m a.s.l.
This paper is organized as follows. The description of the experimental site
and instrumentation is presented in Sect. 2. The applied methodology is
introduced in Sect. 3. Section 4 presents the results and discussion of the
combination of RL and MWR measurements for obtaining RH profiles and the
analysis of the aerosol hygroscopic cases based on the remote sensing and in
situ measurements. Finally, conclusions are given in Sect. 5.
Experimental site and instrumentationSLOPE I field campaign
In summer 2016, the Sierra Nevada Lidar AerOsol Profiling Experiment (SLOPE
I) intensive field campaign was carried out in southeastern Spain in the
framework of the ACTRIS European infrastructure. The goal of this campaign
was to perform a closure study by comparing remote sensing and in situ
measurements at different altitudes, taking advantage of a unique
experimental setup (Román et al., 2018). This setup consisted of several
experimental stations located at different altitude levels on the slope of
the Sierra Nevada, located 20 km away in horizontal distance from the remote
sensors at IISTA-CEAMA station (urban station at Granada). In the present
study, we only make use of the data from the in situ instrumentation of the
mountain Sierra Nevada station (SNS) located at 2500 m a.s.l., SNS in Fig.
1. Combined active and passive remote sensing measurements using multiple
instrumentation at the Andalusian Institute of Earth System Research
(IISTA-CEAMA) station and simultaneous in situ measurements at
2500 m a.s.l. on the northern slope of the Sierra Nevada were performed
from May to September 2016 during this campaign. In addition, 25 RS were
launched during this period, 6 of them during nighttime, in order to perform
regular calibration of the Raman lidar water vapor channel.
IISTA-CEAMA station
One of the stations where this study has been carried out is IISTA-CEAMA, an
urban station managed by the University of Granada (UGR) located at Granada,
Spain (37.16∘ N, 3.61∘ W, 680 m a.s.l.). This region is
characterized by its complex terrain surrounded by mountains, mainly affected
by Mediterranean continental climate conditions with hot summers and cool
winters. Navas-Guzmán et al. (2014) analyzed 1 year of measurements of RH
profiles at Granada, showing that this location presents low values of RH
(below 60 %) in 75 % of the cases studied for altitudes between 1.0
and 2.0 km a.s.l. RH values above 60 % are mostly found in the spring
and winter seasons. Regarding the remote aerosol sources, Granada is
predominantly affected by aerosol particles coming from Europe and mineral
dust particles from the African continent (Lyamani et al., 2006a, b, 2010, 2012;
Guerrero-Rascado et al., 2008a, 2009, 2011; Córdoba-Jabonero et al., 2011;
Titos et al., 2012; Navas-Guzmán et al., 2013; Valenzuela et al., 2014; Granados-Muñoz et al., 2016; Benavent-Oltra et al., 2017; Cazorla et al., 2017). The main local sources
are road traffic, domestic heating (during wintertime), and biomass burning
(Titos et al., 2017). Transported smoke principally from North America,
northern Africa, and the Iberian Peninsula can also affect the study area
(Alados-Arboledas et al., 2011; Navas-Guzmán et al., 2013; Preißler
et al., 2013; Pereira et al., 2014; Ortiz-Amezcua et al., 2017). Moreover,
the probability of marine particles reaching the city is low despite the
short distance to the coast (about 50 km away) due to the orography of the
region, with mountains blocking the path from the sea to the city.
Additionally, Titos et al. (2014) showed that the contribution of marine
aerosols to PM10 mass concentration at IISTA-CEAMA station is
almost negligible (<3%).
Topographic profile of the Granada and Sierra Nevada area. The
yellow star refers to IISTA-CEAMA station and the green star refers to SNS in
situ station.
The main instrument used in this study and located at IISTA-CEAMA station is
the multi-wavelength Raman lidar (RL) MULHACEN (Raymetrics S. A., Greece).
MULHACEN is included in EARLINET (European Aerosol Lidar NETwork) (Pappalardo
et al., 2014), now operating in the framework of ACTRIS-2 (Aerosols, Clouds
and Trace gases Research Infrastructure), and also in SPALINET (Spanish and Portuguese Aerosol Lidar Network) (Sicard et al., 2009). It emits laser pulses at 355 and
532 nm (parallel and perpendicular polarization channels) and 1064 nm, and
it receives backscattered photons at 355, 532, and 1064 nm in analog and
photon counting modes. It also collects Raman backscattered photons at 607
and 387 nm from molecular nitrogen (N2) and at 408 nm from water
vapor (H2O) in photon counting mode during routine nighttime
measurements. Such kinds of configurations allow for derivation of not only
vertically resolved particle information, but also water vapor mixing ratio
profiles. The vertical resolution for lidar backscattered signals is 7.5 m.
Atmospheric information retrieved from lower regions is limited by the full
overlap height, which is reached above 1.3 km a.s.l. due to the system
configuration (Guerrero-Rascado et al., 2010; Navas-Guzmán et al., 2011).
A full description of this instrument can be found in Guerrero-Rascado et
al. (2008a, 2009). Aerosol particle backscatter coefficient profiles
(βpar(z)) are retrieved by the Klett–Fernald method
(Fernald, 1984; Klett, 1981, 1985). The total uncertainty for
βpar retrieved with this method is usually within
20 % (e.g., Franke et al., 2001; Preißler et al., 2011).
The ground-based MWR (RPG-HATPRO G2, Radiometer physics GmbH), which is also
located at IISTA-CEAMA station and belongs to MWRnet (Rose et al., 2005;
Caumont et al., 2016), is used here for retrieving temperature profiles. The
MWR is a passive remote sensor that performs automatic measurements of sky
brightness temperature at two bands: the oxygen V band (51–58 GHz) and
water vapor K band (22–31 GHz) associated with temperature and water vapor
and liquid water, respectively. The MWR has a radiometric resolution between
0.3 and 0.4 rms errors at 1.0 s integration time. The retrievals of
temperature profiles from the measured brightness temperatures are performed
using a standard feed forward neural network (Rose et al., 2005). A detailed
description of this system can be found in Granados-Muñoz et
al. (2012) and Navas-Guzmán et al. (2014). Temperature data are
provided at 39 height bins, with variable vertical resolution. The first
25 bins are located below 2 km (mainly within the atmospheric boundary
layer, ABL) with a resolution ranging between 10 and 200 m, whereas the
vertical resolution is much lower in the free troposphere (between 200 and
2000 m), with only 14 bins between 2 and 10 km. The accuracy and precision
of the temperature profiles of this radiometer were evaluated against RS by
Bedoya et al. (2017). This study revealed differences between RS and the MWR
temperature profiles lower than 0.5 K below 2.5 km and up to 1.7 K at
higher altitude levels. Those results are within the accuracy of the
temperature profile reported by the manufacturer, which is lower than 0.75 K
RMSE (1.2–4.0 km range) and
larger than 1.0 K RMSE from 4 to 10 km.
Co-located RS is occasionally launched when Raman lidar measurements are
taken. The RS data are obtained with a GRAW DFM-06/09 system (GRAW
Radiosondes, Germany), which provides temperature (resolution
0.01∘ C, accuracy 0.2 ∘C), pressure (resolution 0.1 hPa,
accuracy 0.5 hPa), and RH (resolution 1 %, accuracy 2 %) profiles
with vertical resolution depending on the sonde ascension velocity, usually
around 5 m s-1. Data acquisition and processing are performed by the
GRAWmet software and GS-E ground station from the same manufacturer.
A co-located Doppler lidar system (HALO photonics Stream Line) has also been
operated at IISTA-CEAMA station since May 2016. This system provides
range-resolved measurements of attenuated backscatter based on the frequency
shift associated with the movement of the particles and clouds in the
atmosphere by means of the heterodyne optical detection principle (Pearson et
al., 2008). As this movement is linked with wind, the 3-D wind vector can be
determined through the Doppler effect. Radial velocity measurements are taken
every 2 s, and conical scans are performed every 10 min with a 75∘
elevation angle and at 12 equidistant azimuth angles. The eye-safe laser
transmitter vertically pointing to zenith operates at 1.5 mum, with
low pulse energy (∼100µJ) and a high pulse repetition rate
(∼15 kHz) on a monostatic coaxial setup. See Päschke et al. (2015)
for further information of the system configuration.
Sierra Nevada station
At SNS (37.09∘ N, 3.38∘ W; 2500 m a.s.l.),
state-of-the-art in situ instrumentation was operated to characterize aerosol
properties. The inlet at SNS is a whole air inlet located on the rooftop of a
three-story building. It is made up of stainless-steel pipes, with dimensions
of 10 cm in diameter and 2.5 m in length. Inside the main pipe there is a
laminar flow of 100 Lpm and there are several stainless-steel pipes that
drive the sampling air to the different instruments. Each one of the
stainless-steel pipes extracts the appropriate flow for each instrument.
Different diameters of the pipes have been selected in order to optimize the
efficiency of the system (Baron and Willeke, 2001). The instrumentation used
in this study includes an Aerodyne Aerosol Chemical Speciation Monitor (ACSM,
Aerodyne Research Inc.), an Aethalometer (AE33 model, Magee Scientific,
Aerosol d.o.o.), an Aerodynamic Particle Sizer (APS, TSI 3321) spectrometer,
and a Scanning Mobility Particle Sizer (SMPS, TSI 3938) spectrometer, all of
them connected to the main inlet. The ACSM was used to measure online
submicron inorganic (nitrate, sulfate, and ammonium) and organic aerosol (OA)
concentrations. Equivalent black carbon, eBC, mass concentration was obtained
from measurements of Aethalometer AE33 at 880 nm. A mass absorption cross
section of 7.77 m2g-1 was used to convert the absorption
coefficients at 880 nm in eBC mass concentrations (Drinovec et al., 2015).
Particle number size distributions were retrieved by a combination of the
measurements performed with the SMPS in the diameter range 13–600 nm and
the APS for the range 0.6–20 µm.
MethodologyRH profiles by synergy of RL and MWR data
As mentioned in Sect. 2, some RL systems can provide simultaneous aerosol and
water vapor profiles with high vertical and temporal resolution. The water
vapor mixing ratio r (z) can be obtained from the ratio of Raman lidar
signals of water vapor (408 nm) and nitrogen (387 nm) multiplied by a
constant C that takes into account the fractional volume of nitrogen, the
ratio between molecular masses, some range-independent constants, and the
Raman backscatter cross sections for nitrogen and water vapor molecules
(Mattis et al., 2002). In the present study, the calibration constant C has
been calculated using the simultaneous and collocated radiosondes launched at
the EARLINET IISTA-CEAMA station during the analyzed periods. C is obtained
as the average value of the ratio between the uncalibrated RL r (z)
profile and the r (z) profile from RS over a height range that presents a
high good signal-to-noise ratio (Guerrero-Rascado et al., 2008b; Leblanc et
al., 2012; Navas-Guzmán et al., 2014; Foth et al., 2015). C remains
constant over periods when the lidar setup is not modified and the system
presents good alignment, allowing us to retrieve r (z) profiles from the
RL even when RS measurements are not available. If several RS launches are
available during a certain period, C is obtained as the average between all
calibrations performed over that particular period.
Temperature profiles from the MWR are continuously measured every 2 min.
These profiles are averaged over 30 min in order to match the temporal
sampling of the averaged r (z) profiles, as proposed by Navas-Guzmán
et al. (2014); 30 min averaged temperature and r (z) profiles are then
used to retrieve the RH profiles required for aerosol hygroscopic growth
studies. The following equation is used to calculate the RH profiles:
RH(z)=100P(z)r(z)ew(z)[621,97+r(z)],
where r(z) is obtained from the calibrated water vapor channel, P(z)
(hPa) is the ground-scaled pressure profile, and ew(z) is
the water vapor pressure (hPa), calculated from the temperature profiles
(List, 1951). Temperature profiles were scaled to lidar vertical resolution
by linear interpolation.
Selection criteria for hygroscopic cases
A simultaneous increase in aerosol properties, such as particle backscatter
(βpar) or extinction (αpar)
coefficients, and RH values over a certain atmospheric layer might be an
indication of aerosol hygroscopic growth. Moreover, a decreasing
Ångström exponent (AE) and particle linear depolarization ratio
(PLDR) are related to larger and more spherical particles, which also points
to aerosol water uptake (Granados-Muñoz et al., 2015; Fernández et
al., 2015; Haarig et al., 2017). However, additional constraints need to be
fulfilled when studying the aerosol hygroscopic growth in the atmosphere by
remote sensing techniques due to the lack of control over the environmental
conditions, as opposed to in situ measurements. These constraints are used
for guaranteeing those variations in the aerosol properties are due to water
uptake and not to changes in the aerosol load or type.
The first constraint that needs to be satisfied is that the origin and
pathways of the air masses arriving at different altitudes within the
analyzed layer must be the same in order to avoid transport of different
aerosol types from different source regions (Veselovskii et al., 2009;
Granados-Muñoz et al., 2015). The evaluation of the aerosol origin and
transport is performed here through backward trajectory analysis using the
HYSPLIT model (Hybrid Single-Particle Lagrangian Integrated Trajectory)
(Draxler and Rolph, 2003) with GDAS data as meteorological input. GDAS data
have a horizontal spatial resolution of 0.5∘× 0.5∘
and a temporal resolution of 3 h. As a second constraint, atmospheric
vertical homogeneity must be ensured. In order to evaluate the atmospheric
vertical mixing, virtual potential temperature (θv(z)) and
r (z) profiles are analyzed. The low vertical variability of those
variables suggests atmospheric vertical homogeneity in the layer of study
(Veselovskii et al., 2009; Fernández et al., 2015; Granados-Muñoz et
al., 2015; Lv et al., 2017). In addition, horizontal and vertical wind
velocities and directions retrieved from the lidar Doppler system operated at
the Granada station were also considered. Low horizontal wind velocity
measured at different altitude levels is used as an indicator of no particle
advection into the layer analyzed, taking into account that wind direction
must be constant during long time periods (longer than 3 h). The third
moment of the frequency distribution of vertical wind velocities (skewness)
has also been calculated in order to evaluate convection of air masses within
the column studied, keeping in mind that positive values of skewness
represent upward wind velocity and negative values the opposite (O'Connor et
al., 2010).
Relative humidity and aerosol properties
Once the requirements described in Sect. 3.2 are fulfilled, the cases of
hygroscopic growth can be studied by means of the enhancement factor
(fξ(λ, RH)), defined as follows:
fξλ(λ,RH)=ξ(λ,RH)ξ(λ,RHref),
where ξ (λ, RH) represents an aerosol optical/microphysical
property evaluated at a certain RH. The value of RHref is
taken from each profile and corresponds to the lowest RH in the evaluated
layer. In this study, the optical property used is βpar
at 355 and 532 nm and, thus, the backscatter enhancement factor is denoted
as fβ(λ,RH). Estimations of fβ(λ,RH) uncertainty are very scarce because of their high complexity.
Some studies (e.g., Adam et al., 2012; Zieger et al., 2013) provided
estimations based on sensitivity analysis using Mie model calculations,
reporting errors around 20 % on fσ(λ,RH),
where σ is the scattering coefficient. Titos et al. (2016) reported
uncertainty estimations based on Monte Carlo techniques, concluding that the
more hygroscopic the aerosol, the higher the uncertainty in fσ(λ,RH), especially at high RH (RH >80 %).
For moderate-hygroscopic aerosol, a lower limit was established for the
uncertainty in fσλ,RH of around 30–40 %
using nephelometry techniques.
In aerosol hygroscopic growth studies, humidograms are usually parameterized
by using fitting equations (e.g., Titos et al., 2016) of varying complexity.
One of the most commonly used parameterizations is the one-parameter equation
introduced by Hänel et al. (1976):
fβλ(RH)=1-RH/1001-RHref/100-γ(λ),
where γ is a parameter related to the aerosol hygroscopicity. This
parameter depends on the aerosol type and wavelength.
Mie model to calculate the enhancement factor at SNS
In order to validate the results obtained with the remote sensors for
fβλ(RH) and γ(λ), theoretical
calculations based on Mie theory (Mie, 1908) have been performed using data
from SNS in situ instrumentation as input for the Mie model. The particle
backscatter coefficients under dry and humid conditions have been calculated
with a model based on Mie theory where the core Mie routine is based on the
code of Bohren and Huffmann (2004). The particles are assumed to be spherical
and homogenously internally mixed. For this analysis, the particle number
size distribution and the complex refractive index (m) of the measured
aerosol are needed as input. We calculated the aerosol complex refractive
index using the chemical composition measured with the ACSM combined with the
black carbon (eBC) mass concentration from the aethalometer. Then, the
refractive index was determined by a volume fraction averaging:
m(λ)=ρ∑Fiρimi(λ),
where ρ is the total density of the aerosol, Fi is the mass
fraction, ρi is the density, and mi(λ) is the
wavelength-dependent complex refractive index of the compound i. The values
of ρi and mi(λ) are taken from the literature and are
listed in Table 1.
Aerosol properties of selected compounds used for the model
predictions, the refractive index (m) at 355 and 532 nm, density (ρ),
and growth factor.
a Hale and Querry (1973); b Nessler et
al. (2005); c Fierz-Schmidhauser et al. (2010);
d Hess et al. (1998); e Ma and Thompson (2012);
f linear interpolation to 355 nm (Kou et al., 1993);
g Nakayama et al. (2010); h Alfarra et al. (2006);
i Rankin (2009); j Riipinen et al. (2015) for
Dp= 100 nm; k Gysel
et al. (2007) for Dp= 60 nm; l BC was assumed to be
insoluble (e.g., Hung et al., 2015).
RH comparison for 22 July 2013 around 20:00–21:00 UTC.
(a) RH profiles retrieved from a combination of lidar + MWR
(black line), lidar + GDAS (blue line), and RS (red line); and
(b) bias calculation between lidar + MWR (red line) and
lidar + GDAS (blue line).
(a, e) Profiles of RH retrieved from RS (black line) and by
the synergy RL + MWR (red line), (b, f) RH bias profiles (cyan
line), (c, g)βpar retrieved by using the
Klett–Fernald algorithm and lidar ratio of 65 Sr (green line), and
(d, f)fβ (RH) calculated for RS (black dots) and by the
synergy RL + MWR (red dots) and the corresponding Hänel
parameterizations (solid lines), where the red line refers to the
RL + MWR method (case I: γ=0.59±0.05, case II: γ=0.95±0.02) and the black line refers to the RS method (case I: γ=0.56±0.01, case II: γ=0.99±0.01). The top row corresponds to
case I (22 July 2011, 20:30–21:00 UTC) and the bottom row to case II
(22 July 2013, 20:00–20:30 UTC). Horizontal dashed lines indicate the
altitude range analyzed for each case (1.3 to 2.3 km for case I and 1.3 to
2.7 km for case II). All these profiles were measured at the EARLINET
IISTA-CEAMA station.
EARLINET IISTA-CEAMA lidar RCS time series at 532 nm, 16 June 2016
(17:00 to 00:00 UTC). The sunset estimated for this day was at 21:30 UTC
local time.
(a) Water vapor mixing ratio; (b) virtual
potential temperature; (c) relative humidity obtained from synergy
RL + MWR; (d) particle backscatter coefficient at 355 and
532 nm; (e) backscatter-related Ångström exponent
(355–532 nm); and (f) particle linear depolarization ratio. All
profiles correspond to a 30 min average from 20:30 to 21:00 UTC on
16 June 2016 at the EARLINET IISTA-CEAMA station.
Hygroscopic growth was also accounted for by considering the aerosol chemical
composition measured with the ACSM and the eBC mass concentrations measured
with the Aethalometer. For this, we used the individual growth factor g(RH)
as reported in Table 1. These g(RH) were extrapolated to different RH using
Eq. (3) from Gysel et al. (2009), which uses the κ-model introduced by
Petters and Kreidenweis (2007). A mean g(RH) is then calculated with the
Zdanovskii–Stokes–Robinson relationship (Stokes and Robinson, 1966) from
the g(RH) of the individual components of the aerosol and their respective
volume fractions. For the wet refractive index, a volume weighting between
the refractive indices of the dry aerosol and water was used (Hale and
Querry, 1973).
Results and discussionCombination of the RL and MWR method for retrieving RH profiles
The synergetic method proposed by Navas-Guzmán et al. (2014) for
retrieving RH profiles is used here for the first time to study aerosol
hygroscopic growth. In this section, two particular cases (case I on
22 July 2011 at 20:00–20:30 UTC and case II on 22 July 2013 at
20:30–21:00 UTC) are analyzed with this new methodology. These two cases
were already presented in Granados-Muñoz et al. (2015) using the
classical approach that combines RH profiles obtained from RS and the lidar
aerosol properties. Results obtained here are compared with those in
Granados-Muñoz et al. (2015) in order to evaluate the synergetic method
proposed here.
In this work, we have also checked the RH calculation (see Eq. 1) for the
case of 22 June 2013 by using temperature profiles from MWR and GDAS
modeled data which were compared to RS RH
profiles. This comparison allows us to investigate the feasibility of the use
of GDAS temperature information to compute the RH profiles in combination
with RL profiles, in order to increase the database for hygroscopicity
studies. However, the results present larger bias when they are compared with
the RS HR profiles, up to 20 % for RHLIDAR+GDAS
in almost the whole profile instead of the 10 % for the
RHLIDAR+MWR (Fig. 2). Thus, the use of GDAS data
seems not to be appropriate in this study, mainly for two reasons: (i) the
complex terrain where the measurement station is located, surrounded by
mountains of high elevation (up to more than 3000 m a.s.l. in a very short
horizontal distance of a few tenths of kilometers) that makes it more
difficult for models to provide accurate thermodynamics profiles for this
location; (ii) GDAS profiles have a lower temporal resolution (3 h) than the
MWR, which gives temperature profiles every 2 min.
Figure 3 shows, from left to right, the RH profiles obtained from both the RS
(black line) and the synergy RL + MWR (red line), the bias between both
profiles (RHRS–RHRL+MWR), and
β532nm profiles retrieved from the lidar system and
fβ(RH). The upper panels correspond to case I on 22 July 2011 and
the bottom panels to case II on 22 July 2013. Horizontal dashed lines mark
the region of interest analyzed for each case, ranging from 1.3 to
2.3 km a.s.l. for case I and 1.3 to 2.7 km a.s.l. for case II.
RH profiles (Fig. 3a and e, red line) calculated by the combination between
the RL calibrated r (z) profile and MWR temperature profiles were
obtained following the methodology presented in Sect. 3.1 by using Eq. (3)
(Navas-Guzmán et al., 2014). Good agreement is observed, with biases
(Fig. 3b and f) lower than 10 % within the analyzed region. The
differences obtained in the RH profiles might be associated with the
discrepancies between the temperature profiles from MWR and RS, due to the
lower vertical resolution of the MWR. Additionally, discrepancies are also
expected because of the radiosonde drift and the different temporal sampling
(the lidar data correspond to a 30 min average, whereas the RS provides
instantaneous values that build the profile in the region of interest in less
than 5 min).
The discrepancies between the two RH profiles are especially relevant in the
lower part of the analyzed data since differences in RH in this region lead
to variations in RHref. For case I, RHref=60%
for RS and RHref= 68 % for the RL + MWR combination,
whereas for case II, RHref=40 % for RS and
RHref=50 % for the RL + MWR methodology. Additionally,
the RH discrepancies in the upper region of the profiles (from 2.1 to
2.3 km a.s.l. for case I and from 2.6 to 2.7 km a.s.l. for case II), which
can reach up to 5 %, are also relevant since they are associated with the
maximum values of RH and may modify the data tendency on Hänel's
parameterization, leading to variations in γ(λ) depending on
the methodology used for the retrieval of RH. Despite these discrepancies,
the differences between γ(λ) parameters obtained from both
methodologies are low (Table 2). In case I, γ(λ)=0.59±0.05 obtained from RL + MWR is larger than that obtained from RS
(γ=0.56±0.01), while in case II the γ obtained with RH
from RS (γ=0.99±0.01) is larger than the one from RL + MWR
(γ=0.95±0.02). We have to keep in mind that uncertainties
reported on γ are obtained by the polynomial fitting, and they do not
include the propagation error result. The relative differences in both cases
are below 5 %, which is relatively good compared to the expected
uncertainties reported in Titos et al. (2016) and considering the differences
between the two methodologies.
The obtained values of fβ (85 %) using both methodologies are
presented in Table 2. For case I, fβ (85 %) = 1.50 for RS and
fβ (85 %) = 1.46 for RL + MWR, with a relative difference
below 3 %. For case II, fβ (85 %) = 2.6 for RS and
fβ (85 %) = 2.3 for RL + MWR, showing a relative
difference of 11 %. Even though the relative difference is larger for
case II, for both cases the discrepancies lie within the uncertainty
associated with the calculation of fβ (85 %), which is around
20 % according to Titos et al. (2016). Thus, the RL + MWR methodology
presented by Navas-Guzmán et al. (2014) to obtain RH profiles in a
continuous time base is a promising technique for hygroscopic growth studies.
This methodology will allow for expanding the RH profile database, and it opens new opportunities for the
detection of hygroscopic cases during nighttime periods.
Hygroscopic study during SLOPE IConditions for hygroscopic growth
Aerosol hygroscopic growth was observed during the SLOPE I campaign in 2016
by combining the remote sensing instruments and the RS. Figure 4 shows the
time series of the range corrected signal (RCS) at 532 nm derived by the
EARLINET lidar system at IISTA-CEAMA station on 16 June 2016. The presence of
clouds is observed in the late afternoon (∼3.0 km a.s.l.) before
19:00 UTC, with clouds vanishing after that during the remaining measurement
period. The red lines in Fig. 3 mark the 30 min set of profiles (from 20:30
to 21:00 UTC) where an intensification of the RCS is observed at
2.5 km a.s.l, which could be an indication of potential aerosol hygroscopic
growth.
Results obtained for each case analyzed by means of the new
methodology combining RL + MWR and the classical approach using RS data.
Figure 5 shows profiles of r (z), θv, RH,
βpar at 355 and 532 nm, the backscatter-related
Ångström exponent between 355 and 532 nm
(AE355-532), and PLDR532 (particle linear
depolarization ratio at 532 nm) obtained on 16 June 2016 between 20:30 and
21:00 UTC. As we mentioned in Sect. 3.2, for aerosol hygroscopicity analysis
it must be ensured that ranges where RH increases correspond to an increase
in βpar, which is well seen along the layer between 1.5 and
2.4 km a.s.l. (see Fig. 5). The RH profile was calculated by using the
method combining RL + MWR. In this case, the calibration constant for the
RL r (z) profile was calculated using the six RSs launched at nighttime
during this campaign. A calibration constant of 110±2 g kg-1 was
obtained as the mean value of the different calibrations.
In order to fulfill all the requirements discussed in Sects. 3.2 and 3.3 for
hygroscopic growth studies, together with the RH and βpar
increase within the layer, atmospheric stability must be ensured through the
evaluation of thermodynamic variables such as θv and
r (z). Here, r (z) shows relatively low vertical variation within the
region of interest (1.5 to 2.4 km a.s.l.), decreasing monotonically with
altitude at a rate of -1.9gkgkm
(Fig. 5a), and θv shows a monotonic increase at a rate of
∂θv∂z=0.03∘Ckm within the same
region.
AE355-532 and PLDR532 were
also retrieved in order to describe the mean size and shape of the aerosol
particles. For this case, we observe a decrease in both parameters in the
region of interest. A decrease in
AE355-532nm (∼ 0.4 km-1)
means an increase in the predominance of larger particles, and a decrease in
the PLDR532nm (∼ 0.13 km-1) is
related to particles becoming more spherical. This correlation between
AE355-532 and PLDR has been observed in previous
studies associated with hygroscopic growth (Granados-Muñoz et al., 2015;
Haarig et al., 2017).
In order to determine the origin of the aerosol particles over the analyzed
layer, we present a horizontal wind speed and direction and vertical wind
analysis from Doppler lidar data. The 10 min resolved horizontal wind
direction time series (Fig. 6b) indicate that from 18:00 to 21:00 UTC the
wind over IISTA-CEAMA station mainly came from the northwest, within the
region of interest (1.5 to 2.4 km a.s.l.) with relative low horizontal wind
velocity (up to 6 m s-1) (Fig. 6a), which means that aerosol particles
were being transported from the same direction, likely coming from the same
source, at relatively low horizontal velocity.
Time series of (a) horizontal wind velocity,
(b) horizontal wind direction, and (c) skewness retrieved
from Doppler turbulence calculations for 16 June 2016 at 20:30 to 21:00 UTC.
The PBLH retrieved from MWR is presented in black stars.
Humidograms calculated (a) at 532 nm and (b) at
355 nm, within the 1.5 to 2.4 km a.s.l. aerosol layer from the
RL + MWR measurements and calculated using Mie theory and measured
chemical composition and size distribution at 2.5 km a.s.l.
RHref = 78 % was used for both methods.
A turbulence analysis was also performed to reinforce the fact that vertical
fluxes within the aerosol column are associated with increases in RCS
observed in Fig. 4. The aerosol RCS increases in a region where RH increases,
as we see in Fig. 5; thus, we associate these increases in RCS with water
uptake by aerosols inside this atmospheric column. The vertical wind velocity
can be statistically studied to obtain the higher moments of the velocity
distribution (O'Connor et al., 2010; de Arruda Moreira et al., 2018). This
statistical analysis is deeply developed for turbulence studies. Here the
third moment of the frequency distribution (skewness) (Fig. 6c) represents
the direction of the convection (positive skewness is associated with
predominance of upward wind velocity, whereas negative skewness means
predominance of downward wind) in the region of interest. Supporting this
analysis, the black stars represent the calculation of the atmospheric
boundary layer height (PBLH, Fig. 6c) obtained from the MWR data by using the
combination of parcel and gradient methods in convective and stable
atmospheric conditions (Holzworth, 1964; Moreira et al., 2018). In this case,
close to 21:00 UTC (Fig. 6c), the particles tend to ascend into the column,
as indicated by positive values reached in the skewness linked with highly
convective movement. The PBLH reaches its maximum at 15:00 UTC
(2.5 km a.s.l.), but after 16:00 UTC the weakening of convection tends to
decrease the ABLH, keeping the ABLH around 2 km a.s.l. until 21:00 UTC.
All this wind information might be interpreted as transported particles
coming from the same direction at relatively low horizontal velocities,
suggesting that the aerosol source is not changing and that new aerosol
particles are not being advected into the studied layer. The turbulence
analysis allows us to support vertical wind movement within the layer of
interest driving well-mixed processes during the analyzed time interval.
The 6-day backward trajectories were calculated at three different heights
(0.9, 1.5, and 1.9 km a.g.l.), which were selected within the region of
interest in order to guarantee the height independency of the air masses'
pathway. The three air masses came from North America, crossing the Atlantic
Ocean, reaching the continental platform through Portugal, and then advected
to Granada, reaching the station at 21:00 UTC (not shown here). This
information supports the horizontal wind analysis performed before.
fβλ (RH) measured and retrieved by combining
in situ data and Mie theory
The humidogram presented in Fig. 6 shows the measured
fβλ (RH) at 355 and 532 nm as a function of RH
between 1.5 and 2.4 km a.s.l., retrieved by using the lidar data. The
calculated fβλ (RH) was obtained by using the
measured chemical composition and size distribution at SNS station
(2.5 km a.s.l.) as inputs to the Mie model (see Table 1 and Fig. 7). The
humidogram exhibits a monotonic positive increase at both wavelengths, for RH
between 78 and 98 %. The RHref=78% was
selected as the lowest RH value into the evaluated column, and this same RH
was used as a reference for the Mie calculation in order to make both
calculations comparable.
During the hygroscopic growth event at SNS, the mean aerosol particle number
size distribution shows two main peaks at around 35 and 115 nm, with most of
the aerosol in fine mode (<1µm). The sub-micron mass
concentration measured with ACSM indicates a high concentration of organic
particles during daytime (from 12:00 to 17:00 UTC), with values around
7 µg m-3 at 15:00 UTC. OA concentrations decreased slowly
to values around 3.0 µg m-3 at 00:00 UTC. In particular,
during the hygroscopic growth case under study (from 20:00 to 21:00 UTC) the
aerosol composition was mainly made up of organic particles (62 %),
followed by sulfate (24 %), nitrate (10 %), ammonia (2 %), and
black carbon (2 %). Thus, the predominant aerosol studied during the
event is a combination of smoke and urban polluted aerosol. This assumption
about the aerosol type is supported by the relatively high sulfate
concentration observed at SNS and the results discussed in Sect. 4.2.1 (lidar
properties and backward trajectory analyses). This chemical composition with
a high predominance of organic particles is consistent with the γ
values obtained with the RL + MWR method. Fernández et al. (2015)
reported a similar γ532 value of 0.59 in Cabauw
(Netherlands) associated with a high concentration of organic particles,
while they observed a significantly larger γ532 of 0.88
associated with marine particles. Lower values are reported by Lv et
al. (2017) in one of their case studies (γ532=0.24 and
γ355=0.12) in Xinzhou (China) associated with the
presence of dust particles. Although the behavior of the backscatter
coefficient at enhanced RH is expected to differ from the scattering
coefficient, a qualitative comparison can be performed due to the scarcity of
backscatter-related γ values in the literature. For example, using in
situ techniques, Zieger et al. (2015) reported a low scattering enhancement
of boreal aerosol in Hyytiälä (Finland)
(γ525=0.25) related to the high contribution of
organic aerosols at this site that contribute to decreasing the hygroscopic
enhancement. At Cape Cod (USA), Titos et al. (2014) reported significantly
lower γ values for polluted aerosols
(γ550=0.4±0.1) compared with marine aerosols
(γ550=0.7±0.1).
Calculated and measured values of fβλ
(RH) are compared in Table 3 and Fig. 7. In general, there is a good
agreement between measured and calculated hygroscopicity parameters. For both
wavelengths, slightly higher values are predicted by the model compared with
the measurements, especially at RH > 90 %, where the
differences are higher than at RH < 90 %. The values
retrieved from the RL measurements are fβ355(85%)=1.07±0.03 and fβ532(85%)=1.20±0.03, and with Mie theory they are fβ355(85%)=1.10±0.01 and fβ532(85%)=1.15±0.01. The good agreement found in this
analysis is confirmed by the low relative differences observed (lower than
4 %). The hygroscopic growth parameter (γ) also shows good
agreement between the measured (γ532=0.48±0.01 and
γ355=0.40±0.01) and calculated ones using Mie theory (γ532=0.53±0.02 and γ355=0.45±0.02), with relative
differences of 9 % at 532 nm and 11 % at 355 nm. The good agreement
between the measured and theoretical backscatter enhancement factors shows
the robustness of the proposed method for hygroscopic studies in a systematic
manner.
The principal sources of error in the comparison between calculated and
measured data are associated with the method for the retrieval of RH
profiles, as well as the errors associated with theoretical Mie calculation
mainly by the assumption of g(RH) based on the chemical composition.
Finally, the horizontal distance between stations could also lead to
differences in the comparison. The uncertainties affecting our study are the
result of the contributions of the particle backscatter uncertainties and
experimental uncertainties associated with determination of the backscatter
enhancement factor; thus, further studies should center their efforts on this
research field to constrain the range of uncertainty.
Results obtained for the hygroscopic case on 16 June 2016, evaluated
with RL (IISTA-CEAMA station) and in situ (SNS) stations.
In addition, the multi-wavelength results lead us to see a clear spectral
dependence on γ(λ). The efficiency due to changes in
fβλ (RH) associated with
βpar is stronger at 532 than at 355 nm, finding
that fβ532(85%)=1.20>fβ355(85%)=1.07. This is also seen in the gamma parameter
(γ532=0.48±0.01>γ355=0.40±0.01, with
correlations of 0.84 and 0.65, respectively). This spectral dependency has
also been reported in Kotchenruther et al. (1999) for in situ measurements at
450, 550, and 700 nm, obtaining increasing enhancement factors with
wavelength, and in Zieger et al. (2013), where the same behavior is observed
for marine aerosols. As is reported in Haarig et al. (2017), the enhancement
factor dependency with wavelength suggests that larger wavelengths have an
enhancement factor larger than short ones, which in fact was also evidenced
in this work.
Conclusions
The methodology proposed for calculating RH profiles by combining calibrated
r (z) from RL and temperature profiles from MWR has been used in this
work to study aerosol hygroscopicity. With this method, a way to retrieve RH
profiles without the necessity for co-located RS is presented at IISTA-CEAMA
station. In order to validate this methodology, hygroscopic growth cases
which use RS data were selected. The relative differences in the
fβλ(RH) obtained using the RH profiles from
the RS and from the combination of RL and MWR measurements were calculated,
finding relative differences below 11 % in
fβ(85%). The relative differences in γ
were below 5 %, supporting the fact that this methodology is valid for
aerosol hygroscopicity studies.
Aerosol hygroscopic growth observed during the SLOPE I field campaign
(16 June 2016, 20:30 to 21:00 UTC) was studied by means of a particle
backscatter coefficient retrieved from the EARLINET multi-wavelength RL,
backscatter-related Ångström exponent (AE355-532),
and particle linear depolarization ratio (PLDR532) as optical properties
and the combined RL + MWR RH profiles. Stability analysis confirmed good
mixing conditions in the atmospheric layer studied. In addition, Doppler wind
lidar data analysis allowed us to evaluate the vertical profiles of
horizontal wind velocity and direction. Thus, we concluded that particles
came mainly from the northwestern region of Granada at low velocities.
Furthermore, the skewness analysis let us infer that particles presented an
upward movement during the 30 min evaluated period within the column of
interest. These results were confirmed by ABLH calculations from MWR data.
From the experimental data from RL, values of fβ355(85%)=1.07±0.03 and fβ532(85%)=1.20±0.03 at
RHref=78% were obtained within the
evaluated column, and also γ532=0.47±0.01(R2= 0.84) and γ355=0.40±0.01(R2= 0.65), which were in agreement with the literature.
For the case study during SLOPE I the results were validated against Mie
simulations with experimental data from SNS data, obtaining a good agreement
between the values retrieved with RL (fβ355(85%)=1.07 and
fβ532(85%)=1.20) and Mie theory
(fβ355(85%)=1.10 and
fβ532(85%)=1.15), reaching relative differences lower than
4 % when taking the calculated data as a reference. We also found good
agreement between the measured hygroscopic growth parameter (γ)
(γ532=0.48±0.01 and
γ355=0.40±0.01) and the calculated one
(γ532=0.53±0.02 and γ355=0.45±0.02), with
relative differences of up to 9 % at 532 nm and 11 % at 355 nm,
taking the calculated data as a reference. These results show that under
favorable atmospheric conditions (vertical homogeneity, consistent aerosol
sources, and low horizontal velocity within the analyzed layer) and in the
absence of advected air masses into the evaluated column, the hygroscopic
behavior of the particles evaluated by remote sensing at IISTA-CEAMA station
is in accordance with that evaluated for those particles transported to SNS.
The results obtained here show the potentiality of combining r (z) from RL and
temperature from MWR to retrieve RH profiles with high temporal/spatial
resolution to analyze aerosol hygroscopic growth. These results will allow us
to expand the database of hygroscopic growth cases studied with remote
sensing techniques. With the proposed procedure the aerosol properties and RH
are obtained within the same atmospheric column, as opposed to the cases when
the thermodynamic profiles are retrieved from RS.
Data availability
All datasets used in this paper are available upon
request to the corresponding author.
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
This work was supported by the Andalusia Regional Government through project
P12-RNM-2409; by the Spanish Ministry of Economy and Competitiveness through
projects CGL2013-45410-R and CGL2016-81092-R, the excelence network
CGL2017-90884-REDT, the FPI grant (BES-2014-068893), and the Juan de la
Cierva grants FJCI-2014-22052 and FJCI-2014-20819; by the University of
Granada trough the Plan Propio Program P9 Call-2013 contract. Andrés
Bedoya has been supported by a grant for PhD studies in Colombia, COLCIENCIAS
(Doctorado Nacional – 647), associated with the Physics Sciences program at
the Universidad Nacional de Colombia, Sede Medellín and Asociación
Universitaria Iberoamericana de Postgrado (AUIP). The study has also been
supported by the Swiss National Science Foundation trough project
PZ00P2_168114. Financial support for EARLINET was through the ACTRIS
Research Infrastructure Project EU H2020 (Grant agreement no. 654109),
particularly trough the TNA GRA-3 HYGROLIRA. We thank the AERO group from
ESRL-GMD at NOAA for providing the CPD software used for routine measurements
at the SNS, and for their technical support. The authors gratefully
acknowledge the FEDER program for the instrumentation used in this
work. Edited by: Matthias
Tesche Reviewed by: two anonymous referees
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