Remote-sensing measurements by light detection and ranging (lidar)
instruments are fundamental for the monitoring of altitude-resolved aerosol
optical properties. Here we validate vertical profiles of aerosol
backscatter coefficient (βaer) measured by two independent lidar
systems using co-located balloon-borne measurements performed by Compact
Optical Backscatter Aerosol Detector (COBALD) sondes. COBALD provides
high-precision in situ measurements of βaer at two wavelengths
(455 and 940 nm). The two analyzed lidar systems are the research Raman
Lidar for Meteorological Observations (RALMO) and the commercial CHM15K
ceilometer (Lufft, Germany). We consider in total 17 RALMO and 31 CHM15K
profiles, co-located with simultaneous COBALD soundings performed throughout
the years 2014–2019 at the MeteoSwiss observatory of Payerne (Switzerland).
The RALMO (355 nm) and CHM15K (1064 nm) measurements are converted to 455 and 940 nm, respectively, using the Ångström exponent profiles
retrieved from COBALD data. To account for the different receiver field-of-view (FOV) angles between the two lidars (0.01–0.02∘) and COBALD
(6∘), we derive a custom-made correction using Mie-theory
scattering simulations. Our analysis shows that both lidar instruments
achieve on average a good agreement with COBALD measurements in the boundary
layer and free troposphere, up to 6 km altitude. For medium-high-aerosol-content measurements at altitudes below 3 km, the mean ± standard
deviation difference in βaer calculated from all considered
soundings is -2 % ± 37 % (-0.018± 0.237 Mm-1 sr-1 at 455 nm) for RALMO-COBALD and +5 % ± 43 %
(+0.009 ± 0.185 Mm-1 sr-1 at 940 mm) for CHM15K-COBALD. Above 3 km altitude, absolute deviations generally decrease, while
relative deviations increase due to the prevalence of air masses with low
aerosol content. Uncertainties related to the FOV correction and spatial- and
temporal-variability effects (associated with the balloon's drift with
altitude and different integration times) contribute to the large standard
deviations observed at low altitudes. The lack of information on the aerosol
size distribution and the high atmospheric variability prevent an accurate
quantification of these effects. Nevertheless, the excellent agreement
observed in individual profiles, including fine and complex structures in
the βaer vertical distribution, shows that under optimal
conditions, the discrepancies with the in situ measurements are typically
comparable to the estimated statistical uncertainties in the remote-sensing
measurements. Therefore, we conclude that βaer profiles measured
by the RALMO and CHM15K lidar systems are in good agreement with in situ
measurements by COBALD sondes up to 6 km altitude.
Introduction
Aerosol particles are ubiquitous in the atmosphere and play a key role in
multiple processes that affect weather and climate. They absorb and scatter
the incoming and outgoing radiation, which affects the Earth's radiative
budget (direct effect), and interact with cloud formation processes,
influencing their microphysical properties and lifetime (indirect effect)
(e.g., Haywood and Boucher, 2000). Atmospheric aerosols are one of the
largest sources of uncertainty in current estimates of anthropogenic
radiative forcing (Bindoff et al., 2013).
Among the most significant causes of this uncertainty is the high
variability in space and time in the aerosol's concentration, composition
and optical properties. Remote-sensing instruments, such as light detection
and ranging (lidar) systems, represent an optimal tool for the monitoring of
altitude-resolved aerosol optical coefficients (backscatter and extinction),
especially in the planetary boundary layer (PBL) (e.g., Amiridis et al.,
2005; Navas-Guzmán et al., 2013). Lidar networks like EARLINET
(https://www.earlinet.org, last access: 27 January 2021) and E-PROFILE (https://www.eumetnet.eu/e-profile, last access: 27 January 2021), comprising
several hundreds of single-wavelength (including ceilometers) and
multi-wavelength (Raman) lidars, provide a comprehensive database of the
horizontal, vertical and temporal distribution of aerosols over Europe
(e.g., Bösenberg et al., 2003; Pappalardo et al., 2014; Sicard et al.,
2015).
Lidar instruments offer the advantages of vertically resolved measurements
and continuous operation in time but are subject to a number of intrinsic
uncertainties of this technique. Single-wavelength elastic-backscatter
lidars are limited by the fact that only one signal is measured, while the
returning intensity is determined by two parameters (backscatter and
extinction). Hence, an a priori assumption on the aerosol
extinction-to-backscatter ratio (the so-called “lidar ratio”) is necessary
for the calculation of the aerosol backscatter profiles (e.g., Collis and
Russel, 1976). Additionally, the retrieval at low altitudes is particularly
challenging because of the incomplete geometric overlap between the incoming
beam and the receiver's field of view (e.g., Wandinger and Ansmann, 2002;
Weitkamp, 2005; Navas-Guzmán et al., 2011). Previous comparison studies
in the context of EARLINET found typical deviations of 10 % in aerosol
backscatter coefficient (Matthais et al., 2004) and up to 30 % in
attenuated backscatter (Tsaknakis et al., 2011; Madonna et al., 2018)
between elastic-backscatter lidars and more advanced Raman lidar
measurements in the PBL.
Multi-wavelength Raman lidars allow the independent measurement of aerosol
backscatter and extinction as functions of altitude by the detection of a
pure molecular-backscatter signal in addition to the elastic backscatter
(Ansmann et al., 1990, 1992). However, the retrieval procedure is complex
and prone to uncertainties, in particular for extinction. It involves the
calculation of the derivative of the logarithm of the ratio between the
atmospheric number density of molecules and the lidar-received power, which
generally requires complex data-handling techniques to isolate the signal
from statistical fluctuations (Pappalardo et al., 2004). The comparison of
different aerosol backscatter retrieval algorithms between 11 Raman lidar
systems in EARLINET, using synthetic input data, showed deviations between
them up to 20 % for altitudes below 2 km (Pappalardo et al., 2004). This
calls for careful validation studies against independent in situ
measurements, as we perform in this work.
In situ instruments are characterized by higher precision and
signal-to-noise ratio compared to remote-sensing measurements but are
typically limited by low spatial and temporal coverage. Altitude-resolved
in situ measurements of aerosol optical properties can be achieved by
various platforms including aircrafts, unmanned aerial vehicles (UAVs) and
meteorological balloons. Specifically, balloon-borne measurements of aerosol
backscatter are typically used to investigate high-altitude cirrus clouds
(e.g., Khaykin et al., 2009; Cirisan et al., 2014) and aerosol layers in the
upper troposphere and stratosphere (e.g., Rosen and Kjome, 1991; Vernier et
al., 2015; Brunamonti et al., 2018), which are not accessible by aircrafts
and UAVs. The aim of this paper is to use balloon-borne measurements of
aerosol backscatter in the lower troposphere to validate the retrievals of
aerosol backscatter coefficient by one co-located Raman lidar and one
co-located ceilometer.
The instruments and data used for the comparison are introduced in detail in
Sect. 2. The method of comparison, including the derivation of a field-of-view (FOV) correction from idealized Mie-theory scattering simulations, is
described in Sect. 3. The results of the comparison are discussed in
Sect. 4 and the conclusions summarized in Sect. 5.
Observations
We analyze vertical profiles of aerosol backscatter coefficient (βaer) measured by two remote-sensing instruments, namely one research
Raman lidar system and one commercial ceilometer, and in situ
(balloon-borne) measurements performed by aerosol backscatter sondes. The
three instruments and measuring techniques are introduced in Sects. 2.1 and 2.2, and their main characteristics are summarized in Table 1. All data
were collected at the MeteoSwiss Aerological Observatory of Payerne,
Switzerland (46.82∘ N, 6.95∘ E), located at an elevation
of 491 m above sea level (a.s.l.), between January 2014 and October 2019. The
selection of the dataset considered for the statistical comparison is
described in Sect. 2.3. Spatial and temporal variability issues, related
to the different characteristics of the lidar and balloon-sounding
techniques, are discussed in Sect. 2.4.
Summary of the main technical characteristics of the three
instruments used in this work, including measuring technique, instrument
type, light-emitting source, wavelengths and receiver FOV
angle.
RALMO (Raman Lidar for Meteorological Observations) is a research Raman
lidar system developed by EPFL Lausanne in collaboration with MeteoSwiss
(Dinoev et al., 2013), operational in Payerne since 2008 and part of the
EARLINET network. It uses a Nd:YAG laser source, which emits pulses of 8 ns
duration at a wavelength of 355 nm and frequency of 30 Hz. The laser beam
divergence is 120 µrad and the mean energy per pulse 400 mJ. The
receiving system consists of four telescopes with 30 cm parabolic mirrors,
with equivalent total aperture of 60 cm and field of FOV angle of 200 µrad. Optical fibers connect the telescope mirrors with two
polychromators, which allow us to isolate the rotational–vibrational Raman
signals of nitrogen and water vapor (wavelengths of 386.7 and 407.5 nm,
respectively) and the pure rotational Raman lidar signals (around 355 nm).
The rotational–vibrational signals are used to derive water vapor profiles
(Brocard et al., 2013; Hicks-Jalali et al., 2019, 2020), while the pure
rotational signals are used for temperature, aerosol backscatter and aerosol
extinction coefficients (e.g., Dinoev et al., 2010; Martucci et al., 2018).
The optical signals are detected by photomultipliers and acquired by a
transient recorder system (Brocard et al., 2013). Thanks to its Raman
technique, RALMO retrievals are unaffected by incomplete overlap issues.
Nevertheless, the signal-to-noise ratio is typically very low in the first
200 m above the station; therefore this altitude region is not considered in
this study. Aerosol backscatter coefficient measurements from RALMO were
recently used to characterize hygroscopic growth during mineral dust and
smoke events (Navas-Guzmán et al., 2019). Here we derive the RALMO
βaer at 355 nm from the ratio between the elastic and inelastic
signal, as described in Navas-Guzmán et al. (2019).
The CHM 15K NIMBUS (hereafter CHM15K) ceilometer is a single-wavelength
elastic-backscatter lidar manufactured by Lufft, Germany (Lufft, 2019),
installed in Payerne since 2012, and a member of E-PROFILE. It uses a Nd:YAG
narrow-beam microchip laser emitting 1 ns pulses at a wavelength 1064 nm and
repetition rate between 5–7 Hz, with a receiver FOV of 450 µrad. It
supports a range up to 15 km with a first overlap point at 80 m and full
overlap reached at 800 m above the station (Hervo et al., 2016). Below this
level, the profiles are corrected for incomplete overlap (Hervo et al.,
2016). CHM15K is employed as a cloud height sensor and for the automatic
detection of boundary layer height (Poltera et al., 2017), and it was used
for the characterization of aerosol hygroscopic properties (Navas-Guzmán
et al., 2019). Here we derive βaer at 1064 nm from the CHM15K
elastic signal using a Klett inversion algorithm (Klett, 1981). This
technique was shown to provide accurate aerosol backscatter profiles despite
the low molecular backscatter at infrared wavelengths and the low
signal-to-noise ratio of a ceilometer in the free troposphere (Wiegner and
Geiss, 2012). In particular, using a similar system (CHM15kx by Jenoptik,
Germany), Wiegner and Geiss (2012) report a relative error of 10 % in
βaer at 1064 nm retrieved by this method. For consistency within
our statistical comparison, here we assume a constant lidar ratio equal to
50 sr for all profiles. The uncertainty related to this assumption is
discussed in Sect. 4.3.
In situ measurements
COBALD (Compact Optical Backscatter Aerosol Detector) is a lightweight (500 g) aerosol backscatter detector for balloon-borne measurements developed at
ETH Zürich, based on the original prototype by Rosen and Kjome (1991).
Using two light-emitting diodes (LEDs) as light sources and a photodiode
detector with FOV of 6∘, COBALD provides high-precision in situ
measurements of aerosol backscatter at wavelengths of 455 nm (blue visible)
and 940 nm (infrared). COBALD was originally developed for the observation
of high-altitude clouds, such as cirrus (e.g., Brabec et al., 2012; Cirisan
et al., 2014) and polar stratospheric clouds (Engel et al., 2014), while
recently it was proven able to detect and characterize aerosol layers in the
upper troposphere–lower stratosphere (e.g., Vernier et al., 2015, 2018;
Brunamonti et al., 2018). In this work, for the first time we use COBALD
measurements for the analysis of boundary layer and lower-tropospheric
aerosols.
For each balloon sounding, the COBALD sonde is connected to a host
radiosonde via their XDATA interface (e.g., Wendell and Jordan, 2016) to
transmit the data to the ground station. The average ascent rate of the
balloon is set to around 5 m s-1, which combined with a measurement frequency
of 1 Hz provides a vertical resolution of approximately 5 m. Typical
balloon burst altitude is about 35 km. Due to the high sensitivity of its
photodiode detector, COBALD sondes can be only deployed during nighttime.
Hence, all soundings analyzed here were started at approximately 23:00 UTC.
More than 100 COBALD soundings were performed in Payerne since 2009,
supported by SRS-C34 radiosondes by MeteoLabor, Switzerland (MeteoLabor,
2010), until December 2017, and RS41-SGP radiosondes by Vaisala, Finland
(Vaisala, 2017), since January 2018.
The COBALD measurements are typically expressed as backscatter ratio (BSR),
defined as the ratio of the total-to-molecular-backscatter coefficient
(Eq. 1), at 455 and 940 nm. The BSR is obtained by dividing the total
measured signal (normalized to the altitude-dependent LED emitted power) by
its molecular contribution, which is computed from the atmospheric
extinction according to Bucholtz (1995). The atmospheric number density of
molecules is derived from the radiosonde measurements of temperature and
pressure (e.g., Cirisan et al., 2014). Accuracy and precision of COBALD BSR
were estimated by Vernier et al. (2015) as 5 % and 1 %, respectively,
at upper-tropospheric conditions. Here, we further derive βaer
from the COBALD BSR assuming a molecular-extinction-to-backscatter ratio of
8π/3 sr.
BSR=βaer+βmolβmol
Dataset
Over their operational periods, the RALMO and COBALD systems were subject to
various technical and design modifications, which affected their
characteristics and performances. In particular, the currently used COBALD
940 nm LED was introduced in January 2014, replacing the older 870 nm LED
(e.g., Brabec et al., 2012), while the pure rotational Raman acquisition
board of RALMO was replaced, from a Licel system to the faster FAST ComTec
P7888 (FastCom, Germany), in August 2015 (see Martucci et al., 2018). For
consistency, we consider in this work only the time periods following these
changes, i.e., the current versions of RALMO and COBALD.
Therefore, we analyze the years 2014–2019 for the CHM15K validation (58
total COBALD soundings) and the years 2016–2019 for the RALMO validation
(34 total soundings; note that no simultaneous RALMO-COBALD soundings are
available between August and December 2015).
Out of all the available COBALD soundings, we exclude those with
simultaneously missing or incomplete (up to at least 6 km altitude) lidar
profiles. This can be due to instrumental failures, maintenance
interventions or forbidding weather conditions (e.g., thick low clouds, fog
or precipitation) at the time of the balloon sounding. In particular, we
reject from the comparison all profiles for which a precise calibration of
the lidar signal cannot be achieved. The calibration of lidar (as well as
COBALD) measurements involves the normalization of the signal to a reference
value in a “clean region” (i.e., the lowest aerosol concentration along the
profile), usually found in the upper troposphere. If no lidar signal is
measured in this region of altitudes, which is typically the case in the
presence of thick low clouds, or if the signal-to-noise ratio above the
cloud is so low that the signal cannot be properly calibrated, then the
profile is excluded from the comparison. After a careful selection, we
obtain 17 simultaneously calibrated profiles of RALMO and COBALD and 31 of
CHM15K and COBALD, which are used for the statistical comparison. The list
of corresponding dates is given by Table S1 in the Supplement.
Spatial and temporal variability
A fundamental difference between the remote-sensing and balloon-sounding
techniques is that lidars measure at every altitude the vertical air column
directly above their laser beam, while the balloon sondes are subject to a
horizontal drift with altitude, dictated by the atmospheric wind field.
Therefore, in the presence of wind shear, the two instruments may not measure
the same air mass at every altitude. The distance between the balloon sonde
and the lidar beam generally increases with altitude and is strongly
dependent on the atmospheric wind profile at the time of measurement. Figure 1 shows the trajectories of all balloon soundings analyzed in our comparison
for the period 2016–2019 as a function of altitude (0.8–6 km). The distance
between the lidar and the sondes ranges between roughly 0–5 km up to 2 km
altitude and may exceed 10 km at 4 km altitude.
Balloon trajectories (longitude vs. latitude) as a function of
altitude (color scale) for all the analyzed soundings of the RALMO vs.
COBALD comparison (17 profiles, 2016–2019). The trajectories are plotted
with a vertical resolution of 30 m between 0.8–6 km altitude a.s.l. The
location of the RALMO and CHM15K lidars (and balloon launching site) is
shown by the solid red circle (46.82∘ E, 6.95∘ N). The two
dotted red circles indicate horizontal distances of approximately 5 and
10 km from the lidar site.
In addition, the two techniques differ in terms of measurement times.
Namely, while the lidar profiles are integrated 30 min in time, COBALD
provides instantaneous measurements at 1 s resolution (reduced to 6 s after
averaging to 30 m intervals). The combination of balloon drift with altitude
and different integration times, coupled with the high spatial and temporal
variability in aerosol optical properties, can lead to discrepancies between
the remote-sensing and in situ measurement which are not due to instrumental
issues but rather to atmospheric-variability effects. In particular, this
may result in the smoothing or slight displacement in altitude between
aerosol backscatter features (especially thin layers), which are seen by
both techniques. Such effects are often observed in our dataset and
therefore affect the results of the statistical comparison. This issue is discussed further in Sect. 4.3.
Method of comparison
For each COBALD sounding, we retrieve simultaneous RALMO and CHM15K βaer profiles with a vertical resolution of 30 m and integration time of
30 min (roughly corresponding to 10 km of balloon ascent time). Since all
COBALD sondes were launched at 23:00 UTC, the integration time window chosen
for all profiles and both lidars is 23:00–23:30 UTC. To obtain a dataset
with consistent vertical levels, the COBALD measurements (with a vertical
resolution ≈ 5 m) are averaged in altitude bins of 30 m, matching
the vertical grid of the lidars. For the statistical comparison we consider
in total 174 vertical levels, covering the altitude interval from 800 m a.s.l.
to 6 km a.s.l. We only select measurements from ≈ 300 m above the
ground station to avoid the region of maximum incomplete overlap of CHM15K
as well as to avoid the region of low signal-to-noise ratio of RALMO at low
altitudes (see Sect. 2.1). Note that all altitude levels given in the
following are meant as altitude a.s.l. unless differently specified.
Along with the COBALD backscatter data, the temperature, pressure and
relative humidity (RH) measurements from the host radiosonde are averaged to
the same altitude levels. The temperature and pressure profiles are used for
the computation of the atmospheric molecular extinction, as described in
Sect. 2.2. The RH measurements are used to reject in-cloud data points.
In-cloud aerosol backscatter measurements are typically much larger (up to
3 orders of magnitude) compared to clear-sky (i.e., aerosol-only)
conditions and characterized by high spatial and temporal variability.
Therefore, we exclude from the comparison all data points with RH > 90 %. Such a highly conservative criterion is chosen in order
to avoid cloud edge regions as well, which can lead to large biases in the
statistical comparison.
For a proper comparison of the COBALD and lidar backscatter retrievals, a
number of methodological aspects and technical differences between the two
techniques need to be taken into account. In the remainder of this section,
we discuss our approach towards wavelength homogenization (Sect. 3.1),
correction of effects related to the different receiver FOVs (Sect. 3.2),
data sorting according to aerosol content and compared quantities (Sect. 3.3).
Wavelength conversion
To compare βaer at different wavelengths (λ) measured
by the different instruments, it is necessary to account for the spectral
dependency of aerosol backscatter. This is done using the Ångström law
(Eq. 2), which describes the spectral dependency of βaer between two wavelengths (λ0 and λ) as a function of the
Ångström exponent (AE) at every altitude level (zi). The AE is an
intensive property of the aerosol that, under certain assumptions on the
particle's size distribution, can be used as a semi-quantitative indicator
of particle size (e.g., Njeki et al., 2012; Navas-Guzmán et al., 2019).
Through Eq. (2) we convert the lidar profiles into the COBALD
wavelengths so they can be quantitatively compared.
βaerλ,zi=βaerλ0,zi⋅λλ0-AE(zi)
Thanks to its high signal-to-noise ratio and two operating wavelengths,
COBALD allows us to characterize the backscatter spectral ratio (between 455
and 940 nm) at every altitude, including regions of low aerosol load (e.g.,
Brunamonti et al., 2018). Conversely, the signal-to-noise ratio of remote-sensing instruments (in our case especially CHM15K) decreases with altitude,
and the AE derived from lidar measurements is typically characterized by
large statistical fluctuations in the free troposphere. Therefore, here we
choose to retrieve the AE(z) profiles from COBALD data. To minimize the
uncertainty associated with the conversion, we couple each lidar with the
closest COBALD channel in terms of wavelength. Hence, the RALMO profiles at
355 nm (ultraviolet) are converted to 455 nm and compared to the COBALD blue
visible channel, and the CHM15K profiles at 1064 nm (infrared) are converted
to 940 nm and compared to the COBALD infrared channel.
Using the AE from COBALD is equivalent to assuming that the spectral
behavior of the aerosols between 455–940 nm can be extrapolated to the
slightly broader interval of 355–1064 nm, which is justified by the small
difference between the wavelengths that are compared. A number of sensitivity
tests using different assumptions have been conducted, revealing that small
changes in AE have a small effect on the results. The uncertainty associated
with the wavelength conversion of the lidar data is discussed further in
Sect. 4.3.
FOV correction
Besides their wavelengths, the COBALD and lidar systems differ in terms of
FOV of their respective receivers. RALMO and CHM15K use
highly focused laser beams and consequently have narrow FOVs (200 and 450 µrad, respectively, corresponding to 0.01–0.02∘),
while COBALD's photodiode detector has a macroscopic FOV of 6∘
(see Table 1). Considering that the Mie-scattering phase function, i.e., the
distribution of scattered light with angle by a spherical particle, has a
local maximum in the backward direction (180∘), it follows from
its wider FOV that COBALD will measure less backscattered radiation (namely,
the average intensity between 174–180∘) compared to
the lidars (≈ 180∘).
To quantify this effect, we performed idealized Mie-theory scattering
simulations using the optical model by Luo et al. (2003). We assume a single
lognormal size distribution of aerosol particles characterized by mode
radius Rm, number concentration N, fixed width (standard deviation 1.4)
and refractive index (1.4). The BSR of this population is then computed both
assuming the phase function value at 180∘, corresponding to the
lidar observations (FOV ≈ 0∘), and taking the average of
the phase function between angles 174–180∘,
corresponding to the COBALD measurements (FOV = 6∘). The use of
a mono-modal size distribution with fixed width has the advantage that the
correction factors can be described as functions of a single parameter
(Rm), which can be constrained through the observed AE. Furthermore, a
mono-modal distribution represents well the average size distribution of
continental aerosols in the northern mid-latitudes (e.g., Watson-Perris et
al., 2019).
Mie-theory scattering model simulations. Panel (a): ratio of
aerosol-to-molecular-backscatter coefficient, βaer/βmol (i.e., BSR -1) at 455 nm (blue) and 940 nm (red), as a function
of mode radius (Rm), calculated assuming a FOV angle of
174–180∘ (dashed lines: COBALD) and 180∘
(solid lines: lidar), and aerosol number concentration N= 103 cm-3. Panel (b): correction factors, i.e., lidar-to-COBALD ratio of
βaer/βmol (as shown in panel a) for 455 nm (blue)
and 940 nm (red), as a function of Rm. Panel (c): simulated Ångström
exponent (AE) for the COBALD wavelength interval (455–940 nm), as a function
of Rm. Dashed black lines indicate the thresholds of AE = 0.8 and AE = 1.5 used for the parameterization of the correction factors (see Sect. 3.2). Panel (d): resulting FOV correction as a function of AE.
Figure 2a shows the simulated ratio of aerosol-to-molecular-backscatter
coefficient, βaer/βmol (i.e., BSR – 1; see Eq. 1) at 455 nm (blue) and 940 nm (red), as a function of Rm (40 nm–4 µm), calculated assuming FOV ≈ 0∘ (solid lines) and FOV = 6∘ (dashed lines), and N= 103 cm-3. As expected,
the simulations show that for all mode radii the COBALD βaer is lower than the βaer measured by the lidar instruments.
Figure 2b shows the lidar-to-COBALD ratio of βaer (ratio of
solid-to-dashed curves in Fig. 2a), i.e., the correction factor required to
compensate for the FOV effect, for 455 and 940 nm as a function of Rm. For
the considered size interval, the correction factors vary between
approximately 1–1.5 and show a non-linear dependency on Rm, with a local
maximum near 800 nm (λ= 455 nm) and 1.6 µm (λ= 940 nm). This complex optical behavior needs to be corrected. Note that the
correction factors in Fig. 2b are independent of N, unlike the βaer/βmol ratios in Fig. 2a.
To account for the size dependency in Fig. 2b, we use the AE as an
indicator of particle size and develop a parametrization of the correction
factors based on the AE measured from COBALD. Figure 2c shows AE between
455–950 nm calculated from the Mie simulations as a function of Rm. The
AE decreases non-monotonically with mode radius and exhibits the
characteristic Mie oscillations in the range of approximately 40 nm–1 µm (Fig. 2c). More in detail, we observe that AE > 1.5
corresponds to small particles (Rm< 75 nm) and AE < 0.8 to
large particles (Rm> 1.16 µm), while 0.8 < AE < 1.5 corresponds to 75 nm <Rm< 1.16 µm,
but in this intermediate range the change in AE with Rm is not monotonic
(Fig. 2c); hence a one-to-one correspondence cannot be established. To
simplify this behavior, we parametrize the correction factors within the
three fixed intervals of AE just introduced, and for each interval of AE we
take the average correction factor in the corresponding interval of
Rm. Hence, we apply the average correction factors between 75 nm–1.16 µm (namely, 1.23 at 455 nm, 1.10 at 940 nm) to all measurements with
0.8 < AE < 1.5, the average correction factors between 1.16–4 µm (1.29 at 455 nm, 1.28 at 940 nm) for AE < 0.8, and no
correction for AE > 1.5 (both correction factors ≈ 1 for
Rm< 75 nm). The resulting FOV correction as a function of AE is
shown in Fig. 2d.
The FOV correction is applied to all COBALD measurements in the statistical
comparison. Since, for every AE, the correction factors are larger for 455 nm than for 940 nm (Fig. 2d), the FOV correction will affect the RALMO
comparison more than the CHM15K one. We note that, due to the variability in
AE observed in our dataset (see Fig. S1 in the Supplement), the
middle interval of the correction (0.8 < AE < 1.5) accounts
for the large majority of data points in the PBL, and AE > 1.5
typically corresponds to free-tropospheric background measurements, which
are unaffected by the correction, while values of AE < 0.8,
corresponding to very large particles, are rarely encountered in our
dataset. The effect of the FOV correction on two selected profiles is
discussed in Sect. 4.1.
Compared quantities
After the wavelength conversion and the FOV correction, the difference in
aerosol backscatter coefficient (Δβaer) between the
lidars (LIDs) and COBALD (COB) is calculated for each sounding and every altitude
level as in Eq. (3). The mean deviation (δ) of a given subset of
data is calculated according to Eq. (4), where z1 … zN is the ensemble of all vertical levels in the considered dataset and
altitude region. The spread of the individual differences around δ
is quantified using standard deviation (σ), defined by Eq. (5).
Δβaer, δ and σ are expressed in both
absolute backscatter coefficient values (Mm-1 sr-1) and
percent units relative to the COBALD signal (denoted as Δβaerrelδrel,σrel).
3Δβaer(zi)=βaerLID(zi)-βaerCOB(zi)4δ=∑i=1NΔβaer(zi)N5σ=∑i=1N(Δβaer(zi)-δ)2N-1
Atmospheric backscatter profiles are typically characterized by a large
gradient in βaer between the boundary layer, with high aerosol
content (hence high βaer), and the free troposphere, with low
aerosol content (low βaer). This gradient is such that the same
absolute Δβaer may correspond to either a small or large
relative Δβaerrel, depending on altitude. In particular,
free-tropospheric measurements, where statistical fluctuations often
dominate over the atmospheric signal, typically yield large relative
deviations in spite of small absolute differences. While the boundary layer
is the main region of the interest of this study as it contains most of the
aerosol loading in the column, the free troposphere (including low-aerosol-content measurement) cannot be completely neglected since a good agreement
at high altitudes ensures that all profiles are well calibrated (see Sect. 2.3). Therefore, here we focus our analysis on medium-high-aerosol-content
data (defined as explained below), yet for completeness we also display low-aerosol-content measurements in the statistical comparison.
The aerosol content is evaluated according to the average COBALD βaer in each profile and 300 m altitude interval (i.e., mean of 10
vertical levels). Based on the observed range of variability in βaer in our dataset (see Fig. S1), we define
“low aerosol content” as all layers with average COBALD βaer< 0.1 Mm-1 sr-1 at 455 nm (RALMO comparison) and average
COBALD βaer< 0.05 Mm-1 sr-1 at 940 nm (CHM15K
comparison). The averaging in 300 m layers ensures that actual air masses
with low aerosol content are identified rather than individual data points
exceeding the threshold due to statistical variability. When the above
conditions are met, all data points in the considered layer are classified
as “low aerosol content”. All other data points are referred to as
“medium-high aerosol content”. Note that this definition allows individual
data points to exceed the threshold as long as the average criteria in the
layer are not exceeded.
For medium-high-aerosol-content data, in addition to δ and σ, we also evaluate the correlation between the lidars and COBALD using the
Pearson correlation coefficient (ρ). This is defined according to
Eq. 6, where BLID and BCOB are the average lidar
βaer and COBALD βaer, respectively, calculated in 300 m layers.
The Pearson correlation coefficient represents the degree of linearity of
the correlation between βaerLID and βaerCOB,
ranging between values of -1 (total negative linear correlation) and +1
(total positive linear correlation). In the statistical comparison,
δ, σ and ρ are quantified for both RALMO and CHM15K
in three altitude intervals of 0.8–3, 3–6 and 0.8–6 km a.s.l. (i.e., all altitudes).
ρ=∑i=1NβaerLIDzi-BLID⋅βaerCOBzi-BCOB∑j=1NβaerLIDzi-BLID2⋅∑k=1NβaerCOBzi-BCOB2
Results
In this section we present the results of our analysis. Before the
statistical comparison (Sect. 4.2), we discuss the comparison of two
selected individual profiles (Sect. 4.1), highlighting the effect of the
FOV correction. Finally, the results are discussed in Sect. 4.3. Two
additional examples of individual profiles can be found in the Supplement (Figs. S2–S3).
Comparison of individual profiles
To illustrate the main characteristics of the observed βaer
profiles and the effect of the FOV correction, we select as case studies the
soundings performed on 12 July and 4 September 2018. Figure 3 shows an
overview of these measurements – including vertical profiles of βaer (at different λ) by RALMO, COBALD and CHM15K (panels a,
d), AE derived from COBALD measurements (panels b, e), and the temperature
and RH profiles measured by the radiosonde (panels c, f) – as functions of
altitude for the interval of 0.8–6 km a.s.l.
Overview of selected profiles measured on 7 July 2018 (a–c)
and 4 September 2018 (d–f). Panels (a, c): vertical profiles of
aerosol backscatter coefficient (βaer) as a function of altitude,
measured by RALMO (355 nm: green), COBALD (455 nm: blue; 940 nm: red) and
CHM15K (1064 nm: black). Panels (b, d): vertical profiles of Ångström
exponent (AE) for wavelengths 455–940 nm, calculated from the COBALD data.
Panels (c, f): vertical profiles of relative humidity (RH: black) and
temperature (red, top scale) measured by the Vaisala RS41-SGP radiosonde
(flying in tandem with the COBALD sonde).
The case of 12 July 2018 (Fig. 3a–c) shows a typical profile with top of
PBL at about 2.2 km altitude (see temperature inversion in panel c),
characterized by a sharp decrease with altitude in βaer and RH,
plus a thin (≈ 400 m) isolated aerosol layer around 3 km altitude
(note the higher AE compared to the PBL, suggesting finer particles; panel b). Inside the PBL, the vertical structure of βaer observed by
COBALD is qualitatively well reproduced by both RALMO and CHM15K, despite an
evident altitude displacement (of about 60 m) of the top-of-PBL decrease in
βaer between the COBALD and lidar profiles (Fig. 3a). This is
most likely an effect of the atmospheric-variability issues discussed in
Sect. 2.4. Indeed, considering that COBALD crosses the PBL around the
beginning of the lidar integration time window, a downward displacement in
top-of-PBL altitude (as inferred from the βaer profiles) in the
remote-sensing data is consistent with the lowering of PBL altitude during
nighttime reported by Poltera et al. (2017). A similar feature can be seen
in Fig. S2d.
On 4 September 2018 (Fig. 3d–f) a more complex aerosol vertical
distribution is observed, with decreasing βaer with altitude
until 2 km and a thick aerosol layer between 2.5–3.5 km altitude. Again,
the vertical structure of βaer observed by COBALD is very well
reproduced by both remote-sensing products throughout the entire analyzed
altitude range, including both aerosol layers inside and above the PBL. In
this case, no significant altitude displacement is observed between the
βaer features of the COBALD and remote-sensing profiles (Fig. 3d).
Quantitative comparison of RALMO vs. COBALD (a–b, e–f) and
CHM15K vs. COBALD (c–d, g–h) for the selected profiles measured on 7 July 2018 (a–d) and 4 September 2018 (e–h). Panels (a, e):
vertical profiles of aerosol backscatter coefficient (βaer) at
455 nm measured by RALMO (green) and COBALD (blue), both without (dashed)
and with (solid) application of the FOV correction. Panels (b, f): vertical
profiles of the RALMO-COBALD difference in βaer (Δβaer) at 455 nm, both without (dashed) and with (solid)
application of the FOV correction. Panels (c, g): vertical profiles of
βaer at 940 nm measured by CHM15K (black) and COBALD (red), both
without (dashed) and with (solid) FOV correction. Panels (d, h): vertical
profiles of Δβaer for CHM15K-COBALD at 940 nm, both
without (dashed) and with (solid) FOV correction.
Figure 4 shows the results of the quantitative comparison for the two cases
just discussed, meaning the βaer profiles obtained after
converting the lidar wavelengths (355 to 455 nm and 1064 to 940 nm) and
applying the FOV correction to the COBALD measurements. In particular,
Fig. 4 shows vertical profiles of βaer at 455 nm from RALMO
and COBALD (panels a, e), βaer at 940 nm from CHM15K and COBALD
(panels c, g), and their respective differences (Δβaer)
at 455 nm (panels b, f) and 940 nm (panels d, h) for 12 July 2018 (panels a–d) and 4 September 2018 (panels e–h). The COBALD βaer and
Δβaer profiles are shown both before (dashed lines) and
after (solid lines) the FOV correction.
The FOV correction significantly improves the agreement between RALMO and
COBALD measurements. Before the correction (dashed lines), the RALMO
profiles are characterized by a systematic high bias with respect to COBALD of about 0.2 Mm-1 sr-1 in the PBL (Fig. 4a–b, e–f). After the
FOV correction (solid lines), which increases the COBALD βaer by
a factor of 1.23 in this region of altitudes (see Fig. 2d and AE profiles
in Fig. 3b), the discrepancy with RALMO is drastically reduced, and the
profiles are in good agreement within ±0.1 Mm-1 sr-1
(Fig. 4b, f). In relative terms, this corresponds to deviations of less
than 10 % of the observed signal in the PBL, which is comparable to the
estimated statistical uncertainty associated with the remote-sensing
measurements alone (see Sect. 2.1).
As already noted in Sect. 3.2, the effect of the FOV correction on the
CHM15K comparison is smaller. In particular, for the case of 4 September 2018 (Fig. 4g–h) the correction leads to a slight improvement in agreement
with COBALD (≈ 0.05 Mm-1 sr-1), whereas on 12 July 2018
(Fig. 4c–d) it slightly increases the discrepancy. Due to the empirical
implementation of the FOV correction, with many assumptions and
simplifications involved (e.g., single-mode size distribution, coarse
parameterization in AE space), it is to be expected that for individual
sounding the magnitude of the correction might be underestimating or
overestimating the true effect of the different FOVs. The uncertainty
introduced by the FOV correction in the statistical comparison is
discussed more in detail in Sect. 4.3.
Statistical comparison
Here we present the results of the statistical comparison for the dataset
introduced in Sect. 2.3, consisting of 17 simultaneous RALMO vs. COBALD
profiles (Sect. 4.2.1) and 31 CHM15K vs. COBALD profiles (Sect. 4.2.2).
RALMO vs. COBALD
Figure 5 shows all data points of the RALMO-COBALD difference (Δβaer at 455 nm) as a function of altitude, expressed in both absolute backscatter coefficient units (panel a) and percent units
relative to the COBALD signal (panel b), after the FOV correction was
applied to all COBALD measurements. Medium-high- and low-aerosol-content
measurements, classified as in Sect. 3.3, are shown by dark-blue and light-blue circles, respectively. The mean deviation (δ) and mean ± standard deviation (δ±σ) profiles of medium-high-aerosol-content data are shown in both panels as thick and thin solid black
lines, respectively. As discussed in Sect. 3, to avoid in-cloud
measurements, we only consider data points with RH < 90 %
(according to the radiosonde measurements).
Statistical comparison of RALMO vs. COBALD: vertical profiles.
Panel (a): all medium-high-aerosol-content (dark-blue circles) and low-aerosol-content (light-blue circles) data points of the RALMO-COBALD
aerosol backscatter coefficient difference (Δβaer) at
455 nm as a function of altitude. Panel (b): same as panel (a), with
Δβaer expressed in percent units (%) relative to
the COBALD measurements (i.e., Δβaerrel). Mean deviation (δ) and mean ± standard deviation (δ±σ) profiles are shown
in both panels by thick solid and thin dashed black lines, respectively. The
3 km altitude level is highlighted by a thin dashed black line.
Medium-high-aerosol-content measurements of RALMO and COBALD βaer are on average in good agreement over the entire altitude range
(0.8–6 km a.s.l.), yet significant discrepancies can occur in individual
profiles. As expected, the largest absolute differences are observed at low
altitudes (z< 3 km), including most of the PBL (hence medium-high-aerosol-content) measurements in our dataset (Fig. 5a). Conversely,
smaller absolute discrepancies yet large relative differences (Fig. 5b) are found in the free troposphere (z> 3 km), where low-aerosol-content measurements prevail.
For z< 3 km, the mean deviation profile (δ) of medium-high-aerosol-content data stays within ±0.1 Mm-1 sr-1, while
standard deviation (σ) ranges between 0.1–0.4 Mm-1 sr-1,
and individual data points rarely exceed ± 0.5 Mm-1 sr-1
(Fig. 5a). In relative terms, δrel shows an average slight
overestimation of 5 %–10 % below 2 km (with σrel≈ 40 %) and an underestimation of 10 %–25 % between 2–3 km (σrel≈ 30 %) (Fig. 5b). Such large relative standard
deviations can be at least partly attributed to the uncertainties associated
with the wavelength conversion and FOV correction of the data (Sect. 3.1–3.2) and spatial- and temporal-variability effects (Sect. 2.4). These
issues are discussed in more detail in Sect. 4.3. For z> 3 km, nearly all absolute differences are smaller than ±0.1 Mm-1 sr-1 (Fig. 5a). Medium-high-aerosol-content data points above 3 km
altitude generally stay within deviations of ±50 %, whereas low-aerosol-content ones often exceed ±100 % (Fig. 5b).
Figure 6 shows the frequency-of-occurrence distribution of RALMO-COBALDΔβaer, calculated for the altitude intervals of 0.8–3 km
(panels a–b), 3–6 km (panels c–d) and 0.8–6 km (i.e., all altitudes: panels e–f), for medium-high-aerosol-content data (blue bars) and all data (i.e.,
including low aerosol content: black lines). The distributions are
calculated in both absolute units within 40 intervals of 0.1 Mm-1 sr-1 width between ±2 Mm-1 sr-1 (panels a, c, e), and
relative units within 40 intervals of 10 % width between ±200 % (panels b, d, f).
Statistical comparison of RALMO vs. COBALD: frequency-of-occurrence distributions of medium-high-aerosol-content data (blue bars) and
all data (black lines). Panels (a, c, e): frequency-of-occurrence
distributions of the RALMO-COBALD difference in aerosol backscatter
coefficient (Δβaer) at 455 nm for the altitude
intervals 0.8–3 km a.s.l. (a), 3–6 km a.s.l. (c) and 0.8–6 km a.s.l. (i.e.,
all altitudes: panel e). Panels (b, d, f): same as panels (a, c, e), with
Δβaer expressed in percent units (%) relative to
the COBALD measurements (i.e., Δβaerrel). The frequency-of-occurrence distributions
are calculated in Δβaer intervals of 0.1 Mm-1 sr-1(a, c, e) and 10 % (a, c, e).
In all distributions, medium-high-aerosol-content measurements show a higher
frequency of occurrence of small relative deviations compared to all data
(Fig. 6b, d, f) and a lower frequency of occurrence of small absolute
differences (Fig. 6b, d, f). The absolute (relative) δ±σ for medium-high-aerosol-content data is -0.018± 0.237 Mm-1 sr-1 (-2 % ± 37 %) for altitudes 0.8–3 km, +0.015 ± 0.068 Mm-1 sr-1 (+13 % ± 38 %) for
3–6 km and +0.001 ± 0.141 Mm-1 sr-1 (+6 % ± 38 %) for all altitudes (see Table 2). Considering all data, δrel±σrel increases to +5 % ± 40 %
for 0.8–3 km, +19 % ± 53 % for 3–6 km and +13 % ± 47 % for all altitudes. We observe that the skewness of the
medium-high-aerosol-content distribution for 0.8–3 km (Fig. 6a–b) is
strongly influenced by a single strongly outlying profile, showing Δβaerrel> 100 % at z< 2 km (see Fig. 5b), which is likely related to atmospheric-variability effects (see
discussion in Sect. 4.3).
Statistical comparison of RALMO vs. COBALD: results for medium-high
aerosol content. For each altitude interval, we show mean deviation (in both absolute units, δ, and percent units relative to COBALD,
δrel) and standard deviation (in both absolute units, σ, and percent units relative to COBALD, σrel) at 455 nm as well as
Pearson correlation coefficient (ρ).
Finally, to evaluate their correlation, Fig. 7 shows a scatterplot of all
RALMO vs. COBALD measurements of βaer at 455 nm (between 0.03–5 Mm-1 sr-1). As in Fig. 5, medium-high-aerosol-content data are
shown as dark-blue circles and low-aerosol-content data as light-blue
circles. Isolines of Δβaer= 0, Δβaerrel=±25 % and Δβaerrel=±50 % differences are indicated by solid, dashed and dotted black lines,
respectively. The 0.1 Mm-1 sr-1 threshold in COBALD βaer at 455 nm, separating low- from medium-high-aerosol-content layers
as described Sect. 3.3, is also shown as a thin dashed vertical line.
Statistical comparison of RALMO vs. COBALD: scatterplot. All
medium-high-aerosol-content (dark-blue circles) and low-aerosol-content
(light-blue circles) data points of aerosol backscatter coefficient
(βaer) at 455 nm measured by RALMO (y axis) vs. βaer
at 455 nm measured by COBALD (x axis). Thin black lines show 1:1 agreement
(solid), ±25 % difference (dashed) and ±50 %
difference (dotted) isolines. The 0.1 Mm-1 sr-1 threshold in
COBALD βaer, separating low- from medium-high-aerosol-content
data at 455 nm (as described in Sect. 3.3), is shown by a dashed vertical black line.
Figure 7 shows a good correlation between RALMO and COBALD measurements in
medium-high-aerosol-content conditions and, as expected, a larger spread for
low-aerosol-content data. The Pearson correlation coefficient (ρ) of medium-high-aerosol-content data is +0.81 for altitudes 0.8–3 km,
+0.62 for 3–6 km and +0.80 for all altitudes (see Table 2),
indicating a high degree of linear correlation between RALMO and COBALD
measurements up to 6 km. We note from Fig. 7 that the highest density of
medium-high-aerosol-content measurements is found at βaer≈ 0.4–2 Mm-1 sr-1, suggesting that this interval
represents the average PBL aerosol content in our dataset. Here, RALMO and
COBALD show a particularly good agreement, with most individual differences
staying below ±25 % (Fig. 7).
CHM15K vs. COBALD
Following the same structure of the previous subsection, here we analyze the
CHM15K vs. COBALD statistical comparison first in terms of vertical profiles
(Fig. 8), then frequency-of-occurrence distributions (Fig. 9) and
finally a scatterplot of all CHM15K vs. COBALD measurements (Fig. 10).
Statistical comparison of CHM15K vs. COBALD: vertical profiles.
Panel (a): all medium-high-aerosol-content (red circles) and low-aerosol-content (orange circles) data points of CHM15K-COBALD aerosol backscatter
coefficient difference (Δβaer) at 940 nm as a function of
altitude. Panel (b): same as panel (a), with Δβaer expressed in percent units (%) relative to the COBALD measurements
(i.e., Δβaerrel). Mean
deviation (δ) and mean ± standard deviation (δ±σ) profiles are shown in both panels by thick solid and thin dashed
black lines, respectively. The 3 km altitude level is highlighted by a thin
dashed black line.
Statistical comparison of CHM15K vs. COBALD: frequency-of-occurrence distributions of medium-high-aerosol-content data (blue bars) and
all data (black lines). Panels (a, c, e): frequency-of-occurrence
distributions of the CHM15K-COBALD difference in aerosol backscatter
coefficient (Δβaer) at 940 nm for the altitude
intervals 0.8–3 km a.s.l. (a) 3–6 km a.s.l. (c) and 0.8–6 km a.s.l. (i.e.,
all altitudes: panel e). Panels (b, d, f): same as panels (a, c, e), with
Δβaer expressed in percent units (%) relative to
the COBALD measurements (i.e., Δβaerrel). The frequency-of-occurrence distributions
are calculated in Δβaer intervals of 0.1 Mm-1 sr-1(a, c, e) and 10 % (a, c, e).
Statistical comparison of CHM15K vs. COBALD: scatterplot. All
medium-high-aerosol-content (red circles) and low-aerosol-content (orange
circles) data points of aerosol backscatter coefficient (βaer)
at 940 nm measured by CHM15K (y axis) vs. βaer at 940 nm
measured by COBALD (x axis). Thin black lines show 1:1 agreement (solid),
± 25 % difference (dashed) and ±50 % difference
(dotted) isolines. The 0.05 Mm-1 sr-1 threshold in COBALD βaer, separating low- from medium-high-aerosol-content data at 940 nm
(as described in Sect. 3.3), is shown by a dashed vertical black line.
Figure 8 shows all data points of Δβaer at 940 nm for
CHM15K-COBALD as a function of altitude, in both absolute backscatter
coefficient units (panel a) and percent units relative to the COBALD signal
(panel b). Analogously to Fig. 5, medium-high-aerosol-content data are
shown as dark-red circles and low-aerosol-content data as orange circles. Note
that the higher density of data points in Fig. 8 compared to Fig. 5 is
due to the larger number of profiles considered for the CHM15K vs. COBALD
comparison (31) relative to the RALMO vs. COBALD comparison (17) (see
Sect. 2.3).
In absolute terms, medium-high-aerosol-content measurements by CHM15K are on
average in good agreement with COBALD over the entire altitude range (Fig. 8a), yet their relative differences are characterized by a large statistical
variability at all altitudes (Fig. 8b). The absolute differences in
βaer between CHM15K and COBALD are typically larger than
observed for RALMO (Fig. 5a), despite βaer being smaller at 940 nm than at 455 nm due to its spectral dependency. This highlights the lower
signal-to-noise ratio of CHM15K compared to a high-power Raman lidar such as RALMO, which results in the large relative fluctuations in Δβaerrel in the free troposphere in Fig. 8b (especially for low-aerosol-content conditions).
Below 3 km altitude, the mean deviation profile of medium-high-aerosol-content measurements shows a slight overestimation of +5 % with respect
to COBALD and a standard deviation of around 40 % (Fig. 8b). In this
case, such a large spread of relative deviations can be also partly
attributed (in addition to the effects mentioned in Sect. 4.2.1) to the
uncertainty related to the assumption of a constant lidar ratio (50 sr)
for all profiles made in the Klett inversion scheme for the retrieval of
the CHM15K backscatter coefficient (see Sect. 2.1). This uncertainty is discussed in more detail in Sect. 4.3. For z> 3 km, the
majority of medium-high-aerosol-content measurements (except one outlying
profile, showing discrepancies of up to -0.5 Mm-1 sr-1 until 4 km altitude) stay within absolute deviations of ±0.2 Mm-1 sr-1 (Fig. 8a).
Figure 9 shows frequency-of-occurrence distributions of CHM15K-COBALDΔβaer at 940 nm for the altitude intervals of 0.8–3 km
(panels a–b), 3–6 km (panels c–d) and 0.8–6 km (i.e., all altitudes: panels e–f), both for medium-high-aerosol-content data (red bars) and all data
(black solid lines), calculated as in Fig. 6. The absolute (relative)
δ±σ for medium-high-aerosol-content data is +0.009 ± 0.185 Mm-1 sr-1 (+5 % ± 43 %) for 0.8–3 km
altitudes, -0.081 ± 0.291 Mm-1 sr-1 (-43 % ± 72 %) for 3–6 km and -0.058 ± 0.205 Mm-1 sr-1 (-22 % ± 59 %) for all altitudes. As for RALMO,
including low-aerosol-content data increases the frequency of occurrence of
small absolute differences (Fig. 9a, c, e) yet reduces the frequency of
occurrence of small relative differences (Fig. 9b, d, f). In particular
for z> 3 km, we observe that the distributions of relative
Δβaerrel for all data are significantly broader for
CHM15K (Fig. 9d) than for RALMO (Fig. 6d). This is due to the low
signal-to-noise ratio of CHM15K at high altitudes, together with the lower
absolute βaer signal at 940 nm compared to 455 nm.
Finally, Fig. 10 shows the scatterplot of all CHM15K vs. COBALD
measurements of βaer at 940 nm (between 0.01–3 Mm-1 sr-1). As in Fig. 8, medium-high-aerosol-content data points are
shown as dark-red circles and low-aerosol-content data points as orange circles. The
0.05 Mm-1 sr-1 threshold in COBALD βaer, separating
low- from medium-high-aerosol-content data at 940 nm (as described in Sect. 3.3), is shown by a thin dashed black line. CHM15K and COBALD show a
generally good correlation in the medium-high-aerosol-content range,
although discrepancies exceeding ±50 % are often observed, and a
very large spread of deviations for low-aerosol-content data (Fig. 10).
The Pearson correlation coefficient is ρ=+0.72 for altitudes
0.8–3 km a.s.l., +0.24 for altitudes 3–6 km a.s.l. and +0.62 for all
altitudes, indicating a generally high degree of correlation at low
altitudes (yet with smaller ρ than for RALMO) and a lower correlation
at high altitudes. We observe that in the range of most frequently observed
βaer in the PBL (approximately 0.2–1 Mm-1 sr-1),
CHM15K regularly exceeds deviations of ± 25 % with respect to
COBALD (Fig. 10), while for RALMO in the corresponding range of βaer (0.4–2 Mm-1 sr-1 at 455 nm), the fraction of individual
differences exceeding ± 25 % is significantly smaller (Fig. 7).
This highlights a generally better precision of RALMO with respect to CHM15K,
even at medium-high-aerosol-content conditions.
Discussion
The results of the statistical comparison for medium-high-aerosol-content
data are summarized in Tables 2–3. In general, both RALMO and CHM15K achieve
a good agreement with COBALD in terms of mean deviations in the PBL
(δrel=-2 % for RALMO and +5 % for CHM15K for
z< 3 km), while simultaneously they show relatively large standard
deviations, even at low altitudes (σrel= 37 % for RALMO and
43 % for CHM15K for z< 3 km). As mentioned throughout the paper,
this can be at least partly attributed to a number of methodological and
technical aspects of our comparison, namely the uncertainties associated
with the wavelength conversion and the FOV correction as well as spatial- and
temporal-variability effects.
Statistical comparison of CHM15K vs. COBALD: results for
medium-high aerosol content. For each altitude interval, we show mean
deviation (in both absolute units, δ, and percent units relative to
COBALD, δrel) and standard deviation (in both absolute units,
σ, and percent units relative to COBALD, σrel) at 940 nm as well as Pearson correlation coefficient (ρ).
The first uncertainty is related to the assumption of the COBALD-derived AE
profiles to perform the wavelength conversion of the lidar data. From
Eq. 2 we can derive that an error of 0.2 in AE, which is a conservative
estimate considering the small difference between the wavelengths that are
compared, results in an error of 5 % in βaer for the
355–455 nm conversion and 2.5 % for the 1064–940 nm conversion.
The second factor is related to the empirical implementation of the FOV
correction, which involves several assumptions and simplifications (see
Sect. 3.3). From Fig. 2b, we can estimate an uncertainty of up to
±20 % in βaer for the PBL (for both 455 and 940 nm)
due to variability in the correction factors in the range of AE = 0.8–1.5,
which is not resolved by the parameterization of the FOV correction factors
in the AE space (Fig. 2d). Finally, the balloon's horizontal drift with
altitude away from the lidar beam and the different integration times of
the two techniques can also affect the spread of their measurements. These
effects can lead to large discrepancies over small altitude layers, as in
the case of strong vertical gradients in βaer (e.g., top of
boundary layer; Fig. 4a–d), as well as potentially over larger altitude
regions due to the horizontal gradient of the βaer field around
the station (e.g., in the case of the strongly outlying profiles of the
statistical comparison; see Figs. 5, 8). The lack of information on the
aerosol size distribution and the high spatial and temporal variability in
atmospheric aerosols prevent an accurate quantification of these artifacts,
which inevitably affects the standard deviations of our statistical
comparison.
In addition to these effects, the large spread of relative deviations below
3 km in the case of CHM15K-COBALD can also be related to the assumption
of a constant lidar ratio (50 sr) for all profiles made in the Klett
retrieval algorithm (see Sect. 2.1). Using a similar ceilometer (Jenoptik
CHM15kx), Wiegner and Geiss (2012) estimate that an error of ±10 sr
in lidar ratio leads to an error in βaer smaller than 2 % in
the boundary layer. Ackermann (1998) shows that 50 ± 10 sr represents
well the expected range of variability in the lidar ratio of continental
aerosol in the infrared spectrum for all RH conditions between 0 %–90 %.
Therefore, this uncertainty conceivably plays a minor role compared to the
effects discussed above.
Despite these limitations, the comparison of individual profiles (Sect. 4.1) shows that both RALMO and CHM15K are able to achieve an excellent
agreement with COBALD measurements, including the correct representation of
fine and complex structures in the βaer vertical profiles
(Figs. 3–4, S2–S3). In particular, the case study of 12 July 2018 (Fig. 4a-d) shows differences between the lidars and COBALD which are smaller than the expected statistical uncertainty associated with the remote-sensing
measurements alone (10 %–15 %; see Sect. 2.1). This suggests that, under
optimal conditions (such as no wind shear, uniform βaer field,
mono-modal aerosol size distribution), the deviations between the two lidars
and COBALD are typically smaller than the average σ of our
statistical comparison. Considering also the good linear correlation
achieved by both lidars (ρ=+0.81 for RALMO and +0.72 for
CHM15K for z< 3 km), we conclude that βaer measurements
by RALMO and CHM15K are in overall good agreement with in situ measurements
by COBALD sondes up to 6 km altitude.
Conclusions
We have presented the first comparison of lower-tropospheric-aerosol
backscatter coefficient (βaer) profiles retrieved by remote-sensing instruments against independent in situ measurements. The two
analyzed lidar systems, one research Raman lidar (RALMO) and one commercial
ceilometer (CHM15K), were validated using simultaneous and co-located
balloon soundings carrying a Compact Backscatter Aerosol Detector (COBALD),
performed during the years 2014–2019 at the MeteoSwiss observatory of
Payerne, Switzerland. COBALD provides high-precision in situ measurements of
βaer at two wavelengths (455 and 940 nm) and is used as the
reference instrument. The βaer profiles retrieved from RALMO
(355 nm) and CHM15K (1064 nm) are converted to 455 nm and 940 nm, respectively, using the altitude-dependent Ångström exponent (AE) profiles retrieved
from COBALD data. To account for the different receiver field-of-view (FOV)
angles between the remote-sensing instruments (0.01–0.02∘) and
COBALD (6∘), we derived a FOV correction using Mie-theory
scattering simulations. The correction factors are parametrized as functions
of AE to account for the size dependency of the solutions. For the
statistical comparison, low- and medium-high-aerosol-content measurements are
separated according to an empirical threshold in βaer.
The comparison of individual profiles shows that both RALMO and CHM15K
achieve a good agreement with COBALD βaer measurements in the
boundary layer and free troposphere up to 6 km altitude, including fine
structures in the aerosol's vertical distribution. The mean ± standard
deviation of RALMO-COBALDΔβaer (at 455 nm) for
medium-high-aerosol-content data is -0.018± 0.237 Mm-1 sr-1 (-2 % ± 37 %) for altitudes 0.8–3 km a.s.l. and +0.001 ± 0.141 Mm-1 sr-1 (+6 % ± 38 %) for all
altitudes between 0.8–6 km a.s.l. For CHM15K-COBALD, the mean ± standard deviation of Δβaer (at 940 nm) for medium-high
aerosol measurements is +0.009 ± 0.185 Mm-1 sr-1 (+5 % ± 43 %) for altitudes 0.8–3 km and -0.058 ± 0.205 Mm-1 sr-1 (-22 % ± 59 %) for all altitudes. The
Pearson correlation coefficient for medium-high aerosol content below 3 km
altitude is +0.81 for RALMO vs. COBALD and +0.72 for CHM15K
vs. COBALD, indicating a high degree of linear correlation between both
lidars and the in situ measurements. For altitudes above 3 km (i.e., in the
free troposphere), absolute deviations generally decrease, while relative
deviations increase due to the prevalence of low-aerosol-content air
masses. The standard deviations of medium-high-aerosol-content data between
3–6 km altitude are 38 % (0.068 Mm-1 sr-1) for RALMO-COBALD
and 59 % (0.205 Mm-1 sr-1) for CHM15K-COBALD, which denotes
the lower signal-to-noise ratio of CHM15K compared to a high-power Raman
lidar system such as RALMO.
While both RALMO and CHM15K agree well with COBALD in terms of mean
deviations, the statistical comparison is characterized by relatively large
standard deviations for both instruments at all altitudes. As discussed in
Sect. 4.3, this can be at least partly attributed to a number of technical
aspects of our comparison, most notably the uncertainty associated with the
FOV correction and spatial- and temporal-variability effects (related to
the balloon's horizontal drift with altitude and different integration
times), which contribute to the spread of the measurements. Due to the lack
of information on the aerosol size distribution and the high spatial and
temporal variability in atmospheric aerosols, these effects cannot be
accurately quantified. Nevertheless, the excellent agreement observed in
individual profiles, including fine and complex structures in the aerosol's
vertical distribution, shows that under optimal conditions (no wind shear,
uniform βaer field, mono-modal aerosol size distribution), the
deviations between the two lidars and COBALD are typically comparable to the
estimated statistical errors in the remote-sensing measurements alone (10 %–15 %). Similar or even larger discrepancies are also reported in the
literature between single-wavelength elastic-backscatter and Raman lidars
(e.g., Matthais et al., 2004; Tsaknakis et al., 2011; Madonna et al., 2018)
as well as between different Raman lidar algorithms (Pappalardo et al.,
2004).
Considering the many uncertainties that characterize the retrieval of
aerosol backscatter profiles from lidar instruments, from technical and
instrumental effects to issues related to the mathematical treatment of
the data (e.g., Pappalardo et al., 2004), our validation using fully
independent in situ measurements is particularly valuable. Despite the
limitations outlined above, results demonstrate that both single-wavelength
(ceilometer) and Raman lidars can provide altitude-resolved measurements
that are quantitatively consistent with high-precision balloon-borne
measurements over the PBL and free-troposphere altitude regions. Overall,
we conclude that aerosol backscatter coefficient measurements by the RALMO
and CHM15K lidar systems are in satisfactory agreement with in situ
measurements by COBALD sondes up to 6 km altitude.
Code availability
The code used for the data analysis can be obtained from the authors upon request.
Data availability
The RALMO and CHM15K data can be accessed through the EARLINET
(https://data.earlinet.org/, EARLINET, 2020) and E-PROFILE (http://data.ceda.ac.uk/badc/eprofile/data/switzerland/payerne, E-PROFILE Network, 2020) networks,
respectively. The COBALD data can be obtained from the authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-2267-2021-supplement.
Author contributions
SB wrote the paper, performed the data analysis and produced all figures. FNG, GM, MH and AH provided scientific support for the analysis of the lidar data. FGW and YP provided scientific support for the analysis of the COBALD data. GR performed the COBALD measurements. FNG coordinated the project.
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 has been supported by the Swiss National Science Foundation
(project nos. PZ00P2 168114 and 200021_159950/2).
Financial support
This research has been supported by the Swiss National Science Foundation (grant nos. PZ00P2 168114 and 200021_159950/2 ).
Review statement
This paper was edited by Eduardo Landulfo and reviewed by two anonymous referees.
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