This paper is an overview of the progress in sky radiometer technology and the
development of the network called SKYNET. It is found that the technology
has produced useful on-site calibration methods, retrieval algorithms, and
data analyses from sky radiometer observations of aerosol, cloud, water
vapor, and ozone.
A formula was proposed for estimating the accuracy of the sky radiometer
calibration constant F0 using the improved Langley (IL) method, which
was found to be a good approximation to observed monthly mean uncertainty in
F0, around 0.5 % to 2.4 % at the Tokyo and Rome sites and smaller values of
around 0.3 % to 0.5 % at the mountain sites at Mt. Saraswati and Davos. A new cross IL (XIL)
method was also developed to correct an underestimation by the IL method in
cases with large aerosol retrieval errors.
The root-mean-square difference (RMSD) in aerosol optical thickness (AOT) comparisons with other networks took values of less than 0.02
for λ≥500 nm and a larger value of about 0.03 for shorter
wavelengths in city areas and smaller values of less than 0.01 in mountain
comparisons. Accuracies of single-scattering albedo (SSA) and size
distribution retrievals are affected by the propagation of errors in
measurement, calibrations for direct solar and diffuse sky radiation, ground
albedo, cloud screening, and the version of the analysis software called the Skyrad
pack. SSA values from SKYNET were up to 0.07 larger than those from AERONET,
and the major error sources were identified as an underestimation of solid viewing angle (SVA) and cloud
contamination. Correction of these known error factors reduced the SSA
difference to less than 0.03.
Retrievals of other atmospheric constituents by the sky radiometer were also
reviewed. Retrieval accuracies were found to be about 0.2 cm for
precipitable water vapor amount and 13 DU (Dobson Unit) for column ozone amount.
Retrieved cloud optical properties still showed large deviations from
validation data, suggesting a need to study the causes of the differences.
It is important that these recent studies on improvements presented in the
present paper are introduced into the existing operational systems and future
systems of the International SKYNET Data Center.
Introduction
A sun–sky radiometer is a narrow-band filter photometer able to perform
measurements of direct solar and diffuse sky radiation at selected
wavelengths and at several scattering angles. Observed data have large
information content for aerosol, cloud, and gaseous constituents but are
difficult to retrieve because of the need for full radiative transfer
computation to quantify single- and multiple-scattered radiation.
The origin of the idea of the technology dates back to the beginning of the
last century (Shaw, 2006). Long-term direct solar and diffuse sky
measurements were carried out during 1923–1957 by the Smithsonian
Astronomical Observatory by monitoring the solar constant with a
pyrheliometer at Montezuma (Chile) and Table Mountain (California) (Abbot,
1911; Ångström, 1961, 1974; Roosen et al., 1973; Hoyt, 1979a, b).
Diffuse sky irradiance in the circumsolar or solar aureole region was
measured by the pyranometer to correct for the atmospheric effects in the
measured solar constant (Abbot and Aldrich, 1916). This method was also used
by Kalitin (1930), Fesenkov (1933), and Pyaskovskaya-Fesenkova (1957)
(Terez and Terez, 2003). By the 1970s, spectral measurements of the direct solar
radiation became popular for air pollution monitoring via the introduction of a
low-cost compact narrow-band radiometer called a sun photometer, with a
silicon photodiode and cutoff or interference optical filters (Volz, 1959,
1974). In parallel, pioneering measurements of spectral diffuse sky
radiance started from the ground and aircraft (Bullrich, 1964; Bullrich et al.,
1967, 1968; Murai, 1967; Eiden, 1968; Green et al., 1971; Gorodetskiy et
al., 1976; Twitty et al., 1976). They were attracted by the characteristic
radiance distributions, including bright circumsolar region and neutral
points of the degree of polarization in the sky dome. Theoretical and
inversion schemes for the involved ill-conditional problems were studied for
data analysis (Deirmendjian, 1957, 1959; Phillips, 1962; Twomey 1963; de
Bary, 1964; Turchin and Nozik, 1969; Yamamoto and Tanaka, 1969; Dave, 1971;
Shifrin et al., 1972; Shifrin and Gashko, 1974).
By the 1980s, analyses of combined sun and sky radiation data became
comprehensive (e.g., O'Neill and Miller, 1984a, b; Tanaka et al., 1986;
Tanré et al., 1988) after full yet fast radiative transfer
computation became possible, allowing quantification of the multiple-scattering component of sky radiance and retrieval of the column-averaged
size distribution and the complex refractive index of polydispersed aerosol
(Twitty, 1975; Weinman et al., 1975; Box and Deepak, 1978, 1979; Nakajima et
al., 1983; O'Neill and Miller, 1984b; Tanré et al., 1988; Tonna et al.,
1995; Dubovik and King, 2000; Dubovik et al., 2000, 2002). Networks of
radiometers have been developed to utilize sun and sky measurement data for
various applications, such as satellite remote sensing validation, air
pollution monitoring, and the study of the climate effects of atmospheric
constituents, as overviewed by Holben et al. (2001). The largest network is
NASA AERONET (Holben et al., 1998) developed in the early 1990s and
currently with more than 500 sun–sky photometers. Later,
in the 2000s SKYNET was formed with sky radiometers (Nakajima et
al., 2007). Compared to the AERONET technology, SKYNET has several
differences in measurement and analysis methods.
SKYNET is for research purposes without a centralized data analysis
system and its information is scattered in independent papers and documents,
which makes SKYNET difficult to understand for the science community. As a result, this paper intends to put forward an overview of the key findings of and issues
with SKYNET to provide better information for the community.
Sun and sky measurements from the sky radiometer
SKYNET is a research group of users of sky radiometers that was initiated
around the time of the East Asian Regional Experiment (EAREX) 2005 (Nakajima
et al., 2007), one of the regional experiments under the UNEP Atmospheric
Brown Cloud (ABC) project (Ramanathan et al., 2007). A number of sky
radiometers were deployed in the East Asian region for measuring aerosol
optical properties in order to estimate the aerosol impact on the Earth's
radiation budget (Takamura et al., 2004; Khatri et al., 2010). Since then,
users of sky radiometers have kept growing globally and the number of sky
radiometers now exceeds 100 units. Table 1 and Fig. 1 show the sky
radiometer sites as recognized by the International SKYNET Committee (ISC).
Users established regional sub-network groups in China, Europe, India,
Japan, South Korea, Mongolia, and Southeast Asia for data analysis and formed the
ISC to discuss international collaboration issues (Fig. 2). Historically, two
major groups were grown for regional data collection and analysis: the
SR-Center for Environmental Remote Sensing (SR-CEReS) of Chiba University
(Takamura et al., 2004, 2009, 2013) and the European SKYNET Radiometers
network (ESR) (Campanelli et al., 2004, 2007, 2012). Analysis systems were
developed by the sub-networks independently, and thus analysis methods and
data archive systems have not been unified.
Sites recognized by the International SKYNET Committee.
The structure of the International SKYNET Committee.
In 2017, SKYNET became a contributing network of the WMO Global
Atmospheric Watch (GAW) (https://community.wmo.int/activity-areas/gaw, last access: 21 July 2020). In
this expanding situation of SKYNET having more burden and responsibility, the
ISC decided to establish the International SKYNET Data Center (ISDC) at the
National Institute for Environmental Studies (NIES) in Japan to start a
shared data collection and analysis based on the memorandum of understanding (MOU) between users and the
ISDC. Among the sites in Table 1, the ISDC started receiving data from 25
sites across the world. The ISDC is going to provide standard products from the
SKYNET network, whereas the regional sub-networks will develop new research
products and test new methodologies.
The main instrument of SKYNET is the sky radiometer manufactured by
PREDE Co., Ltd. Several versions of the radiometer have been made at user
requests. POM-01 is the standard version, with seven wavelengths of
λ=315, 400, 500, 675, 870, 940, and 1020 nm, and POM-02 is an
extended version, with UV wavelengths of 340 and 380 nm and shortwave
infrared wavelengths of 1600 and 2200 nm. Channels of 315 and 940 nm are
installed for ozone and water vapor amount retrievals. Full widths at
half maximum of band-pass filters are 3 nm or less for channels shorter than
380 nm, 10 nm between 400 and 940 nm, and 20 nm for longer wavelengths. There
is a modified version of POM-02 for lunar photometry (Uchiyama et al.,
2019). Shipborne versions have been also built (Kobayashi and Shiobara,
2015).
Sky radiometer readings of direct solar and diffuse sky measurements,
Vd and Vs, are related to the direct solar irradiance Fd and sky
radiance L‾s at the mean Earth orbit as follows:
Fd=CRRes2Vd,L‾s=FsΔΩ=CRRes2VsΔΩ,
where CR is the radiometric sensitivity or calibration coefficient of
the radiometer to translate the radiometer reading to irradiance unit, e.g.,
W m-2 nm-1; ΔΩ is the solid viewing angle (SVA) of
the radiometer; and Res is the sun–Earth distance in astronomical units.
SKYNET remote sensing uses the beam transmittance Td of the atmosphere
and relative sky radiance R (Nakajima et al., 1986)
2aTd≡FdF0=exp(-m0τ),2bτ=τa+τm,ωτ=ωaτa+ωmτm,2cR(θ,ϕ;θ0,ϕ0)≡L‾s(θ,ϕ;θ0,ϕ0)/mFd=1mΔΩVsVd,
where τ is the optical thickness (OT) of the atmosphere consisting of
molecular optical thickness τm, single-scattering albedo (SSA)
ωm, aerosol optical thickness (AOT) τa, and SSA
ωa in the clear sky condition; F0 is the extraterrestrial
solar irradiance (ESI); (θ0, φ0) and (θ,
φ) are zenith and azimuthal angles of the sun and the line of sight
of the sky radiometer, respectively; m0 and m are optical air masses for
solar insolation and the line of sight of the radiometer, which are approximated
as 1/cos(θ0) and 1/cos(θ) for plane-parallel geometry
of the atmosphere. SKYNET adopts on-site calibration routines to determine
the two radiometric constants, F0 and ΔΩ, using the
improved Langley plot method (hereafter, IL or ILP) and the disk scan method
(Nakajima et al., 1996; Boi et al., 1999; Uchiyama et al., 2018a, b), as
discussed in Sect. 3 and 4. Under the condition that CR and F0 do
not change between the time of measurement and time of F0 determination,
Td and R do not depend on the calibration coefficient CR, and thus we
can select the radiometer reading for F0, i.e., CR=1, without the
absolute radiometric calibration. Under this assumption, F0 in the
radiometer reading is sometimes called a calibration constant. In order to
meet this condition, the on-site calibration is required to be
performed as frequently as possible to monitor change of CR due to machine
condition change and F0 change due to solar luminosity change.
Standard measurement protocols of SKYNET are as follows. Direct solar
irradiance is measured every 1 min. Diffuse sky radiance is measured by
full almucantar scan at scattering angles from the set of Θ={2∘(1∘)5∘,7∘,10∘(5∘)30∘(10∘)160∘} for θ0≤78∘, whereas the
forward almucantar scan is made in Θ≤30∘ for
obtaining quick scan data for ILP and/or in a condition of rapid air mass
change for θ0>78∘. AERONET adopts a
two-side scan of the sun for a symmetry check and spatial averaging of sky
radiances to minimize the inhomogeneous effects. On the other hand, SKYNET
basically uses a one-side almucantar scan of the sun to save on observation
time. At some sites, however, the almucantar scan is made on either
side of the sun alternatively, and the retrieval is made for each side
separately to evaluate inhomogeneous aerosol distribution in space and time.
The sky radiometer has several angle scan modes, i.e., almucantar scan,
principal plane scan, cloud scan, and solar disk scan. There are two
temporal sampling modes of a regular time interval of 10 min (mode 1) and a
regular solar air mass interval of 0.25 (mode 2). Most of the sites in Table 1 adopt the mode 1 measurement with a one-side almucantar scan. The disk scan
mode is scheduled once a week at 10:00 LT, though the scan time
can be changed according to the user's plan. A cloud scan mode at nadir is taken every
10 min at POM-02 sites and some POM-01 sites.
Once the radiometric constants are determined, the direct solar irradiance
F and relative sky radiance R are used for the level 2 (L2) analysis, i.e.,
retrievals of the geophysical parameters of aerosol, cloud, water vapor, and
ozone, as discussed later in Sect. 5. The flow of sky radiometer
measurements and data analysis are schematically depicted in Fig. 3. As
overviewed in the following sections, F0 and SVA are obtained on-site
through various Langley plot methods and the solar disk scan method using data
from direct solar and forward scan measurements. Cloud screening is also
performed differently by different sub-networks. The ESR performs a cloud screening
for a direct solar measurement at 1 min frequency using a procedure based on
the methodology developed by Smirnov et al. (2000), Estellés et al. (2012) and Song et al. (2014). Cloud screening for sky measurements uses the
downward shortwave radiative flux measured by a co-located pyranometer
(Khatri and Takamura, 2009), otherwise they do not perform cloud screening
for sky data. CEReS conducts the cloud screening with the method of Khatri
and Takamura (2009) but without using global irradiance data from a
pyranometer (Irie et al., 2019). It corresponds to the combination of a
spectral variability test (Kaufman et al., 2006) and statistical analysis
test of Smirnov et al. (2000) including checking the number of data, the
diurnal stability check, smoothness criteria, and three standard deviation
criteria but without a triplet stability criteria test. We do not use several
quality control tests, such as the angular steepness of the solar aureole, for a stricter
cloud filter as is done for AERONET (Giles et al., 2019).
A flowchart of the SKYNET analysis. Quantities in parentheses are
research products.
Geophysical parameter products, versions of Skyrad pack, and
availability of the known data archives.
ESR:L2 products: AOT, AE, SDF, SSA, CRI, phase function, asymmetry factor, lidarhttp://www.euroskyrad.netratio, linear depolarization ratio(last access: 25 July 2020)Analysis software: Skyrad pack v4.2, MRI v2Data availability: L2 data are open via the web systemSR-CEReS:L2 products: AOT, AE, SDF, SSA, CRIhttp://atmos3.cr.chiba-u.jp/skynet/Analysis software: Skyrad pack v5(last access: 25 July 2020)Data availability: L2 data are open via the web systemToyama U:L2 products: AOT, AE, SDF, SSA, CRIhttp://skyrad.sci.u-toyama.ac.jp/Analysis software: Skyrad pack v4.2, 5(last access: 25 July 2020)Data availability: L2 data are open via individual requestMRIL2 products: AOT, AE, SDF, SSA, CRI, phase function, asymmetry factor, lidar ratio, linear depolarization ratioAnalysis software: Skyrad pack MRI v1 and v2Data availability: L2 data are available from PIs upon requestCAMS-SKYNETOperational L2 products: AOT, AE, SDF, SSA, CRIAnalysis software: Skyrad pack v4.2 and 5Data availability: L2 data are used by CMA, data are available from PIs upon requestIMDOperational L2 products: AOT, AE, SDF, SSAAnalysis software: Skyrad pack v4.2Data availability: L2 data are used by IMD, no open web system
To obtain data for L2 data analysis for retrieval of geophysical parameters
for atmospheric constituents, an analysis software called the Skyrad pack has
been developed (Nakajima et al., 1996; Hashimoto et al., 2012) and is publicly
available on the OpenCLASTR shareware site (http://157.82.240.167/~clastr/data_policy.html, last access: 26 July 2020) for use by the research community. Various L2 products are retrieved
by the Skyrad pack, such as spectra of AOT, its slope called Ångström
exponent (AE), size distribution function (SDF), SSA, complex refractive
index (CRI) , asphericity, cloud optical thickness (COT), cloud effective
particle radius (CER), water and ice phase from data in the non-gas-absorbing channels, precipitable water vapor (PWV), and column ozone amount
(O3) from the gas-absorbing channels, as explained in the following
sections. Common operational products of the sub-networks are AOT, AE, SDF,
SSA, and CRI-assuming Mie particles. Other products have been retrieved by
research studies. The current operating versions are version 4.2 and 5, and
a version from the Meteorological Research Institute of Japan Meteorological
Agency (MRI version) developed by Kobayashi et al. (2006, 2010).
Table 2 lists the archived geophysical parameters, versions of the retrieval software Skyrad pack, and the data availability in the known data archives. Table 2 indicates that the features of the archives are different from each other and difficult to use for the science community.
Radiometric calibration of the direct solar irradiance measurements
In the case of non-gas-absorption channels, the standard Langley plot method
(SL or SL plot method) can be used to obtain F0 by plotting the
logarithm of the Lambert–Beer's law Eq. (2a) versus m0,
ln(Fd)=ln(F0)-m0τ,
to extrapolate the linear regression line to m0=0. It is known,
however, that an air mass dependence or a quadratic time dependence of AOT
introduces a serious error in the SL, as claimed by Shaw (1976). Correction
methods to this problem were proposed by O'Neill and Miller (1984a, b) and
Tanaka et al. (1986) with the use of a time dependence of the circumsolar
radiance of which the major part is approximated by the single-scattered
radiance proportional to the OT along the solar almucantar circle (θ=θ0), given as follows:
R(θ,ϕ;θ0,ϕ0)=ωτP(Θ)+Rmult(θ,ϕ;θ0,ϕ0),
where P is the normalized scattering phase function at the scattering angle
of Θ and Rmult is the multiple scattered radiation. Tanaka et
al. (1986) used a forward scattering around Θ=20∘ at
which the phase function is relatively independent of the SDF of the
atmospheric particulate matter. Extending this principle, SKYNET adopts the
IL method to extrapolate Eq. (3) regarding the total scattering optical
path,
x=m0ωτ,
or its aerosol part,
xa=m0ωaτa,
which can be retrieved from the forward scattering part, Θ≤30∘, of the relative sky radiance R, Eq. (4). The formulae in Eqs. (4) and (5) indicate that xa is relatively accurately retrieved from the
inversion of the forward scattering part of the sky radiance. We use Eq. (5b) in most of ILPs of the sub-networks.
The accuracy of F0 estimation by the IL method depends on the turbidity
condition of the site. The theory of a linear regression model is formulated
with a normal random observation error u as
6ayi=a+bxi+ui,i=1,…,n,6ba=ln(F0),x=m0ωτ,y=ln(F),
where n is the number of observations. Here, we omit subscript a from
τa and ωa for the sake of compact notation unless
otherwise specified. Equation (6) gives estimates of regression coefficients
and their dispersion as
7ab=<x-x‾y-y‾>σx2,a=y‾-bx‾,7bσb2=εu2nσx2,σa2=εu2n1+x‾2σx2,
where upper bar and <> stand for averaging operation and
εu is the root-mean-square error (RMSE) for u. The standard
linear regression theory assumes x is an independent variable to be related
to a dependent variable y that includes a random residual of the fitting u.
Based on this assumption, the dispersion of x is given as
σxx‾2∼σm02m‾02+στ2τ‾2+σω2ω‾2,
where σm02, στ2,
and σω2 are dispersions of sampling
air masses {m0i} and natural variations in {τi} and {ω} during the ILP,
respectively. The dispersion of residual {ui} is
approximated by the sum of mean-square errors of τ and ω, i.e., ετ2 and εω2, caused by
the inversion process of Eq. (4) as
εu2=b2m‾02ω‾ετ2+τ‾εω2+εF2,
where εF2 is the mean-square
error of {yi} caused by observations of the
radiometer, which is usually small and neglected from the
formula. The budget of dispersions Eq. (8a) leads to the
following estimate for a typical air mass sampling from
m1=1.3 to m2=3.5 and atmospheric
conditions of large optical parameter change from τ1=0.2 to τ2=0.4 and from ω1=0.85 to ω2=0.95 during the ILP
as
σxx‾2≈13m2-m1m2+m12+13τ2-τ1τ2+τ12+13ω2-ω1ω2+ω12=0.070+0.037+0.001,
if we assume a regular sampling of linear change
models for m, τ, and ω.
This budget indicates that the wide sampling of air mass
is the main contributor to decrease σa2.
The IL method allows selection of the atmospheric
condition in which τ and ω
undergo natural variations that help to increase σx and thus decrease σa. But such
selection of unstable atmospheric conditions may increase
inversion errors, ετ and εω, wasting the benefit of natural changes in
ω and τ. It is also possible
to have a change in atmospheric conditions over a
short time of less than 5 min in one full angle
scan, causing unexpected errors. Sub-networks, therefore, have
their own screening protocols for ILP using stability of the
time sequence of variables to reject ill conditioned data
for ILP. They also reject large AOT cases to secure
the calibration accuracy, e.g., AOT >0.4 by ESR (Campanelli
et al., 2004).
Combining Eqs. (7) and (8), we have the following
estimate for σa assuming b and
ω are close to 1,
10aσa,IL=3.5nm‾02m2-m1γτ‾∼9.2nγτ‾∼1.3nτ‾,10bγ≡εττ‾2+εωω‾2.
The third expression on the right-hand side of Eq. (10a) is an estimate
for m1=1.3 and m2=3.5, and the
rightmost expression is an approximation with 10 % relative
errors in inversion of τ and ω
as a typical example of ILP. This estimate indicates that the
accuracy of ln(F0) from the IL method is
proportional to the OT during ILP operation at the site.
Table 3 lists mean values of n, τa
and σa,IL every 30 d (month) obtained by ILP operation carried out at the Tokyo
University of Science (TUS) and Rome sites. The table
shows that the monthly value of σa,IL ranges
from 0.5 % to 2.4 %, with a tendency to increase with
decreasing wavelength. We also estimated σa,IL
by Eq. (10) with optimum γ values of 7 %
and 15 % for Tokyo and Rome, respectively. These
estimates correspond to 5 % and 11 % for relative
retrieval errors ετ/<τ>
and εω/<ω> during ILP
operation.
(a) Monthly mean values of n, τa, and σa,IL obtained by ILP at the Tokyo University of Science (TUS) site averaged for the period of February–May 2017 and (b) those at the Roman site averaged for October 2017 and May–September 2019, other than the 380 nm data, which were taken only in October
2017. Estimates of σa,IL are also given by Eq. (10) assuming γ value of 7 % for Tokyo and 15 % for Rome. All wavelength means are shown in the bottom of each table.
The monitoring ability of F0 by IL on-site methods
has merits, such as low-cost frequent calibration to
detect the changing constants and a short-term ESI change,
and minimizes the radiometer environmental change, avoiding
shipping for calibration. The error in F0 is
propagated to cause an error in OT from Eq. (3) as
εdirect,τ∼σam0.
A rough estimate of AOT error by the IL calibration is expected to be on the
order of 0.003 to 0.01 for m0=2 in the case of Table 3, though real
errors depend on detailed setup and observation sequence at each site. It is
important to compare this accuracy of IL with that of SL. In the SL case, we
assume x=m in Eq. (6a), so that the error estimate Eq. (7b) is reduced to
the following expression as
σa,SL2=τ′2n1+m‾2σm2,
where we assume the error in a is caused by a part of OT change during the SL
plot, which tends to the inverse of the optical air mass as
τ=τ‾+τ′m.
A measure of OT change during air mass change from m1 to m2 can be
defined as
δτ≡|τ2-τ1|2=121m2-1m1τ′=0.24τ′.
The rightmost estimate is given for m1=1.3 and m2=3.5 as an
example. If we assume δτ/τ‾=0.1 to be the same as the
inversion error in the estimate of IL accuracy, the following estimate is
given as
σa,SL=1.6nτ‾.
This estimate of the SL error is similar to that of IL given in Eq. (10),
suggesting the SL performance is similar to or slightly larger than that of
IL under conditions of 10 % change in OT during the SL plot. Selection of
the calibration methods, therefore, depends on the character of the
turbidity conditions at the site. There are reports from city-area sites,
such as Rome, Beijing, and Chiba, that the accuracy of SL method is more than
1 % to 2 % worse than that of IL method, suggesting
ετ/τ‾>0.1
commonly happens at these sites, and thus we recommend comparison of
F0 values from both SL and IL methods to diagnose the calibration
quality of the SL and IL methods. At the same time, we recommend high
mountain calibration and/or transfer of calibration constants from a
well-calibrated standard radiometer to keep the on-site IL calibration
healthy.
The SKYNET community performed high mountain calibrations at Mauna Loa (USA,
3397 m a.m.s.l.) and two similar pristine aged-background sites
(AOT500 ∼0.05, AOT at λ=500 nm) from the Indian
Astronomical Observatory (IAO) at Hanle (Mt. Saraswati, 32∘47′ N,
78∘58′ E, 4500 m a.m.s.l.) and Merak (33∘48′ N, 78∘37′ E, 4310 m a.m.s.l.), located in the high-altitude Ladakh region in the
northwestern Himalaya. Figure 4 shows retrieved values of F0 and SVA
from the observation taken by a single instrument (POM-01) from IAO-Hanle
during January 2008–December 2010 and June 2015–December 2018 and Merak
during January 2011–May 2015. They used the Skyrad pack software for data
screening with a condition of root-mean-square difference (RMSD) of SVAs below 0.20, while the median
value of the long-term data is as much as 0.05. The observations were taken from
a wide range of AOTs with minimum (instantaneous) 0.01 to maximum 0.22 with
the annual averaged AOT as 0.045±0.026 at 500 nm during 2008 to 2018
at the two sites. Due to limiting cloudy conditions in the afternoon, 35 %
of the disk-scanning work is performed between 08:00 and 09:00 LT at this site. Since
the disk-scanning procedure takes around 20–25 min to complete the
entire wavelengths, it is apparent that in some cases, some wavelengths may
have been affected by thin (cirrus) clouds, which are carried by strong winds
(above 15 m s-1) at both the sites. The figure indicates that the RMSD of
ln(F0) from SL and IL methods agree within about 0.5 %. This F0
uncertainty is smaller than the minimum value of σa,IL
at Tokyo and Rome shown in Table 3 and corresponds to an estimate of Eqs. (10)
and (13), assuming the mean AOT at the site is on the order of 0.03 at λ=500 nm and n=100. The figure shows that the disk scan method, discussed in
the next section, was obtained with monthly mean SVA within 1.5 % for all
the spectral channels. The disk scan was performed from observations taken
under full clear-sky conditions with minimum of 3–5 d of data in every month
(Ningombam et al., 2014). Therefore, there are 12 values of SVA in all the
spectral channels in a year. The vertical bar indicates a representative
RMSD of monthly means in each year.
Time series of the ratio of F0 values from SL and IL methods (a)
and SVA (b) from the observations taken by a single instrument (POM-01) at
two pristine sites, IAO-Hanle during January 2008–December 2010 and June
2015–December 2018 and Merak during January 2011–May 2015. The error bar
indicates a representative monthly RMSD in each year.
The first QUAlity and TRaceability of Atmospheric aerosol Measurements
(QUATRAM, http://www.euroskyrad.net/quatram.html, last access: 22 July 2020) Campaign compared the
F0 value from the IL method with that of the standard Precision Filter
Radiometer (PFR) (Kazadzis et al., 2018b) of the World optical depth
Research and Calibration Center (PMOD/WRC). A preliminary analysis showed
the difference is 0.3 % at Davos (1590 m a.m.s.l.), where the mean AOT500 is 0.15
and AOT500 in clean aerosol conditions is 0.05. This F0 uncertainty is
similar to those of the IAO sites and again smaller than the minimum value
in Table 3, indicating the importance of the careful constant calibration
effort on high mountains.
Another important point to note is that comparison of Eqs. (3) and (6) leads
to the following relation
b=-1ω.
The forward scattering analysis of the IL method prescribes the refractive
index, and thus it is highly possible for x in Eq. (5a) to include a factor
type systematic error like
x=Cx0.
In this case, Eq. (6) results in the following relation between fitted and
true values of a and b, a0 and b0, as
b=1Cb0,a=y‾-1Cb0Cx‾0=y‾-b0x‾0.
This result shows that the formula of a in Eq. (7a) is invariant to the
factor type error, indicating the robustness of the IL calibration. On the
other hand, the b value changes depending on the value of C and takes a value
-1/ω in the no error condition according to Eq. (14). Boi et al. (1999) utilized this point
and proposed an iterative IL method to improve the F0 value and find the
optimum CRI by trying several refractive indices. They reported the method
can improve the precision of F0 by 30 %, e.g., 2 % to 1.5 %.
There is another caution regarding the use of the formulae of
Eq. (7a). In the real observation, it is difficult to
separate natural variations and inversion errors of τ and ω, and thus the dispersion
σx tends to include undesired inversion
errors that lead the IL method to an underestimation of
a and b as understood by Eq. (7b).
We are testing a new solution to this problem, named
the cross IL method (XIL), which exchanges the role of
x and y in the regression analysis,
i.e.,
16axi=α+βyi+vi,i=1,…,n,16bb=1β,a=-αβ.
Figure 5 presents retrieved values of a (=lnF0) from the IL and XIL
methods with 10 ensemble runs of an idealized experiment with
F0=1; ω=1; τ=0.1; n=20; and m=1.3 to 3.5 as a
function of normal random errors εx in x. The figure shows
that the IL method underestimates the a value, while the XIL stays accurate
within RMSE less than 0.03 up to εx=0.01 (10 % of
τ=0.1) and 0.05 at εx=0.025 (25 % of τ=0.1), consistent with Eq. (10). Figure 6 and Table 4 compare results
of the IL and XIL methods with the following screening conditions applied to 38
sets of real Langley plot data at the TUS site for 4 months from February
through May 2017:
m2/m1≥2,|b(SL)|<10,
and
0.8≤-b(IL),-b(XIL)≤1.2,
and
εu(IL),εu(XIL)≤εu0,
where m1 and m2 are lower and upper limits of air mass in the ILP.
The threshold residual εu0 is given as 0.02, 0.03, and
0.05. Figure 6 and Table 4 indicate that the a value from SL is largely
scattered, suggesting determination of F0 by SL at turbid sites like
Tokyo is not recommendable. On the other hand, a values from IL and XIL
converge on a regression line within differences of 2 %–3 %, with a tendency
of systematically smaller values by IL than those from the XIL method by amounts
of εu0/2. Although the difference between IL and XIL is not large when
we select low-noise data, we would like to recommend the XIL method to be
applied to 5 to 10 Langley plot data sets in order to secure an accuracy of
1 % to 2 % in F0 using the screening conditions of Eq. (17). The
figure also shows that we can detect a long-term decreasing trend of
a value by about 10 % during the period at the TUS site.
Retrieved values of a=ln(F0) from IL and XIL methods with 10
ensemble runs of an idealized experiment (n=20 and m=1.3 to 3.5) as a
function of normal random error εx in x. True values are
assumed to be F0=1; ω=1; and τ=0.1.
Time series of estimated a values by IL and XIL methods for ILP data
at the Tokyo University of Science (TUS) site for 4 months from February
through May 2017. Presented are the results of two screening conditions of Eq. (17) with εu0=0.05 and 0.03 at λ=500 nm.
Estimates of a and b values at λ=500 nm and their RMSD values
(σa, σb) in the F0 retrieval by IL and XIL
methods for ILP data at the Tokyo University of Science (TUS) site for 4
months from February through May 2017. Results of three screening conditions
of Eq. (17) with εu0=0.05, 0.03, and 0.02 are
listed.
εu0=0.05Methodaσa-bσbSL-8.2200.3890.2960.321IL-8.2470.0500.9680.082XIL-8.2190.0691.0350.117εu0=0.03Methodaσa-bσbSL-8.2530.2380.2370.163IL-8.2490.0390.9730.070XIL-8.2330.0391.0190.073εu0=0.02Methodaσa-bσbSL-8.1900.1680.2470.160IL-8.2430.0300.9900.064XIL-8.2330.0311.0250.075Sky radiance calibration for the sky radiometer
Several methods have been proposed for on-site calibration of the sky
radiance measured by the sky radiometer, such as the solar disk scan method,
point-source or lamp scan method, and diffuse plate method (Nakajima
et al., 1986, 1996; Boi et al., 1999). Among them, the solar disk scan
method has been routinely used in the SKYNET measurement of the SVA of the
sky radiometer by scanning a circumsolar domain (CSD) of ±1∘ by ±1∘ around the sun at every
0.1∘ interval.
The irradiance received by the radiometer, which is aimed at the direction
(x, y) in a Cartesian coordinate system of angular distance from the center of
the solar disk at origin (x=0, y=0), is an angular integration of
radiances weighted by the response function of the radiometer fR in the
field of view (FOV),
F(xy)=∫∫FOVdx′dy′fR(x′-x,y′-y)L(x′,y′).
In the case of diffuse sky radiance measurement, the
SVA of the radiometer is given from Eqs. (1) and (18)
as
ΔΩ=∫∫FOVdx′dy′fR(x′,y′).
In the case of the solar disk scan, the main term for F is given as follows
under conditions of small contributions from diffuse sky radiation in the
CSD,
F(xy)=∫∫FOVdx′dy′fR(x′-x,y′-y)Ld(x′,y′),
where Ld is the radiance distribution of the solar disk. The angular
aperture of the sky radiometer is about 1∘, whereas the solar
disk diameter is about 0.5∘, and thus we can measure the solar
disk-averaged value of the radiometer response function as
f‾R(xy)=F(x,y)Fd.
From Eqs. (1), (20a), and (20b), the following normalization condition has to
be fulfilled,
f‾R(0,0)=1.
The SVA can be obtained by the angular integration of the radiance in the
CSD as follows:
I≡∫∫CSDdxdyf‾R(xy)=1Fd∫∫CSDdxdy∫∫FOVdx′dy′fR(x′-xy′-y)Ld(x′,y′)=1Fd∫∫FOVdx′dy′Ld(x′,y′)∫∫CSDdxdyfR(x′-xy′-y)=ΔΩ.
The last expression is obtained using Eqs. (19) and (20c) if the size of CSD
is large enough to include FOV or the contribution outside the CSD is small.
These equations indicate that flatness of the response function around the
optical axis should be secured in manufacturing the sky radiometer for
stable measurement of the direct solar radiation through Eq. (20b). The
perfect flatness is realized by optics without an objective lens, which is
useful for moving platforms such as aircrafts and ships (Nakajima et al.,
1986).
Analyzing data from the solar disk scan, Uchiyama et al. (2018b) found an
underestimation of SVA from the disk scan method of 0.5 % to 1.9 % and
proposed a correction method by extending CSD size up to a scattering angle of
2.5∘, assuming an extrapolation function as illustrated in Fig. 7.
They also discussed that the SVA error for the disk scan can exceed 1 % for
large AOT conditions such as AOT550 >0.5 and proposed a
subtraction method using sky radiance calculated from the size distribution
retrieved from the relative radiance. This subtraction method can reduce the
error to 0.5 % for AOT550 <2 for sky radiance measurements with
the minimum scattering angle Θ=3∘. The recent CEReS
system has introduced a quality control for setting the optimal value of SVA for
each site including Uchiyama's method, but no other sub-networks implement
these correction methods in their operational analysis.
Response functions of the sky radiometer at λ=0.5 and 1.6 µm measured by the solar disk scan method.
Though not performed routinely, a Xe lamp scan has been performed in CEReS
for the current version of the sky radiometer (Manago et al., 2016). The
merit of the method is that we can narrow the size of the point source below
0.5∘ and can extend the CSD size beyond ±1∘
without a significant effect from the sky light. Following this, measured SVAs were
compared with those derived from the solar disk scan in daytime. From the
experiments, uncertainty in SVA was estimated to be less than ±0.01 msr or ±4 % (Irie et al., 2019). This value is larger than that of
Uchiyama et al. (2018b) and more experiments may be needed for more precise
estimates and a unit variety.
Retrievals of parameters for atmospheric constituents
Once the values of radiometer calibration constants, F0 and SVA, are
determined by the calibration methods described in the preceding two
sections, the geophysical parameters of aerosols, clouds, water vapor, and
ozone are retrieved by inversion of F and/or R in Eqs. (2a) and (2c) at full
or specific scattering angles (Fig. 3). Aerosol retrievals are done using
Skyrad pack version 4.2 and/or version 5. The former is based on inversion
scheme of the Phillips–Twomey type solution of the first kind of Fredholm
integral equation with a homogeneous smoothing constraint, and the latter is
based on the second kind of the equation with an inhomogeneous constraint and
a priori climate data for aerosols (Twomey, 1963) to retrieve the inherent
aerosol optical properties. These methods can be generalized by minimization
of a cost function φ for realization of an observation vector y as
a function of a state vector x with observation error e using a multi-term
least-squares method (LSM) (Dubovik and King, 2000; Dubovik, 2004; Dubovik et
al., 2011),
21ay=f(x)+e,21bϕ=etSε-1e+ϕ1+ϕ2,21cϕ1=x-xatSa-1x-xa,ϕ2=xtGx,
where superscript t stands for matrix transpose operation, Sε is the
error covariance matrix, φ1 is the norm of the solution from
the a priori data xa with its associated covariance Sa, and φ2 is the cost for smoothness of the solution with the G matrix related to
the norm of the second derivatives of x. The AERONET analysis uses both the
constraints, φ1 and φ2, but with only two
elements for φ1 at the smallest and largest size bins and with
the value at the largest size bin as small as is possible to still be able to give a contribution to
AOT440 wavelength (Dubovik et al., 2006). Skyrad pack versions 4.2 and 5,
respectively, adopt the third and second term of the right-hand side of Eq. (21b), but not both.
The latter case of version 5 corresponds to the maximum a posteriori
solution (MAP) based on the Bayesian theorem (Rodgers, 2000). The MRI
version of Skyrad pack uses a φ1 constraint similar to version 5. An iterative search of the nonlinear solution is made by the
Gauss–Newton method as
22axi+1=xi+KitSε-1Ki+Sa-1+G-1KitSε-1y-f(xi)-Sa-1(xi-xa)-Gxi,22bKi=∇xF(x)x=xi.
Version 4.2 uses Eq. (22a) without Sa terms, and version 5 uses
the one without G terms. Observation and state vectors are given as
23ay=τa(λi),R(λiθjϕj)i=1,…,Nλ;j=1,…,Na,23bx=ln(Vj),ln(ñr(λi)),ln(ñi(λi))i=1,…,Nλ;j=1,…,Nv.
where geophysical parameters for the state vector are aerosol volume SDF as
a function of logarithm of particle radius r, x=lnr, and real and imaginary
parts of CRI, i.e., ñ=ñr-ñii, as functions of wavelength. The SDF
is represented by a linear combination of base functions {fk},
v(x)≡dVdx=∑k=1NvVkfk(x),x=ln(r).
The package allows two types of base functions, i.e., box-car functions or
lognormal functions with mode radii {xk} that are
regularly spaced in x axis,
fk(x)=12πσexp-12x-xkσ2.
The standard analysis in sub-networks assumes 20 lognormal base
functions (Nv=20) from r=0.02 to 20 µm with
dispersion σ=0.4, though there is an argument for a narrower
value (Momoi et al., 2020). The a priori value of the CRI is usually given as
ñ=1.5–0.005i. The version 4.2 retrieves x through the following four
steps: (step 1) the SDF for xa is assumed to be a bimodal lognormal
size distribution (Nv=2) with r1=0.1µm and r2=2µm and σ1=0.4, σ2=0.8, and the volumes of the
two modes are set to be the same, V1=V2, and are estimated from the forward
radiance data (Θ≤30∘); (step 2) retrieve
ñr from the radiance data in (20, 70∘); (step 3)
retrieve {Vk} from the forward radiance data
to revise xa; (step 4) retrieve SDF and CRI from the full angle scan
data. Step 4 is iterated until a conversion criteria is fulfilled. On
the other hand, version 5 follows steps 1, 2, and 4 without step 3. The
standard analysis of sub-networks does not treat asphericity of mineral dust
and sea salt particles and assumes Mie particles, except for in research
studies.
The package adopts the IMS method for solar aureole radiance calculation
(Nakajima and Tanaka, 1988) for full scalar radiative transfer code, Rstar,
with polarization correction by Ogawa et al. (1989) to save computing time.
A full polarization vector code, Pstar (Ota et al., 2010), is also used for
research purposes (e.g., Momoi et al., 2020). Asphericity is treated by an
approximation of the method of Pollack and Cuzzi (1980) and by several aspherical kernels.
Those softwares are available at OpenCLASTR. Other than polar region
measurements, the surface albedo is prefixed at 0.05 or 0.1 for wavelengths
shorter than 400 nm and 0.1 at longer wavelengths.
Comparison of AOT values at λ=870 nm obtained by the sky
radiometer, Cimel sun photometer, and PMOD PFR.
Statistics of AOT differences from other networks. RMSD values of
Estellés et al. (2012) are differences between AERONET values and
SUNRAD values for the same Cimel-CE318 sun photometer data with mode 1
(SKYNET-like) and mode 2 (AERONET-like) algorithms.
SourceStatistics3403804405006758701020Che et al. (2008), Beijing,mean0.536–0.3300.2480.211with AERONETRMSD0.025–0.0180.0180.018Figure 8a, 4 sites,∗mean0.1240.0890.080Khatri et al. (2016),RMSD0.0190.0150.016with AERONETFigure 8b, Davos,mean0.041–0.037–Kazadzis et al. (2018a, b),RMSD0.007–0.001–with PFRGo et al. (2020), Seoul,mean0.2630.2350.2050.1730.1190.0880.087with AERONETRMSD0.0360.0330.0290.0150.0090.0070.015Estellés et al. (2012),Valencia,with AERONET (mode 1)RMSD0.0180.0130.0110.0100.0100.0080.010with AERONET (mode 2)RMSD0.0050.0040.0020.0020.0020.0010.002
∗ These four sites are Chiba, Pune, Valencia, and Seoul.
Figure 8a and Table 5 compare observed AOT values with those of AERONET at
four co-located sites of Chiba (Japan), Pune (India), Valencia (Spain), and
Seoul (South Korea) (Khatri et al., 2016). They found that RMSDs were 0.019 at 675 nm and
about 0.015 at 870 and 1020 nm with a site dependence of 0.010, 0.033,
0.009, and 0.022 at 870 nm at the four sites, respectively, though this is not shown
in Table 5. Che et al. (2008) compared the AOTs between the POM-02 sky radiometer
and Cimel CE-318 sun photometer at the top of the Institute of Atmospheric
Physics (IAP) in Beijing, which belongs to SKYNET and AERONET.
The POM-02 data were processed by Skyrad pack 4.2. They found an RMSD of 0.025
at 440 nm and 0.018 at other wavelengths, which is similar to the findings of
Khatri et al.(2016), even with the mean AOT at this site being as large as 0.33 at 675 nm.
RMSDs of the Ångström exponent were 0.19 between 440 and 870 nm and
0.28 between 500 and 870 nm, though this is not shown in Table 5.
SKYNET instruments are regularly compared with Precision Filter Radiometer
(PFR) instruments belonging to the World Optical depth Research and
Calibration Center (WORCC) and the Global Atmospheric Watch PFR network.
Results of three POM instruments compared with the reference WORCC triad in
2015 showed differences of less than 0.005 and 0.01 in all cases and for 500
and 865 nm during the fourth filter radiometer comparison
(Kazadzis et al., 2018a). During the same campaign, Ångström exponent
mean differences were less than 0.5. Under low aerosol conditions, a small
relative bias in the AOT determination at 500 and 865 nm can theoretically
lead to large deviations in the calculated Ångström exponents (AEs).
As an example, for AOTs of about 0.05 and 0.02 at 500 and 865 nm,
respectively, AOT differences of 0.01 and 0.005 can lead to
AE differences up to ∼1. Since 2015, PFR versus POM long-term
comparisons have been performed at various stations, i.e., Valencia (Spain),
Chiba (Japan), Davos (Switzerland), and during the QUATRAM campaign in Rome (Italy)
(Kazadzis et al., 2018a; Monica Campanelli, personal communication, 2020). Figure 8b
and Table 5 compare AOTs at Davos to those of PMOD PFR. The PFR
comparison uses the result from the SUNRAD pack (Estellés et al., 2012), where
only direct measurements from the sky radiometer are used to retrieve AOT,
which have a higher time resolution with respect to direct measurements
performed during the almucantar scenarios. They found an RMSD as small as 0.007
and 0.001 at 500 and 870 nm, respectively.
Using multi-radiometer observation data since 2016 at Yonsei University,
South Korea, in a validation study for the upcoming Geostationary Environment
Monitoring Satellite (GEMS) (Kim et al., 2020), Go et al. (2020) compared
AOTs from a Cimel sun photometer, Ultraviolet Multifilter Rotating Shadowband
Radiometer (UV-MFRSR), NASA Pandora sun spectrometer, and POM-02 sky
radiometer. As shown in Table 5, they found RMSDs between AOT values from the
POM-02 and Cimel sun photometer of 0.029 to 0.036 for λ≤440 nm
and 0.009 to 0.015 for λ≥500 nm.
The statistics shown in Table 5 indicate that the RMSD took a value less
than 0.02 for λ≥500 nm and a larger value of about 0.03 for
shorter wavelengths in city areas, whereas mountain comparisons show smaller
RMSDs of less than 0.01. This location difference can be understood using
F0 uncertainties from around 0.5 % to 2.4 % in Tokyo and Rome and
smaller values around 0.3 % to 0.5 % at the mountain sites of Mt. Saraswati and
Davos, as discussed in Sect. 3, though uncertainties in AOT comparisons can
include other error sources, such as pointing error, time variation, and
errors in the retrieval software. Estellés et al. (2012) discussed this
point using a comparison of AERONET AOT values with those retrieved by their
SUNRAD pack for the same sun photometer, but with two different analysis
modes, i.e., mode 1, which implements the SKYNET extinction model, and mode 2
with an AERONET-like model. As listed in Table 5, they found an RMSD of about 0.01
for λ≥440 nm and a larger value in UV channels with a mode 1
setup, whereas a mode 2 setup gives a very small RMSD of less than 0.005.
Therefore, more than half of the RMSDs found in the comparison between SKYNET
and AERONET can be attributed to differences in the analysis software.
The Skyrad pack assumes a simplified extinction model with a plane-parallel
assumption in the optical air mass formula, ignores water vapor absorption in
IR channels, and has an ozone absorption extinction model in the UV channels that is
different from the AERONET model. Slightly larger values at 1020 nm than at
875 nm may be due to the omission of water vapor absorption. Further work is needed
to study the effects of these simplifications, which need improvements. For
example, SKYNET poses an IL operation limit of m0≤3 instead of
m0<5 in the data analysis of Estellés et al. (2012) shown
in Table 5.
Reported SSA differences from other networks: mean bias (Che et
al., 2008) and RMSD (Kim et al., 2005; Khatri et al., 2016; Mok et al.,
2018). Simulated changes of SSA between Skyrad pack versions 4.2 and 5 and
SSA retrieval errors of version 4.2 in an enhanced mineral dust case are
also obtained by a numerical simulation (Hashimoto et al., 2012).
SourceMethod34038044015006758701020Kim et al. (2005),Diffuse to direct0.027RMSDChe et al. (2008),0.010.030.030.060.07with AERONET, meanKhatri et al. (2016),Before correction0.0690.0740.0684 sites2, with AERONETAfter correction0.0270.0300.037RMSDMok et al. (2018),Spectral Ag0.0170.0150.0160.0250.047with AERONETAg=0.10.0250.0180.0200.0240.048RMSDHashimoto et al. (2012),Beijing observed cirrus0.0170.0230.0290.0350.023simulation meancontamination, version 4.2 and 5differenceEnhanced mineral dust0.0080.004-0.005-0.013-0.017case, version 4.2and 5-0.013-0.017-0.026-0.031-0.030
1 400 nm in Hashimoto et al. (2012).
2 Chiba, Pune, Valencia, Seoul.
Table 6 lists reported SSA differences from other networks. SSA values from
SKYNET are known to be overestimated, as pointed out by Che et al. (2008).
Mean values of SSA in Beijing retrieved from the PREDE sky radiometer were
significantly larger than those from the Cimel sun photometer, with
differences reaching 0.06 to 0.07 for λ≥870 nm, whereas the
mean differences were less than 0.03 at shorter wavelengths. This wavelength
dependence can be understood by a tendency for error to increase with
decreasing AOT (Dubovik et al., 2000). Similarly Khatri et al. (2016) had a
positive difference of about 0.07 RMSD for λ≥675 nm from
AERONET values at the four sites (Chiba, Pune, Valencia, and Seoul), and
they found that the values can be reduced to around 0.03 if various corrections
are applied. The major error source was SVA underestimation of 1.4 % to
3.7 % causing an SSA increase of 0.03 to 0.04. There was an AOT
underestimation of 0.02 RMSD at 675 nm, as shown in Table 5, which caused an
SSA increase of 0.02 at 675 nm and less than 0.004 at longer wavelengths.
Version 4.2 of the Skyrad pack tended to give larger SSA than version 5, but the
difference was less than 0.01 for usual aerosol conditions in this case.
Effects of surface albedo and asphericity on the SSA difference were less
than 0.01. These effects are consistent with those obtained by sensitivity
simulations by Pandithurai et al. (2008) and Hashimoto et al. (2012) in a
similar way to those described by Dubovik et al. (2000). Pandithurai et al. (2008)
found that a 5 % error in F0 and SVA and a 0.5∘
error in the azimuth angle pointing in SKYNET can induce an error of 0.03
in retrieved AOT, and mean and maximum differences in retrieved SSA are about
0.004 and 0.02. Hashimoto et al.(2012), found, in a numerical simulation at 500 nm, as
shown in Table 6, a positive SSA retrieval error of +0.03 can be
caused by SVA underestimation of about 5 %, AOT underestimation of about
-0.02, and ground albedo underestimation of about -0.1.
Aerosol properties in the UV spectral region were extensively measured in
the KORUS-AQ campaign (https://espo.nasa.gov/korus-aq/content/KORUS-AQ, last access: 23 July 2020). Mok
et al. (2018) compared SSA retrievals, as shown in Fig. 9 and Table 6, from
SKYNET SR-CEReS, AERONET, and Pandora AMP radiometers from April to August
2016 at Yonsei University, South Korea. They found differences of around 0.02 for
λ≤500 nm and a larger value of 0.05 at 870 nm, similar to
those of Che et al. (2008) and Khatri et al. (2016) shown in Table 6. They
also found that the SSA difference increased by 0.004 to 0.008 at short
wavelengths when they adopted a spectrally fixed ground albedo Ag at 0.1 as was
assumed in the SKYNET analysis, instead of the original setup of spectrally
varying AERONET ground albedo Ag.
Combined spectral SSA from AMP retrievals (blue symbols) and
SKYNET retrievals (orange symbols) using MODIS-derived surface albedo. 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 (interquartile range). 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. Cited
from Mok et al. (2018).
Cloud contamination is another significant error source, as studied by
Hashimoto et al. (2012). They studied a case of cirrus contamination
detected by a lidar observation in Beijing and found that Skyrad pack
version 4.2 retrieved SSA values larger by between 0.017 and 0.035 than those from
version 5, as shown in Table 6. Version 4.2 simply retrieves a cloud particle
volume as coarse-mode aerosol volume with the smoothness constraint
φ2 in Eq. (21), but version 5 can filter out the cloud
particles, owing to the a priori constraint φ1 on SDF. This
robustness of version 5 to cloud contamination makes the inversion of the
aerosol SDF robust to various noises, as reported by Che et al. (2014) and
Jiang et al. (2020), who demonstrated a clear aerosol bimodal size
distribution over Beijing in China by using Skyrad pack version 5. Hashimoto
et al. (2012), therefore, proposed a data screening protocol to reject
unusually large coarse-particle volume: (C1) AOT500 <0.4, (C2)
ετ<0.07, and (C3) 2×V2.4µm<max(V7.7µm,V11.3µm,V16.5µm). Application
of this screening protocol reduced SSAs by version 4.2 to be closer to version 5
and AERONET values within 0.03 for 8 to 9 month data at Pune (India)
and Beijing (China).
It is also interesting to compare the sky radiometer method with other
methods. Kim et al. (2004, 2005) compared SSAs from a sky radiometer with
those estimated by the diffuse direct method (King and Herman, 1979) using
data from a co-located pyranometer network in the APEX campaign (Asian
Atmospheric Particle Environmental Change Studies) (Nakajima et al., 2003).
This method is especially beneficial for the climate study community,
because the method gives effective SSA values consistent with the Earth
radiation budget. They found an RMSD at 500 nm of about 0.03 from data on
Amami Ōshima Island. This value is consistent with other values in Table 6.
Retrieved and observed aerosol size distribution functions in the
African dust event cases in the sun photometer Airborne Validation Experiment
in Dust (SAVEX-D) campaign during 16–25 August 2015 (Estellés et al.,
2018; Marenco et al., 2018; Ryder et al., 2018).
One reservation about the SSA retrieval by version 5, though, is that it
tends to underestimate the SSA due to underestimation of the coarse aerosols
when the a priori SDF for constraint tends to zero for radii larger than
10 µm. Hashimoto et al. (2012) showed by their numerical simulation of
an enhanced mineral dust case that version 5 tends to underestimate SSA
by 0.017 to 0.035 compared to version 4.2, as shown in Table 6, because
version 5 mistakenly filters out coarse aerosols using the a priori SDF data
xa in Eq. (21c). Estellés et al. (2018) found similar
underestimation of the coarse aerosols by version 5 compared to aircraft
in situ observations (Marenco et al., 2018; Ryder et al., 2018) for African
dust events in the sun photometer Airborne Validation Experiment in Dust
(SAVEX-D) campaign during 16–25 August 2015, as shown in Fig. 10. The figure
indicates that version 4.2 retrieved coarse-mode SDF similar to the observed
SDF, though the error bar is large. These examples suggest an improvement of
the a priori SDF data is needed for severe dust storm cases.
Water vapor amount is retrieved from direct solar irradiance measurement in
the 940 nm channel. F0 value in the water vapor channel is retrieved by
the modified Langley plot (ML or MLP) method based on the following OT
formula instead of Eq. (3):
25ay=ln(F0)-agx,25by=ln(F)+m(τa+τR),x=(mgCg)bg,
where τa and τR are AOT and OT for molecular
scattering, respectively, and Cg is the column-integrated burden of
gaseous species, i.e., PWV W in this case; mg is optical air mass for the
gaseous species; ag and bg are two prescribed constants to
approximate the beam transmittance due to the gaseous species; and ag can be
regarded as an equivalent absorption coefficient for band-averaged
absorption of the gaseous species. It is common to assume mg to be the same as
that of atmospheric air mass, i.e., mg=m in the water vapor case. The
value of τa is obtained by an interpolation of the AOT spectrum
retrieved from the non-gas absorption channels. There are two algorithms for
the SKYNET analysis. One is to use the measured spectral response function
of the interference filter of the sky radiometer to prescribe values of
ag and bg by the theoretical absorption calculation (Uchiyama et
al., 2014). This method is similar to that of the AERONET method. The strong
line absorption theory of the 930 nm spectral band yields bg=0.5
(Goody and Yung, 1989) in Eq. (25b). However, there is some dependence of
bg on the vertical structure of the atmosphere, and therefore an improved
method is proposed by Campanelli et al. (2010, 2014, 2018) to determine
ag and bg values using a statistical regression technique of daily
observation data at the site. They obtained a range of bg values of 0.53
to 0.61 as monthly mean values of the 3 years from 2007 to 2009 at the San
Pietro Capofiume site (SPC; 44∘23′ N, 11∘22′ E, 11 m a.m.s.l.), Italy, with some seasonal dependence. One
complexity of this method, however, is a need for measurements of W for making
the regression analysis. They used PWV either from radiosonde data or a
proxy of PWV constructed from surface meteorological data of temperature and
relative humidity. Figure 11 compares PWV by the two methods with GPS and
AERONET retrievals in Tsukuba, Japan, and Valencia, Spain, for data taken in
2011. Figure 11 shows that the RMSD from the validation data is less than 0.2 cm using both
methods, with some systematic underestimation of the slope of the
regression line of 10 % in the former method. Estellés et al. (2012)
compared PWV at Valencia, Spain, between AERONET values and those retrieved
by the SUNRAD pack for the same Cimel sun photometer. They found an RMSD of 0.20 cm
when the SUNRAD pack uses the mode 1 (SKYNET-like) setup, whereas it is reduced to
0.17 cm if SUNRAD uses the mode 2 (AERONET-like) setup, indicating
performances of the two modes are similar in water vapor
retrievals compared to a significant difference in the AOT case, as shown in
Table 5.
Precipitable water retrieved by Uchiyama et al. (2014) in (a)
and by Campanelli et al. (2018) in (b) and (c).
In order to get rid of the F0 retrieval process in the water vapor
channel, Momoi et al. (2020) proposed a new method of using water vapor
dependence of the relative radiance along the almucantar circle of the sky.
Although this method has a limited range of retrievable PWV that is less than 2 cm,
there is merit in using the value from the method, e.g., Wsky, as a
proxy of Cg=W in Eq. (25b) to perform the MLP on site, similar to the
IL method for the non-absorption channels, but with
x=Wskybg,
instead of Eq. (25b).
The columnar ozone amount (O3) is retrieved from the direct solar
irradiance measurement of 315 nm channel for the Huggins band. Khatri et al. (2014) determined the F0 value using an ML method Eq. (26) assuming
bg=1 for ozone without a significant line absorption structure. The
formula of mg is given by Robinson (1966). In the F0 determination
process, they simultaneously obtained an optimal value of the equivalent
ozone absorption coefficient ag, which brings the slope of the ML plot to
unity using data of ozone column burden Cg=U measured by the Dobson
spectrometer. The RMSD of the fitting for campaign data at the Tsukuba site from
13 December 2012 to 8 January 2013 was 13 DU (Dobson unit) as shown in
Fig. 12. They also reported a large degradation of filter transmission in
the ozone channel.
Comparison of column ozone amount (DU) retrieved from the sky
radiometer at MRI Tsukuba site and from Dobson spectrometer at the JMA Tateno
Observatory from 13 December 2012 to 8 January 2013.
Cloud microphysical properties have been obtained from diffuse sky radiance
measurements from satellites (Nakajima and King, 1990). A similar approach can
be applied to the ground-based radiance measurements. Chiu et al. (2010,
2012) retrieved cloud optical thickness (COT) and effective particle radius
(CER) from AERONET data. SKYNET uses the POM-02 sky radiometer, which has 1.6 and 2.2 µm channels (Kikuchi et al., 2006; Khatri et al.,
2019). Figure 13 compares COT retrieved from POM-02 at zenith observations
at the three sites of Chiba, Fukue, and Hedo combined with retrievals from
Himawari-8/AHI satellite-borne imager in a period of October 2015 to
December 2016 (Khatri et al., 2019). Satellite retrieval results were
obtained by the Comprehensive Analysis Program for Cloud Optical Measurement
(CAPCOM) (Nakajima and Nakajima, 1995) in the system of AMATERASS (Takenaka
et al., 2011; Damiani et al., 2018). Geostationary satellite observation has
the merit of frequent time-matching with the ground-based observation. Figure 13 shows that there is a large scatter of RMSD at 10.2 and a correlation of
0.89. They also studied cloud effective particle radius but did not find a
significant correlation between SKYNET and AHI observations. Figure 14 also
compares the broadband radiance at zenith measured by a ground-based
pyrheliometer and with broadband horizontal radiative flux measured by a
pyranometer with those theoretically calculated using the cloud parameters
from sky radiometer measurement. Figure 14 indicates that the down-welling
radiance at zenith was consistent between the two radiometers, but
horizontal radiative fluxes were not well represented by the cloud optical
properties retrieved from the sky radiometer at nadir. Figures 13 and 14
suggest that the inhomogeneity of cloud fields is the main source of
differences between the cloud parameters obtained by the sky radiometer and
satellite measurements.
Comparison of cloud optical thickness (COT) retrieved from sky
radiometer at the Chiba, Fukue, and Hedo sites and the Himawari-8/AHI satellite-borne
imager in the period of October 2015 to December 2016 (Khatri et al., 2019).
The regression line is shown with zero intercept constraint at the origin.
The same as in Fig. 13 but for the comparison between modeled and observed
broadband (a) radiances and (b) horizontal radiative fluxes. Regression
lines are shown with zero intercept constraint at the origin.
Conclusions
The SKYNET community has undertaken efforts at improving their on-site
calibration and analysis systems to provide retrieved aerosol and other
atmospheric constituents.
An estimate of the retrieval accuracy of F0 is given by Eq. (10) for the
IL method, which can serve as an approximation of observed monthly mean
uncertainty in F0 as 0.5 % to 2.4 % at the Tokyo and Rome sites and
smaller values of around 0.3 % to 0.5 % at the mountain sites of Mt. Saraswati and
Davos. These values are consistent with RMSD values, in the AOT comparisons
with other networks, of less than 0.02 for λ≥500 nm and a larger
value of about 0.03 for shorter wavelengths in city areas and smaller values of
less than 0.01 in mountain comparisons. We also developed a new XIL method
to correct an underestimation by the IL method in the case of large aerosol
retrieval errors.
Several causes of larger SSA values reaching 0.07 than those of other
networks have been identified as underestimation of SVA measured by the disk
scan method and the new lamp scan method, cloud contamination, and others.
Recent reported values of the difference are found to be less than 0.03
after these corrections.
Retrievals of other atmospheric constituents by the sky radiometer are also
reviewed. We found accuracies of about 0.2 cm for the precipitable water
vapor amount and 13 DU for the column ozone amount. A new on-site
calibration method for water vapor has been developed. The cloud optical
properties were found to have some (but not large) correlation with satellite
remote sensing values, suggesting cloud inhomogeneity may be one source of
error.
There are several aims for the next step of the SKYNET to make its system
more reliable and useful for the science community. The reported useful
improvements of the product quality are still in the research phase, and it is
important to introduce them into the existing operational systems and future
system of the ISDC. Comparison studies also showed that the analysis
software Skyrad pack may need improvements in its simplified optical model.
We want to pursue our on-site calibration system for sustainable operation
of the network. However, it is still required for a full accuracy assessment
to conduct continuous comparison of on-site calibrations of our standard sky
radiometer with high mountain calibrations and other network
calibrations.
Data availability
L2 products of SKYNET are available at the archives listed in Table 2. Other datasets used in the present study are available from the corresponding author on reasonable request.
Author contributions
The main contributions to network management were made by TeN, MC, HC, HI, SWK, JK, DL, ToN, GP, VKS, BT, NUT, KA, and AY; to algorithm developments by TeN, MC, VE, HI, MH, PK, RK, MM, and AU; and to data analyses and archiving by MC, HC, VE, HI, SWK, JK, DL, ToN, GP, VKS, BT, NUT, KA, SG, AH, SK, PK, NK, RK, FM, MM, SSN, CLR, AU, and AY.
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 (AMT/ACP inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We are grateful for the support from the JAXA (Japan Aerospace Exploration Agency) SGLI, MOEJ (Ministry of the Environment Japan)-NIES (National Institute for Environmental Studies)-JAXA GOSAT and GOSAT2, ERCA (Environmental Restoration and Conservation Agency) ERDF/S-12, and JST (Japan Science and Technology Agency)/CREST/TEEDDA (JPMJCR15K4) projects. Kazuhiko Miura of
Tokyo University of Science is gratefully acknowledged for providing us
with Langley plot data. A group of the co-authors were supported by
ERCA/ERDF/2-1901, JSPS (Japan Society for the Promotion of Science) /KAKENHI/JP19H04235, P17K00529, and the JAXA 2nd
research announcement on the Earth Observations (grant number 19RT000351). ESR is grateful for the support of the Spanish Ministry of Economy and Competitiveness
and European Regional Development Fund through funding to the University of
Valencia within several projects, such as CGL2015-70432-R and
CGL2017-86966-R. The SAVEX-D campaign
was funded by EUFAR TNA (European Union Seventh Framework Programme, grant
agreement no. 312609), making use of airborne data obtained using the
BAe-146-301 Atmospheric Research Aircraft operated by Airtask Ltd and
managed by the Facility for Airborne Atmospheric Measurements (FAAM). It was
a success thanks to many staff at the Met Office, the University of Leeds, the University of
Manchester, the University of Hertsfordshire, FAAM, Directflight Ltd, Avalon Engineering
and BAE Systems. ESR also thanks Emanuele Costantini of the Research Area of Tor Vergata for the data center and server maintenance, Dr. Igor Bertello for his help on the development of the ESR system, and Dr. Henri Diémoz from ARPA Valle d'Aosta for continuous support of ESR activities.
Jhoon Kim was supported by the “Technology development for Practical Applications
of Multi-Satellite data to maritime issues” project, funded by the Ministry of Ocean
and Fisheries, South Korea.
We thank Alexander Smirnov of NASA GSFC for useful discussions about the
history of sun and sky measurements.
Financial support
This research has been supported by the JAXA (grant nos. SGLI and 19RT000351), the MOEJ-NIES-JAXA (GOSAT and GOSAT2), the ERCA (grant nos. S-12 and 2-1901), the JST (grant no. JPMJCR15K4), the JSPS (grant nos. JP19H04235 and P17K00529), the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (grant nos. CGL2015-70432-R and CGL2017-86966-R), and the EUFAR TNA (grant no. 312609).
Review statement
This paper was edited by Omar Torres and reviewed by two anonymous referees.
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