We present the evaluation activity of the European
Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of
the Level 2 aerosol backscatter coefficient product derived by the
Cloud-Aerosol Transport System (CATS) aboard the International Space
Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter
measurements within a 50 km distance between the ground station and the ISS
overpass and as close in time as possible, typically with the starting time or
stopping time of the EARLINET performed measurement time window within 90 min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter
coefficient and EARLINET. Three ISS overpasses close to the EARLINET
stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are
analyzed here to demonstrate the performance of the CATS lidar system under
different conditions. The results show that under cloud-free, relative
homogeneous aerosol conditions, CATS is in good agreement with EARLINET,
independent of daytime and nighttime conditions. CATS low negative biases are
observed, partially attributed to the deficiency of lidar systems to detect
tenuous aerosol layers of backscatter signal below the minimum detection
thresholds; these are biases which may lead to systematic deviations and slight
underestimations of the total aerosol optical depth (AOD) in climate
studies. In addition, CATS misclassification of aerosol layers as clouds,
and vice versa, in cases of coexistent and/or adjacent aerosol and cloud
features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low
negative biases in CATS backscatter coefficient profiles, of the order of
6.1 %, indicate the good nighttime performance of CATS. During daytime,
a reduced signal-to-noise ratio by solar background illumination prevents
retrievals of weakly scattering atmospheric layers that would otherwise be
detectable during nighttime, leading to higher negative biases, of the order
of 22.3 %.
Introduction
The Cloud-Aerosol Transport System (CATS) is a satellite-based elastic
backscatter lidar developed to provide near-real-time, vertically resolved
information on the vertical distribution of aerosols and clouds in the
Earth's atmosphere (McGill et al., 2015). Developed at NASA's Goddard
Space Flight Center, CATS is based on the Cloud Physics Lidar (CPL; McGill
et al., 2002) and the Airborne Cloud-Aerosol Transport System (ACATS; Yorks
et al., 2014), designed to operate aboard the high-altitude NASA ER-2
aircraft. CATS operated as a scientific payload aboard the Japanese
Experiment Module – Exposed Facility (JEM-EF), utilizing the International
Space Station (ISS) as a space science platform (Yorks et al., 2016).
Starting from 10 February 2015, CATS provided aerosol and
cloud profile observations along the ISS flight track for more than 33
months, until 30 October 2017, when the system suffered an
unrecoverable fault.
CATS was developed to meet three main scientific goals. The primary objective
was to measure and characterize aerosols and clouds on a global scale. The
spaceborne lidar orbited the Earth at an altitude of approximately 405 km
and a 51∘ inclination. The use of the ISS as an observation platform
facilitated, for the first time, global lidar-based climatic studies of
aerosols and clouds at various local times (Noel et al., 2018; Lee et al.,
2018). In addition, near-real-time data acquisition of the CATS observations
was developed towards the improvement of aerosol forecast models (Hughes et
al., 2016). A secondary objective was related to the need for long-term and
continuous satellite-based lidar observations to be available for climatic
studies. The first spaceborne lidar mission, the Lidar In-Space Technology
Experiment (LITE; McCormick et al., 1993) in 1994, was succeeded by the
joint NASA and Centre National d'Études Spatiales (CNES) Cloud-Aerosol
Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission in
June, 2006 (Winker et al., 2007). Since 2009, the Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP) instrument (Winker et al., 2009) aboard
CALIPSO operates on the secondary backup laser. The launch of the
post-CALIPSO missions, the joint European Space Agency (ESA) and JAXA's
satellite Earth Cloud, Aerosol and Radiation Explorer (EarthCARE; Illingworth
et al., 2015) and NASA's Aerosols, Clouds, and Ecosystems (ACE) are
planned for 2021 and post-2020 respectively. The CATS project was partially
intended to fill a potential gap in global lidar observations of aerosol and
cloud profiling. The third scientific objective of CATS was to serve as a
low-cost technological demonstration for future satellite lidar missions
(McGill et al., 2015). Its scientific goal to explore different technologies
was fulfilled through the use of photon-counting detectors and of two low-energy (1–2 mJ) and high repetition rate (4–5 kHz) Nd:YVO4 lasers
(multi-beam and high spectral resolution lidar – HSRL; UV demonstrations), aiming to provide simultaneous
multiwavelength observations (355, 532, and 1064 nm). Additional gains of the
CATS project were related to the exploitation and risk reduction of newly
applied laser technologies to pave the way for future spaceborne lidar
missions (high repetition rate, injection seeding, and wavelength tripling at
355 nm).
CATS performance has been validated against ground-based AERONET (Holben et al., 1998) measurements and evaluated against
satellite-based aerosol optical depth (AOD) retrievals of the Aqua and Terra
Moderate Resolution Imaging Spectroradiometer (MODIS; Levy et al., 2013) and active CPL
(McGill et al., 2002) and CALIPSO CALIOP (Winker et al., 2009) profiles of
the extinction coefficient and AOD at 1064 nm. Lee et al. (2018) compared
daytime quality-assured CATS V2.01 vertically integrated extinction
coefficient profiles (1064 nm) and AERONET AOD (1020 nm) values, spatially
(within 0.4∘ longitude and latitude) and temporally (±30 min) collocated, and found a reasonable agreement, with a correlation of
0.64. A comparative analysis of CATS and MODIS C6.1 Dark Target (DT) AOD
retrievals, through spectral interpolation between 0.87 and 1.24 µm
channels, reported a correlation of 0.75 and slope of 0.79 over ocean. In
addition, Lee et al. (2019) evaluated AOD and extinction coefficient
profiles from CATS through intercomparison with CALIOP. Regarding AOD,
analysis of 2681 CATS and CALIOP collocated observation cases (within
0.4∘ longitude and latitude and ±30 min ISS and CALIPSO overpass
difference) showed correlations of 0.62 and 0.52 over land and ocean
respectively during daytime (1342 cases) and 0.84 and 0.81 over land and
ocean respectively during nighttime (1339 cases). Comparison of CATS and
CALIOP collocated extinction coefficient profiles based on the closest
Euclidian distance on the Earth's surface shows also good shape agreement
despite an apparent CALIOP underestimation in the lowest 2 km height. CATS
and CALIOP observations were used by Rajapakshe et al. (2017) to study the
seasonally transported aerosol layers over the SE Atlantic Ocean. The
performed comparative analysis reported on similar geographical patterns
regarding above-cloud aerosol (ACA), cloud fraction (CF), and ACA occurrence
frequency (ACA_F) between CATS and CALIOP retrievals.
However, the authors reported also on differences between CATS and CALIOP
vertical aerosol distributions, with the ACA bottom height identified by CATS
being lower than the respective of CALIOP. Noel et al. (2018) implemented
measurements from CATS to investigate the diurnal cycle and variations in
clouds over land and ocean. The authors showed that both CATS and CALIOP
profiles and CF agree well on both the vertical patterns and values at 01:30
and 13:30 LT, over both land and ocean, with minor differences of the order
of 2 %–7 % throughout all cloud profiles. Finally, CATS
depolarization measurements, which are critical in the processing algorithms
of aerosol-subtype classification, were investigated in the case of desert
dust, smoke from biomass burning, and cirrus clouds (Yorks et al., 2016) and
were found to be consistent and in good agreement with depolarization measurements
from previous studies and historical datasets implementing CPL (Yorks et
al., 2011) and CALIOP (Liu et al., 2015).
Overall, CATS retrievals have been evaluated and found to be in reasonable
agreement with ground-based AERONET, airborne CPL, and satellite-based MODIS
and CALIOP measurements. However, for the quality assessment of CATS
backscatter coefficient profiles, a large-scale and dense network of
ground-based lidar systems is needed in order to facilitate high-quality
collocated and concurrent measurements. This necessity is largely related to
the ISS orbital characteristics, the CATS near-nadir viewing (0.5∘ off
nadir), the lidar narrow footprint (14.38 m diameter), and the limited
number of ISS overpasses. The European Aerosol Research Lidar Network
(EARLINET) consists of a unique infrastructure for assessing the validation
needs for spaceborne lidar missions. EARLINET operates in the framework of
Aerosols, Clouds and Trace Gases Research Infrastructure (ACTRIS) as a
pan-European effort to develop a coordinated lidar research infrastructure
(Pappalardo et al., 2014) of advanced Raman lidar systems and is
characterized by extensive geographical coverage.
In this paper, we utilize EARLINET for the evaluation of the CATS Level 2
aerosol backscatter coefficient product at 1064 nm. The paper is structured
as follows: in Sect. 2 we introduce aspects of CATS and EARLINET relevant
to the study. and additionally the comparison methodology is presented and
discussed. Specific study cases are evaluated and discussed in Sect. 3.
Section 4 presents the generic intercomparison results between CATS and
EARLINET, while the concluding remarks on the CATS–EARLINET backscatter
coefficient evaluation are summarized in Sect. 5.
Data and methodologyCATS
The CATS elastic backscatter lidar was designed to provide near-real-time
measurements of the vertical profiles of aerosol and cloud optical
properties at three wavelengths (355, 532, and 1064 nm). As a payload of the
JEM-EF on the ISS, CATS was designed to operate two high repetition rate
lasers in three different modes and at four instantaneous fields of view
(iFOVs). Mode 1 was designed as multi-beam backscatter and depolarization
configuration at 532 and 1064 nm, where a beam splitter would produce two
footprints of 14.38 m diameter on the Earth's surface to the left-side FOV
(LSFOV) and the right-side FOV (RSFOV) of the ISS orbit track, separated by
approximately a distance of 7 km. Mode 2 was designed as a demonstration of
the HSRL to provide backscatter profiles at 532 nm and backscatter and
depolarization ratio profiles at 1064 nm (forward FOV – FFOV). Mode 3 was designed
to operate and provide backscatter at 355, 532, and 1064 nm and at a
depolarization ratio at 532 and 1064 nm. CATS was a technological demonstration
designed to operate on orbit between 6 months and 3 years. Due to a
failure in the CATS optics at the 355 nm wavelength, CATS did not operate in
Mode 3, while the use of Mode 1 was limited between 10 February 2015 and
21 March 2015 due to a failure in the electronics of laser 1. Nevertheless, the
successful long-term operation of Mode 2, between February 2015 and October 2017, allowed CATS to fulfill its science objectives.
CATS processing algorithms (Pauly et al., 2019) rely heavily on the
processing algorithms developed in the framework of the CPL, ACATS, and
CALIPSO lidar systems (Palm et al., 2002; Yorks et al., 2011; Hlavka et al.,
2012), while the products of CATS are provided in different levels of
processing. CATS Level 1B data include vertical profiles of total and
perpendicular attenuated backscatter signals, which are range-corrected, calibrated,
and annotated with ancillary meteorological parameters based on previous
work using CPL and CALIPSO (McGill et al., 2007; Powell et al., 2009;
Vaughan et al., 2010). Level 2 products provide the vertical distribution of
aerosol and cloud properties (depolarization ratio, backscatter, and
extinction coefficient profiles at 1064 nm – FFOV), with a horizontal and
vertical resolution of 5 km and 60 m respectively. In addition, Level 2 data
include geophysical parameters of the identified atmospheric layers
(vertical feature mask – feature type and aerosol subtype), the required
horizontal averaging, and information on the feature type classification
confidence (Yorks et al., 2019). In addition to CATS Level 2 feature type
(namely clear air, cloud, aerosol, and totally attenuated), the algorithm
provides the confidence level of the feature type classification, which is similar to
the CALIOP cloud–aerosol discrimination (CAD) algorithm (Liu et al., 2004, 2009). The CATS feature type score is a multidimensional probability
density function (PDF) developed based on multiyear CPL observations that
discriminates cloud and aerosol features, assigning an integer between -10
and 10 for each detected atmospheric layer.
In this study, we used CATS Level 2 V2.01 profiles (Palm et al., 2016). A
comprehensive overview of the CATS instrument and CATS scientific goals is
given by McGill et al. (2015) and Yorks et al. (2016), while detailed
information related to CATS datasets and a CATS lidar quick-look browser can
be found in the CATS Data Release Notes, Quality Statements and Theoretical
Basis, available at https://cats.gsfc.nasa.gov/ (last access: 20 December 2018).
EARLINET
EARLINET (https://www.earlinet.org/index.php?id=earlinet_homepage, last access: 20 December 2018) was founded by the European
Commission (Bösenberg et al., 2001) as a research project within the
framework of the Fifth Framework Programme (FP5). Currently the network
activity is integrated and constitutes a major component of the ACTRIS
research infrastructure (https://www.actris.eu/, last access: 20 December 2018). The main objective of EARLINET is to establish an extended,
coordinated, and continental network of sophisticated ground-based Raman
lidar systems (Ansmann et al., 1992). The vertical distribution of aerosols in the atmosphere as
well as their temporal evolution are provided by high-resolution EARLINET
measurements over Europe. The long-term continuous operation of EARLINET infrastructure has fostered a quantitative, comprehensive, and
statistically significant database of the distribution of aerosol on a
continental scale (Bösenberg et al., 2003; Pappalardo et al., 2014).
Since the beginning of the initiative in 2000, EARLINET has significantly
increased its observing and operational capacity. Currently, EARLINET is
composed of 29 operating lidar stations distributed over Europe
(Fig. 1), including seven admitted or joining stations. EARLINET stations
are classified as “active”, “not permanent”, “joining”, and “not
active”. An EARLINET station is classified as active on the condition that
measurements are performed regularly and simultaneously with the other stations
composing the lidar network, and accordingly, contribute with uploading
the performed measurements to the EARLINET database
(https://www.earlinet.org/, last access: 20 December 2018). Lidar
observations in the framework of EARLINET are performed according to a
common schedule – on preselected dates. The schedule involves three
measurements per week, namely one during daytime at around local noon (Monday, 14:00 ± 1 h) and two during nighttime (Monday and Thursday, sunset + 2/3 h) to
enable Raman extinction retrievals. In addition to the preselected dates of
the operation schedule, dedicated measurements are performed to monitor
special events such as major volcanic activity (Ansmann et al., 2010, 2011; Pappalardo et al., 2013; Perrone et al., 2012; Sicard
et al., 2012; Wang et al., 2008), long-range transport of Saharan dust
(Ansmann et al., 2003; Solomos et al., 2017, 2018), and smoke particles
(Ortiz-Amezcua et al., 2017; Janicka et al., 2017; Stachlewska et al., 2018).
Some of the EARLINET systems perform 24/7 continuous measurements as, for
example, the PollyXT systems (Engelmann et al., 2016; Baars et al., 2016).
The quality assurance and improvement of the performance of the EARLINET
systems are tested through the intercomparison of both the infrastructure
(Wandinger et al., 2016) and the optical products (Böckmann et al.,
2004; Pappalardo et al., 2004). In addition, the homogenization of the lidar
data in a standardized output format is facilitated, and an automatic
algorithm is developed to further address the quality assurance of the lidar
measurements (the Single Calculus Chain – SCC; D'Amico et al., 2015, 2016; Mattis et al., 2016). The SCC has been used in near-real time
to show the potential operationality of the network in a 72 h continuous
measurement exercise in 2012 (Sicard et al., 2015).
Distribution of EARLINET lidar stations over Europe and
western Asia. Green dots: stations used in the intercomparison. ISS orbits
between February 2015 and September 2016 are overlaid in red for daytime and in blue for
nighttime overpasses.
Due to its implicit characteristics, EARLINET is an optimum tool for supporting
satellite-based lidar missions with extensive experience in satellite
calibration and validation activities. EARLINET and CALIPSO (Winker et al.,
2009) correlative measurements are regularly performed in order to
investigate the quality of CALIOP observations, to test the presence of
possible biases, and to assess different aspects of spaceborne lidar
measurements (e.g., Pappalardo et al., 2010; Mamouri et al., 2009; Mona et
al., 2009; Perrone et al., 2011; Wandinger et al., 2011; Amiridis et al.,
2013; Grigas et al., 2015; Papagiannopoulos et al., 2016). Similarly, ESA
validation programs of the Atmospheric Laser Doppler Instrument (ALADIN)
aboard Aeolus (Stoffelen et al., 2005; Ansmann et al., 2007) and the
ESA–JAXA EarthCARE (Illingworth et al., 2015) are highly dependent on
ground-based EARLINET correlative measurements. In addition, EARLINET
supports the homogenization of the different satellite missions. CALIOP is
a two-wavelength polarization-sensitive lidar that operates at 532 and 1064 nm, while the ESA's ALADIN aboard Aeolus and the ESA–JAXA Atmospheric Lidar (ATLID) aboard
EarthCARE operate at 355 nm, and NASA's CATS lidar operates at 532 and 1064 nm in Mode
1 and 1064 nm in Mode 2 (Yorks et al., 2014). EARLINET supports the
continuity of satellite lidar missions through the calculation of
aerosol-dependent spectral conversion factors between different wavelengths
to homogenize different missions at different operating wavelengths in order
to provide a long-term 3-D climatic record from space (Amiridis et al., 2015; Chimot et al., 2017;
Marinou et al., 2017; Proestakis et al., 2018).
To obtain a significant number of collocated and concurrent CATS–EARLINET
cases, a large number of EARLINET stations contributed to the CATS
evaluation activity. Figure 1 shows the geographical distribution of the
active EARLINET stations during the study over Europe and Asia, including
the daytime and nighttime overpasses of ISS within the evaluation period,
between February 2015 and September 2016, encompassing the first 20 months of CATS
operation. The green circles denote the stations participating in the
CATS–EARLINET intercomparison activity (namely – in alphabetical order –
Athens-NOA, Athens-NTUA, Barcelona, Belsk, Bucharest, Cabauw, Dushanbe,
Évora, Hohenpeißenberg, Lecce, Leipzig, Potenza, Thessaloniki, and
Warsaw). All participating stations operate high-performance multiwavelength
lidar systems. Six of the contributing stations (Athens-NOA, Cabauw,
Dushanbe, Évora, Leipzig, and Warsaw) are part of the PollyNET subnetwork
(http://polly.tropos.de/, last access: 17 September 2019), operating 24/7 portable, remote-controlled
multiwavelength-polarization Raman lidar systems (PollyXT; Baars et al.,
2016; Engelmann et al., 2016). Due to the geographical distribution of
EARLINET stations, the evaluation activity accounts for a large variety of
aerosol types (marine, urban, desert dust, and smoke). Table 1 provides the
locations of the EARLINET stations contributing to this analysis along with
the surface elevation and the respective identification codes.
Contributing EARLINET lidar stations, including
identification codes, geographical coordinates, and elevation.
In order to quantitatively address the accuracy and representativeness of
CATS retrievals, we follow the methodology introduced by EARLINET for CALIOP
validation, which is based on correlative independent measurements
(Pappalardo et al., 2010). For the validation of spaceborne lidar
observations, of fundamental significance is the spatial and temporal
variability in the atmospheric scene. The effect of the distance between
ground-based lidar measurements and space-based lidar measurements was
investigated in the framework of the CALIPSO validation. In particular,
EARLINET-based studies attribute an introduced discrepancy of the order of
5 % to the intercompared signal analysis when the horizontal distance
between the EARLINET stations and the spaceborne lidar footprint is below
100 km (Mamouri et al., 2009; Mona et al., 2009; Pappalardo et al., 2010;
Papagiannopoulos et al., 2016). In the context of the applied validation
criteria, we selected CATS measurements within 50 km horizontal distance
between the EARLINET stations and the ISS subsatellite overpass position. In
addition, the correlative measurements should be as close in time as
possible. EARLINET contributed with performed measurements as close in time
as possible, typically with a starting time or stopping time of the performed
measurements window within 90 min of the ISS overpass. The
CATS–EARLINET cases considered in the assessment of the accuracy and
representativeness of CATS backscatter coefficient profiles are provided in
Table 2, including the name of the EARLINET station, the EARLINET
measurements window, the ISS overpass time, and the ISS minimum distance between
the corresponding EARLINET station and the lidar footprint of CATS and the
daytime and nighttime information.
ISS–CATS and EARLINET cases considered in the evaluation process of
CATS backscatter coefficient profiles at 1064 nm.
The number of available cases for the intercomparison is subject to a
certain number of constraints. First and foremost, the orbital inclination
of the ISS does not allow overpasses close to EARLINET stations north of
52.2∘ latitude. Second, the ISS crossing time and
ground track over an area are highly variable, enhancing the probability of
the overpass time to fall outside of the predefined common and fixed schedule
of EARLINET measurements. In addition, to account for contamination effects
of multiple scattering and specular reflection in the intercomparison
process, only cloud-free atmospheric scenes are used. Cases with detected
cirrus clouds, either at the EARLINET range-corrected signal quick looks or
at the ISS–CATS backscatter coefficient profiles and feature type profiles,
are not considered in the study. Initially, the presence of clouds is
investigated through the implementation of the CATS backscatter coefficient and
depolarization time–height images and EARLINET range-corrected signal. Cases
for which the retrieval of EARLINET temporally averaged profile is not
feasible due to the presence of clouds, and/or CATS cases in which the presence
of clouds propagated into the CATS spatially averaged profile, are discarded
from the analysis. Regarding CATS, the “Sky_Condition” flag
is also used to screen cloudy (no aerosols) and hazy or cloudy (both
clouds and aerosols) profiles from the analysis. The “Feature_Type_Score” parameter stored in the Level 2 data was
additionally used to remove aerosol cases of medium or low confidence in the
comparison process (“Feature_Type_Score”
≥-1). Applying all match-up selection criteria resulted in a total of
47 correlative CATS–EARLINET cases suitable for quantitatively addressing the
accuracy and representativeness of the CATS Level 2 backscatter coefficient
product at 1064 nm. CATS requirements applied in the methodology are
summarized in Table 3.
List of CATS quality-assurance thresholds applied in the
EARLINET comparison.
Mode7.2Level2ParameterBackscatter coefficientWavelength1064 nmDistance≤50 km radius from the EARLINET stationsFeature type score≤-2Sky condition0 – clean skies – and 1 – clear skies (no clouds)Backscatter coefficient0≤b1064nm≤2 (Mm-1 sr-1)Vertical range window≤10 km a.s.l.Particle backscatter coefficient retrievals from ground-based lidars
at 1064 nm
In order to evaluate the CATS Level 2 aerosol backscatter product at 1064 nm,
we utilized backscatter coefficient profiles calculated either with the SCC
algorithm or, in case of PollyXT lidar systems, with independently developed
user-assisted retrieval algorithms (Baars et al., 2016). The EARLINET backscatter
coefficient profiles used in this study are calculated with the SCC version
4 algorithm (for the stations that are not part of PollyNET) and with the
methodology described in Haarig et al. (2017; for the stations that are part
of PollyNET). The SCC algorithm (D'Amico et al., 2015, 2016;
Mattis et al., 2016) is developed with the concept of sustaining the
homogeneity of aerosol products derived from different EARLINET lidar
systems while satisfying the need for coordinated, quality-assured
measurements. It consists of five different modules, including one for
handling the pre-processing of raw lidar data by applying all the necessary
instrumental corrections to the signal and a module for providing the final
aerosol optical products, namely the particle backscatter and extinction
coefficient. In particular, the SCC algorithm calculates the backscatter
coefficient with the iterative method (Di Girolamo et al., 1995), using only
the elastic lidar channels. To calculate the b1064nm with these
methods, an assumption of the lidar ratio value is required (as a profile or
a height-independent value, representative of the corresponding atmospheric
scene) and the selection or determination of a reference height (R0),
usually chosen at an altitude range with the minimum aerosol contribution.
All methods applied within the SCC have been tested against synthetic
(Mattis et al., 2016) and real lidar data (D'Amico et al., 2015). The
comparison showed that by using only the signal from the elastic channels,
the mean relative deviation in the calculation of the aerosol backscatter
coefficient at 1064 nm is less than 30 % (Althausen et al., 2009; Baars
et al., 2012; Engelmann et al., 2016; Hänel et al., 2012), thus meeting
the quality-assurance requirements of EARLINET. None of the lidar systems
participating in the present study are equipped with a
rotational–vibrational Raman channel excited by the 1064 nm, as, for example,
recently reported by Haarig et al. (2017). In the case of PollyXT lidars, for
the daytime backscatter coefficient calculations, the Fernald–Klett method
(Klett, 1981; Fernald, 1984) is implemented, assuming a height-independent
lidar ratio. For the nighttime calculations, the Raman channel at 607 nm is
additionally used (Baars et al., 2016). Specifically, the basic lidar
equation at 1064 nm can be described by
P1064R=C1064ORR2βpar1064R+βmol1064Rexp-2∫0Ramol1064r+apar1064rdr,
and the corresponding lidar equation at 607 nm can be described by
P607R=C607ORR2βmol607Rexp-∫0Ramol532r+apar532r+amol607r+apar607rdr.
A solution for the particle backscatter coefficient at 1064 nm is obtained
using the ratio
P607(R0)P1064(R)P1064(R0)P607(R),
where P607 and P1064 stand for the power received from a
distance R, with respect to the lidar system, at 607 nm and 1064 nm
respectively. The constant C at 607 or 1064 nm contains all range-independent system parameters. The overlap function O(R), which is less than
unity for the altitude range where the laser beam is not completely inside
the receiving telescope field of view (Wandinger and Ansmann, 2002), is assumed
to be identical between the two channels, which is the case for PollyXT systems,
which use one beam expander for all three emitted wavelengths. βmol and βpar represent molecular and particle
backscattering respectively, whereas αmol and αpar
are the molecular and particle extinction coefficients.
(a) Nighttime ISS orbit over Athens, Greece, on 1 February 2016 (blue line). The concentric white circles denote regions of
10, 20, 30, 40, and 50 km from the location of PollyXT NOA lidar system
(white dot). Red color in the ISS footprint indicates CATS observations
within 50 km distance from the NOA PollyXT lidar system. (b) CATS
backscatter coefficient at 1064 nm on 1 February 2016, 17:24 UTC. The white box
depicts CATS observations used for the profile intercomparison. (c) PollyXT NOA range-corrected signal time-series at 1064 nm. The white box
delineates the temporal averaging of the lidar signals (17:45–19:30 UTC),
while the red line denotes the ISS overpass at 1 February 2016, 17:24 UTC –
the closest distance time. (d) CATS (blue line) and PollyXT NOA (red line) mean
profiles and standard deviations of backscatter coefficient at 1064 nm (0–6 km).
Finally, in order to perform the intercomparison between CATS and EARLINET
profiles, the high resolution of EARLINET profiles was lowered to match the
vertical resolution of CATS profiles (i.e., 60 m). The objective of obtaining
profiles of similar vertical resolution was addressed through computing the
EARLINET mean backscatter coefficient value from all EARLINET bins within
each CATS 60 m backscatter coefficient height range. The computed EARLINET
profiles of similar vertical resolution to CATS followed, with high
accuracy, the characterizes and tendencies, both qualitative and
quantitative, of the initial EARLINET profiles despite the loss of vertical
resolution (Iarlori et al., 2015).
Demonstration of the comparison methodology for a case study over Athens
To illustrate the evaluation methodology for the CATS Level 2 aerosol
backscatter coefficient at 1064 nm, a pair of collocated and concurrent CATS
and EARLINET lidar observations is shown in Fig. 2. The example refers to
a nighttime ISS overpass of the coastal city of Athens, Greece, on
1 February 2016. During that period, the PollyXT NOA system was
operating in a 24/7 mode in Athens, at the premises of the National
Observatory of Athens, to fulfill the needs of an ACTRIS Joint Research
Activity (JRA) related to aerosol absorption (Tsekeri et al., 2018). At the
same time, on Monday 1 February 2016, the lidar station operating
at the National Technical University of Athens (NTUA) was performing
nighttime measurements according to the EARLINET schedule of regular and
simultaneous measurements in order to enable Raman extinction retrievals.
The closest distances between the CATS footprint of the ISS overpass and the
locations of the EARLINET-at (NTUA) and EARLINET-no (NOA) stations were
approximately 18.58 and 23.3 km at 17:24 UTC respectively (Fig. 2a). The vertical
distribution of aerosols and clouds is shown in the CATS 1064 nm backscatter
coefficient quick look (Fig. 2b) and the PollyXT NOA
lidar range-corrected signal at 1064 nm between 1 February 2016 at 12:00 UTC and
2 February 2016 at 00:00 UTC (Fig. 2c). The temporal averaging window of the
ground-based lidar signal is shifted to a few minutes after the ISS overpass
(17:45–19:30 UTC), due to routine and automatic depolarization calibration
measurements conducted with PollyXT NOA system at the exact time of the
overpass (Engelmann et al., 2016), while for the EARLINET-at system the
temporal averaging window between 18:20:51 and 19:57:41 UTC was used. Both
CATS and PollyXT NOA quick looks advocate for the horizontal and vertical
homogeneity of the scene. For the comparison of CATS and EARLINET
observations, the latest are regridded to the CATS Level 2 vertical
resolution (60 m). Accordingly, the CATS spatially averaged and the EARLINET
(NOA–NTUA) temporally averaged backscatter coefficient profiles are
qualitatively compared (Fig. 2d). The observed disagreements between the two
EARLINET profiles are related to differences between the two systems, the
different surface elevations of the locations of the two stations (86 m for
EARLINET-no and 212 for EARLINET-at), and the different overlap regions. The
horizontal bars in the CATS profile (Fig. 2d) correspond to the standard
deviation of the spatially averaged backscatter coefficient profiles.
(a) Nighttime ISS orbit over the EARLINET Leipzig station
on the 13 September 2016 at 03:37:49 UTC and of
closest distance between the footprint of CATS and the EARLINET Leipzig
station of 3.79 km. The white dot denotes the location of Leipzig lidar
system, while the blue line shows the lidar footprint of CATS. (b) CATS
backscatter coefficient at 1064 nm. (c) CATS (blue line) spatially and
EARLINET Leipzig (red line) temporally averaged backscatter coefficient
profiles (1064 nm). The implemented EARLINET Leipzig time window of
cloud-free measurements was between 00:00:00 and 02:30:00 UTC. The
horizontal blue and red lines denote the variability (1 standard
deviation) in the CATS- and EARLINET-measured atmospheric scenes
respectively.
The comparison of the mean backscatter coefficient profiles retrieved by
CATS and the two corresponding EARLINET NOA and NTUA profiles presented in
Fig. 2 is an initial demonstration of the good agreement between the two
products. The CATS instrument reproduces the observed aerosol features in
terms of aerosol load as well as their vertical distribution (Fig. 2d). The
assessment of CATS backscatter coefficient is performed in the region
between 0.5 km a.g.l. of the EARLINET sites to account for
overlap effects between the laser beam and the telescope (Wandinger and
Ansmann, 2002), topographic effects, surface returns, and differences of
atmospheric samples within the planetary boundary layer (Fig. 2d – shaded
area iii) and 10 km a.s.l.. An upper limit of 2 Mm-1 sr-1
is applied to the aerosol backscatter coefficient values in order to
account for cloud features possibly misclassified as aerosols (Fig. 2d –
shaded area ii). Finally, cases of EARLINET backscatter coefficient values
below the CATS minimum detectable backscatter limit at 1064 nm are not
included in the comparison when the corresponding CATS backscatter
coefficient is reported to be zero (Fig. 2d – shaded area i). The latter
constraint is applied to account for very thin detected layers from
ground-based lidar systems with backscatter values below the CATS minimum
detection limit due to the low signal-to-noise ratio (SNR) values. The
discussed constraints are employed because of our basic objective to
quantitatively assess the representativeness and accuracy of the detected
CATS aerosol features while preventing possible contaminations (e.g., presence of clouds) from propagating into the CATS–EARLINET dataset.
ResultsCATS–EARLINET correlative cases
To illustrate strengths and limitations of CATS products, we discuss in
detail three selected cases of collocated and concurrent CATS–EARLINET
observations close to the (EARLINET) stations of Leipzig, Évora, and
Dushanbe. The three study cases represent different atmospheric conditions
with an increasing degree of difficulty in the detection of representative
aerosol layers by CATS.
Case I: ISS–CATS over Leipzig – 13 September 2016 at 03:37 UTC
The first overpass considered here shows a representative case study of a
nighttime ISS orbit on 13 September 2016 (blue line), at a minimum
distance of 3.78 km from the EARLINET Leipzig, Germany, PollyXT lidar
system (indicated by a white dot), at 03:37 UTC (Fig. 3a). The CATS particulate
backscatter coefficient cross section at 1064 nm (Fig. 3b) shows the
presence of aerosols up to 2.6 km a.s.l. The CATS feature-mask algorithm
classifies all of the detected layers as aerosols (not shown). The ground-based lidar measurements at the Leipzig station between 00:00 and 12:00 UTC did
not report any cloud features either, including cirrus clouds. CATS
spatially averaged and Leipzig temporally averaged profiles were derived
from CATS profiles within horizontal distance of 50 km, between the Leipzig
station and the ISS footprint, and Leipzig measurements within 90 min of
the ISS overpass respectively (Fig. 3c). The direct comparison of the
backscatter coefficient profiles, measured from the EARLINET Leipzig station
(red line) and CATS (blue line), along with their standard deviations
(horizontal error bars), indicate also the presence of aerosol up to 2.6 km a.s.l. The intercompared profiles between ISS–CATS and
EARLINET Leipzig station are characterized by high agreement, although
discrepancies are also present. To the uppermost part of the profiles,
between 2.5 and 3 km a.s.l., due to the higher SNR, the Leipzig lidar is
capable of detecting tenuous atmospheric features of low backscatter
coefficient values. Although the case presented and discussed in Fig. 3
corresponds to a nighttime ISS overpass, the case is representative for
cloud-free and relative homogeneous atmospheric scenes in terms of aerosols,
for both daytime and nighttime solar background illumination, demonstrating
the overall high performance of CATS under such conditions.
Case II: ISS–CATS over Évora – 31 May 2016 at 19:43 UTC
Small biases between the EARLINET and CATS backscatter coefficient are also
identified in specific cases. CATS particulate backscatter coefficient
profiles are available for the identified atmospheric features and not as
full profiles, as in the case of the attenuated backscatter profiles. The
feature classification algorithm, assuming no cloud or aerosol layers are
detected and no overlaying opaque layers are present, classifies the
atmospheric layers as clear air. Clear-air segments though are not pristine
and aerosol-free, as they frequently contain tenuous particulate layers (Kim
et al., 2018). Layers of atmospheric features that are not detected contain
either fill values (0.0 km-1 sr-1) or are marked as invalid in
cases when the calculation of the particulate backscatter coefficients was
not possible (-999.9). This scheme of assigning appropriate backscatter
coefficient values to the detected atmospheric features (e.g., aerosol and
clouds) propagates through many of the Level 2 products in the comparison of
CATS Level 2 data and thus in the assessment of the representativeness of CATS
observations. Consequently, the comparison of CATS Level 2 backscatter
coefficient profiles against EARLINET observations is only possible over the
detected atmospheric features. In addition, the identification of the
atmospheric features strongly depends on the calibrations of the CATS lidar
system and to the level of the background signal – solar illumination
conditions – due to the different SNRs between daytime and nighttime.
(a) Daytime ISS orbit over the EARLINET Évora station
on the 31 May 2016 at 19:43:31 UTC and of
closest distance between the footprint of CATS and the EARLINET Évora
station of 39.42 km. The white dot denotes the location of Évora lidar
system, while the red line shows the lidar footprint of CATS. (b) CATS
backscatter coefficient at 1064 nm. (c) CATS (blue line) spatially and
EARLINET Évora (red line) temporally averaged backscatter coefficient
profiles (1064 nm). The implemented EARLINET Évora time window of
cloud-free measurements was between 19:29:56 and 19:59:35 UTC. The
horizontal blue and red lines denote the variability (1 standard
deviation) in the CATS- and EARLINET-measured atmospheric scenes
respectively.
Figure 4 shows a daytime ISS match-up, on 31 May 2016 (red line), at a
minimum distance of 39.4 km from the EARLINET station of Évora,
Portugal (indicated by a white dot) at 19:43:41 UTC, during a time window
of cloud-free atmospheric conditions (Fig. 4a). The CATS particulate backscatter
coefficient cross section at 1064 nm (Fig. 4b) shows the absence of aerosol
and/or cloud features, while the Évora temporally averaged profile during
the cloud-free window (Fig. 4c) indicates the presence of thin aerosol
layers in the altitude range between 1 and 2.5 km a.s.l. The
aerosol layer detected by the Évora PollyXT lidar system is
characterized by backscatter coefficient values lower than 0.3 Mm-1 sr-1. Although CATS is characterized by relatively low minimum
detection thresholds (Yorks et al., 2016), CATS capabilities are limited in
terms of detecting similarly tenuous aerosol layers at levels that lie below
the detection thresholds (e.g., CATS 7.2 minimum detectable backscatter 1064 nm – night: 5.00×10-5±0.77×10-5 km-1sr-1; day: 1.30×10-3±0.24×10-3 km-1 sr-1 – for cirrus clouds; Yorks et al., 2016). The
detection limitation of CATS may propagate in scientific studies
implementing CATS through introduced underestimations and possible biases.
Case III: ISS–CATS over Dushanbe – 25 May 2015 at 18:53 UTC
The assessment of accuracy of CATS Level 2 against EARLINET collocated and
concurrent observations is performed on the basis of backscatter coefficient
profiles because this product constitutes the CATS Level 2 parameter with
the lowest influence of a priori assumptions (e.g., lidar ratio). In addition
CATS Level 2 provides the feature classification of the detected layers and
associated confidence level of the classification. The cloud–aerosol
discrimination though is not performed perfectly. Thus misclassified aerosol
layers may be classified as clouds and vice versa. In the framework of the
study, for the assessment process of the CATS Level 2 aerosol quality,
strict cloud filtering is applied. In particular, cloud-contaminated
profiles (sky condition 2 and 3) and aerosol layers characterized by medium or low
classification confidence (Feature_Type_Score ≥-1) are filtered. The strict cloud screening is applied because of
our basic idea to establish the accuracy of CATS aerosol backscatter
coefficient profiles based on intercomparison against EARLINET, preventing
any contamination of cloud features from propagating into the dataset.
(a) Nighttime ISS orbit over the EARLINET Dushanbe station
on the 25 May 2015 at 18:53:19 UTC and of closest distance between the
footprint of CATS and the EARLINET Dushanbe station of 24.3 km. The white
dot denotes the location of Dushanbe lidar system, while the blue line shows
the lidar footprint of CATS. (b) CATS backscatter coefficient at 1064 nm.
(c) CATS (blue line) spatially and EARLINET Dushanbe (red line) temporally
averaged backscatter coefficient profiles (1064 nm). The implemented
EARLINET Dushanbe time window of cloud-free measurements was between
18:00:00 and 20:00:00 UTC. The horizontal blue and red lines denote the
variability (1 standard deviation) in the CATS- and EARLINET-measured
atmospheric scenes respectively.
As discussed in the case of Leipzig overpass, on average, the agreement
between CATS Level 2 backscatter coefficient profiles and EARLINET is good,
especially under relative homogeneous cloud-free atmospheric conditions.
Under complex atmospheric conditions, though, of coexistent and adjacent
aerosol and cloud features, the impact of the CATS feature type score on the
CATS aerosol retrievals becomes significant. Figure 5 shows the CATS
footprint for the nighttime ISS orbit, on 25 May 2015 (blue line), at a
minimum distance of 24.3 km from the EARLINET Dushanbe, Tajikistan, station
(Hofer et al., 2017), at 18:53:19 UTC (Fig. 5a). This EARLINET station is
located in a natural basin surrounded by mountain ridges of variable height,
between 0.7 and 4 km a.s.l. The CATS particulate backscatter
coefficient cross section at 1064 nm (Fig. 5b) shows the predominant
presence of aerosols up to 3.6 km a.s.l., adjusted to broken thin
clouds. These cloud characteristics though are not consistent with the
observations performed at Dushanbe station between 13:00 and 23:00 UTC on
25 May 2015 that reported the absence of cloud features below 6 km. CATS
lidar profile and the EARLINET Dushanbe profile yield different behavior in
terms of backscatter coefficient (Fig. 5c). The Dushanbe lidar reports a
weak presence of aerosols, up to approximately 4 km a.s.l. The
backscatter comparison against CATS profile reveals enhanced discrepancies
in segments of the CATS profile, denoted by the high backscatter coefficient
values (> 2 Mm-1 sr-1). The cloud features that cause
the observed discrepancies are classified by the CATS CAD algorithm as aerosol
layers, contaminating the CATS profile despite the strict cloud screening.
Features with an invalid CATS CAD score, although not frequently observed, may
impact the quality of the column AOD and related
climatological studies. In addition, complex topography in terms of
geographical characteristics, erroneous mean backscatter coefficient
profiles due to the high variability in aerosol load in the planetary
boundary layer, the horizontal distance between the CATS lidar footprint and
the ground-based lidar stations and surface returns further enhance these
discrepancies, especially in the lowermost part of the profiles. Based on
this analysis and comparisons with CALIPSO, the CATS cloud–aerosol
discrimination algorithm was updated for the V3-00 Level 2 data products
(released in the end of 2018) to improve the accuracy of the feature type
and feature type score, especially during daytime.
CATS–EARLINET comparison statistics
In this section an overall assessment of the CATS backscatter coefficient
product at 1064 nm is given, using the entire dataset of CATS–EARLINET
collocated profiles. To address quantitatively the accuracy and
representativeness of the satellite-based lidar retrievals, the estimation of
possible biases in the CATS backscatter coefficient is performed. Towards
this assessment, in the comparison of CATS against EARLINET, we implement the
CATSi EARLINETi residuals for each pair of observations “i”, as
a statistical indicator of CATS average overestimation or underestimation of
the aerosol load, in terms of backscatter coefficient values.
Figure 6 shows the distributions of CATSi EARLINETi backscatter
coefficient differences. On average, the agreement is good, demonstrating the
high performance of CATS, with mean and median residual values close to zero
and typically within 0.4 Mm-1 sr-1. The intercomparison between
CATS satellite-based and EARLINET ground-based lidar retrievals reveals the
presence of negative biases in the CATS 1064 nm backscatter coefficient
profiles. The CATSi EARLINETi differences, for all the available
21 daytime (Fig. 6a) and 26 nighttime (Fig. 6b) cases of paired correlative
observations, show an underestimation of the CATS retrievals, being more
pronounced during daytime than nighttime. In the case of daytime
observations, the calculated mean (median) CATS difference from EARLINET is
-0.123 Mm-1 sr-1 (-0.095 Mm-1 sr-1). In the case of
nighttime observations, the corresponding mean (median) difference from
EARLINET is -0.031 Mm-1 sr-1 (-0.065 Mm-1 sr-1). The
observed standard deviation (SD) is 0.431 Mm-1 sr-1 over daytime
and 0.342 Mm-1 sr-1 during nighttime. During daytime, minimum and
maximum CATS–EARLINET residual values of -1.802 and 1.189 Mm-1 sr-1 are observed, while the corresponding minimum and maximum
values for nighttime are -1.348 and 1.149 Mm-1 sr-1. The CATSi EARLINETi daytime mean absolute bias
and root-mean-square error (RMSE) statistical indicators (Binietoglou et
al., 2015) of daytime observations are 0.323 and 0.448 Mm-1 sr-1, while the respective statistical indicators for the
nighttime cases are 0.249 and 0.343 Mm-1sr-1.
CATS performance is also quantified through the linear correlation
coefficient between the CATS and EARLINET backscatter coefficient
distributions, with correlation coefficients of 0.54 and 0.69 during daytime
and nighttime respectively. The correlations between CATS and EARLINET
distributions are not very good, as expected due to the significant
influence of the topography, the high inhomogeneities within the local
planetary boundary layer (PBL), and the effect of the horizontal distance
and temporal measurement differences. The fractional bias values for daytime
and nighttime are -0.676 and -0.773 respectively, while the fractional gross
error ranges between 1.061 for daytime and 0.999 for nighttime cases.
Overall, the agreement between CATS and EARLINET is good. On average though,
slight underestimations of CATS compared to EARLINET are observed: 6.3 %
during nighttime and 22.3 % during daytime. The intercomparison
statistical values between CATS and EARLINET are summarized in Table 4.
Distributions of the differences between CATS Level 2 and
the corresponding EARLINET backscatter coefficient measurements, calculated
for (a) daytime (21 collocated cases) and (b) nighttime (26 collocated
cases).
CATS–EARLINET comparison statistics on mean bias, median,
mean absolute bias, standard deviation, root-mean-square error (RMSE), and
minimum and maximum values on the observed backscatter coefficient profiles at
1064 nm (Mm-1 sr-1) for daytime and nighttime correlative cases.
Figure 7 reports the mean aerosol backscatter coefficient profiles at 1064 nm as provided by CATS and EARLINET daytime (Fig. 7a) and nighttime (Fig. 7b) lidar observations. On average, the mean aerosol backscatter coefficient
profiles reveal similar characteristics between CATS and EARLINET, although
the comparisons are subject to the different number of available cases: 21
and 26 for daytime and nighttime respectively. Both CATS and EARLINET
daytime and nighttime backscatter coefficient profiles yield higher values
close to the surface level, gradually decreasing with altitude. Especially
in the range between the full overlap region of the laser beam and the
telescope of the EARLINET systems (approximately 1 km) and the middle
free troposphere (∼6 km a.s.l.), the mean backscatter
coefficient profile of CATS is well within the standard deviation of the
scenes provided by EARLINET. Nonetheless, discrepancies are also evident. CATS,
as a result of the high spatial atmospheric variability, yields usually
higher values of standard deviation than EARLINET. In addition, at altitudes
higher than 6 km a.s.l., the CATS mean backscatter coefficient profile yields
zero or close-to-zero values, while EARLINET shows the presence of elevated
aerosols, with rather low mean backscatter values, which are lower than 0.2 Mm-1 sr-1.
CATS (blue line) and EARLINET (red line) mean profiles of
backscatter coefficient at 1064 nm for (a) daytime and (b) nighttime.
The horizontal lines represent the SD of CATS (blue colour) and EARLINET
(red colour) profiles.
The CATS Level 2 backscatter coefficient product evaluation study shows that
CATS agrees reasonably well with ground-based EARLINET measurements,
although they are generally biased low. To assess the ability of the CATS lidar to detect
aerosol features and optical properties and to shed light on the origin of
observed CATS–EARLINET discrepancies, the conducted CALIOP validation studies
offer an unprecedented basis. This is due to the similar viewing geometry
between CATS and CALIOP and to the similarities between Level 1B and Level 2
processing algorithms (McGill et al., 2015; Yorks et al., 2016, 2019).
Since CALIPSO joined the A-Train constellation of Earth observation
satellites in June 2006 (Winker et al., 2007), several studies have been
conducted to validate and evaluate CALIOP Level 1B, Level 2, and Level 3
products against ground-based, airborne, and spaceborne measurements.
Airborne NASA Langley HSRL (Hair et al., 2008) and CPL (McGill et al., 2002)
flights, of close spatial and temporal coincidence with the CALIPSO
satellite, documented the high performance of CALIOP, although with the
presence of low negative biases (Burton et al., 2010, 2013; McGill et al.,
2007; Rogers et al., 2011, 2014). Kacenelenbogen et al. (2014) reports on
the detection of aerosols above cloud (AAC) in only 151 of 668 CALIOP HSRL
coincident airborne cases (23 %). The use of ground-based Raman lidar
observations also reports that CALIOP Level 1B and Level 2 products are
biased low (Mamouri et al., 2009; Mona et al., 2009; Pappalardo et al., 2010;
Tesche et al., 2013). In terms of columnar measurements, the conducted
validation activities based on collocated observations between CALIOP and
AERONET (Dubovik et al., 2000) showed CALIPSO AOD
underestimations (Amiridis et al., 2013; Omar et al., 2013; Schuster et al.,
2012). In addition, evaluation studies of AOD observations from the passive
spaceborne MODIS (Remer et
al., 2005) show that CALIOP provides reasonably well-known climatic
features, although with apparent AOD underestimations (Amiridis et al.,
2013; Kittaka et al., 2011; Oo and Holz, 2011; Redemann et al., 2012). The
magnitude of the documented agreements and biases in the detection of
aerosol features varies from study to study with respect to the different
CALIOP versions. Substantial improvement in the detection of aerosol
features is documented in the latest CALIPSO version 4 (AMT CALIPSO special
issue).
Discussion
Overall, CATS, much like CALIOP, observes the vertical
distribution of atmospheric aerosol backscatter coefficient reasonably well, although with
slight underestimations. The observed discrepancies in the compared
CATS–EARLINET profiles are attributed to several sources.
First, the retrieval accuracy of CATS Level 2 data products, such as the
aerosol and cloud backscatter and extinction coefficient profiles, the
vertical feature mask, and the integrated parameters (e.g., AOD), depends
crucially on the calibration of the lidar system and the calibration region
(Kar et al., 2018). CATS total attenuated backscatter from molecules and
particles in the atmosphere is performed in the calibration region between
22 and 26 km, starting with V2-08 of the Level 1B data (Russell et al., 1979; Del
Guasta, 1998; McGill et al., 2007; Powell et al., 2009). Uncertainties in the
CATS Level 1B backscatter calibration are attributed to random and
systematic errors (CATS Algorithm Theoretical Basis Document – ATBD). Random errors result mainly from normalizing
the 1064 nm lidar signal to modeled molecular signal and are dominated by
lidar noise. On the contrary, systematic errors result from a number of
different sources, including uncertainties in the CALIOP stratospheric
scattering ratios and molecular backscatter coefficient values generated
from the Goddard Earth Observing System (GEOS) atmospheric general
circulation model and assimilation system used to calculate molecular and
ozone atmospheric transmission (Rienecker et al., 2008) and from the
non-ideal performance of CATS. The total uncertainty due to the CATS
calibration constants is estimated at between 5 % and 10 % (CATS ATBD).
Secondly, CATS detection and classification schemes, similar to CALIOP,
provide Level 2 aerosol products only in regions where aerosol features are
detected and identified. This implies that optically thin aerosol layers can
go undetected by CATS due to weak backscattering intensities below the CATS
detection thresholds (Kacenelenbogen et al., 2014; Thorsen et al., 2015). To
increase the detection of tenuous aerosol layers, CATS incorporates an
iterated horizontal averaging scheme (5 and 60 km; Yorks et al., 2019).
Failures of spaceborne lidar instruments and algorithms to detect tenuous
aerosol layers (Toth et al., 2018) result in range bin backscatter
coefficient assignments to 0.0 Mm-1 sr-1. The faint undetected
aerosol layers do not contribute to the CATS aerosol backscatter profiles and,
consequently, neither contribute to extinction coefficient profiles nor to estimates of
CATS AOD, which is similar to CALIOP AOD (Kim et al., 2018; Rogers et al., 2014;
Thorsen and Fu, 2015). The detection sensitivity is attributed to the solar
background and sunlight illumination conditions due to the significantly
lower CATS SNR during daytime than nighttime (Rogers et al., 2014). The
undetected aerosol layers, although of low aerosol load, introduce negative
biases in the CATS–EARLINET comparison. The total uncertainty, the sum of
the systematic and random errors, in the CATS ATBD at 1064 nm is estimated at
10 %–20 % for nighttime data and 20 %–30 % for daytime data.
Another source of discrepancy between CATS and EARLINET is attributed to the
effect of horizontal distance between the ground-based lidar systems and the
space-based lidar footprint. Studies performed in the framework of EARLINET
attribute an introduced discrepancy of the order of 5 % to the
intercompared profiles, when the horizontal distance is below 100 km
(Mamouri et al., 2009; Pappalardo et al., 2010; Papagiannopoulos et al.,
2016). The different – opposite – viewing geometry (upward for
EARLINET and downward for CATS and CALIPSO) and the different transmittance terms
are further sources of discrepancies (Mona et al., 2009). In addition,
enhanced disagreements observed between CATS and EARLINET in the lowermost
part of the mean backscatter coefficient profiles are attributed to the high
spatial and temporal variability in the aerosol content within the PBL, to
the complexity of the local topography, and to surface returns.
Finally, regarding the utility of CATS in climatic studies, another common
reason of satellite-based lidar overestimations or underestimations is
attributed to the absence of detailed aerosol properties in the
classification of the detected aerosol layers. The aerosol-subtype
classification scheme frequently results in aerosol layer
misclassifications, as has been shown in the case of coincident HSRL CALIPSO
under-flights (Burton et al., 2012). Misclassified aerosol layers
incorporate erroneous values of lidar ratio. Possible underestimation or
overestimation of aerosol backscatter coefficient profiles, considered with
erroneous aerosol-subtype classification, introduces biases in corresponding
extinction coefficient profiles and eventually in total columnar AOD
retrievals. The CATS V3.00) Level 2 data products improve errors in
cloud–aerosol typing identified in these CATS–EARLINET comparisons.
Furthermore, Wandinger et al. (2010), based on CALIOP extinction coefficient
profiles in case of dust aerosol layers and collocated ground-based Raman
lidar measurements, showed that multiple-scattering effects can result in
negative biases if not considered in the algorithm inversion schemes. Data
users should be aware of these multiple-scattering effects and cloud–aerosol
typing errors when using the CATS data.
Summary and conclusions
This study implements independent retrievals carried out at several EARLINET
stations to qualitatively and quantitatively assess the performance of
NASA's CATS lidar operating aboard the ISS from February 2015 to October 2017. We compared satellite-based CATS and ground-based independent
measurements over 12 high-performance EARLINET stations across Europe
and one located in Central Asia. Our analysis is based on the first 20
months of CATS operation (February 2015–September 2016). Comparison of CATS Level 2 and
EARLINET backscatter coefficient profiles at 1064 nm is allowed only in
cases in which the maximum distance between the ISS overpass and the EARLINET stations
is below 50 km. EARLINET contributed with observations as close in time as
possible, typically with a starting time or stop time of the measurements
within 90 min of the ISS overpass. The analysis was restricted to
cloud-free profiles to avoid possible cloud contamination of the
intercompared aerosol backscatter coefficient profiles.
In the quantitative assessment of the performance of CATS, 47 collocated,
concurrent, and cloud-free measurements of CATS the EARLINET were identified
(21 daytime and 26 nighttime), offering a unique opportunity for the
evaluation of the spaceborne lidar system. The results of the generic
comparison are encouraging, demonstrating the overall good performance of
CATS, although with negative biases. The agreement, as expected due to
higher SNR, is better during nighttime operation, with observed
underestimation of 22.3 % during daytime and 6.1 % during nighttime
respectively.
In addition to the generic comparison, three CATS–EARLINET comparison cases
were examined to demonstrate the system's performance under different study
conditions. The comparison showed that under cloud-free, relative
homogeneous atmospheric aerosol conditions, the spatially averaged CATS
backscatter coefficient profiles are in good agreement with EARLINET,
independent of light conditions. The deficiency of CATS though to detect
tenuous aerosol layers, due to the inherent limitations of space-based lidar
systems, may lead to systematic deviations and slight underestimations of
the total AOD in climatic studies. In addition, the CATS V2.01 feature type
score misclassification of aerosol layers as clouds, and vice versa, in
cases of coexistent and/or adjacent aerosol and cloud features, may lead to
non-representative, unrealistic, and cloud-contaminated aerosol profiles.
While CATS feature identification will improve in V3.01 data products, the
most crucial reason for the observed discrepancies between CATS and EARLINET
in the lowermost part of the profiles is related to the complexity of the
topography and the geographical characteristics. Especially in the case of
large elevation or slope differences, the effects of both inadequate sampling
lower than the maximum elevation and of the different atmospheric sampling
volumes result in large AOD biases and unrealistic AOD values.
The qualitative and quantitative agreement between CATS and EARLINET
reported in this study is encouraging, especially during nighttime; this is
agreement that will hopefully facilitate further studies implementing CATS
observations in the future. CATS, for a period of almost three years,
provided an unprecedented global dataset of vertical profiles of aerosols
and clouds, much like CALIOP, taking advantage, though, of the unique orbital
characteristics of the ISS. The ISS enabled CATS to provide for the first time
satellite-based lidar measurements of the diurnal evolution of aerosols and
clouds over the tropics and midlatitudes and to be more specific to
latitudes below 52∘. Since CALIPSO and Aeolus (and in the future also
EarthCARE) are polar sun-synchronous satellites of a fixed equatorial crossing
time (01:30 and 13:30 LT for CALIOP; 06:00 and 18:00 LT for ALADIN), it is
expected that, at least for the near future, the CATS dataset will remain the
only available satellite-based lidar source of nearly global diurnal
measurements of atmospheric aerosols and clouds. In addition, while CALIOP
is a two-wavelength lidar system operating at 532 and 1064 nm with
depolarization capabilities at 532 nm, CATS provided satellite-based aerosol
and cloud depolarization profiles at 1064 nm, thus in a different
wavelength. This dataset, much like the CALIOP dataset, is especially useful for
studies of the three-dimensional distribution of non-spherical aerosol
particles in the atmosphere (e.g., mineral dust and volcanic ash) and,
especially since it is an active sensor, over regions of high reflectivity
(e.g., deserts and ice). Future studies including the exploitation of CATS
unique observations may help the scientific community shed new light on
physical processes of aerosols and clouds in the Earth's atmosphere.
Data availability
CATS browsed images and data products are freely distributed via the CATS website at http://cats.gsfc.nasa.gov/data/ (last access: 15 September 2019). The lidar data used in this study are available upon registration at http://data.earlinet.org (last access: 15 September 2019).
Author contributions
EP coordinated the project,
communicated with all EARLINET groups and CATS Team, collected all EARLINET
and CATS data, directed the study, and prepared the paper, with
contributions from all co-authors. VA directed the preparation of
the paper and supervised the study. EARLINET co-authors
contributed with performed and processed ground-based lidar measurements of
the vertical distribution of aerosols at 14 stations: VA, EM, AG, and ET at the EARLINET-no station
(Athens, Greece, during the study period); UW, AA, RE, and HB at the EARLINET-le station (Leipzig, Germany); DA, JH, AM, and SA at the EARLINET-du
station (Dushanbe, Tajikistan); AP and MM at the
EARLINET-at station (Athens, Greece); DB, NS, and KAV
at the EARLINET-th station (Thessaloniki, Greece); DN and IB at the EARLINET-bu station (Bucharest, Romania); IM at
the EARLINET-oh station (Observatory Hohenpeissenberg, Germany); ISS at the EARLINET-wa station (Warsaw, Poland); MS and CMP at the EARLINET-ba station (Barcelona, Spain); ArA
and DM at the EARLINET-ca station (Cabauw, Netherlands); DaB
and MJC at the EARLINET-ev station (Évora, Portugal); MRP
and PB at the EARLINET-lc station (Lecce, Italy); GLL and
DD at the EARLINET-lm station (Rome – Tor Vergata, Italy); AlP and AS at the EARLINET-be station (Belsk, Poland); and GP, LM, and NP at the EARLINET-po station
(Potenza, Italy). EARLINET co-authors participated in the maintenance and
calibration of the lidar systems throughout the study period, data curation,
and preprocessing. JY, EN, and RP provided advice and
support throughout the process regarding NASA's CATS lidar system.
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
The authors acknowledge EARLINET for providing aerosol lidar profiles
(https://www.earlinet.org/index.php?id=earlinet_homepage, last access: 20 December 2018). This project receives funding from
the European Union's Horizon 2020 research and innovation program under
grant agreement nos. 654109 and 739530. The authors acknowledge the ISS NASA
Research Office (NRO) for the CATS instrument and the NASA Science Mission
Directorate (SMD) for the CATS data products and processing algorithms.
Emmanouil Proestakis, Anna Gialitaki, and Eleni Tetoni acknowledge support
from the Stavros Niarchos Foundation. The research leading to these results
was supported through the European Research Council (ERC) under the European
Community's Horizon 2020 research and innovation framework program – ERC
grant agreement 725698 (D-TECT). We acknowledge support of this work by the
project “PANhellenic infrastructure for Atmospheric Composition and climatE
chAnge” (MIS 5021516), which is implemented under the action “Reinforcement
of the Research and Innovation Infrastructure”, funded by the operational
program “Competitiveness, Entrepreneurship and Innovation” (NSRF
2014–2020) and co-financed by Greece and the European Union (European
Regional Development Fund). The Portuguese team acknowledges the support
from the Portuguese Science Foundation (FCT), in the frame of the European
Regional Development Fund – COMPETE 2020 – under the project
UID/GEO/04683/2013 (POCI-01-0145-FEDER-007690). Lucia Mona and Nikolaos
Papagiannopoulos acknowledge the European Union through the EU's Horizon
2020 research and innovation program for societal challenges – Smart, Green
and Integrated Transport under grant agreement no. 723986 (project
EUNADICS-AV – European Natural Disaster Coordination and Information System
for Aviation). The measurements in Tajikistan were funded by the German
Federal Ministry of Education and Research (BMBF) in the context of
“Partnerships for sustainable problem solving in emerging and developing
countries” under the grant number 01DK14014. Lidar measurements in Barcelona
were also supported by the Spanish Ministerio de Economía y
Competitividad (project TEC2015-63832-P) and EFRD (European Fund for
Regional Development); the Spanish Ministry of Science, Innovation and
Universities (project CGL2017-90884-REDT); and the Unidad de Excelencia
Maria de Maeztu (project MDM-2016-0600) financed by the Spanish Agencia
Estatal de Investigación.
Financial support
This research has been supported by the ERC Consolidator Grant 2016 D-TECT: “Does dust TriboElectrification affect our ClimaTe?” (grant no. 725698) and the “PANhellenic infrastructure for Atmospheric Composition and climatE chAnge” (grant no. 5021516) and the Stavros Niarchos Foundation (SNF).
Review statement
This paper was edited by Eduardo Landulfo and reviewed by three anonymous referees.
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