Quantifying aerosol absorption at ultraviolet (UV)
wavelengths is important for monitoring air pollution and aerosol amounts
using current (e.g., Aura/OMI) and future (e.g., TROPOMI, TEMPO, GEMS, and
Sentinel-4) satellite measurements. Measurements of column average
atmospheric aerosol single scattering albedo (SSA) are performed on the
ground by the NASA AERONET in the visible (VIS) and near-infrared (NIR)
wavelengths and in the UV-VIS-NIR by the SKYNET networks. Previous
comparison studies have focused on VIS and NIR wavelengths due to the lack
of co-incident measurements of aerosol and gaseous absorption properties in
the UV. This study compares the SKYNET-retrieved SSA in the UV with the SSA
derived from a combination of AERONET, MFRSR, and Pandora (AMP) retrievals
in Seoul, South Korea, in spring and summer 2016. The results show that
the spectrally invariant surface albedo assumed in the SKYNET SSA retrievals
leads to underestimated SSA compared to AMP values at near UV wavelengths.
Re-processed SKYNET inversions using spectrally varying surface albedo,
consistent with the AERONET retrieval improve agreement with AMP SSA. The
combined AMP inversions allow for separating aerosol and gaseous (NO2
and O3) absorption and provide aerosol retrievals from the shortest
UVB (305 nm) through VIS to NIR wavelengths (870 nm).
Introduction
Aerosols affect both the surface and outgoing radiation affecting Earth's
radiative balance. To quantify the radiative effects of aerosols, the
aerosol optical depth (AOD) and single scattering albedo (SSA) are monitored
using ground-based, orbital and sub-orbital platforms. The potential climate
effects of absorbing aerosols have received considerable attention lately
(Myhre et al., 2013). In addition to climate effects, aerosol absorption
effects on surface ultraviolet (UV) irradiance and photolysis rates have important
implications for tropospheric photochemistry, human health, and agricultural
productivity (Dickerson et al., 1997; Krotkov et al., 1998; He and
Carmichael, 1999; Castro et al., 2001; Mok et al., 2016). Measurements of
column atmospheric aerosol absorption and its spectral dependence in the UV
remains one of the most difficult tasks in atmospheric radiation measurements
due to the lack of co-incident measurements of aerosol and gaseous
absorption properties in the UV.
The enhanced column UV absorption (lower SSA at wavelengths shorter than 440 nm) is commonly attributed to organic aerosols (OA) that absorb
predominantly in the UV, explaining much stronger wavelength dependence than
a purely black carbon (BC) absorption would suggest (Kirchstetter et al.,
2004). Martins et al. (2009) showed that the absorption efficiency of urban
aerosol is considerably larger in the UV than in the VIS wavelengths and is
probably linked to the absorption by OA. This enhanced UV absorption by OA
results in a doubling of absorption efficiency compared to BC alone and can
reduces surface UV fluxes by up to 50 % in highly polluted areas.
Similarly, the enhancement of aerosol absorption at UV wavelengths was
observed in urban cities such as Rome, Italy (Ialongo et al., 2010) and
Athens, Greece (Kazadzis et al., 2016), especially in winter. Mok et al. (2016) first measured enhanced UV absorption with the strong spectral
dependence attributed to light absorbing component of organic carbon (OC)
known as “brown carbon” (BrC) for aged Amazonian biomass burning smoke.
Although urban aerosols have different chemical and physical composition,
they also exhibit enhanced UV absorption with significant impact on
tropospheric photochemistry and biologically active surface UV irradiance
(Krotkov et al., 1998; 2005b; Li et al., 2000; Ciren and Li, 2003; Bergstrom
et al., 2007, 2010; Arola et al., 2009; Mok et al., 2016).
Recently, the need for measurements of column atmospheric aerosol absorption
in the UV wavelengths are highlighted in the global aerosol and chemistry
transport model (CTM) simulations. Current CTMs treat all OC from biomass
burning as purely scattering, which underestimates the heating effect of the
total carbon (BC+OC) – the primary absorbing component of carbonaceous
aerosols (Cooke et al., 1999; Chung and Seinfeld, 2002; Bond et al., 2013;
Myhre et al., 2013). However, recent laboratory studies (Kirchstetter et
al., 2004; Yang et al., 2009; Chakrabarty et al., 2010; Chen and Bond, 2010;
Lack et al., 2012; Saleh et al., 2013, 2014; Zhong and Jang, 2014) suggest
that BrC is capable of enhancing total absorption efficiency of OC,
potentially altering the direct radiative forcing (DRF) from negative to
positive (Bond, 2001; Kirchstetter et al., 2004; Feng et al., 2013; Saleh et
al., 2014). Recently, Hammer et al. (2016) showed that carbonaceous aerosol
absorption over most biomass burning regions is underestimated if OC is
regarded as purely scattering in a global 3-D CTM GEOS-Chem, while a better
agreement is obtained with satellite observations from the Ozone Monitoring
Instrument (OMI) on board NASA's Aura satellite after implementing the BrC
absorption parameterization.
The aerosol column absorption in the visible (VIS) and near-infrared (NIR) wavelengths is measured
routinely at many locations by the AERONET (Dubovik et al., 2000; Holben et
al., 2001) (http://aeronet.gsfc.nasa.gov, last access: 14 April 2018) and the SKYNET (Nakajima et al.,
1996, 2007) networks, both of which utilize sun–sky scanning radiometer
instrumentation. Aerosol absorption retrievals have also been demonstrated
by Multifilter Rotating Shadowband Radiometer (MFRSR) instruments (Harrison
et al., 1994) at VIS (Kassianov et al., 2005) and UV wavelengths (Bigelow et
al., 1998; Petters et al., 2003; Krotkov et al., 2005a, b) as well as
spectrometers (Harrison et al., 1999; Bais et al., 2005; Barnard et al.,
2008). The shadowband technique for aerosol absorption retrievals does not
require separate calibrations for direct and diffuse measurements and allows
more frequent (up to one minute) measurements. This technique is more
accurate at small solar zenith angles (SZA) (Krotkov et al., 2005a, b)
complementing AERONET standard almucantar inversions, which are less
sensitive for small SZAs (Dubovik et al., 2002).
SKYNET is a ground-based international remote sensing network dedicated for
aerosol-cloud-radiation interaction research (Nakajima et al., 1996, 2007).
Using the direct sun and diffuse sky radiance aerosol column average optical
properties (e.g., AOD, SSA, refractive index, and volume particle size
distribution (PSD)) are retrieved every 10 min using standard processing
software SKYRAD.pack (Nakajima et al., 1983, 1996). The ability for UV (340 and 380 nm) channels mounted on the PREDE POM-02 sky radiometer used by
SKYNET is investigated in this study. Recent comparison studies focused on
VIS and NIR wavelengths (Che et al., 2008; Estellés et al., 2012; Khatri
et al., 2016) due to the lack of co-incident measurements of aerosol and
gaseous absorption properties in the UV. Using SKYNET measurements in Hefei,
China, Wang et al. (2014) reported smaller SSA at 380 nm during the autumn
and winter (0.91–0.93) than that in spring and summer (0.95–0.97).
They explained lower SSA by combined BC / BrC absorption in smoke from the
local farm land-clearing burning in autumn and from local heating in winter.
Their study showed that SSA seasonal variability is smaller than
∼ 0.05. Thus, evaluation and reduction of the uncertainty in
the SKYNET SSA retrieval, particularly at UV wavelengths, is important for
aerosol speciation and radiative forcing studies.
This study compares the SKYNET SSA retrievals in extended UV–NIR
wavelengths with the SSA derived from a combination of AERONET (Dubovik et
al., 2002), MFRSR (Krotkov et al., 2005a, b), and Pandora (Herman et al.,
2009) inversions (hereafter referred to as AMP) in Seoul, South Korea during
and after KORUS-AQ international field campaign in 2016 (Holben et al.,
2018). This study provides first comparisons of the SKYNET and MFRSR SSA
retrievals in the UV wavelengths. It also facilitates future comparisons of
independent satellite SSA retrievals in the UV from the OMI (Torres et al.,
1998, 2007, 2013; Jethva and Torres, 2011; Jethva et al., 2014).
Experimental site and instrumentation
The data used in this study include measurements from Hampton University's
UV- and VIS-MFRSR shadowband radiometers (head number 582 and 579,
respectively), a SKYNET sun–sky radiometer (Nakajima et al., 1996, 2007) and
an AERONET sun–sky radiometer (Holben et al., 1998) from April to August 2016 on the roof of the Science Hall, Yonsei University in Seoul, South
Korea. Concurrently, an international air quality field study, called the
Korea-US Air Quality (KORUS-AQ), was carried out over the South
Korean peninsula from May to June 2016 (https://espo.nasa.gov/home/korus-aq/content/KORUS-AQ, last access: 14 April 2018). Seoul has high
levels of urban pollution, since it is a metropolitan region with a
population of 25 million, including significant transportation and
industrial emissions sources. Seoul is also located downwind of regions
that include heavy aerosol pollution sources: primarily fossil fuel
combustion from industrial and urban areas in Inchon, South Korea and East
China, plus biomass burning aerosols from wildfires and crop fires locally
and remotely in North Korea, China, Russia, as well as airborne dust from
the Taklimakan and Gobi deserts.
To measure aerosol column optical properties from these sources, the
modified UV- and VIS-MFRSR instruments were installed on the roof of the
Science Hall, Yonsei University in Seoul, South Korea. The Yankee
Environmental Systems (YES) UV- and VIS-MFRSR sensors were modified at the
US Department of Agriculture (USDA) UV-B Monitoring and Research Program
(UVMRP) at the Natural Resource Ecology Laboratory, Colorado State
University, to facilitate their operation in conjunction with AERONET Cimel
sun-photometers. The manufacturer supplied 300, 317, and 368 nm UV-MFRSR
filters were replaced with 440, 340, 380 nm filters, respectively,
used by AERONET. In addition, a 440 nm filter was added to an
unfiltered pyranometer slot of the VIS-MFRSR sensor. Domes were also added
to both instruments to prevent Teflon diffuser contamination (Krotkov et
al., 2009). These UV and VIS-MFRSR instruments are part of the USDA UV-B
monitoring and Research Program (UVMRP: http://uvb.nrel.colostate.edu/UVB/index.jsf, last access: 14 April 2018). All MFRSR instruments in the
UVMRP network are regularly characterized for their spectral, angular and
radiometric responses at the NOAA Central UV Calibration Facility (CUCF:
https://www.esrl.noaa.gov/gmd/grad/calfacil/cucfhome.html, last access: 14 April 2018) in
Boulder, Colorado, U.S. The combined set of modified UV- and VIS-MFRSR
instruments measures direct solar and diffuse sky irradiances at 13 narrow
spectral bands with central wavelengths from the UV to the NIR: 305, 311,
325, 332, 340, 380, 415, 440, 500, 615, 673, 870, and 940 nm. The 440 nm
filter common to both MFRSR sensors and to the CIMEL photometer provides
spectral overlap between the inversion procedures applied to the three
sensors using the procedure described by Krotkov et al. (2005a, b) and
discussed here in detail. Furthermore, Yonsei University has been operating
a CIMEL sun-photometer as part of the AERONET network, as well as a new
Pandora spectrometer instrument to measure trace gases (ozone, NO2,
SO2, and HCHO) (Herman et al., 2009). These co-located instruments
facilitate the AERONET-to-MFRSR calibration transfer and help in comparing
aerosol absorption products such as the imaginary part of the refractive
index (k), single scattering albedo (SSA), and absorption aerosol optical
depth (AAOD). A summary of the instruments can be found in Table 1.
Instruments and wavelengths of retrieved absorption
properties.
InstrumentsMeasurementsWavelengths (nm)CIMEL sun and sky photometers (AERONET)Direct sun and almucantar sky radiance, Filters (2–10 nm)440, 675, 870, 1020Modified UV-MFRSR (#582)Diffuse and total irradiance, Filters (2 nm)305, 311, 325, 332, 340, 380, 440Modified VIS-MFRSR (#579)Diffuse and total irradiance, Filters (2 nm)415, 440, 500, 615, 673, 870, 940Sky radiometer (SKYNET)Sun and sky radiance, Filters (10 nm)340, 380, 400, 500, 675, 870, 1020Data and methodologyMFRSR on-site calibration
Improving the MFRSR observational protocol and daily on-site calibration are
critical for accurate measurements of aerosol column absorption. The MFRSR
on-site calibration is determined by daily comparisons with the AERONET
sun-photometers, since AERONET measured AOD is highly accurate at
∼ 0.01 to 0.02 with the higher values in the UV (Eck et al.,
1999).
We apply corrections for dark current offset, angular response, and
instrumental tilt to produce corrected voltages derived from raw voltages
measured by MFRSRs. The angular response correction was performed by using
the spectral and cosine response measured at NOAA Central UV Calibration
Facility (Krotkov et al., 2005a). To compensate for possible leveling
errors, the tilt correction was applied in conjunction with the cosine
correction (Alexandrov et al., 2007; Mok, 2017).
We use an estimate of the calibration constant for each individual 1 min
MFRSR measurement at each wavelength (i.e., extraterrestrial voltage,
V0(λ,t)) calculated using Eq. (1) to normalize measured
direct and diffuse voltages (same calibration in shadowing technique) and as
a quality assurance tool to retain only the best quality measurements
consistent with the AERONET AOD measurements.
lnV0λ,t=ln(Vdirn(λ,t))+secSZA(t)[τa(λ,t)+τR(λ,t)+τNO2(λ,t)+τO3(λ,t)],
where Vdirnλ,t is
the MFRSR-measured direct normal voltage, τaλ,t is gaseous corrected and spectrally
interpolated or extrapolated AOD to the MFRSR wavelengths applying a fit of the
equation (lnτa=a0+a1lnλ+a2(lnλ)2)
(Eck et al., 1999) using AERONET spectral level 2 AOD, τRλ,t is the Rayleigh optical
depth inferred from the measured surface pressure, and τNO2λ,t and
τO3λ,t are NO2 and ozone optical depths, calculated using Pandora
column NO2 and ozone measurements, interpolated to MFRSR 1 min
measurements (Herman et al., 2009; Tzortziou et al., 2012). For cases when
NO2 and O3 values are not available from Pandora spectrometer,
satellite NO2 (OMNO2 L2 v3.0) and ozone (OMTO3 L2 v8.5) measurements
from the OMI are used (data are available at http://avdc.gsfc.nasa.gov under the Aura submenu). In polluted urban
regions like Seoul, OMI NO2 measurements are typically lower than
ground-based retrievals (Irie et al., 2009, 2012; Ialongo et al., 2016;
Krotkov et al., 2017).
Outlier measurements with ln(V0λ,t)
exceeding 2 standard deviations from the daily average V0(λ) are iteratively removed and the
daily average V0(λ) is
re-calculated iteratively as described in Krotkov et al. (2005a). Any
low-frequency diurnal V0λ,t
variability indicates possible systematic errors (e.g., not perfect leveling,
non-complete shadowing, and/or electronics problems). To reduce systematic
errors and outliers, time periods are selected when V0 does not vary
with time (Krotkov et al., 2005a) and only those MFRSR measurements meeting
these quality assurance criteria are retained for inversions.
Using only the best quality MFRSR measurements, the mean V0 value for a
given day (V0(λ)) is
calculated and then MFRSR values (τa(MFRSR)λ,t) are calculated by inverting Eq. (1):
τa(MFRSR)λ,t=cosSZAtlnV0(λ)/Vdirnλ,t-τRλ,t-τNO2λ,t-τO3λ,t,
Finally, the measurements are only used when the root mean squared (RMS) of
(τa(MFRSR)λ,t-τaλ,t)<0.01. The spectral interpolation error is typically less than
0.01.
MFRSR inversion technique
Currently ground measurements of column effective refractive index and
single scattering albedo (SSA) are limited to the 4 discrete VIS and NIR
wavelength bands by AERONET almucantar inversions (440, 675, 870, and 1020 nm). An AERONET CIMEL sun-photometer has 340 and 380 channels, but does not
provide SSA inversions. However, sky radiance measurements are currently
made by many instruments at 380 nm so that the SSA at 380 nm will be a
future data product. To extend SSA retrievals into UV and other wavelengths
(Table 1), our method combines synchronous co-located measurements by
AERONET, MFRSR, and Pandora ensuring consistent retrievals of AOD, particle
size distribution (PSD), real part of the refractive index (n), and gaseous
absorption (e.g., by ozone and NO2). We also use consistent spectral
surface albedos (monthly climatological values) derived from MODIS satellite
surface albedo data (Moody et al., 2005; Eck et al., 2008). MFRSR-measured
Diffuse/Direct (DD) irradiance ratios are fitted with a forward radiative
transfer model coupled with a Mie scattering code (Arizona code, Herman et
al., 1975) to estimate only one forward model parameter: column effective
imaginary part of refractive index (k) independently for each MFRSR spectral
channel (Krotkov et al., 2005b).
The procedure of the combined AMP retrievals is summarized as a flowchart
(Fig. 1). Required ancillary input parameters such as PSD, n, surface
pressure, and surface albedo are taken from co-located near simultaneous
AERONET inversions (Dubovik et al., 2002). Gaseous absorption of column
ozone and NO2 are accounted for using ground-based direct-sun
retrievals by Pandora spectrometers (Herman et al., 2009; Tzortziou et al.,
2012) or satellite data from Aura/OMI overpass when Pandora data are not
available. AOD is obtained either from MFRSR inferred direct (total-diffuse) irradiances (corrected for laboratory measured angular response) or
AERONET direct sun measurements. In this study, we only used gaseous
corrected AERONET AOD for consistency. Then, the Mie-RT model is iterated to
find the k value, which minimizes the difference between calculated and
measured the DD irradiance ratio. The fitted k value together
with AERONET inferred PSD and n at 440 nm is converted to SSA using Mie
calculations assuming spherical particles (Krotkov et al., 2005b). As shown
in Fig. 2, the Ångström exponent (AE) observations from AERONET are mostly
higher than unity, which is typical for predominantly fine mode pollution
aerosols.
Flowchart showing the combined AERONET-MFRSR-Pandora (AMP) SSA
inversion methodology.
We estimate retrieval errors of k (Δk) and SSA (Δω)
using combined Mie-RT code to calculate the finite difference normalized
Jacobians (J):
Jk,DD=ΔkkΔDDDD,Δk=Jk,DDΔDDDDk,Jω,k=ΔωωΔkk,Jω,DD=Jω,kJk,DD,Δω=Jω,DDΔDDDDω,
Using Eq. (7), the error of SSA (Δω) is calculated as
shown by the vertical bar in Fig. 3b and c. Assuming constant 3 % accuracy
in the measured DD ratio (ΔDD) (Eqs. 3–4), the calculated
SSA retrieval error Δω is inversely proportional to AOD, but is
typically less than 0.02 for AOD at 440 nm, AOD440≥0.2.
The Ångström exponent (AE) (440–870 nm) as a function of AOD at
675 nm. The prevailing values of AE greater than unity characterize the
relative influence of fine mode particles during April to August in 2016.
The average Ångström exponent (440–870 nm) is 1.3 and its standard
deviation is 0.26.
Comparison between SSA at 440 nm retrieved from AERONET-only and
AMP retrievals in Seoul: (a) all 1 min UV-MFRSR versus VIS-MFRSR
retrievals, (b) AERONET inversions versus 32 min average UV-MFRSR
retrievals, and (c) AERONET inversions versus 32 min average VIS-MFRSR
retrievals. MFRSR SSA mean errors are shown assuming 3 % error in diffuse
to direct ratio. The UV- and VIS-MFRSR SSA in (b) and (c) are averaged
within ±16 min from the AERONET retrieval time. The dashed lines
show SSA agreement within ±0.03, which is assumed SSA error. The
dotted lines are ±0.05 of the 1:1 line. Red color shows comparisons
for AOD440≥0.4, consistent with the best quality level 2 AERONET
inversions. Blue dots indicate retrievals for 0.2 ≤ AOD < 0.4.
Combined SSA statistics for AOD ≥ 0.2 are shown in black. Standard
deviation of SSA is indicated in parentheses.
Sky radiometer (SKYNET)
In analyzing SKYNET sky radiometer measurements conducted here, we use the
Sky Radiometer analysis package from the Center for Environmental Remote
Sensing (SR-CEReS) version 1. As the main program, SKYRAD.pack version 5
(Hashimoto et al., 2012) is implemented to retrieve aerosol properties in
SR-CEReS along with all pre- and post-processing programs for the purpose of
the near-real time data delivery. Two kinds of calibration approaches were
considered for the present study. The first approach is to use the so-called
static calibration constants. We derived the static calibration constants
through comparison with the reference sky radiometer, which was calibrated
at the Mauna Loa Observatory (MLO) in December 2015, and through the direct
calibration at the MLO in October and November 2016. The second approach is
to use the dynamic on-site calibration method, based on the Improved Langley
method (Campanelli et al., 2007; Khatri et al., 2016). Since the first
method is not able to account for the possible temperature variations on a
monthly time scale during very hot summer for instance, the latter
calibration method was selected in this study to estimate the daily
calibration constant (<F0>). To account for the
temporal variations of <F0> by ±1–3 %
caused by temperature variations, the following method was used in this
study.
Assuming the field of view (FOV) of the SKYNET instrument is known by the
solar disk scan method (Nakajima et al., 1996; Uchiyama et al., 2018),
F0 was calculated for each measurement, where aerosol parameters were
retrieved utilizing ratios of aureole radiance to direct radiance (Tanaka et
al., 1986; Nakajima et al., 1996):
F=F0R2exp-mτ,
where F, m, τ, and R are the measured intensity, the air mass, total
(Rayleigh + aerosol + ozone) optical depth, and the Sun–Earth distance,
respectively, and all are given quantities.
However, uncertainties arise because (1) τ has uncertainty in the
absorption component and (2) m has uncertainty due to the refraction at high
SZAs (corresponding to high m values). To estimate <F0>, we use a statistical approach as follows: (1) a
two-month period (±30 days of the target day) is used to calculate
measurement statistics, (2) only clear sky F0 values obtained within the
lowest 1/3 of all mτ values are used, and (3) only F0 values within
their 3 standard deviations are used. Regarding the threshold of 1/3, we
tested other thresholds and found that the choice is not critical. This
threshold was likely best to keep a sufficient number of data points to
determine <F0> at small mτ values. Then, the
average of those data is regarded as the final <F0> value for the target day. This statistical approach is
taken during pre-processing and is different from previous studies. While
daily <F0> values for entire UV-VIS-NIR channels
have not been given in previous studies, reanalysis of their observation
data by this approach is preferable to confirm consistency. For cloud
screening, this study uses the method of Khatri and Takamura (2009) without
including global irradiance data from a pyranometer.
Comparison of SSA between AERONET almucantar and MFRSR DD
inversions at (a) 675 nm (673 nm VIS-MFRSR) and (b) 870 nm. Increased
scatter results from larger inversion uncertainties from smaller AOD.
Comparison of SSA at 440 nm between AERONET and AMP
inversions via UV-MFRSR and VIS-MFRSR.
0.2 ≤ AOD440< 0.4 AOD440≥ 0.4 AERONETMFRSRAERONETMFRSRAERONET and UV-MFRSR matchup Mean0.8920.8690.9290.918Standard deviation0.0380.0380.0380.042Correlation0.77 0.89 Number24 45 RMSD0.034 0.022 AERONET and VIS-MFRSR matchup Mean0.8970.8780.9330.922Standard deviation0.0390.0390.0370.041Correlation0.82 0.89 Number30 50 RMSD0.030 0.022 Results and discussionComparison of single scattering albedo between AERONET and
MFRSRs
Comparisons of AMP-retrieved with SKYNET-retrieved SSA (±16 min average) at (a) 340, (b) 380, (c) 415 (400 nm SKYNET), (d) 500, (e) 673
(675 nm SKYNET), and (f) 870 nm using spectrally flat surface albedo
(0.1) at all wavelengths. Red dots are filtered using AOD440≥0.4
to correspond the best quality level 2 AERONET data. The horizontal bars
show estimated uncertainties of the AMP SSA mean values (i.e., excluding
natural variability) within ±16 min time window. The vertical bars
show one standard deviation of the SKYNET retrieved individual SSA values
within ±16 min time window (i.e., including natural variability).
Statistical differences between AMP and SKYNET retrieved SSA with
spectrally invariant surface albedo = 0.01 (in parenthesis) and spectrally
varying surface albedo (Fig. 6). Statistics, such as root mean square
deviation (RMSD), mean difference (MBD), standard deviation (SD), and 95
percentile (U95) of the differences are computed for AOD440≥ 0.4
consistent with the quality assured level 2 AERONET inversion data.
First, the individual 1 min SSA retrieved at 440 nm (SSA440) by the
UV- and VIS-MFRSR instruments are compared to demonstrate the high degree of
consistency for a combined set of modified UV- and VIS-MFRSR instruments
(Fig. 3a). The correlation coefficient between UV-MFRSR and VIS-MFRSR
retrieved SSA440 is 0.98, the estimated standard deviation of MFRSR
SSA440 uncertainty (standard MFRSR uncertainty, Fioletov et al.,
2016) is ∼ 0.007, and the mean SSA440 difference (bias)
is less than 0.002. Next, SSA440 from AERONET level 1.5 inversions are
compared with the ∼ 32 min average SSA440 retrievals
from either the UV-MFRSR (Fig. 3b) or VIS-MFRSR (Fig. 3c). For the time
averaging interval we use ±16 min based on the AERONET inversion
time. Both instruments provide the best quality SSA retrievals at high
turbidity conditions (AOD440≥0.4) (Dubovik et al., 2002; Krotkov
et al., 2005b; Mok et al., 2016). For these conditions, the average
SSA440 from either UV-MFRSR or VIS-MFRSR (∼ 0.92) is less
by about 0.01 from the corresponding AERONET average SSA440
(∼ 0.93).
Relaxing the AERONET level 2 inversion AOD440≥0.4 criterion
(Holben et al., 2006) allows for analyzing a larger statistical sample of
the MFRSR-AERONET matchups (Fig. 3). However, the mean SSA440 values
using relaxed AOD filter (AOD440≥0.2, shown as blue and red
dots) are reduced by ∼ 0.02 compared to the restricted sample
using AERONET level 2 criteria (AOD440≥0.4, shown as red dots).
The SSA variability (standard deviation) using the relaxed filter is
insignificantly increased (less than 0.01) compared to using the restricted
filter. The increased variability reflects cases with smaller AOD, showing
stronger absorption (SSA ∼ 0.9). The root mean square
deviation (RMSD) is higher for lower AOD cases (∼ 0.030–0.034 for 0.2 ≤ AOD440<0.4) than for higher AOD cases
(∼ 0.022 for AOD440≥0.4) (Table 2) as shown in
previous studies (Dubovik et al., 2002; Estellés et al., 2012). The good
agreement in SSA at the common overlapping wavelength 440 nm from UV-MFRSR,
VIS-MFRSR, and AERONET level 1.5 provide additional justification to using
the MFRSR and AERONET level 1.5 inversions with AOD440≥0.2.
Thus, we utilize the combined AMP SSA retrievals for AOD440≥0.2
to compare with the SKYNET SSA retrievals.
Surface albedo used for AMP (blue symbols) and SKYNET (red line)
SSA inversions. The bottom and top edges of the boxes are located at the
sample 25th and 75th percentiles; the whiskers extend to the minimal and
maximal values within 1.5 interquartile range (IQR). The outliers are shown
in circles. Constant surface albedo of 0.1 assumed for all wavelengths in
SKYNET retrievals, is shown as red solid line.
Compared to the low scatter in SSA440 differences between UV-MFRSR and
VIS-MFRSR (Fig. 3a), Fig. 3b and c show larger scatter between either
UV-MFRSR (Fig. 3b) or VIS-MFRSR (Fig. 3c) and AERONET SSA440. We
explain this by several possible reasons. The two MFRSR instruments measure
the total sky hemispherical irradiance affected by even small cloud
fraction, whereas AERONET has the ability to filter out scattered cumulus
from the symmetry check done on directional sky radiances in the almucantar
scan. Therefore, it is possible that some MFRSR SSA retrievals are more
affected by the presence of scattered clouds than the AERONET retrievals.
Another potential source of scatter between AERONET and MFRSR SSA440
retrievals could be gaseous absorption by NO2 that is not completely
accounted for in the AERONET Version 2 retrievals. Next, coarse mode
fraction, which varies approximately from ∼ 5 to 50 % in
South Korea for these paired measurements (Fig. 2), primarily by the
mixture of dust and urban aerosols, could affect the MFRSR retrievals which
assume spherical particles, while dust is complex in shape. Additionally,
coarse mode size particles scatter much more strongly in the forward
direction than fine mode particles, thereby resulting in additional variable
uncertainty in the solar aureole corrections made to account for the sky
fraction blocked by the shading band in the MFRSR instrument (di Sarra et
al., 2015). The aureole correction is less important to the AERONET
measurements because of the small FOV ∼ 1.2∘ (Sinyuk et
al., 2012) than to the shadowing measurements from MFRSR (Krotkov et al.,
2005a). The empirical MFRSR aureole correction (Harrison et al., 1994) tends
to underestimate the aureole contribution to the diffuse irradiance for
coarse aerosol particles and cirrus clouds (Min et al., 2004; Yin et al.,
2015). The aureole under-correction causes systematic underestimation of the
diffuse irradiance and retrieved SSA by the MFRSR. Quantitatively, the bias
varies for different locations: e.g., from +0.004 at the Santa Cruz,
Bolivia (Mok et al., 2016) to -0.005 in Greenbelt, Maryland with fine mode
dominated aerosols (Krotkov et al., 2009). We estimate that aureole SSA bias
should be less than ∼ 0.01 at Seoul.
Re-processed SKYNET SSA at (a) 340, (b) 380, (c) 415 (400 nm
SKYNET), (d) 500, and (f) 870 nm using spectrally varying surface albedo,
which corresponds the MODIS-derived surface albedo shown in Fig. 6. SKYNET
SSA at 675 nm is the same with Fig. 5e. The horizontal bars show
estimated uncertainties of the AMP SSA mean values (i.e., excluding natural
variability) within ±16 min time window. The vertical bars show
one standard deviation of the SKYNET retrieved individual SSA values within
±16 min time window (i.e., including natural variability).
Comparisons of AMP-retrieved AOD with SKYNET-retrieved AOD at (a) 340, (b) 380, (c) 415, (d) 500, (e) 673 (675 nm SKYNET), and (f) 870 nm
using SKYNET retrievals with spectrally varying surface albedo. AMP/AOD is
AERONET/AOD used for inversions and/or interpolated to UV wavelengths and
times. Dotted and dashed lines are 0.03 and 0.05 offset, respectively. The
horizontal bars show constant reported uncertainties of the AERONET AOD at
each wavelength (Eck et al., 1999; Sinyuk et al., 2012). The vertical bars
show standard deviation of the SKYNET measured AODs within ±16 min
time window (i.e., including natural variability).
Figure 4a and b compare AERONET and MFRSR SSA at longer NIR wavelengths:
675 and 870 nm (SSA675 and SSA870), respectively. Note that the
average AOD at 675 and 870 nm (0.34 and 0.24, respectively) are lower than
the AOD440∼ 0.6, as the average Ångström exponent (440–870 nm) is 1.30 (Fig. 2). The lower AOD at 675 and 870 nm is the main
reason for the larger SSA retrieval noise (RMSD = 0.025 and 0.026 for
AOD440≥0.4). However, the discrepancies between mean AERONET SSA
and mean MFRSR SSA at 675 and 870 nm are less than 0.02 regardless of
whether the relaxed or strict filter is adopted. The MFRSR calculated SSA
uncertainties are less than ∼ 0.03, which is typical AERONET
SSA retrieval uncertainty. Such agreement allows us to compare the AMP SSA
with the SKYNET SSA as discussed below.
Comparison of single scattering albedo between AMP and SKYNET
Previous comparison studies of retrieved aerosol optical properties between
AERONET and SKYNET (Che et al., 2008; Estellés et al., 2012) show
typically good agreement for AOD. However, Khatri et al. (2016) found that
the SKYNET SSA was overestimated compared to AERONET SSA inversions at VIS
and NIR wavelengths mainly due to systematic difference in absolute
calibration of sky radiances. Differently from previous studies, we found
that average SKYNET SSA is in good agreement with average AMP SSA at VIS and
NIR ranges (Fig. 5 and Table 3). This is at least partly because we used
the improved quality checks for the solar disk scan data used to determine
the FOV. In addition, we used daily <F0> values for
all UV-VIS-NIR channels, which have not been done in previous studies (See
details in Sect. 3.3).
None of previous studies (Che et al., 2008; Hashimoto et al., 2012; Khatri
et al., 2016) performed the intercomparison of SKYNET SSA in the UV
wavelengths. This study is the first to compare SKYNET SSA retrievals at UV
to NIR wavelengths using co-located near simultaneous (±16 min)
AMP retrievals in Seoul in 2016. Figure 5 shows SSA comparison results
between AMP and SKYNET in extended wavelength range from 340 to 870 nm.
Correlation between the two SSA retrievals is moderately high, decreasing at
675 and 870 nm due to higher uncertainty in the SSA retrievals at lower AOD.
The SSA scatter could result from small AOD differences, which are
independently measured in SKYNET and AMP retrievals. Nevertheless, the mean
absolute SSA differences are less than 0.02, within uncertainties in the SSA
retrievals. We found that, on average, the SKYNET SSA at UV wavelengths is
lower compared to the AMP SSA (Fig. 5). The likely source of the bias
could be the spectrally invariant surface albedo (0.1, Fig. 6) assumed in
SKYNET SSA retrievals. This incorrect assumption leads to the underestimated
SSA values in UV, even if AOD retrievals are accurate (Hashimoto et al.,
2012).
Combined spectral SSA from AMP-retrievals (blue symbols) and
SKYNET retrievals (orange symbols) using MODIS-derived surface albedo shown
in Fig. 6. The bottom and top edges of the boxes are located at the sample
25th and 75th percentiles; the whiskers extend to the minimal and maximal
values within 1.5 IQR. The outliers are shown in circles. The center
horizontal lines are drawn at the median values. The whisker-boxes are
computed using AOD440≥ 0.4 criteria to correspond the best
quality level 2 AERONET data.
Main factors of discrepancySurface albedo
Surface albedo has an important impact on the retrievals of SSA in the UV
region (Corr et al., 2009). The AMP inversions use the AERONET-provided
spectral surface albedos at 440, 670, and 870 nm derived from MODIS surface
BRDF/albedo product (Moody et al., 2008). The shortest wavelength at which
surface albedo is available is 440 nm. Therefore, we assumed that the
surface albedo at 440 nm applies to MFRSR retrievals in all UV
wavelengths.
Figure 6 compares surface albedo used in AMP inversions with that assumed in
SKYNET inversions. There is little variability in MODIS-derived
climatological surface albedo (Moody et al., 2008) assumed in AERONET
inversions (±0.01) at 440 nm. The SKYNET retrievals compared here use
the spectrally invariant surface albedo (0.1) at all wavelengths. The
spectrally independent SKYNET-assumed surface albedo 0.1 is close to the
AERONET surface albedo at 675 nm (Fig. 6). However, it greatly deviates
from the MODIS surface albedo at 440 and 870 nm (∼ 0.04 and
∼ 0.2, respectively used by AERONET and AMP retrievals). The
overestimated value of surface albedo in the SKYNET inversions will lead to
an underestimated value of SSA at near UV wavelengths: 340, 380, and 400 nm
(Hashimoto et al., 2012). As seen in Fig. 5, this explains the lower
SKYNET SSA compared to AMP retrievals.
Re-processing the SKYNET inversions using spectrally varying surface albedo
(Fig. 6), consistent with the AERONET retrievals, improves agreement
between the SKYNET SSA and the AMP SSA (Fig. 7 and Table 3). The updated
surface albedo in the SKYNET inversions increases the SSA (by
∼ 0.01) at wavelengths from 340 to 500 nm. The mean SSA
differences between AMP and re-processed SKYNET are reduced to
∼ 0.013, 0.002, and 0.003 (for AOD440≥0.4) at 340,
380, and 400 nm, respectively. The root mean squared differences are also
reduced (RMSD < 0.02) at these wavelengths (Table 3). Thus, using
consistent surface albedo reduces systematic biases between SKYNET, MFRSR
(AMP) and AERONET retrievals, particularly at UV wavelengths.
AOD
The close agreement of AOD (i.e., better than 0.01) is a critical
pre-condition for SSA comparison, since the overestimation in AOD leads to
the underestimation in SSA and vice versa (Dubovik et al., 2000; Khatri et
al., 2016). The discrepancy of AOD is typically attributed to problems in
instrumental calibrations (Khatri et al., 2016). Figure 8 shows the only
significant AOD differences between AMP and SKYNET are at a wavelength of
340 nm, where the mean bias difference (MBD) and RMSD were
∼ 0.030 and ∼ 0.044, respectively. The
differences of mean AOD were less than ∼ 0.01 at all other
wavelengths. We conclude that AOD differences were not significant in our
SSA comparisons at wavelengths longer than 340 nm.
Atmospheric gas absorption
The AMP inversions account for effects of gaseous (ozone and NO2)
absorption in the UV and VIS wavelengths. However, the gaseous absorption
(ozone and NO2) is not taken into account in the sky radiances that are
inverted in the AERONET Version 2 retrievals. In the SKYNET retrievals, only
fixed column ozone (300 DU) is considered without the NO2 absorption.
In the upcoming AERONET Version 3 data base, the ozone and NO2
absorption will be accounted for in sky radiances by using monthly
climatological values from Aura/OMI satellite retrievals (Bhartia, 2005;
Krotkov et al., 2017). There will still be an NO2 related error, since
NO2 amounts from OMI are much smaller than the strongly time-dependent
NO2 amounts from Pandora retrievals (Herman et al., 2009). Errors in
the daily SSA retrievals will be introduced if one uses a fixed
climatological value of column NO2 (Corr et al., 2009) at UV and blue
wavelengths.
As discussed in Sect. 4.3.1, the agreement between the AMP and SKYNET SSA
is improved by using consistent MODIS-derived surface albedo (0.04) in the
SKYNET SSA retrievals at 340, 380, and 400 nm. Still, the SKYNET-derived SSA
(for AOD440≥0.4) shows a slight underestimation compared to the
AMP-derived SSA at these wavelengths. To investigate NO2 gaseous
absorption as possible cause, we modified our AMP SSA inversion assuming
zero NO2 absorption and found SSA decreased by ∼ 0.004–0.007 at 340, 380, and 415 nm, closer to SKYNET retrievals. Thus, accounting
for NO2 absorption should further reduce the negative bias in SKYNET
SSA retrievals. The NO2 effect on SSA retrieval is largest for small
AOD and could lead to incorrect interpretation of aerosol composition
(Krotkov et al., 2005c). We also found that including SO2 absorption
(average SO2 column amount in Seoul is < 1 Dobson Unit, 1 DU = 2.69×1016 molecules cm-2) (Krotkov et al., 2016) results in
negligible increases in SSA (∼ 0.003 at 305 nm and less at
longer wavelengths).
SSA spectral dependence
As shown in Fig. 9, AMP and SKYNET SSA retrievals using the AERONET
spectrally varying surface albedo are in good agreement at all wavelengths.
The SSA typically decreases with wavelength in the VIS and NIR wavelengths,
reaches flat maximum between 415–500 nm and decreases sharply in shorter
UV wavelengths. This can be explained by the mixture of spectrally flat
absorbing black carbon and selectively UV-absorbing aerosols (i.e., brown
carbon, dust). The detailed investigation relating aerosol type and SSA
spectral dependence will be discussed in future studies. Here we conclude
that AMP and SKYNET retrievals are in good agreement, both allowing for
measuring aerosol absorption and its spectral dependence.
Summary and conclusion
This study uses simultaneous measurements from co-located AERONET, MFRSR,
and Pandora instruments to ensure accurate measurement of aerosol extinction
optical depth, in order to provide consistent inversions of aerosol column
absorption properties between UV and VIS wavelengths, and to partition
absorption between aerosol and gases. Using this technique, we retrieved the
column spectral SSA in the UV, VIS, and NIR wavelength and performed the SSA
comparisons between AERONET and MFRSR retrievals. The SSA comparisons
between AERONET and MFRSR are in good agreement, showing the mean SSA
difference is less than 0.01 at common wavelength 440 nm for both conditions
of AOD440≥0.4 and AOD440≥0.2. The latter condition,
called the relaxed filter, increases the number of AERONET-MFRSR matchup by
a factor of ∼ 1.5 and is used for comparisons with SKYNET. As
a result, our approach can provide SSA at wavelengths AERONET cannot provide
and can be compared with the SKYNET SSA.
The new finding is the underestimation of the SKYNET SSA in the UV, which
has not been previously discussed. The underestimation could be explained,
in part, by the use of the unrealistically high surface albedo (0.1). The UV
surface albedo should not be larger than the MODIS derived values at 470 nm
(∼ 0.04), used in AERONET SSA retrievals at 440 nm. The value
0.04 is similar to the land surface values derived from the Total Ozone
Mapping Spectrometer, TOMS (Herman and Celarier, 1997). Following this
recommendation, updating the surface albedo in the SKYNET inversions to the
average AERONET value of ∼ 0.04 significantly reduces average
differences in SSA (∼ 0.01) in the near UV. Future studies
relevant to SKYNET SSA inversions might determine the optimal surface albedo
from the MODIS climatology (Moody et al., 2008) and/or combined with BRDF
models (Wang et al., 2018) if no other co-located instrument is available.
The relatively poor correlations between AMP and SKYNET SSA at 675 and 870 nm compared to shorter wavelengths should reflect, at least partly, the fact
that AODs at 675 and 870 nm were much lower than AODs at other shorter
wavelengths. The second issue is smaller Rayleigh scattering, which greatly
reduces diffuse sky irradiance and causes larger noise in diffuse to direct
ratio. Future studies using more observations with higher AODs are needed to
better quantify SSA at 675 and 870 nm.
This study demonstrates the consistency of the column aerosol spectral
absorption derived from the AMP and SKYNET inversions in the extended
wavelength region. Specifically in UV wavelengths this study presents the
first comparison of the column average SSA measured by independent
ground-based techniques. It is found that SKYNET provides more reliable SSA
at UV wavelengths (340 and 380 nm) on the condition that the spectrally
varying surface albedo and NO2 absorption are taken into account.
Considering the results of this study, the SSA measurements presented here
are more essential to answer how the UV light absorbing aerosols affect air
quality, surface UV radiation, and tropospheric oxidation capacity, which
remains highly uncertain. In addition, retrieved aerosol absorption in the
UV contributes to improving the classification algorithm of the columnar
aerosol types (Kim et al., 2007; Choi et al., 2016; Mok et al., 2016) and
validating satellite SSA retrievals from the current (Aura OMI (Jethva and
Torres, 2011) and SNPP OMPS) and future satellite atmospheric composition
missions (TROPOMI, TEMPO, GEMS, and Sentinel-4).
Data availability
AMP and SKYNET data are available in the Supplement. AERONET data must be requested
from the
AERONET web site (https://aeronet.gsfc.nasa.gov, GSFC NASA, 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-2295-2018-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “SKYNET – the international network for aerosol, clouds, and solar radiation studies and their applications”.
It does not belong to a conference.
Acknowledgements
Jungbin Mok was supported by the University of Maryland/ESSIC–NASA cooperative
agreement and a NASA grant (NNX16AN61G). Jhoon Kim, Ja-Ho Koo, and Sujung Go were supported by the Korea Ministry
of Environment (MOE) under the grant: “Public Technology Program based on
Environmental Policy (2017000160001)”. The authors acknowledge support from
NASA Earth Science Division, Radiation Sciences and Atmospheric Composition
programs and the AERONET project at GSFC. The authors also thank the AERONET
team, Charles M. Wilson from the NOAA Central UV Calibration Facility (CUCF),
plus George Janson, and Becky Olson from the USDA UV-B monitoring and Research
Program (UVMRP).Edited by: Stelios Kazadzis
Reviewed by: three anonymous referees
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