AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-12-169-2019Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurementsAdvancements in the AERONET version 3 databaseGilesDavid M.david.m.giles@nasa.govhttps://orcid.org/0000-0001-6093-2051SinyukAlexanderSorokinMikhail G.SchaferJoel S.SmirnovAlexanderhttps://orcid.org/0000-0002-8208-1304SlutskerIlyaEckThomas F.HolbenBrent N.https://orcid.org/0000-0002-1251-9809LewisJasper R.CampbellJames R.https://orcid.org/0000-0003-0251-4550WeltonEllsworth J.KorkinSergey V.LyapustinAlexei I.https://orcid.org/0000-0003-1105-5739Science Systems and Applications Inc. (SSAI), Lanham, MD 20706, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAUniversities Space Research Association (USRA), Columbia, MD 21046, USAJoint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21250, USAMarine Meteorology Division, Naval Research Laboratory (NRL), Monterey, CA 93943, USADavid M. Giles (david.m.giles@nasa.gov)11January201912116920916August201810September201812December201813December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/12/169/2019/amt-12-169-2019.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/12/169/2019/amt-12-169-2019.pdf
The Aerosol Robotic Network (AERONET) has provided highly
accurate, ground-truth measurements of the aerosol optical depth (AOD) using
Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2)
of the AERONET database, the near-real-time AOD was semiautomatically
quality controlled utilizing mainly cloud-screening methodology, while
additional AOD data contaminated by clouds or affected by instrument
anomalies were removed manually before attaining quality-assured status
(Level 2.0). The large growth in the number of AERONET sites over the past
25 years resulted in significant burden to the manual quality control of millions
of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm
provides fully automatic cloud screening and instrument anomaly quality
controls. All of these new algorithm updates apply to near-real-time data as
well as post-field-deployment processed data, and AERONET reprocessed the
database in 2018. A full algorithm redevelopment provided the opportunity to
improve data inputs and corrections such as unique filter-specific
temperature characterizations for all visible and near-infrared wavelengths,
updated gaseous and water vapor absorption coefficients, and ancillary data
sets. The Level 2.0 AOD quality-assured data set is now available within a
month after post-field calibration, reducing the lag time from up to several
months. Near-real-time estimated uncertainty is determined using data
qualified as V3 Level 2.0 AOD and considering the difference between the AOD
computed with the pre-field calibration and AOD computed with pre-field and
post-field calibration. This assessment provides a near-real-time
uncertainty estimate for which average differences of AOD suggest a +0.02 bias
and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty
can be significantly larger for specific instrument deployments. Long-term
monthly averages analyzed for the entire V3 and V2 databases produced
average differences (V3–V2) of +0.002 with a ±0.02 SD (standard
deviation), yet monthly averages calculated using time-matched observations
in both databases were analyzed to compute an average difference of -0.002
with a ±0.004 SD. The high statistical agreement in
multiyear monthly averaged AOD validates the advanced automatic data
quality control algorithms and suggests that migrating research to the
V3 database will corroborate most V2 research conclusions and likely lead to
more accurate results in some cases.
Introduction
Space-based, airborne, and surface-based Earth observing platforms can
remotely retrieve or measure aerosol abundance. Each method has its own
assumptions and dependencies in which the aerosol total column abundance
quantified by aerosol optical depth (AOD) introduces uncertainty in the
retrieval or measurement. At the forefront, ground-based Sun photometry has
been considered the ground truth in the measurement of AOD given minimal
assumptions, reliable calibration, and weak dependency on trace gases at
carefully selected wavelength bands, thus resulting in highly accurate data
(Holben et al., 1998, 2001). Meanwhile, AOD inferred from other observing
platforms such as satellite retrievals provides quantitative AOD but with
significantly higher uncertainty (Remer et al., 2005; Li et al., 2009; Levy
et al., 2010; Sayer et al., 2013). Further, in situ measurements lack the
ability to provide a reliable columnar AOD due to the requirement of
measuring aerosols vertically in each layer while not perturbing or
modifying the particle properties during the measurement (Redemann et al.,
2003; Andrews et al., 2017). Lidar is
fundamental in the determination of the vertical aerosol extinction
distribution (Welton et al., 2000; Omar et al., 2013). Quantification of
columnar AOD from ground-based lidar, for example, may be less reliable due
to low signal-to-noise ratio during the daylight hours at high altitudes and
below the overlap region in which the aerosols very near the surface are
poorly observed by lidar. Satellite retrieval issues include determining the
AOD for very high aerosol loading episodes, cloud adjacency effects,
land–water mask depiction, surface reflectance, highly varying topography,
and aerosol type assumptions (Levy et al., 2010, 2013; Omar et
al., 2013). With each of these measurement platforms, uncertainties exist
with AOD; however, these concerns are minimized with AOD measurements from
surface-based Sun photometry such as from the federated Aerosol Robotic
Network (AERONET). Ground-based Sun photometry, a passive remote-sensing
technique, is robust in measuring collimated direct sunlight routinely
during the daytime in mainly cloud-free conditions (Shaw, 1983; Holben et
al., 1998; Takamura and Nakajima, 2004; Smirnov et al., 2009; Kazadzis et
al., 2018). While these surface-based measurements are only point
measurements, the federated AERONET provides measurements of columnar AOD
and aerosol characteristics over an expansive and diverse geographic area of
the Earth's surface at high temporal resolution.
Standardization of Sun photometer instrumentation, calibration, and freely
available data dissemination of AOD and related aerosol databases highlights
the success of the federated AERONET. For more than 25 years, the AERONET
federation has expanded due to the investments and efforts of NASA (Goddard
Space Flight Center, GSFC) (Holben et al., 1998), the University of Lille
(PHOtométrie pour le Traitement Opérationnel de Normalisation
Satellitaire – PHOTONS) (Goloub et al., 2008), University of Valladolid (Red
Ibérica de medida Fotométrica de Aerosoles – RIMA) (Toledano et al.,
2011), other subnetworks (e.g., AEROCAN, Bokoye et al., 2001; AeroSpan,
Mitchell et al., 2017; AeroSibnet, Sakerin et al., 2005; CARSNET, Che et
al., 2015), collaborators at agencies, institutes, and universities, and
individual scientists worldwide. Conceived in the late 1980s, AERONET's
primary objective was to provide an aerosol database for validation of Earth
Observing System (EOS) satellite retrievals of AOD and atmospheric
correction (Kaufman and Tanré, 1996). In addition to columnar direct Sun
AOD, sky radiances were used to infer aerosol characteristics initially from
Nakajima et al. (1996) (SkyRad.PAK) and later by the Dubovik and King (2000)
inversion algorithm to obtain products such as aerosol volume size
distribution, complex index of refraction, single scattering albedo, and phase functions.
AERONET is a network of autonomously operated Cimel Electronique Sun–sky
photometers used to measure Sun collimated direct beam irradiance and
directional sky radiance and provide scientific-quality column-integrated
aerosol properties of AOD and aerosol microphysical and radiative properties
(Holben et al., 1998; https://aeronet.gsfc.nasa.gov, last access: 12 December 2018). The development and
growth of the program relies on imposing standardization of instrumentation,
measurement protocols, calibration, data distribution, and processing
algorithms derived from the best scientific knowledge available. This
instrument network design has led to a growth from two instruments in 1993
to over 600 in 2018. During that time, improvements were made to the Cimel
instruments to provide weather-hardy, robust measurements in a variety of
extreme conditions. While the basic optical technology has evolved
progressively from analog to digital processing over the past 25 years, the
most recent Sun–sky–lunar CE318-T instruments provide a number of new
capabilities in measurement protocols, integrity, and customizability (Barreto et al., 2016).
All of the slightly varying models of the Cimel instruments can have
measurement anomalies affecting direct Sun measurements, which include
measurements in the presence of clouds, various obstructions in the
instrument's field of view, or systematic instrumental issues such as
electrical connections, high dark currents, and clock shifts to name a few.
Some of these issues depend on the instrument model and, for more than a decade,
these anomalies have been removed semiautomatically utilizing the cloud-screening method developed by Smirnov et al. (2000) and further quality
controlled by an analyst to remove additional cloud-contaminated data and
instrument artifacts from the database. Chew et al. (2011) identified up to
0.03 of AOD bias at Singapore due to optically thin cirrus clouds for Version 2
Level 2.0 data. Coincidentally, Huang et al. (2011) examined how cirrus
clouds could contaminate AOD measurements in up to 25 % (on average) of the
data in April at Phimai, Thailand, in the Version 2 Level 2.0 data set. The
number of AERONET sites has increased to more than 600 sites in the network
as of 2018 and the labor-intensive effort of quality controlling hundreds of
thousands of measurements manually had resulted in a significant delay of
quality-assured data (Level 2.0) in the AERONET Version 2 database.
With these issues at hand, the cloud-screening quality control procedure as well as all other aspects of the AERONET processing algorithm
including instrument temperature characterization, ancillary data set
updates, and further quality control automation were
reassessed. Utilizing these
improvements, the Version 3 Level 2.0 quality-controlled data set requires
only the pre-field and post-field calibrations to be applied to the data so
these data can now be released within a month of the final post-field
instrument calibration instead of being delayed up to several months. As
encouraged by the AERONET community, automatic quality controls in Version 3
are now also applied to near-real-time Level 1.5 AOD products allowing for
improved data quality necessary for numerous applications such as numerical
weather prediction, atmospheric transport models, satellite evaluation, data
synergism, and air quality.
The AERONET Version 3 processing algorithm marks a significant improvement
in the quality controls of the Sun photometer AOD measurements, particularly
in near real time. The revised AERONET algorithm is introduced by first
reviewing the calculations made to compute the AOD plus changes in the input
data sets and the resulting calculation of optical depth components. Next,
the preprocessing steps and data prescreening are discussed for the Version 3
quality control algorithm. Cloud screening and instrument quality control
algorithm changes are discussed with reference to Smirnov et al. (2000), and
the solar aureole cirrus cloud-screening quality control is introduced for
the first time. The automation of instrument anomaly quality controls and
additional cloud screening is described in the subsequent sections. Lastly,
the AERONET Version 2 and Version 3 database results are analyzed for the
entire data set as well as for selected sites.
Aerosol optical depth computation
Sun photometry is a passive remote-sensing measurement technique in which
mainly collimated light generally not scattered or absorbed by the
atmosphere illuminates a photodiode detector and this light energy is
converted to a digital signal. The digital signal (V) measured by the
instrument is proportional to the solar irradiance. The relative solar
calibration is derived from the Langley method (Ångström, 1970; Shaw et
al., 1973) utilizing the digital counts from the instrument versus the
optical air mass to obtain the calibration coefficient (Vo) by choosing
the intercept at which optical air mass is zero at the top of the atmosphere
(Shaw, 1983). The relative extraterrestrial solar irradiance is proportional
to Vo. As shown by Holben et al. (1998) and for completeness in this
discussion, the Beer–Lambert–Bouguer law converted to instrument digital
counts is shown in Eq. (1):
V(λ)=Vo(λ)⋅d2⋅exp-τ(λ)Total⋅m,
where V(λ) is the measured spectral voltage of the instrument
dependent on the wavelength (λ), Vo(λ) is the
relative extraterrestrial spectral calibration coefficient dependent on λ,
d is the ratio of the average to the actual Earth–Sun distance
(Michalsky, 1988; USNO, 2018), τ(λ)Total is the total
optical depth, and m is the optical air mass, which is strongly dependent on
the secant of the solar zenith angle (Kasten and Young, 1989). For the Cimel
Sun photometer, the voltage signal is expressed as integer digital counts or
digital number (DN). The error in the τ(λ)Total is
generally dependent on the optical air mass (m) by δτ proportional
to m-1 and hence the AOD computation error will tend to be at a
maximum at m=1 (Hamonou et al., 1999). Cimel instrument repeatability is
tested during calibration procedures by comparing voltage ratios between the
field instrument and reference instrument to be less than ±1 %
(Holben et al., 1998). The absolute uncertainty in the AOD measurement can
be described as Eq. (2), with calibration uncertainty of Vo being the
overwhelmingly dominant error source:
δτ=1m⋅δVV+δVoVo+τ⋅δm≅1m⋅δVoVo.
The spectral AOD (τ(λ)Aerosol)
should be computed from the cloud-free spectral total optical depth τ(λ)Total)
and the subtraction of the contributions of Rayleigh scattering optical depth
and spectrally dependent atmospheric trace gases as shown in Eq. (3).
τ(λ)Aerosol=τ(λ)Total-τ(λ)Rayleigh-τ(λ)H2O-τ(λ)O3-τ(λ)NO2-τ(λ)CO2-τ(λ)CH4
The Rayleigh optical depth (τRayleigh) is calculated based on the
assumptions defined in Holben et al. (1998), optical air mass (Kasten and
Young, 1989), and the formula by Bodhaine et al. (1999), except correcting the
result based on the NCEP-derived station pressure. The ozone (O3)
optical depth (τO3) is dependent on the O3 absorption
coefficient (aO3) for the specific wavelength, the geographic and
temporally dependent multiyear monthly climatological Total Ozone Mapping
Spectrometer (TOMS) O3 concentration (CO3), and the O3 optical
air mass (mO3) (Komhyr et al., 1989) using the following formulation:
τO3=aO3⋅CO3⋅mO3/m. Similarly, nitrogen
dioxide (NO2) optical depth (τNO2) is computed using absorption
coefficient (aNO2) and geographic and temporally dependent multiyear
monthly climatological Ozone Monitoring Instrument (OMI) NO2
concentration (CNO2) assuming NO2 scale height is equal to
aerosol: τNO2=aNO2⋅CNO2. The water vapor optical
depth (τH2O) is calculated based on filter-dependent (e.g.,
1020 and 1640 nm) A and B coefficients (discussed further below) and precipitable
water (PW) in centimeters (u) using the following linear formulation: τH2O=A+Bu.
The carbon dioxide (CO2) optical depth (τCO2) and
methane (τCH4) use station-elevation-dependent formulations:
τCO2=0.0087⋅P/P0 and
τCH4=0.0047⋅P/P0, assuming the US standard atmosphere (1976) and absorption
constants derived from HITRAN. Further descriptions of these calculations
are provided below.
Table 1 provides a list of the spectral corrections used in the calculation
of AOD and PW from 935 nm. The nominal standard aerosol wavelengths are 340,
380, 440, 500, 675, 870, 1020, and 1640 nm.
For wavelengths shorter than and equal to 1020 nm, these channels are
measured using a silicon photodiode detector with a spectral range from
320 to 1100 nm. If the Cimel instrument has an InGaAs detector with a
900 to 1700 nm spectral range, then the 1640 nm wavelength is measured along with
a redundant 1020 nm measurement used to compare instrument optical
characteristics among detectors, lenses, and collimator tubes. The Cimel
SeaPrism instrument models, which are deployed on ocean or lake platforms as
part of the AERONET Ocean Color component to retrieve normalized water
leaving radiances at 8–12 additional visible band wavelengths for ocean and
lake remote-sensing studies, are similarly corrected for atmospheric effects
(Zibordi et al., 2010).
Rayleigh optical depth calculations require the use of the station pressure
(Bodhaine et al., 1999) as well as the optical air mass (Kasten and Young
1989). To determine AERONET site station pressure (PS), the NCEP/NCAR
reanalysis mean sea level pressure and geopotential heights at standard
levels (1000, 925, 850, 700, and 600 hPa) are fitted by a
quadratic function in logarithmic space to infer the station pressure at the
corresponding interpolated geopotential height. The NCEP/NCAR reanalysis
data are available routinely at 6-hourly temporal resolution and
2.5∘ spatial resolution (Kalnay et al., 1996). Errors in the station
pressure are generally less than 2 hPa when the station elevation is accurate
and the weather conditions are benign (i.e., atmospheric pressure tends to
be stable) since aerosol measurements are typically performed in mainly
cloud-free conditions.
Nominal AERONET wavelengths for ion-assisted deposition filters used
for aerosol remote sensing and spectral corrections or components for each channel.
The 935 nm wavelength is used to determine the water vapor optical depth
contribution, which is consequently subtracted from the longer aerosol
wavelengths (i.e., 709 nm SeaPrism, 1020, and 1640 nm). The AOD at 935 nm is
extrapolated based on the Ångström exponent (AE) computed from the
linear regression of the AOD and wavelengths in logarithmic space within the
range of 440–870 nm excluding channels affected by water vapor absorption
(Eck et al., 1999). To extract the PW in centimeters from the
935 nm measurements, the Rayleigh optical depth and the AOD components need
to be subtracted from the total optical depth at 935 nm. As a result, the
dimensionless column water vapor abundance (u) is obtained using the following
equations (4–7):
TW=lnT935nm[Measured]-lnT935nm[Extrapolated],-lnTW=lnVo935nm⋅d2-lnV935nm-m⋅τ935nmAOD+τ935nmRayleigh,lnTWC=-A⋅mW⋅uB,u=lnTW-A1/BmW,
where TW is the water vapor transmission, constants A and B are
absorption constants unique to the particular 935nm filter, C is an
absorption constant assumed to be equal to 1 (Ingold et al., 2000), d and
m are defined in Eq. (1), mW is the water vapor optical air mass
(Kasten, 1965), and u is the total column water vapor abundance (Schmid et
al., 2001; Smirnov et al., 2004). The total column water vapor abundance
(u) is converted to total column water content or PW by using the
normalization factor (uo=10 kg m-2) and dividing it by the mean
value of water density (po=1000 kg m-3) to obtain water column
height units of centimeters (Bruegge et al., 1992; Ingold et al., 2000).
In the calculation of the filter-dependent A and B constants, the water vapor
absorption optical thickness is determined by the integration of water vapor
extinction coefficient over height from the bottom to the top of the
atmosphere. This calculation requires the following inputs to determine the
extinction at each height: HITRAN spectral lines with assumed US Standard Atmosphere, 1976 temperature and pressure profiles, the absorption
continuum lookup table from the Atmospheric and Environmental Research (AER)
Radiative Transfer Working Group (Clough et al., 1989; Mlawer et al., 2012),
and total internal partition sums that define the shape and position of
lines dependent on temperature (Gamache et al., 2017). Nine defined total
column water vapor amounts (0.5, 1.0, 1.5, 2.0, 2.5, 3.0,
4.0, 5.0, and 6.5 cm) are used to generate water vapor absorption
optical depth lookup tables. From these lookup tables, transmittances are
calculated based on the bandpass and averaged spectral solar irradiance for
the quiet Sun obtained from the University of Colorado LASP/NRL2 model
(Coddington et al., 2016) to generate filter-specific A and B coefficients.
The one sigma uncertainty in the calculation of PW in centimeters is expected to be
less than 10 % compared to GPS PW retrievals (Halthore et
al., 1997; Bokoye et al., 2003; Sapucci et al., 2007; Alexandrov et al.,
2009; Prasad and Singh, 2009; Bock et al., 2013; Van Malderen et al., 2014;
Pérez-Ramírez et al., 2014; Campenelli et al., 2018). The spectral
water vapor optical thickness (τH2O(λ)) is determined by
computing the average of all A and B constants from the suite of filters
affected by water vapor absorption (i.e., 709 nm SeaPrism, 935, 1020, and
1640 nm) in the AERONET database. The τH2O(λ) (Eq. 8) is also
dependent on the dimensionless total column water vapor abundance (Michalsky
et al., 1995; Schmid et al., 1996):
τH2O(λ)=A‾(λ)+B‾(λ)⋅u.
The contribution of ozone (O3) optical depth is determined utilizing
the total column TOMS monthly average climatology (1978–2004) of O3
concentration at 1.00∘× 1.25∘ spatial resolution, the
O3 optical air mass using O3 scale height adjustment by
latitude (Komhyr et al., 1989), and the O3 absorption coefficient
(Burrows et al., 1999). The OMI O3 data set is not used here due to
instrument sampling anomalies (McPeters et al., 2015). While the TOMS
O3 data set is extensive and generally characterizes the distribution
of O3, recent changes in concentration could introduce some minor
uncertainty in AOD. Similarly, the nitrogen dioxide (NO2) optical depth
is calculated using the total column OMI monthly average climatology (2004–2013)
of NO2 concentration at 0.25∘× 0.25∘
spatial resolution and the NO2 absorption coefficient (Burrows et al.,
1998). Tropospheric NO2 is highly variable spatially due to various
source emissions, and stratospheric NO2 concentrations are more stable
spatially than the tropospheric NO2 and can bias the calculation of
AOD if neglected (Arola and Koskela, 2004; Boersma et al., 2004). Therefore,
regions with high tropospheric NO2 emission will tend to have greater
proclivity for deviating from climatological means. Further, NO2 can
vary significantly on the diurnal scale (Boersma et al., 2008). Improved
satellite observations, models, or collocation with surface-based PANDORA
instruments measuring temporal total column O3 and NO2 may assist
in reducing the uncertainty and determination of the total column NO2
optical depth contribution in later versions of the algorithm (Herman et
al., 2009; Tzortziou et al., 2012). Concentrations for carbon dioxide (CO2)
and methane (CH4) are assumed constant and optical depths
are computed based on the HITRAN-derived absorption coefficients of 0.0087
and 0.0047 for the 1640 nm filter, respectively, and adjusted to the station elevation.
The calibration of the AOD measurements is traced to a Langley measurement
performed by a reference instrument (Shaw, 1983; Holben et al., 1998). The
reference instruments obtain a calibration based on the Langley method
morning-only analyses based on typically 4 to 20 days of data performed at a
mountaintop calibration site. The primary mountaintop calibration sites in
AERONET are located at Mauna Loa Observatory (latitude 19.536,
longitude -155.576, 3402 m) on the island of Hawai'i and Izana Observatory
(latitude 28.309, longitude -16.499, 2401 m) on the island of Tenerife
in the Canary Islands (Toledano et al., 2018). These reference instruments
are routinely monitored for stability and typically recalibrated every 3
to 8 months. Reference instruments rotate between mountaintop
calibration sites and inter-calibration facilities at NASA GSFC
(latitude 38.993, longitude -76.839, 87 m) in Maryland, Carpentras
(latitude 44.083, longitude 5.058, 107 m) in France, and Valladolid
(latitude 41.664, longitude -4.706, 705 m) in Spain, where reference
instruments operate simultaneously with field instruments to obtain
pre-field and post-field deployment calibrations. For periods when the AOD
is low (τ440nm<0.2), optical air mass is low
(m<2), and aerosol loading is stable, the reference Cimel
calibration may be transferred to field instruments (Holben et al., 1998).
Eck et al. (1999) estimate the reference instrument calibration uncertainty
impact on AOD varies from 0.0025 to 0.0055 with the maximum representing
uncertainty only in the UV channels (340 and 380 nm). In Version 3, the
field instrument AOD uncertainty is still estimated to be from 0.01 to 0.02
with the maximum representing the uncertainty only in the UV channels (340 and 380 nm).
The Version 2 processing used default temperature corrections based on three
sensor head temperature (TS) ranges (TS<21∘C,
21 ∘C ≤TS≤32∘C, and
TS>32∘C) using a constant nominal temperature sensitivity only for
the 1020 nm filter direct Sun measurements. In Version 3, measurement
temperature sensitivity has been updated for all wavelengths ≥ 400 nm and
all measurement types (i.e., direct solar, sky, water, and lunar viewing
measurements). Beginning in 2010, the temperature sensitivity was
characterized for almost all wavelengths uniquely for each Cimel instrument.
The temperature effect on signal (i.e., DN per degree Celsius) is
a function of the combined sensitivity of the detector and the filter
material itself. If any Cimel data relying on a filter were in use prior to 2010
and the filter was not temperature characterized, then the default
values for the filter and manufacturer type are applied, if established.
Filters in the ultraviolet (i.e., 340 and 380 nm) are not measured for
temperature dependence because of low integrating sphere radiance output at
these wavelengths. Due to temperature dependence of the field instrument and
the reference instrument, the Sun and sky calibration transfer needs to be
adjusted by computing the ratio of the Cimel temperature coefficients for
each wavelength and for the temperature observed at the time of the
calibration. In addition, when the AOD is computed for field instruments,
the sensor head temperature is measured for each direct Sun measurement so
these data can be adjusted to the temperature response of the instrument
optics (i.e., combined effect of the detector and filters) and electronics.
The temperature response is measured at the AERONET calibration facilities
using an integrating sphere and a temperature chamber in which the temperature
is varied from -40 to +50∘C. The wavelength-dependent temperature coefficient is typically determined from the slope of
ordinary least-squares (OLS) regression fit of the digital voltage counts versus
the sensor head temperature reading. For this relationship, the second-order
polynomial fit is computed for 1020 nm, while other filters use either a
linear or second-order polynomial fit (depending on the larger correlation
coefficient). For Cimel model 4 and some model 5 instruments with two
silicon photodiode detectors, the digital counts for solar aureole and sky
instrument gains are used to determine temperature coefficients for each
detector (Holben et al., 1998; https://aeronet.gsfc.nasa.gov, last access: 12 December 2018). Some model 5
and all CE318-T instruments perform the direct Sun and sky measurements on
the same detector (silicon or InGaAs) and typically utilize the solar
aureole gain digital counts (Barreto et al., 2016; https://aeronet.gsfc.nasa.gov, last access: 12 December 2018).
According to Holben et al. (1998), all instruments generally perform
measurements sequentially from longer wavelength to the shortest wavelength
filters on a rotating filter wheel inside the sensor head, which positions
each filter in front of the photodiode detector and behind the sensor head
lenses and collimator tube. The robotically controlled sensor head points
automatically at the Sun based on the time and geolocation of the
instrument. The laboratory-tuned four-quadrant detector provides nearly perfect
solar and lunar tracking to one motor step or ∼0.1∘
immediately following the geographic pointing. A dual-tube external
collimator with internal baffles attached to the top of the sensor head
reduces stray light effects into the sensor head 1.2∘ field-of-view optical train.
The instrument performs measurements of the Sun using measurement triplets, that is,
performing the series of measurements of all filters starting at 0 s of the minute
for a duration of about 8 s, and then repeating this measurement sequence at 30 s and 60 s from the initial measurement time.
The resulting 1 min averaged measurement sequence is defined
as a triplet measurement and the maximum to minimum range of these
measurements is termed the triplet variability. The triplet measurement
advantageously allows for separation of homogeneously dispersed aerosols
versus highly temporally variable clouds. The triplet measurements are
performed either every 15 min for older model 4 instruments or every
3 min for newer model 5 and CE318-T instruments increasing the
temporal availability of the AOD measurements in the AERONET database.
Automatic quality controls of Sun photometrically measured aerosol optical depth
The AERONET database has provided three distinct levels for data quality:
Level 1.0, Level 1.5, and Level 2.0. In Version 2, Level 1.0 was defined as
prescreened data, Level 1.5 represented near-real-time automatically
cloud-cleared data, and Level 2.0 signified an automatically cloud-cleared,
manually quality-controlled data set with pre- and post-field calibrations
applied. In Version 3, the definitions have been modified substantially for
Level 1.5 and Level 2.0. Version 3 Level 1.5 now represents near-real-time
automatic cloud screening and automatic instrument anomaly quality controls
and Level 2.0 additionally applies pre-field and post-field calibrations.
The Version 3 fully automated cloud screening and quality control checks
eliminate the need for manual quality control and cloud screening by an
analyst and increases the timeliness of quality-assured data. Note that in
all cases each subsequent data quality level requires the previous data
level to be available as input (e.g., Level 1.5 requires Level 1.0 and Level 2.0
requires Level 1.5). The following sections will describe these new
definitions and automatic quality controls and the impact these
new quality assurance measures have on the AERONET database in detail.
Preprocessing steps and prescreening
Most preprocessing data quality criteria operate on voltage (V, expressed as
the integer DN) or sensor head temperature (TS). The
impact of these conditions may immediately remove data from Level 1.0
consideration or later only impact Level 1.5 and Level 2.0 AOD. Each quality
control section describes the reasoning for the screening at the specified
data quality level. Digital count anomalies typically result from anomalous
electronic issues such as very low or high battery voltages, malfunctioning
amplifiers, or loose connections of internal control box components. These
digital count anomalies mostly affect older Cimel model 4 (CE318-1) and
model 5 (CE318-N) instruments (Holben et al., 1998;
https://aeronet.gsfc.nasa.gov, last access: 12 December 2018), while several of these connection issues
have been mitigated in the Cimel model T (CE318-T) instruments (Barreto et al., 2016).
Electronic instability
Cimel model 4 instruments use a 16-bit analog–digital (A/D) converter in the
processing unit in which the analog signal from the sensor head detector to
the control box is subject to electronic noise. Cimel model 5 instruments
use a 16-bit A/D converter inside the sensor head and the instrument invokes
electronic chopping to reduce electronic noise. Cimel model T instruments
utilize an increased quantization from 16 to 24 bits, which
significantly reduces noise effects. Cimel model 5 and model T instruments
internally adjust for the dark current (VD) with each measurement and
no separate record is logged. Cimel model 4 instruments perform
VD measurements after each sky scan (approximately hourly) for each spectrally
dependent instrument gain parameter (i.e., Sun, aureole, and sky). Large
VD values generally represent significant instrument electronic
instability. Quality controls applied to the VD will remove the entire
day for model 4 instrument data from all of the quality levels for either of
the following conditions: (1) a single dark current measurement is greater
than 100 counts for greater than N-1 wavelengths, where N is the total
number of wavelengths, or (2) more than three dark current measurements are
greater than 100 counts for three or more wavelengths.
Amplifiers in the Cimel model 4 instruments can produce unphysical increases
in the digital counts or decreases in the AOD for the 340 and 380 nm
wavelengths at large optical air mass (Fig. 1). These instability issues are
evaluated simply using a relative threshold with respect to the available
visible wavelength AOD measurements. If the τ380 is greater than
0.5⋅τ340 and (τ440+τ500or675<τ380+τ340-2.0), then the triplet
measurements for 340 and 380 nm are removed from the database for Level 1.5
and subsequent levels. These quality controls are limited to model 4
instruments that were not manufactured after 2001; however, the early
AERONET database (1993–2005) contains many of these data. New Cimel model T
instruments are replacing model 4 instruments but over 40 model
4 instruments remain active in 2018.
The instrument may rarely malfunction by producing constant digital voltages
for triplet measurements and the result of keeping these data in the
database leads to unphysical variations in the AOD. A frequency analysis is
performed to determine if any DN values occur more than
10 times in a day. If more than 50 % of the DNs are from the same triplet
measurement, then this measurement is identified as an anomalous
measurement. If more than 50 % of the triplet measurements in the day are
considered anomalous, then the entire day will be removed from Levels 1.5 and 2.0.
Aerosol optical depth (AOD) data from AERONET Ussuriysk site
(43.70∘ N, 132.16∘ E) on 30 November 2005 show electronic
instability. For the Cimel model 4 instruments, the electronic sensitivity of
the UV AOD data (340 and 380 nm) can be high due to a bad amplifier. The
resulting AOD data for the UV channels are out of spectral dependence the
entire day with a maximum error for large optical air mass due to large dark
current values. The UV channels (identified by line plots) are removed by the
quality control while preserving other wavelengths that are not affected by this condition.
Radiometer sensitivity evaluation
The Cimel four-quadrant solar near-infrared detector requires enough
sensitivity to track the Sun, and a DN threshold of 100 in the near infrared
is needed to have sufficient signal. Near-infrared wavelengths (e.g.,
1020 nm) typically have a higher measured solar DN(V) due to higher
atmospheric transmission in the presence of fine-mode-dominated aerosols
even in very high aerosol loading conditions. When the DN (V870nm or
V1020nm) is less than 100 counts for any measurement of the solar
triplet, then the entire solar triplet AOD will be removed for all
wavelengths from Level 1.0 and subsequent levels due to potential solar
tracking accuracy issues.
Spectral-dependent low digital number removal at NASA Goddard Space
Flight Center (GSFC; lat 38.99, long -76.84). (a) Level 1.0
AOD data from GSFC on 8 July 2002 are plotted for the Quebec forest fire smoke
event. Significantly fewer Level 1.0 AOD data are available for the shorter
wavelengths near local sunrise (∼ 11:00 UTC) and sunset (∼ 23:30 UTC).
(b) The distribution of the AOD measurements with respect to optical
air mass clearly shows the removal of short wavelengths for large air mass in
this fine-mode aerosol event. The high aerosol loading due to smoke and haze
results in significant extinction at UV and visible wavelengths, which
corresponds to low digital counts. The low digital count quality control removes
AOD measurements impacted by diffuse radiation scattered into the instrument
field of view (Sinyuk et al., 2012).
Version 2 data processing assessed the instrument electronic and diffuse
light sensitivity by defining a DN of 10 to remove solar
AOD triplet measurements. Electronic issues impact Cimel model 4 instruments
in the UV and short visible wavelengths due to high DN(VD). Scattered
diffuse light into the collimated field of view can affect all instruments
and produce unusual AOD changes with optical air mass, especially when the
aerosol loading is high and optical air mass is large. The signal-to-noise
ratio of the Cimel instrument requires setting a minimum threshold for the
determination of the solar measured DN(V) to limit the effect of diffuse
radiance in the instrument field of view (Sinyuk et al., 2012). A dark
current DN(VD) (e.g., ∼50–100) nearly equal to or
larger than the measured solar DN(V) (e.g., ∼25–50) will
result in V and τ decreasing with increasing optical air mass. All
wavelengths are evaluated to determine if the measured solar DN(V)
(subtracted from the closest temporal dark current DN(VD) for model 4
instruments only) is less than DN(VO)/1500; then the identified
wavelength will be removed from all AOD levels. A threshold of 1500 is
calculated from a DN of 15 000, a typical average DN(VO) for Cimel
models 4 and 5, normalized to a minimum signal DN of 10. The maximum product
of AOD times optical air mass (τm=τ⋅m) of
approximately 7.3 is computed by the natural logarithm of 1500 (i.e., ln(15000/10)) for
Cimel model T instruments. For non-model T instruments, the 100 DN threshold
for 870 and 1020 nm limits the τm to approximately 5.0 (i.e.,
ln(15000/100)) for only those two wavelengths. The τm maximum
threshold applies to all channels; however, the signal count can decrease
significantly with optical air mass and depend on the wavelength dependence
of VO. For values exceeding the τm maximum threshold, the
diffuse radiation increases the signal and, as a result, unfiltered AODs
show a decrease in magnitude as optical air mass increases for high AOD even
when DN(VD) equals zero. A measured solar DN(V) lower than the ratio
DN(VO)/1500 threshold will result in the removal of the solar triplet
AOD for the specific wavelength (Fig. 2).
Digital number triplet variance
As mentioned in Sect. 2, the Cimel instrument performs a direct Sun triplet
measurement at regular intervals throughout the day. A variance threshold is
applied based on the root-mean-square (RMS) differences of the triplet
measurements relative to the mean of these three values. If the
(RMS/mean) ⋅ 100 % of the DN triplet values is greater than
16 %, then these data are not qualified as Level 1.0 AOD (Eck et al.,
2014). The DN temporal variance threshold is sensitive to clouds
with large spatial-temporal variance in cloud optical depth and optically
thick clouds such as cumulus clouds as well as issues due to poor tracking of the instrument.
Sensor head temperature anomaly identification
Each Cimel instrument has a fixed resistance (model 4) or band gap (models 5
and T) temperature sensor inside the optical head within 0.5 cm of the
detector, filter wheel, and optical train assembly. As discussed in Sect. 2,
the instrument optics and digital counts can have dependence on the sensor
head temperature (TS), which is saved with each measurement triplet.
Sensor head temperatures may be erroneous due to instrument electronic
instability or communication issues. These potentially unphysical values
of TS are evaluated by a number of algorithm steps such as checks for
(1) constant TS values, (2) unphysical extreme high or low TS,
(3) potentially physical yet anomalously low TS with respect to the
NCEP/NCAR reanalysis ambient temperatures, and (4) unphysical TS
decreases (dips) or increases (spikes). When the algorithm removes a
TS reading or the TS measurement is missing, an assessment is made
on the instrument temperature response based on ±15∘C of
the NCEP/NCAR reanalysis temperature for the date and location to determine
whether the temperature characterization coefficient for a specific
wavelength would result in a change of AOD by more than 0.02. If this
condition is met for a specific wavelength, then data associated with this
wavelength-specific triplet measurement will be removed at Level 1.5 and
subsequent levels while preserving other less-temperature-dependent spectral
triplet measurements.
Eclipse circumstance screening
During episodic solar or lunar eclipses, AOD will increase to the maximum
obscuration of the eclipse at a particular location on the Earth's surface.
The AOD increases due to the reduction of the irradiance and the celestial
body (Moon or Earth) obscuring the calibrated light source (Sun or Moon).
While any one point on Earth infrequently experiences an eclipse, when an
eclipse episode does occur, the eclipse can affect many locations nearly
simultaneously, making manual removal tedious at sites distributed globally.
To automate the removal of eclipse episodes, the NASA solar and lunar
eclipse databases are queried for eclipse circumstances based on geographic
position of the site to produce a table of eclipse episodes starting from 1992.
The eclipse tool utilizes established Besselian elements based on the
Five Millennium Canon of Solar Eclipses: -1999 to +3000 (Espenak and
Meeus, 2006) to quantify the geometric and temporal position of the celestial
bodies (Sun, Earth, and Moon), determine the type of eclipse (e.g., partial,
annular, total), and predict times of the various stages of the solar or
lunar eclipse. For the Version 3 database, the eclipse site-specific tables
are used to discretely remove triplet measurements affected by any stage of
the eclipse circumstance. For example, during a solar eclipse, solar
triplets will be removed between the partial eclipse first contact and the
partial eclipse last contact regardless of the eclipse obscuration or
magnitude for Level 1.5 data and subsequent levels (Fig. 3). The partial
eclipse first contact is defined as the time at which the penumbral shadow
is visible at a point on the Earth's surface and the partial eclipse last
contact is defined as the time at which the penumbral shadow is no longer a
visible point on the Earth's surface. Efforts to retain AOD during solar
eclipse episodes have been attempted by the authors in which up to 95 % of
the AOD can be corrected based on adjusting calibration coefficients by the
eclipse obscuration. However, spectral calibration coefficients also need to
be adjusted to account for the solar atmosphere spectral irradiance, which
becomes more dominant during the solar eclipse episode and is a topic of further investigation.
Very high AOD retention
Cloud-screening procedures in the next section may inadvertently remove
aerosol in very high aerosol loading cases due to biomass burning smoke and
urban pollution as discussed by Smirnov et al. (2000). For Version 3, each
triplet reaching Level 1.0 is evaluated for possible retention in the event
that a specific Level 1.5 cloud-screening procedure removes the triplet.
When the AOD measurement for 870 nm is >0.5 and AOD 1020 nm > 0.0,
these conditions will potentially qualify the triplet for
very high AOD retention. Further analysis is performed on those qualified
triplets to remove the effect of heavily cloud-contaminated data using the
AE for the wavelength ranges of 675–1020 or 870–1020 nm (Eck et al.,
1999). If the AE675-1020nm1.2 (or
AE870-1020nm>1.3, if AOD675nm is not available), and
the AE for the same range is less than 3.0, then the triplet qualifies for
very high AOD retention and the triplet can be retained at Level 1.5 even if
the measurement does not pass Level 1.5 cloud-screening quality control steps in Sect. 3.2.
Eclipse circumstance at the NASA Goddard Space Flight Center (GSFC;
lat 38.99, long -76.84) on 25 December 2000 between 16:04:13 and
19:16:25 UTC. The maximum AOD during the eclipse occurs at the maximum
obscuration of 0.42, which results in a change of ∼0.28 for AOD at 500 nm
compared to data before and after the solar eclipse. Utilizing the NASA Solar
Eclipse database, the AOD measurements are removed between the partial eclipse
first contact and partial eclipse last contact as denoted by the vertical dashed lines.
Total potential daily measurements
Cloud-screening methods in Sect. 3.2 may incompletely remove all
cloud-contaminated points and leave data fragments. To mitigate this issue,
a methodology was developed based on the total number of potential
measurements in the day and calculated AE values. The total number of
potential measurements in the day is defined as the number of triplet
measurements plus the number of wet sensor activations. If the number of
remaining measurements after all screening steps in Sect. 3.2 are performed
is less than three measurements or less than 10 % of the potential
measurements (whichever is greater), then the algorithm will remove the
remaining measurements. This condition is repeated after each cloud-screening step in Sect. 3.2 and will only be activated when the very high
AOD restoration is not triggered (see Sect. 3.1.6) or when the
AE440-870nm is less than 1.0 for a triplet measurement, indicating large
particles such as clouds may contaminate the remaining measurements.
Optical air mass range
The basic Cimel Sun photometer Sun and sky measurement protocols were
specified to NASA requirements in Holben et al. (1992, 1998, 2006) and
have only been slightly modified since that time for improved measurement
capability of the model 5 and model T instruments (Barreto et al., 2016).
All instruments systematically perform direct Sun measurements between the
optical air mass (m) of 7.0 in the morning and m of 7.0 in the evening. In
Version 2 and earlier databases, AERONET data processing limited the
Level 1.5 and Level 2.0 AOD computation from m of 5.0 in the morning to m of 5.0 in
the evening. The m limitation may avoid potential error in the computation of
the optical air mass at large solar zenith angles (Russell et al., 1993) and
possible increased cloud contamination (Smirnov et al., 2000). For Version 2
and 3 processing, the Kasten and Young (1989) formulation was used to account
for very small differences in the optical air mass calculations at high
solar zenith angles. Noting that the AOD error (δτ/m) has a
minimum at large m values (conversely a maximum at solar noon), the maximum m of 5.0
was extended to m of 7.0 in Version 3 processing. The larger optical air mass
range leads to an increase in the number of solar measurements occurring in
the early morning and the early evening contributing to additional AOD
measurements used for input for almucantar and hybrid inversions plus an
increase in AOD measurements at high-latitude sites when solar zenith angles
may be large even at solar noon. The impact on the cloud-screening
performance appears to be minimal for measurements closer to the horizon.
The fidelity of the Version 3 cloud-screening (see Sect. 3.2) AODs supports
the extended optical air mass range for Level 2.0.
Level 1.5 AOD cloud-screening quality controls
As discussed in Sect. 3.1, several preprocessed criteria and parameters are
necessary to quality control the AOD data quality in near real time.
Cloud-screening procedures proposed by Smirnov et al. (2000) were designated
to remove or reduce cloud-contaminated AOD measurements. However, these
procedures also had the effect of surreptitiously occasionally removing
other non-cloud anomalies such as repeated AOD diurnal dependence when AOD
had a large maximum at midday and minimum at high optical air masses due to
environmental impacts on the optical characteristics of the instrument
(e.g., moisture on the sensor head lens or spider webs in the collimator
tube). While these cloud-screening methods have been implemented for about
25 years, the state of knowledge has progressed over this period and thus
necessitates review and modification of cloud-screening quality control
procedures (Kaufman et al., 2005, Chew et al., 2011; Huang et al., 2011). The
calculation of the AOD at Level 1.0 essentially represents the following in Eq. (9):
τappTotal=1Γanomalyτaerosol+τcirrusCcirrus+τliquidcloud+τeclipse,
where τappTotal is the apparent total optical depth, which at
this point in the data processing may be affected by the contributions of
liquid cloud droplets (τliquidcloud), cirrus amplification
factor (Ccirrus) applied to the cirrus crystal optical
depth (τcirrus) due to strong forward scattering into the field of view of the
instrument, solar or lunar eclipses (τeclipse), and instrument
anomalies (Γanomaly adjustment factor). Given cloud-free
conditions and perfect instrument operation, the additional non-aerosol
τ components would be zero and Ccirrus and Γanomaly
would be 1. However, the Cimel Sun photometer always attempts to measure
the Sun if it can be tracked regardless of the total optical depth magnitude.
Clouds are a major factor in the effort to quality control remotely sensed
aerosol data (Smirnov et al., 2000; Martins et al., 2002; Kaufman et al.,
2005; Chew et al., 2011; Kahn and Gaitley, 2015). A significant portion of
the liquid cloud contribution is removed by the prescreening prior to Level 1.0
as discussed in Sect. 3.1.3. The τappTotal should be
adjusted based on a multiplier dependent on the cirrus crystal size
(τcorrect=Ccirrus⋅τappTotal) according to Kinne et
al. (1997). While this cirrus coefficient (Ccirrus) is not specifically
modeled by Kinne et al. (1997) for the Cimel instrument field of view half
angle of 0.6∘, this multiplier is likely to be close to 1 for
small cirrus crystals (e.g., reff=6–16 µm), but near
2 for larger cirrus crystal sizes (e.g., reff=25–177 µm).
These adjustment factors would result in the reduction of
the τappTotal due to forward scattering in the presence of
cirrus. Conversely, liquid water cloud droplets would significantly
increase the τappTotal in a manner similar to large dust particles.
Cimel instruments may also have internal and external anomalous conditions
that modify the optical characteristics or response of the instrument,
resulting in amplification or dampening impacts (Γanomaly) of
varying magnitudes on the computation of the τappTotal. These
anomaly adjustments can be difficult to quantify and can have strong
dependence on optical air mass (m) or the sensor head temperature (TS).
As a result, the following sections will describe the mechanisms
in which these additional cloud and anomaly components are automatically
eliminated or reduced to as close to zero as possible to provide a quality-assured AOD (τaerosol) after final calibration is applied (see
Sect. 4) across the global AERONET AOD database.
Cloud-screening quality controls
As Level 1.0 AOD data may have cloud contamination, these data should be
considered potentially cloud contaminated where the triplet measurement
represents the apparent AOD (τappaerosol) as defined in the
previous section. Table 2 provides a summary of the cloud-screening quality
control changes from Version 2 to Version 3 and these changes are discussed
in detail below and Sect. 3.2.2.
Summary of cloud-screening-related quality control changes from
Version 2 to Version 3.
Algorithm/parameterVersion 2Version 3Very high AOD restorationn/aτ870>0.5; α675-1020>1.2 or α870-1020>1.3, restore ifeliminated by cloud screeningOptical air mass rangeMaximum of 5.0Maximum of 7.0Number of potentialNremain<3, reject allAfter all checks applied, reject all measurements in themeasurementsmeasurements in the dayday if Nremain< MAX{3 or 10 % of N}Triplet criterionAll wavelengthsAOD triplet variability > MAX{0.01 or 0.015⋅τaerosol}checked; AOD tripletfor 675, 870, and 1020 nm wavelengths simultaneouslyvariability > MAX{0.02or 0.03⋅τaerosol}Ångström exponent (AE)n/aIf AE440-870nm<-1.0 or AE440-870nm>3.0, then eliminatelimitationtriplet measurement.Smoothness checkD<16For AOD 500 nm (or 440 nm) Δτaerosol>0.01 per minute,then remove larger τaerosol in pair. Repeat condition foreach pair until points are not removed.Solar aureole radiancen/aUsing 1020 nm solar aureole radiances, compute thecurvature checkcurvature (k) between 3.2 and 6.0∘ scattering angle (φ)(Sect. 3.2.2)at the smallest scattering angle. If k<2.0×10-5φ and ifslope of curvature (M) is greater than 4.3 (empiricallydetermined), then radiances are cloud contaminated. Forsky scan measurements, all τaerosol measurements areremoved within 30 min of the sky measurement. ForModel T, special aureole scan measurements willremove all τaerosol within a 2 min periodsuperseding any sky scan aureole measurements.Stand-alone measurementsn/aIf no data exist within 1 h of a measurement, then rejectit unless AE 440–870 nm > 1.0.AOD stability checkSame as Version 3If daily averaged AOD 500 nm (or 440 nm) has σ less than0.015, then do not perform 3-σ check.3-σ checkSame as Version 3AOD 500 nm and AE 440–870 nm should be within theMEAN ± 3σ; otherwise, the points are rejected.
n/a = not applicable.
Cimel triplet measurements are performed typically every 3 min
(every 15 min for older instrument types) and these triplet measurements
can detect rapid changes in the τappaerosol by analyzing the
maximum to minimum variability (i.e., the Δτappaerosol{MAX–MIN}). Assuming that spatial
and temporal variance of aerosols plus clouds is much greater than aerosols
alone, in many cases, Δτaerosol would be near zero and
Δτcloud should be much larger than zero when especially
liquid-phase cloud droplets exist. For Version 2 and earlier databases,
Smirnov et al. (2000) methodology utilized all available wavelengths to
perform τappaerosol triplet screening for cloud contamination.
Therefore, large triplet variability would indicate the presence of clouds
due to large Δτcloud. Analyses (e.g., Eck et al., 2018)
have shown that removing the entire triplet measurement when only one or
more of the shorter wavelengths indicates a large variation (Δτaerosol(λ) much greater than zero) may not be the most robust
approach. For example, cases of highly variable fine-mode aerosols such
as smoke can produce large triplet variability as a result of the
inhomogeneous nature of the aerosol plume, especially for shorter wavelengths
(e.g., 340, 380, 440 nm) at which fine-mode-dominated aerosol particles can
have radii similar to short wavelength measurements.
Considering these factors, several potential techniques were explored
utilizing various wavelength combinations and utilizing the spectral
deconvolution algorithm (SDA) fine- and coarse-mode triplet separation
(O'Neill et al., 2001, 2003). While the SDA-algorithm-derived triplets for
coarse-mode AOD relative change tended to show utility in cloud removal,
the SDA algorithm itself could not be applied universally to the AERONET
database due to anomalous results in which fine- and coarse-mode AODs can
have a negative relationship when the number of available wavelengths or
wavelength range is not satisfied. Anomalies in SDA retrievals can occur
when the uncertainty in AOD is relatively large near solar noon compared to
the magnitude of AOD as is sometimes the case when only the pre-field
deployment calibration has been applied. Upon further consideration of the
triplet variability technique, analyses indicated that using all three
longest standard AERONET wavelengths (i.e., 675, 870, and 1020 nm) could
be used to remove a triplet measurement when they have high triplet
variability that exceeds 0.01 or 0.015 ⋅ AOD (whichever is greater). The
reduction in the threshold of the triplet variability criterion is
proportional to the magnitude of decrease in AOD uncertainty compared to
UV wavelengths (0.02) and those of visible and near-infrared wavelengths (0.01).
While Smirnov et al. (2000) did not impose an AE
limitation, Version 3 processing constrains the AE440-870nm of
Level 1.5 data to be within -1.0 and +3.0. In general, the AE440-870nm
values outside this range are unphysical and should not be used due to the
inconsistency of the AOD spectral dependence. These inconsistencies
typically occur at very low optical depth (<0.05) at which the
uncertainty of the AOD may be up to 100 % of the actual value, thus
producing AE values that are invalid.
The AOD time series smoothness uses a number of numerical methods and fits
dependent on the application. For an AOD time series, rapid and large
increases are usually the result of cloud contamination. Version 2 and
prior versions include a technique proposed by Smirnov et al. (2000) to implement a
smoothness methodology similar to Dubovik et al. (1995). In this scheme, the
triplet measurements were considered to be discrete points, and differences in
logarithm of τappaerosol and relative difference in times
between those measurements were utilized to calculate the first derivative
differences in which an arbitrary parameter D (similar to the norm of the
second derivative) is calculated. In Version 2 and earlier versions, when
the value of D was greater than 16 for an AOD measurement time sequence for
500 or 440 nm, then this triplet was removed from the data set. Further,
the smoothness procedure was repeated or measurements were rejected for the
day if fewer than three triplets remained for the day as discussed in Smirnov
et al. (2000). While the D=16 threshold was empirically derived, the
smoothness parameter is somewhat arbitrary in origin and operates in
logarithmic coordinates rather than natural ones. For example, the
distribution of aerosol measurements in a single day is typically normally
distributed rather than logarithmically distributed. Further, the
D parameter smoothness procedure was not always successful at removing
cloud-contaminated data and this may be related to the fact that the
empirically derived D parameter was tuned for 15 min triplet measurement
intervals rather than 3 min intervals now commonly observed in the
network. Therefore, an approach adhering to the relative change in the total
optical depth with time is feasible and a more straightforward physical
quantification of the change in τappaerosol with time.
The AOD time series smoothness in Version 3 evaluates the same τappaerosol
500 nm wavelength (or 440 nm if 500 nm is not available).
The Version 3 smoothness method computes the relative rate of change of
τappaerosol per minute and if Δτappaerosol/Δt>0.01 per minute, then the larger triplet
measurement in the pair is removed and the smoothness procedure will
continue to remove triplets until measurement pairs in the day do not
surpass the smoothness threshold. The selection of this threshold of 0.01
per minute hinges on the premise that the triplet average does not change
rapidly within 1 min. The Version 3 smoothness procedure could be
affected by extreme changes in AOD due to anomalous aerosol plumes (e.g.,
biomass burning or desert dust plumes) for which a strong gradient exists.
After the cirrus cloud-screening quality control (to be discussed in the
Sect. 3.2.2), triplets are evaluated for spurious or isolated measurements
remaining during the day after applying the cloud-screening quality control
procedures. So-called “stand-alone points” may be relevant given the
ability of the instrument to perform measurements in cloud breaks or gaps.
Here, the definition of a stand-alone triplet is when no triplets are
available within 1 h of the measurement. If the AE440-870nm is
greater than 1.0, the algorithm retains the triplet measurement; otherwise,
the measurement will be removed from the data set. Further, daily averaged
data are evaluated for temporal stability using the AOD stability during the
day at 500 nm (or 440 nm) and daily outlier triplets using the 3-sigma check
for AOD at 500 nm (or 440 nm) and AE440-870nm to be within
±3 SD (standard deviations) (Smirnov et al., 2000). Finally, each wavelength is
evaluated to be greater than or equal to -0.01 (based on uncertainty of 0.01;
Eck et al., 1999). At this point in the quality control algorithm, the
remaining triplet measurements are not expected to have a major component of
τcloud or τcirrus.
Novel cirrus removal method utilizing solar aureole curvature
Utilizing satellite and surface-based lidar, studies have shown the AERONET
Version 2 Level 2.0 AOD data are impacted by homogeneous optically thin
cirrus clouds with a bias of up to 0.03 in AOD (DeVore et al., 2009; Chew et
al., 2011; Huang et al., 2011). The optically thin cirrus bias can influence
radiative forcing calculations and satellite validation when clouds
contaminate the measurement (DeVore et al., 2012). In addressing the
shortcoming of Smirnov et al. (2000) and manual checks in which the
identification of optically thin cirrus clouds give relatively weak signal
in the AOD or AE, the authors leveraged high-angular-resolution radiance
measurements routinely performed in the solar aureole region
(3.2–6.0∘ scattering angle range). While cirrus detection may be
possible with other scattering angle ranges, Cimel Sun photometer radiance
measurements do not presently have high enough angular resolution from
6.0 to 35.0∘ to reliably and consistently detect cirrus-induced atmospheric phenomena (e.g., solar halos and sun dogs) since these
events depend on cirrus crystal shape and orientation and are not always
detectable beyond levels of cloud optical depth variability.
The use of the solar aureole radiance (LA; µW cm-2 sr-1 nm-1)
with respect to the scattering angle (φ; in radians) has been
demonstrated using the Sun and aureole measurement (SAM) aureolegraph
instrument to indicate the presence of large particles such as cirrus
crystals (DeVore et al., 2009, 2012; Haapanala et al., 2017). The effect of
the surface reflectance is much less than the radiance of the solar aureole
so it is ignored; however, this may become important at very large solar
zenith angles and bright surfaces such as snow (Eiden, 1968). All Cimel
instrument models perform solar aureole measurements at the nominal 1020 nm
wavelength. The Cimel performs solar triplet measurements directly on the
solar disk, while solar aureole radiances are measured mainly during the
almucantar, principal plane, and hybrid sky scans. These solar aureole
measurements are performed hourly for model 4 and 5 instruments during sky
scan scenarios and for model T instruments before each solar triplet as well
as for the hourly almucantar and hybrid sky scan measurements.
The AERONET measurements of the solar aureole directional radiances (LA)
depend on the absolute calibration of the integrating sphere. The
integrating spheres at the AERONET calibration centers provide an absolute
calibration traceable to a NIST standard lamp hosted at the NASA GSFC
calibration facility. The uncertainty in the radiance calibration is
typically less than 3 % due to systematic degradation in the lamp levels,
changes in integrating sphere characteristics, and instrument spectral
signal response. The solar aureole radiance magnitudes also depend on the
instrument Sun sensitivity gain settings for each wavelength for Cimel model 4
and 5 instruments, while the model T instruments use an internal
instrument gain switch applied to all wavelengths (Barreto et al., 2016).
The LA measurements have calibration and temperature correction applied
and are measured by all Cimel instruments at the 440, 675, 870, and
1020 nm wavelengths. Due to lower AOD in fine-mode aerosol loading
situations, less Rayleigh scattering, and lower calibration uncertainty, the
LA measurements at 1020 nm have less noise for evaluating cirrus cloud presence.
Given that the LA measurements are performed at discrete φ, we
calculate the OLS linear regression fit on a logarithmic
scale when more than three scattering angles are available to determine the
intercept (a), slope (b), and the correlation coefficient (R). If R is less than
or equal to 0.99, then we do not proceed to check for cirrus contamination.
When R is greater than 0.99, the curvature (ko) for the first available
scattering angle (φo) in the 3.2–6.0∘
scattering angle range is calculated using the equation of curvature of the
signed planar curve, which gives the rate of turning of the tangent vector
in Eq. (10) (Kline, 1998):
k=y′′1+y′232.
The curvature (k) can be formulated by assuming the power-law function and
its derivatives and, in our application, using the first scattering
angle (φo) in radians for φ below:
y=a⋅φb,y′=a⋅b⋅φb-1,y′′=a⋅b⋅(b-1)⋅φb-2.
According to the k formulation, the stronger the forward scattering peak,
then the smaller the value of curvature since the second derivative is small
and the first derivative is large due to the steepness of the solar aureole
radiances. Further, the overall slope of curvature for all of the scattering
angles (3.2–6.0∘) can be calculated using the
assumption that y′2≫1, rendering the
addition of 1 in the denominator of Eq. (10a) insignificant. The slope of
the logarithm of curvature versus logarithm of scattering angle is desired
and this slope can be calculated using a and b from the linear regression
above by converting from logarithmic coordinates. Therefore, we derive
Eq. (11) to determine the slope of curvature dependent only on the slope of
the linear regression fit of LA and φ on a logarithmic scale as follows:
lnk=a+(1-2b)⋅lnφ.
Here, the slope of curvature (M) is defined as (1-2b). The value of M will
typically be positive since b will tend to be negative due to the dimming of
the solar aureole with increasing scattering angle. Alternatively, M can be
calculated numerically for each k and φ to obtain similar results. A
small value of curvature (ko) at the smallest scattering angle available
represents the possible existence of large particles producing a forward
scattering peak. The slope of curvature (M) represents the average
characterization of the solar aureole shape across the scattering angle
3.2–6.0∘ range in which a large magnitude signifies the
potential presence of large particles as curvature increases with increasing
scattering angle across the forward scattering peak.
AERONET and MPLNET sites and date ranges used for assessing cirrus and
non-cirrus cloud presence.
The Micro-Pulse Lidar Network (MPLNET) is a global network of lidars
monitoring the vertical distribution of aerosols and clouds (Welton et al.,
2000; Welton and Campbell, 2002; Campbell et al., 2002). To determine the thresholds for these
Sun photometer solar aureole curvature parameters for different surface
types and aerosol environments, the MPLNET lidar cloud identification
database was used at eight collocated AERONET sites as shown in Table 3.
Multiyear MPLNET lidar deployment data were analyzed and matched with
AERONET observations when the solar zenith angle was less than 30∘
to minimize the spatiotemporal differences of the zenith pointing lidar
versus the slantwise pointing of the Sun photometer in which sky condition
can be quite different at large solar zenith angles. The MPLNET cloud base
height data product was matched with the MERRA reanalysis vertical temperature
profile corresponding to the geopotential height pressure surface. When a
cloud top temperature is less than -37∘C, a cloud is designated
to be cirrus, while other non-cirrus clouds may contain liquid or mixed-phase particles (Sassen and Campbell, 2001; Campbell et al., 2015; Lewis et
al., 2016). Partitioning of the AERONET data set of solar aureole radiances
in terms of cirrus clouds, non-cirrus clouds, all clouds, and clear (no cloud
base detected) sky condition categories allowed for the empirical
determination of potential thresholds for the curvature parameters. For each
site, AERONET curvature parameters (k and M) were computed for almucantar and
principal plane solar aureole (LA) measurements (i.e., left and right
scans separately) and further categorized based on the coincident lidar-detected sky condition. These solar aureole radiances have calibration and
temperature characterization applied for the 1020 nm channel and these
LA measurements were only quality controlled based on the correlation
threshold of 0.99 discussed above.
Figure 4a shows the number distribution of the k at NASA GSFC
(lat 38.99, long -76.84) for each of the four lidar sky
condition categories. The number of the potential clouds is large for
magnitudes of k less than 2.0×10-5. Similarly, Fig. 4b and c show the
number distributions of the M at NASA GSFC for each lidar sky condition
category. In Fig. 4b, the number of potential clouds generally dominates
when the M is greater than 4.3 with generally clear or possibly cloudy
conditions when M is less than or equal to 4.3. Some overlapping of the
categories for M may be related to the differences in the viewing geometry of
the sky between the Sun photometer and the lidar or inhomogeneous cloud conditions.
Algorithmically combining the two thresholds of k and M produces a defined
distribution of the clear versus cloudy sky condition categories. When the
threshold of k<2.0×10-5 is applied first, then the distribution of
mainly cloudy conditions becomes more distinct as shown for NASA GSFC in
Fig. 4c. The maximum in the number distribution for cirrus is near M=4.6
and the maximum in the number distribution of clear sky condition is at
M=4.3 (Fig. 4c). At Singapore (lat 1.29, long 103.78),
Fig. 5c suggests that the distinction of small aerosol particles and larger
cirrus cloud ice crystals allows for adequate separation to identify an
observation as cloud contaminated using a threshold of M greater than 4.3.
Figure 6a shows the number distribution of the curvature at the first
scattering angle for coincident AERONET and MPLNET observations at Sede
Boker (lat 30.85, long 34.78). Figure 6c shows the
distinction is distributed similarly to GSFC and Singapore to potentially
identify cirrus-contaminated observations. For Fig. 6a, the clear sky
condition category is much higher in number than other sky condition
categories; however, the k values less than the first scattering angle
threshold of 2×10-5 (shown by the orange vertical line) indicate a
significant presence of dust particles rather than cirrus clouds due to
forward scattering of dust. Note that as for Figs. 4 and 5, the x axis
of Fig. 6a is truncated to 1×10-4 but the number distribution continues at
values near zero for larger first point curvatures. Sede Boker data in
Fig. 6c exhibit a significant contribution of clear conditions are preserved,
indicating that this method does not appear to misidentify dust as cirrus at
this mixed dust and urban pollution site.
NASA Goddard Space Flight Center (GSFC; lat 38.99,
long -76.84) AERONET data coincident with MPLNET lidar-derived sky
condition categories (clear, both cirrus and non-cirrus clouds, non-cirrus
clouds, and cirrus clouds) from 2001 to 2013. The AERONET solar aureole 1020 nm
radiances are used to calculate the curvature at the first scattering angle (ko)
and the slope of curvature (M) between 3.2 and 6.0∘ scattering angles.
(a) The number distribution of ko is shown and the dashed vertical
line at ko equal to 2×10-5 indicates the threshold at which values
less than 2×10-5 are considered possibly cirrus cloud contaminated
(the x axis is truncated at 1×10-4 for viewing purposes).
(b) The number distribution of M is shown and M values greater than 4.3
are considered to be possibly cirrus cloud contaminated (the dashed vertical
line indicates the threshold of 4.3). (c) Similar to (b)
except that the ko threshold (ko<2×10-5) is applied first and,
as a result, data greater than 4.3 in this panel are considered to be cirrus cloud contaminated.
When evaluating all of the collocated AERONET–MPLNET sites in Table 3
(Fig. 7), the maximum in the number distribution for cirrus is at M=4.3 after the
k<2.0×10-5 threshold is applied with a relative minimum for the
clear conditions for M>4.3. Given this information, an empirical
threshold of M>4.3 can be established for maximizing the removal
of cirrus clouds and minimizing removal of potentially clear data points. As
mentioned previously, the almucantar and principal plane sky scans are
performed on an hourly basis. If cirrus clouds are homogeneously distributed
in the sky, then this assumption allows for the application of the temporal
screening of triplet measurements within 30 min of the solar aureole
measurement time. As a result, a significant number of cirrus-contaminated
measurements for M≤4.3 are likely removed with this procedure given the
normally distributed number distribution of cirrus-identified solar aureole
measurements around M=4.3. For the Cimel model T instruments, sky scan
aureole measurements are superseded by a special solar aureole scan (CCS)
performed in the 3.0 to 7.5∘ scattering angle range at
0.3∘ increments (left and right) after each triplet solar
measurement; therefore, temporal screening for these triplet measurements is
applied within 2 min of the CCS scan. Overall, the aureole curvature
cirrus cloud-screening quality control decreases the probability of a cirrus
bias in the AOD data set globally by using this standard procedure. However,
the Version 3 Level 1.5 AOD data set may still be influenced by optically
thin or sub-visible cirrus clouds with ice crystals similar in diameter to
coarse-mode aerosols such as those found at polar latitudes or when solar
aureole measurements are not available due to instrument malfunction or
incomplete data transfer.
Similar to Fig. 4, except for Singapore (lat 1.29,
long 103.78) from 2009 to 2013.
Figure 8 shows solar aureole radiances have significant nonlinearity with
scattering angle when impacted by cirrus clouds while measurements without
cirrus are more linear. The Sede Boker site is influenced by desert dust.
Dust particles can affect the calculation of the k parameter to be close to
the threshold of 2×10-5 even when cirrus clouds are not present (Sede Boker
case 1); however, the overall slope is more linear for the non-cirrus case
compared to the cirrus case (Sede Boker case 2). As a result, the M parameter
is much lower and the algorithm action would be to preserve the Sede Boker
case 1 data and remove data for Sede Boker case 2. Note that the k parameter
is quite low for Sede Boker case 1 and in general dusty sites may frequently
have k less than 2×10-5; therefore, the M curvature parameter is needed to
prevent inadvertent removal of aerosol data. For the fine mode at GSFC case 1
and Singapore, small values of k and large values of M result in removal of
the cirrus-contaminated data. For comparison, GSFC case 2 shows
significant linearity when cirrus clouds are not present. GSFC case 3
and Trinidad Head case show the variation in these curvature parameters at
low optical depths in which only one of the curvature parameters indicates
the possibility of cirrus clouds. While these two curvature parameters may
be used independently in certain conditions, the current algorithm must
employ both curvature parameter thresholds to avoid inadvertently
identifying aerosols as clouds in dust and low aerosol loading conditions.
Similar to Fig. 4, except for Sede Boker (lat 30.85,
long 34.78) from 2007 to 2013.
Level 1.5 quality controls to screen instrument anomalies
While cloud-screening quality controls remove a significant portion of data
impacted by cloud contamination and some instrument anomalies, a portion of
the remaining AOD data set can be impacted by internal or external
instrument anomalies. Most instrument anomalies can be removed utilizing the
prescreening steps outlined in the Sect. 3.1, but a number of issues still
exist that are more evident after the cloud-screening quality controls have
been applied to the data set. A data set with some clouds can mask or offset
patterns in the AOD spectra that can clearly identify data anomalies
dependent on optical air mass. For AERONET instruments, data anomalies can be dependent on the optical air mass, the sensor head temperature, or
leakage, degradation, or looseness of the optical interference filter.
Section 3.1 addresses the quality control procedure with respect to the
instrument temperature dependence. Some instrument anomalies dependent on
the optical air mass include deviations of the measurement time to the true
time (i.e., time shift) and obstruction of light into the silicon or InGaAs
detector (e.g,. dust, moisture, spider webs). Measurements performed at high
latitudes have a slowly varying optical air mass and thus optical air mass
pattern recognition is more difficult. The AOD spectra may have optical air
mass dependence for out-of-band leakage or degradation of transmittance due
to irregularities in the optical filter composition, or the AOD may have
significant variability due to a loose filter inside the sensor head.
Similar to Fig. 4c including all analyzed sites in Table 3.
The retained spectral AOD measurements passing the quality controls from
Sect. 3.1 and 3.2 are evaluated as input for the quality controls in
the present section. The removal of nearly all of the clouds and most
instrument anomalies from the previous steps allow for more defined pattern
recognition. This section will discuss the pattern recognition techniques
utilized for the time shift and AOD diurnal dependence, provide a
description of the detector consistency, and discuss AOD spectral dependence quality
controls. Further, the AOD diurnal dependence algorithm can be used jointly
with the detector consistency and AOD spectral dependence quality controls
to remove anomalous data with more certainty. These quality controls can be
applied for multiple days to remove data impacted by anomalies for more than
1 day even when clouds interrupt the day-to-day AOD pattern. The final
data set is evaluated for the remaining number of observations in a day and deployment period.
The solar aureole 1020 nm radiance versus the scattering angle in
degrees for selected sites. Data plots with the dashed lines (i.e., Sede Boker 2,
GSFC 1, and Singapore) all qualify for the removal of data due to optically thin
homogeneous cloud contamination.
Time shift screening
AERONET data are transferred by satellite data collection platform (DCP),
personal computer (PC), or SIM card data transfer. The older Vitel satellite transmitters
provided a handshake between the instrument and transmitter allowing for
time adjustment, and newer Sutron SatLink transmitters provide a GPS time
stamp for each message. While time shift is not an issue for satellite
transmissions, the time shift can become more significant for PC data
transfer and even some instruments using SIM card data transfer. AERONET has
developed a program called cimel_https_connect
that can update the processing unit clock of Cimel model 5 instruments.
Older instruments (model 4) and old non-AERONET data transfer software
(e.g., Cimel ASTPwin) do not have the capability to synchronize the Cimel
control box with the time-synced AERONET server. Most non-AERONET software
requires the PC time to be updated from a timeserver or GPS system to
provide accurate clock synchronization. Even some newer model T instruments
transferring data by PC or SIM can have faulty GPS modules in which the
clock deviated significantly. Cimel model T instruments may allow for the
PC software (e.g., cimelTS_https_connect) updating the time and overriding the GPS module.
A Cimel clock that deviates from true time can result in an optical air mass
calculation not appropriate for the actual time, especially when the optical
air mass varies relatively rapidly diurnally. This instrument anomaly can
result in significant changes in the AOD, which affects all wavelengths but
most greatly shorter wavelengths (e.g., 340, 380, and 440 nm) at large
optical air mass when it changes rapidly. In general, longer wavelength AODs
(675, 870, and 1020 nm) have less impact from erroneous optical air mass
calculations due to less influence of molecular (Rayleigh) scattering. As a
result, AODs from the longer wavelengths tend to be more stable and AODs
from the shorter wavelengths will tend to cross over the longer wavelengths
only at one end of the day (near sunrise or near sunset). The timing of the
wavelength crossover depends on whether the Cimel clock is too fast or too
slow with respect to the actual time. For example, if the time is slow
(fast) relative to the actual time, the temporally deviated optical air mass
magnitude will be larger (smaller) than the actual optical air mass and thus
the short wavelength AODs will be lower (higher) and possibly cross the
longer wavelength AODs (significantly increase spectral dependence). In
general, Cimel clock temporal deviations in AOD data can be identified using the following:
when the shortest available wavelength AOD crosses neighboring UV,
visible, and near-infrared channel AODs near sunset and the short wavelength AOD is
decreasing significantly relative to a longer stable wavelength (e.g., 870 nm)
AOD, this condition indicates the Cimel clock is too fast (Fig. 9a);
when the shortest available wavelength AOD crosses neighboring UV,
visible, and near-infrared channel AODs near sunrise and the short wavelength AOD is
increasing significantly relative to a longer stable wavelength (e.g.,
870 nm) AOD, this condition indicates the Cimel clock is too slow (Fig. 9b).
The AOD differences and trends are used for a specific optical air mass
interval (2.5–7.0), in which the temporal clock deviation amplifies the error
in optical air mass calculations. Individual day screening is limited to
mainly cloud-free periods with low AOD in areas with significant variation
in optical air mass from ∼1.0 to 7.0.
The time shift algorithm is applied over a multiday period. The algorithm
scans the current day plus 19 days in the past (∼3-week
period) to determine if three or more days indicate the occurrence of a time
shift. If the multiday time shift criterion of 3 or more days is met,
then data between the current day and the last occurrence of the time shift
are removed from the field deployment. Although the Cimel clock could
possibly be adjusted periodically, most time shift issues tend to occur at
remote sites and this approach will maximize the removal of data over the
multiday period to minimize the negative impact on the data from the
clock-shifted anomalies. Moderate to high aerosol loading can partly mask
the temporal AOD time shift pattern and these data periods may not be
removed completely unless they occur between periods of lower aerosol
loading when the clock shift spectral AOD pattern is more defined.
Time-shifted aerosol optical depth (AOD) data examples at Málaga
(lat 36.72, long -4.48) and Toronto (lat 43.79,
long -79.47). Note the line plot is used to emphasize the 340 and
380 nm AOD impact for the time shift. (a) The Level 1.5 AOD cloud-screened-only data measured at the Málaga site on 30 January 2014. These data
show the time-shifted AOD particularly at short wavelengths, representing that the
instrument clock is too fast. (b) The Level 1.5 AOD cloud-screened-only data measured at the Toronto site on 24 September 2013. The time-shifted
aerosol optical depth particularly at short wavelengths represents when the
instrument clock was too slow. (a) Also shows the algorithm can be
used with data gaps and a lower-temporal-resolution measurement interval compared to (b).
Detector consistency quality control
The instrument external collimator on the sensor head avoids stray light and
reduces front lens contamination, while the internal sensor head defines the
field of view of the instrument (nominally 1.2∘) by the
achromatic front lens, filter, and field stop before each detector. The
external collimator is composed of two tubes and the aperture design varies
slightly by instrument type. The Cimel model 4 instrument type has two
silicon photodiode detectors in the sensor head to measure the Sun and sky
while newer model instruments have one silicon photodiode and one InGaAs
photodiode detector to measure the Sun and sky on both detectors. One of the
detectors could be impacted by an obstruction such as a spider web, insect
debris, dust, or moisture. For Cimel model 4 and some model 5 instruments,
the sky scan scenario performs two measurements at the 6∘ azimuth
angle for the almucantar and 6∘ scattering angle for the principal
plane at each wavelength over both detectors. For these older instruments,
the solar aureole gain is used for the solar silicon diode detector and the
sky gain is used for the sky silicon diode detector. These redundant
measurements can allow for detection of the change in the relative signal
but this method is currently more appropriate to use for quality controlling
the inversion products due to uncertainty in sky calibration. Newer model 5
and model T instruments (with the solar and sky measurements performed on
both detectors) do not have the redundant sky measurement; instead, these
instruments have a redundant solar measurement at 1020 nm in both collimator
tubes, where each solar measurement of the triplet is performed within
8 s of each other. The AOD 1020 nm measurements on silicon and InGaAs
detectors can be compared directly to determine if an obstruction exists in
front of either of the detectors. Applying a similar approach to Giles et
al. (2012), the difference limit (ΔτLimit) can be
computed using the optical air mass and AOD magnitude-dependent formulation (Eq. 12):
ΔτLimit=0.04+0.02⋅MINτ1020nmm,
where MIN[τ1020nm] is the minimum of the AOD at 1020 nm obtained from the
redundant AOD 1020 nm measurements on silicon and InGaAs detectors and m is
the optical air mass. The difference limit for an AOD 1020 nm minimum of 1.0
will result in the 0.06/m 1020 nm difference limit described in Giles et
al. (2012). A more lenient approach is used here based on the AOD magnitude to
prevent removal of data for low AOD at 1020 nm. At low AOD, the average field
instrument uncertainty (up to 0.01) becomes more significant while the
maximum AOD error occurs at midday and differences due to their temperature
dependency can contribute up to 0.02 AOD bias. Given the relative difference
in the AOD 1020 nm measurements, the maximum uncertainties in both 1020 nm
measurements must be considered. Therefore, the 0.02 threshold is derived
from the average uncertainty (up to 0.01) and the 0.04 limit is derived from
the maximum midday error in AOD and temperature dependency (up to 0.02).
When more than 10 % of the total measurements for the day exceed the
ΔτLimit, data are removed in the following manner.
If the AOD 1020 nm silicon subtracted by the AOD 1020 nm InGaAs detector is
greater than ΔτLimit, then the silicon side has an
obstruction and the entire measurement is removed for both silicon and InGaAs AOD
data.
If AOD 1020 nm silicon subtracted by the AOD 1020 nm InGaAs is less than
-ΔτLimit, then the InGaAs detector has an obstruction
and only the InGaAs AOD for 1020 and 1640 nm measurements is removed.
If the redundant AOD 1020 nm values are nearly the same (-ΔτLimit≥Δτ≥ΔτLimit), then an
obstruction could possibly exist in the event that a substance (e.g., spider
webs, dust, moisture) similarly obstructs both detectors.
For condition (3), this case is further evaluated by the AOD diurnal dependence
quality control in the next section.
Thresholds used to determine the independent and dependent AOD diurnal
dependence. Satisfying both the slope and correlation coefficient (R)
conditions would constitute the possible removal of all measurements for a day.
Day removalAODAnalyzedSlopeRtypediurnalperiodthresholdthresholdshapeIndependentConcaveAM, PM, day>0.25>0.974DependentConcaveAM, PM>0.04>0.94DependentConcaveday>0.1>0.94DependentConvexAM, PM, day<-0.02<-0.94Dependent – τavg<0.1ConvexAM, PM, day<-0.1<-0.94Independent –ConcaveAM, PM, day>0.1 day or>0.94Two or more siliconAM & PM > 0.02wavelengths(440, 675, 870,1020 nm) or 1640 nmInGaAsAerosol optical depth diurnal dependence
The AERONET instrument has spectral calibrations made and typically applied
both before and after field deployment. When the instrument operates in the
field, the pre-field spectral calibration applied to the near-real-time data
is constant. If the calibration changes significantly during the instrument
deployment, the error in the computation of the AOD increases with
decreasing optical air mass where the maximum error occurs when optical air
mass approaches 1 (δτ⋅m; Hamonou et al., 1999). As a result,
an apparent diurnal dependence in the AOD can occur depending on the
magnitude of the deviation from the pre-field calibration. When both the
pre-field and post-field calibrations are applied and data still show a
diurnal dependence in the AOD, then the deviation in the field measurements
is due to a nonlinear change in the calibration coefficient since Level 2.0
data utilize a linear interpolation between the pre-field and post-field
calibration coefficients.
Midday maximum (concave pattern) or midday minimum (convex pattern) of AOD
diurnal dependence can be observed at any AOD magnitude but is typically
more pronounced at lower aerosol loading due to calibration offset (Cachorro
et al., 2004) or instrument anomalies. Quality controls developed for the
analysis of the AOD diurnal dependence need to consider the impact of clouds
and missing data to assess whether to remove these data while minimizing the
removal of data exhibiting true diurnal dependence. For example, one
cloud-free day may show diurnal dependence, but on another day, the morning
or afternoon data may not be available due to missing data during cloudy or
rainy periods. The algorithm must have a sufficient number of observations
to perform a robust assessment of the AOD diurnal dependence.
AERONET data collected at Rio Branco (lat -9.96, long -67.87)
on 30 August 2011. The AOD 1020 nm Level 1.5 with only the cloud-screening
algorithm applied to the data. (a) The AOD diurnal dependence presents
a concave shape during the solar day. (b) The AOD 1020 nm and the
inverse optical air mass show a highly correlated linear fit and the slope is
significant for the full day (day), morning (AM), and afternoon (PM). Data
separation for AM and PM is defined by the local solar noon, which is
16:31:28 UTC at Rio Branco.
Variation in the number of available measurements in a day due to clouds or
instrument issues can limit the application of a single-day-only approach.
As a result, the morning and afternoon periods must have at least five
measurements separately and the analysis of the full day must have at least
10 measurements. To analyze the diurnal dependence and reduce the impact of
outliers, the GNU Scientific Library robust least-squares (RLS) linear
regression fit is performed for AOD versus the inverse optical air mass
(m-1, where m is approximately the cosine of the solar zenith angle). The
slope and correlation coefficient (R) values derived from the linear
fit are used as thresholds to determine the magnitude and strength of the
diurnal dependence (Table 4).
AERONET data collected at Rio Branco (lat -9.96, long -67.87)
from 15 August to 30 September 2011. (a) The time series of Level 1.5
spectral AOD (cloud screened only) data is plotted from 26 August to
5 September 2011 and shows repeated diurnal dependence for varying magnitudes
of AOD. (b) The robust linear fit slope and correlation coefficient (R)
are calculated from the AOD 1020 nm versus the inverse of the optical air mass (m-1).
For the full day evaluation, the green dashed line indicates the threshold for
the slope parameter at 0.1 and the solid green line indicates the threshold for
the correlation coefficient (R=0.94). Both the slope and R must exceed these
thresholds for at least 3 days scanning from the current day to the last
occurrence within the 20-day period to remove the spectral AOD, and in this
circumstance, all of the data are removed for the period for Levels 1.5 and 2.0.
The nominal AERONET 440, 675, 870, and 1020 nm wavelengths for the
silicon detector and 1640 nm for the InGaAs detector are assessed for diurnal
dependence and potential removal of all spectral channels. An example of the
AOD diurnal dependence of the 1020 nm wavelength is shown in Fig. 10 at the Rio
Branco (lat -9.96, long -67.87) AERONET site where the site
manager indicated spider webs were obstructing measurements. If data are
removed for the InGaAs detector, then only InGaAs detector data are removed,
while removal of the silicon detector data will remove all data including
InGaAs detector data, if any. The AOD diurnal dependence is classified as
two categories: independent and dependent. If the algorithm meets the strict
thresholds for independent diurnal dependence, then all channels
exhibiting diurnal dependence can remove data for a day, except the 1020 nm
channel since some old data with temperature defaults may exhibit false
diurnal dependence. Otherwise, all of the above channels are used for the
dependent diurnal dependence quality control. The dependent diurnal
quality control relies on more lenient thresholds for the slope and R;
however, the removal of data generally requires that another quality control
flag is set such as the detector consistency quality control (Sect. 3.3.2),
in which an obstruction was identified in front of one of the detectors or at
least one additional qualified wavelength meeting the slope and R thresholds.
When a qualified wavelength indicates dependent AOD diurnal dependence for
day or both AM and PM and the AM and PM slopes are positive, then the entire
day can qualify for independent removal. This methodology allows for a more
skilled approach in removing only data affected by instrumental anomalies
while minimizing the removal of data coincidently producing a true diurnal
dependence signature.
Flowchart of the reverse spectral dependence algorithm used to remove
cloud contamination artifacts and instrument anomalies. The 1640 nm wavelength
is available on some Cimel model 5 instruments and all model T instruments.
The AOD diurnal dependence identification can be complicated by changes in
aerosol loading during the day, cloud artifacts, and missing data. A
multiday scan must be performed to maximize the removal of data impacted by
instrument anomalies. A multiday assessment example is provided in Fig. 11
for Rio Branco. Figure 11a shows that the spectral AOD varies significantly
diurnally for the period from 26 August to 5 September 2011, especially for
the 870 and 1020 nm near-infrared wavelengths. Figure 11b shows evaluation
of the slope and correlation coefficient (R) for the AOD 1020 nm daily
variation, which shows 7 of the 10 days exceeding the thresholds
(slope > 0.1 and R>0.94) and wavelengths established in
Table 4. For these data to qualify for dependent AOD diurnal dependence
removal, additional information is needed such as another qualified
wavelength with slope and R exceeding the thresholds. For this case, the AOD
870 nm daily slope and correlation parameters (not shown) also exceed the
thresholds, which lead to the elimination of these data from Levels 1.5
and 2.0. Similar to the time shift screening in Sect. 3.3.1, the AOD diurnal
dependence algorithm scans the last 19 days including the current day to
determine the first occurrence and last occurrence of the dependent and
independent AOD diurnal dependence. When three or more days are identified,
data are removed from the first occurrence to the last occurrence of AOD
diurnal dependence during the 20-day period. The multiday screening allows
for the elimination of data affected by an obstruction in the instrument
field of view even with moderately high aerosol loading in the near-infrared
wavelengths and when days have an incomplete number of measurements from the
established protocol due to clouds.
Reverse spectral dependence
While the majority of the cloud-screening quality controls remove aerosol
measurements contaminated by clouds, some spurious points or slowly varying
changes in cloud properties may still affect the data set at this point in
the algorithm. A new method (Fig. 12) utilizing the AE
is applied to the remaining data set for evaluation of cloud
contamination. AEs derived from anomalous AOD
measurements due to instrument artifacts may produce a similar signature.
The spectral dependence among the wavelengths is now much improved compared
to Version 2 by removing temperature dependencies that influenced the
calculation of the AE at low AODs, reducing the effect of improper spectral
dependence due to temperature anomalies.
The AE is computed utilizing the OLS fit of the
logarithms of AOD and wavelength for the ranges of 440–870, 870–1640 nm
(if 1640 nm is available), and 870–1020 nm (for silicon detectors only) (Eck et al., 1999). The reverse spectral dependence algorithm in
Fig. 12 removes cloud-contaminated points utilizing these AE ranges depending on
the instrument model. Figure 13 shows the removal of the anomalously high
AOD at the Bratt's Lake (lat 50.20, long -104.71) AERONET
site in southwest Canada. In Fig. 13b, all negative and a few positive AE
values are identified and the algorithm removes nearly all of the residual
cloud contamination in this case. However, the penultimate and final
measurements in Fig. 13c have slightly higher AOD than the previous hour of
data, which may be due to marginal contamination by optically thin cirrus
clouds. Additional algorithm development is still needed to further enhance
the removal of cloud-contaminated data with small ice crystals while not removing dust aerosols.
Data from Bratt's Lake (lat 50.20, long -104.71) on
7 January 2007. (a) The Level 1.5 data with only the cloud-screening (CS)
algorithm applied show cloud-contaminated data remain after 18:10 UTC.
(b) For the same period as in (a), the Ångström exponent
values decreased significantly to a level at which coarse-mode aerosol particles
are not expected. (c) The final Level 1.5 and Level 2.0 data series
after the reverse spectral dependence quality control or additional cloud-screening method has been applied to the stand-alone Level 1.5 CS data.
Aerosol optical depth spectral dependence
The wavelength dependence of AOD is typically strong for fine-mode aerosols
(e.g., pollution or smoke) and weak for coarse-mode aerosols (e.g., dust or
sea salt). The AE provides an index of the strength of the spectral
dependence related to the estimation of the possible aerosol size (Eck et
al., 1999). In general, the AE440-870nm will typically provide values
between approximately 0.0 and 3.0. These prospective values indicate no
spectral dependence at AE440-870nm of 0.0 and very strong spectral
dependence with an AE440-870nm near 3.0 (AE values of 3.0 have not been
observed in good-quality data with sufficiently high AOD). The spectral
dependence can be used to evaluate the quality of each channel given that
most channels in the measurement suite adhere to the stated AOD uncertainty
of 0.01 for wavelengths ≥ 400 nm and 0.02 for wavelengths < 400 nm
(Eck et al., 1999). The fit of the AOD with wavelength on a logarithmic scale
should generally be linear for coarse-mode-dominated or fine- or coarse-mode
particle mixtures. However, in moderate- to high-aerosol-loading cases
(especially when fine mode dominated), a quadratic or cubic assumption is
needed to fit the data depending on the wavelength range under evaluation
(Eck et al., 1999; O'Neill et al., 2008). The OLS
methodology is perturbed by the presence of outliers and therefore skews the
fit towards outliers. If the boundary wavelengths are impacted by anomalies,
the OLSs can poorly fit other intermediate wavelengths.
In an effort to reduce the influence of outliers, the GNU Scientific Library
(GSL Version 2.2.1 C compilation) RLS technique is
utilized to improve the removal of spectral AOD outliers. In general, the
OLS technique is sensitive to the endpoints and to the number of points used
in the regression. For example, the outlier detection will have less skill
with a few points or anomalous endpoints. The RLS scheme uses an iterative
approach with up to 100 passes using the Tukey biweight function and
assigning the outliers a lower weight with each pass. The RLS approach
allows for the more meticulous removal of wavelengths out of spectral
dependence and more importantly preserves mid-visible wavelengths that could
be removed incorrectly when utilizing the OLS method.
Outlier detection is performed utilizing the uncertainty of the AOD
measurement and providing an allowable tolerance in the fit given the potential
irregular nature of the uncertainty (0.01 to 0.02). For wavelengths ≥ 400
and <1600 nm, the allowable AOD difference between the
measurements and fit for a candidate wavelength is (0.02 ⋅ AOD) + 0.02, based
on the stated AOD uncertainty for these wavelengths (Holben et al., 1998;
Eck et al., 1999). For wavelengths < 400 and 1640 nm, the allowable
AOD difference between the measurements and fit for a candidate wavelength
is (0.02 ⋅ AOD) + 0.04, which is adjusted for greater uncertainty at the
UV wavelengths and greater uncertainty in the larger spectral range to fit the
1640 nm wavelength.
The spectral outlier procedure begins by identifying and removing any
negative AOD values that are not within the allowable AOD difference from
the RLS linear fit. Negative AOD due to slight calibration drift can be
observed at very clean locations; otherwise, these negative values may be
anomalous. The algorithm will evaluate each wavelength separately and
compute the RLS linear fit based on the remaining wavelengths producing the
slope, intercept, and R2 values, for which the slope and intercept are used
to compute the AOD fit at the wavelength under evaluation. If the algorithm
does not identify any wavelengths for removal, then the procedure is
complete. If AOD is low (AOD440nm<0.1) and one wavelength AOD
exceeds the maximum allowable difference, then the wavelength will be
removed due to the linear fit deviation. However, if more than one
wavelength has AOD marked for removal for the low AOD condition, then the
wavelength with the largest departure from the linear fit to the measurement
and largest R2 will qualify for removal.
In the case of higher AOD (AOD440nm≥0.1), the algorithm stores the
information from the RLS linear fit and continues to perform a RLS quadratic
fit (400 nm ≤λ≤1020 nm) or a RLS cubic fit
(λ=1640 nm). If the candidate wavelength deviates from the allowable
difference in fit to the measurements for the higher-order fits, then the
wavelength will be removed if it is identified as a wavelength that
corresponds to the maximum deviation for the RLS linear fit. Figure 14
provides an example of this condition at the Osaka (lat 34.65,
long 135.59) AERONET site. After each wavelength removal regardless
of order of the fit, the algorithm repeats until no wavelength removals
occur or when fewer than three wavelengths remain.
Large aerosol optical depth triplet variability
In addition to growth of hygroscopic aerosols near cumulus cloud boundaries
and large triplet variability at short wavelengths in highly variable fine-mode plumes, a misaligned filter due to improper filter wheel movement or
dust on the filter may produce large AOD triplet variability (AOD max–AOD min).
The cloud-screening triplet variability quality control (Sect. 3.2.1)
removes the entire measurement when 675, 870, and 1020 nm AOD triplets
all have large triplet variability exceeding the threshold (0.01 or
0.015 ⋅ AOD, whichever is greater). A situation may exist in which one of those
wavelengths or shorter wavelengths are impacted by a filter anomaly, making
it necessary to assess the large AOD triplet variability. If the triplet
measurement is identified for high AOD retention (Sect. 3.1.6), then the
following large adjacent triplet quality control is not performed because
very high aerosol loading in fine-mode events can lead to large triplet
variability naturally. Occasionally, if the triplet is very large and
exceeds the limit of 0.03+0.2⋅ AOD, then the wavelength is removed
independently of the next longer wavelength.
AERONET data from the Osaka (lat 34.65, long 135.59)
site on 16 October 2006 at 22:02:11 UTC. The plot shows AOD versus the
wavelength with lines identifying the linear and quadratic robust regression
fits on a logarithmic scale used by the AOD spectral dependence algorithm. The
675 nm channel is clearly anomalous with fits differing by 0.12 for linear fits and
0.09 for quadratic fits. In addition, the AOD at 340 nm appears anomalous with
deviations of 0.06 from linear fit and 0.07 from quadratic fit. While both
wavelengths exceed their respective AOD thresholds (0.023 for 675 nm and
0.051 for 340 nm), the algorithm determines the maximum deviation for linear
and quadratic fits and removes the AOD at 675 nm measurement. A subsequent scan
by the algorithm determined that the remaining AOD measurements from 340 to
1020 nm were within the established fit deviation thresholds.
To further screen anomalous triplets individually or the entire day, each
triplet and wavelength is evaluated using the triplet variability from the
shortest wavelength (e.g., 340 nm) and the next longer wavelength (e.g.,
380 nm). The allowable triplet variability limit is computed based on the
aerosol loading and the AOD triplet variability in the next longer
wavelength: 0.03+0.02⋅ AOD + triplet_variability_of_next_longer_wave.
If the total number of triplets for a wavelength
exceeding the large triplet variability threshold is more than 25 %, then
the AOD measurements for the wavelength are removed completely for the
entire day. Figure 15 shows the large triplet variability removal at the
Polar Environment Atmospheric Research Laboratory (PEARL) (lat 80.05, long -86.42) AERONET site in northern
Canada. The triplets at shorter wavelengths may naturally exhibit relatively
large triplet variability; hence it is necessary to check the shorter
wavelength in comparison to the next longer wavelength, which typically will
be more stable if clouds do not impact the measurements.
Spectral AOD exhibiting large triplet variability at PEARL
(lat 80.05, long -86.42) on 25 August 2013. (a) Version 3
Level 1.5 cloud-screened-only data are plotted with large triplet variability
and these data were not removed by the cloud screening. The error bars represent
the triplet variability (AOD max–AOD min) divided by 2 so the full range
represents the AOD triplet variability. The large triplet variability occurs
mainly at shorter wavelengths than 675 nm. (b) Data affected by large
triplet variability (i.e., AOD 380 nm, AOD 440 nm, and AOD 675 nm) are
removed by using the Level 1.5 large triplet variability quality controls.
Remaining measurement evaluation
After the previous quality control algorithms have been applied, extraneous
data points may remain and are identified for possible removal. A number of
conditions have been implemented based on the total data removed for the
day, number of wavelengths remaining for the day, and number of measurements
for a wavelength for a deployment. These “cleanup” conditions below will
remove all wavelengths in a day for any of the following conditions
dependent on the high AOD retention from Sect. 3.1.6 and the number of
wavelengths in a day:
if high AOD retention and fewer than two wavelengths remain in a day;
if high AOD retention and two wavelengths but are not 870 and 1020 nm in a day;
if no high AOD retention and fewer than three wavelengths remain in a day;
if no high AOD retention and fewer than half of the wavelengths remain in a day.
Each wavelength must be evaluated for remnant data artifacts. If greater
than 50 % of the total cloud screened AOD data for a wavelength in a day
are removed, then AOD measurements for the candidate wavelength will be
removed for the day. Further, a condition is implemented to remove specific
wavelengths for an entire deployment. For example, if the number of
measurements for a wavelength is less than 20 % of the total cloud
screened data set for a deployment, then all of the measurements for the
specified wavelength will be removed for the deployment. These removal
conditions are necessary to fully quality control the spectral AOD data set
and avoid unphysically irregular and fragmented data sets.
Algorithm performance assessment
Data quality controls applied to the quality-controlled Level 1.0 data set
are evaluated for removal performance for each part of the Level 1.0
prescreening and Level 1.5 algorithm. The Level 1.0 prescreening is applied
to about 84 million solar triplet measurements from 1993 to 2018. The
radiometric sensitivity screening (see Sect. 3.1.2) for the DN of 1020 nm
removes about 36 % and the digital voltage triplet variance greater than 0.16
(see Sect. 3.1.3) removes nearly 11 % of the Level 1.0 data. The
remaining Level 1.0 prescreening that checks for radiometric sensitivity
screening for DN of 870 nm, extreme temperatures (TS≤-40 or
TS>100∘C), and bad
measurement configuration conditions removes approximately 0.5 % of the
Level 1.0 data. Therefore, nearly half (48 %) of the initial 84 million
solar triplet measurements are removed by the Level 1.0 prescreening steps
due to the presence of clouds in the solar measurements that greatly reduce
the signal (e.g., stratus clouds) or exhibit significant temporal
variability within the 1 min triplet measurement sequence (e.g., cumulus clouds).
The Level 1.0 AOD measurement removal by the Level 1.5 cloud-screening
algorithm from 1993 to 2018. The plot shows the impact of the major cloud-screening steps in the Level 1.5 cloud-screening algorithm and removal of these
data applies to all wavelengths. The triplet criterion removes more than 23 %
of the Level 1.0 data. Nearly 5 % of the Level 1.0 data are removed due to
cirrus cloud contamination. The “remaining” category indicates the check
performed after each cloud-screening step to determine if enough measurements
are available and do not meet the high AOD retention criteria. The “unqualified”
category indicates data that are not triplets or lack sufficient channels to
participate in the cloud-screening algorithm.
The Level 1.5 quality control algorithm is divided into the two main steps
for cloud screening and instrument data anomaly removal. Figure 16 shows the
percentage of the Level 1.0 data removed by the Level 1.5 cloud-screening
quality control. Over 23 % of the removal in the cloud-screening algorithm
was due to the large triplets at the long wavelengths (675, 870, and
1020 nm). Nearly 5 % of the removal of the Level 1.0 data was due to the
presence of cirrus clouds as detected by the solar aureole curvature
algorithm and is significant since a cirrus contamination bias is evident in
the AOD in Version 2 Level 2.0 data set. The “unqualified” category
indicates data that are not triplets or lack the sufficient channels to
participate in the cloud-screening part of the algorithm and these
measurements are rejected from Level 1.5. Finally, spectral AOD removed due
to too low negative values (AOD <-0.01) has maximum removal of
approximately 0.5 % for 380 nm and 1 % for 340 nm of the total Level 1.5
AOD measurements due to 0.02 uncertainty in the UV range at very low optical
depths, while other AOD wavelengths have generally much less than 0.5 %
removal. After all of the data are cloud screened, about 66 % of the Level 1.0
data are passed to the second part of the Level 1.5 instrument quality
control algorithm for examination of the instrument anomalies and other
spurious clouds and artifacts.
Level 1.5 quality control algorithm wavelength-dependent impacts for
each major step for the period analyzed from 1993 to 2018. The most significant
removal for most channels is due to AOD diurnal dependence, time shift, and
difference between AOD at 1020 nm for the silicon and InGaAs detectors (resulting
from collimator inconsistency). The AOD at 340 nm has significant removal of AOD
spectral dependence. The 1640 nm InGaAs channel has significant removal by
“remaining measurements” since this wavelength cannot be checked for quality
when the silicon channels are not available. Temperature screening mostly
applies to the 1020 nm silicon wavelength due to its strong temperature
dependence near the edge of the signal sensitivity of the silicon photodiode detector.
The second stage of the Level 1.5 quality control algorithm utilizes
measurements passed from the cloud-screening algorithm. While the cloud-screening algorithm rejects the entire measurement in the presence of
clouds, the instrument quality controls can also reject the entire
measurement or remove data by wavelength depending on the anomalous
condition. Figure 17 shows the removal of Level 1.5 cloud screened data due
to mainly instrument anomalies for each wavelength. More than 2.5 % of the
data are removed due to the AOD diurnal dependence screening, about 2 %
for the time shift screening, and 1.5 % for the AOD 1020 nm difference
screening. These three instrument quality control algorithms remove, in
general, the most across all wavelengths. Some removal occurs significantly
spectrally for the InGaAs channel (1640 nm). The InGaAs channels can be
affected in some instruments more significantly by water contamination as
the InGaAs side of the collimator is facing away from the Sun when in the
parked or resting position. Further, when the algorithm removes all of the
silicon channels, the remaining InGaAs channels are also removed since no
other independent method exists to check the InGaAs channel data quality.
The “remaining” measurement removal shows that nearly 4 % of the cloud
screened data are removed from the InGaAs data set. The AOD spectral
dependence removes more than 2 % of the 340 nm wavelength data, which tends
to be the most unstable wavelength (due to filter degradation), and about
0.5 % for all other wavelengths. The temperature screening removal of
missing or anomalous temperatures mostly affects the silicon 1020 nm
wavelength with nearly 1 % of the cloud-screened data removed due to their
large temperature dependence compared to the other wavelengths.
Difference in AOD response between Version 3 and Version 2 temperature
correction applied to Version 3 AOD data based on the sensor head temperature
from 1993 to 2018. The Version 2 temperature correction assumes temperature ranges
for 1020 nm and no temperature correction for all other wavelengths, while
Version 3 temperature correction characterizes the temperature response for each
filter or set of default filters for each instrument for wavelengths ≥ 400 nm.
(a) The AOD average difference plotted for each 1 ∘C
temperature bin from -25 to +55∘C. The AOD at 1020 nm exhibits an
opposite trend compared to the other wavelengths varying from -0.01 at low
temperatures to up to +0.01 at high temperatures. Other wavelengths have
slight differences at cold temperatures but apparent dependencies at high
temperatures greater than 40 ∘C possibly due to extrapolation of the
temperature coefficients to higher temperatures. (b) The number of
measurements plotted for each 1 ∘C temperature bin with a minimum of 1000 observations.
Assessment of the quality assurance data set
The AOD data will be qualified for consideration of
Level 2.0 once it passes the Level 1.5 checks. To reach Level 2.0, these
data must meet the following conditions:
Data must have pre-field and post-field calibration applied, or in some
cases, the pre-field deployment or post-field deployment calibration may be
made constant for the deployment after evaluation of the best calibration
values.
Temperature characterization must be applied utilizing the temperature
correction for the instrument or default values for each wavelength.
Instrument must be designated as the primary instrument for the site.
Once the above conditions are met, these data are considered to reach Level 2.0.
These Level 2.0 data are recommended for publication and use in various
atmospheric applications. The automated quality control algorithm attempts
to preserve aerosol data while removing data artifacts. Some unusual
atmospheric conditions (e.g., small cirrus particles r<5µm)
or rare instrument anomalies (e.g., loose filters or partially removed
multiday AOD diurnal dependence) affecting the AOD may rarely pass through
the algorithm and users are advised to consider inspecting these data
carefully when using them for detailed studies. Further, optical air-mass-dependent anomalies such as the time shift and AOD diurnal dependence
quality controls may allow data to pass when aerosol loading is high or too
few data exist to make an assessment. These quality controls can determine
patterns more skillfully at lower aerosol loading, which could result in
retaining potentially contaminated high aerosol loading periods when the
pattern may be less defined and does not meet the quality control thresholds.
The subsequent sections discuss the impact of the temperature
characterization on the Version 3 Level 2.0 AOD data to quantify the change
in regards to the Version 2 Level 2.0 data set. Further, the assessment of
the Version 3 near-real-time product is made to determine the average bias
of the AOD based on the applied calibration. Finally, an analysis is made of
the Version 3 Level 2.0 AOD long-term averages for select AERONET sites and
these are compared to the Version 2 Level 2.0 AOD long-term averages.
Using data qualified as Version 3 Level 2.0, aerosol optical depth (AOD)
average difference comparing measurements only with the pre-field calibration
applied versus instruments with both the pre-field and post-field calibrations
applied from 1993 to 2018. Calibration sites are excluded from the analysis. The
histogram of AOD differences is provided for the optical air mass 1.0≤m<7.0
range in (a) and 1.0≤m<1.5 range in (b). The average
difference is largest for the UV wavelengths and smallest for the longer wavelengths.
Temperature characterization evaluation
The accurate measurement of the spectral direct-beam Sun intensity (from
which AOD is computed) depends on the sensor head temperature of the
instrument as discussed in Sect. 2. The sensor head temperature can vary
significantly since the optical head canister is heated by the Sun and can
be much higher (>10∘C) than the ambient temperature,
especially near solar noon. The temperature sensitivity of the silicon
detector is more significant for the 1020 nm filter due to the proximity to
the edge of the spectral range of the detector in which temperature
dependence becomes more significant. The temperature dependence for all
wavelengths may vary due to the composition and/or manufacturing quality of
the filters and/or detectors. Due to technical difficulty, the ultraviolet
wavelength (λ<400 nm) filters have not been temperature
characterized in Version 3; however, UV filters may have a temperature
dependence. Figure 18 shows the difference in the AOD temperature
coefficients for Version 3 temperature correction applied to Version 3 data
and Version 2 temperature correction applied to Version 3 AOD data
from 1993 to 2018. The AOD varies most significantly for the silicon 1020 nm channel
with a full range of ∼0.02 for sensor head temperatures
between -25 and +55∘C. Notably, the shorter
wavelength channels and the InGaAs wavelengths (i.e., 1020 and 1640 nm) do
not show significant change in AOD less than 40 ∘C. All of the
wavelengths, except the silicon 1020 nm, show an AOD difference decrease
from -0.005 to -0.010 for temperatures greater than 40 ∘C, which
may be due to changes in instrument characteristics (e.g., electronic
instability in the instrument) at high temperatures. The decreasing AOD
difference with increasing temperature may be related to the smaller number
of observations at high temperatures and contribution by instruments with
temperature characterization measurements that did not reach temperatures
greater than 40 ∘C. Temperature characterization has proven to be a
small yet necessary adjustment to the AOD computation and this improvement
is especially exhibited in polar regions or at sites with very low aerosol
loading in which the Version 3 AOD spectra have much less crossover, allowing
for the computation of more accurate AEs than in the Version 2 data set.
Using data qualified as the Version 3 Level 2.0 aerosol optical depth (AOD)
500 nm average difference comparing measurements only with the pre-field
calibration applied versus instruments with both the pre-field and post-field
calibrations applied from 1993 to 2018. The AOD average differences are provided
for the optical air mass 1.0≤m<7.0 range in (a) and 1.0≤m<1.5
range in (b). Vertical bars represent the standard deviation for each
day bin. The secondary y axis on a logarithmic scale represents the number of
measurements of AOD at 500 nm for each day bin.
Level 1.5 near-real-time aerosol optical depth bias and uncertainty
The Version 3 near-real-time data set provides improved data quality
compared to Version 2 since the algorithm has improved cloud screening and
instrument quality controls applied to the data. The data set can vary in
the near-real-time interval from current day up to 1 month as ancillary
data sets are received and processed; hence, these database changes invoke
reprocessing of the AOD throughout the near-real-time phase. Once AOD data
have been pre-field and post-field calibrated, then these data may be raised
to Level 2.0 as described in Sect. 4. The near-real-time data using only
constant pre-field calibration are compared to the quality-assured data set
that uses both the pre-field and post-field calibrations applied to the data
with the assumption of linear interpolation. Figure 19 shows the
distribution by wavelength for this comparison of the near-real-time and
quality-assured data set for the entire database of Level 2.0 qualified data
excluding calibration site data and deployments using a copied pre-field or
post-field calibration. These results are based on the Version 3 Level 2.0
data set in which the Level 1.5 algorithm scans the entire deployment. The
AOD difference histograms were computed for optical air mass ranges
(1.0≤m<7.0 and 1.0≤m<1.5). The optical air mass
1.0≤m<7.0 range includes all of the data; however, these AOD difference
magnitudes will be constrained by the improved AOD measurements at large
optical air mass and influenced toward Northern Hemisphere winter
midlatitude sites when AOD tends to be low. The optical air mass
1.0≤m<1.5 range includes data that will provide AOD measurements near solar
noon and these measurements are generally less accurate (δτ⋅m)
than at larger optical air mass. In addition, optical air mass 1.0≤m<1.5
range data include a greater influence of tropical locations
and data from the midlatitude summer when AOD tends to be moderate to high.
Figure 19 shows the AOD average differences for the 1.0≤m<7.0
range indicate a positive bias in which the AOD for the pre-field only
calibration tends to be on average +0.003 to +0.009 higher than the AOD
using the interpolated calibration. Similarly, AOD average differences for
the 1.0≤m<1.5 range show a positive bias and similar wavelength
variations but up to 2 times larger differences than for the
1.0≤m<7.0 range. The largest average differences and standard
deviations are for the UV wavelengths, which have greater uncertainty as
discussed in Sect. 2. The AOD differences for the wavelengths longer than
500 nm have about less than half the bias of the UV wavelengths. The Level 1.5
algorithm performance improves with increased data availability, such as
a greater number of wavelengths or number of days. When an instrument
deployment begins, some of the Level 1.5 algorithm steps such as multiday
removal schemes are not available until several days into the deployment,
producing larger differences in the near-real-time AOD with respect to the
final product. While wavelength-dependent biases of +0.003 to +0.009 for
the 1.0≤m<7.0 range and +0.006 to +0.015 for the
1.0≤m<1.5 range exist when only the pre-field calibration is applied,
the difference can vary significantly depending on each instrument
deployment, necessitating continued post-field calibration and maintenance effort.
When an instrument is deployed in the field, the pre-field calibration is
used constantly until the post-field calibration is assessed and applied to
the data using linear interpolation. The difference of pre-field calibration
AOD minus the post-field calibration AOD average difference and standard
deviation are computed in day bins for the number of days since the
pre-field calibration. Figure 20 shows the AOD 500 nm average difference for
the optical air mass ranges: 1.0≤m<7.0 and 1.0≤m<1.5.
Instruments typically operate in the field between 12 and
18 months from the pre-field calibration date; however, the instrument
deployment may be delayed and the instrument may not begin operation for a
few months after the pre-field calibration. Thus, the number of AOD
measurements in the days since pre-field calibration bins increases to a
maximum at about 100 days. Some instruments may operate longer in the field
to support field campaigns and other scientific priorities. Figure 20 shows
that the AOD average difference and the standard deviation slowly but
steadily increase for each optical air mass range. At about 1.5 years after
pre-field calibration (∼550 days), the AOD average difference
is about +0.010 with a standard deviation of 0.015 for optical air mass
1.0≤m<7.0 range and +0.017 with a standard deviation of 0.021
for 1.0≤m<1.5. For the UV wavelengths, the average differences,
and the standard deviations tend to increase slightly while the longer visible
and near-infrared wavelengths tend to decrease slightly. Therefore, the
quality of the Level 1.5 near-real-time AOD changes with time with high-quality data at the start of the deployment but up to a +0.02 bias and
0.02 uncertainty for data collected more than 1.5 years since pre-field calibration.
Multiyear monthly comparisons of Version 3 Level 2.0 to Version 2 Level 2.0 databases
Long-term average differences between the Version 3 and Version 2 Level 2.0
data sets provide insight into the changes to be expected across most
AERONET sites. The analysis of the Version 3 and Version 2 data sets shows
mainly the differences in the AOD, AE440-870nm, PW
in centimeters, and the number of days clustered near zero (Fig. 21). Note that
PW data quality depends on the quality of the input
wavelengths (675 and 870 nm) and no further quality control is made on the
935 nm wavelength. The increases in the Version 3 Level 2.0 multiyear
monthly average AOD are often due to the increased presence of fine-mode
particles from high aerosol loading events as well as aerosols in near-cloud
environments (Eck et al., 2018). The decrease in the multiyear monthly
average AOD is due to the improved removal of clouds in the Version 3
quality control algorithm. Generally, the results should be very similar
between Version 3 and Version 2 in AOD calculation since the temperature
characterizations as well as NO2 absorption contributions typically have
relatively minor contributions.
Comparison of Version 3 and Version 2 Level 2.0 multiyear monthly
average data sets. (a) The aerosol optical depth (AOD) interpolated
to 500 nm to include data from instruments without 500 nm. (b) The
Ångström exponent (AE) is calculated utilizing the inclusive ordinary least-squares regression fit from 440 to 870 nm. (c) The precipitable water
in centimeters is derived from the 935 nm water vapor channel. (d) The difference
in the number of days is determined for each monthly long-term average.
Other factors affecting the AOD calculation include the adjustment of site
coordinates and elevation information for about 100 AERONET sites utilizing
GPS or a digital elevation model. A few rare extreme coordinate adjustments of
more than 25 km included Petrolina_SONDA (latitude -9.0691,
longitude -40.3201), Ilorin (latitude 8.4841, longitude 4.6745),
and Ouagadougou (latitude 12.4241, -1.4872). A large site
coordinate adjustment can complicate satellite matchups for these few cases
but the review of all AERONET sites showed that a less than 5 km distance
adjustment and less than 100 m elevation adjustment were needed for most
of these 100 suspected sites.
Comparison of Version 3 and Version 2 Level 2.0 multiyear monthly
average data sets for time-matched instantaneous observations in both data sets.
The panels are similar to those in Fig. 21.
Figure 22 shows plots similar to Fig. 21 except that for the observations used
for the multiyear monthly averages in both data sets the instantaneous
observations are time matched; hence, each data set has the same number of
observations and number of days. The time-matched long-term average
comparison provides insight into the AOD calculation differences rather than
impacts due to cloud screening and instrument quality controls applied in
Level 1.5. Table 5 shows the multiyear monthly overall standard deviation,
and AOD maximum to minimum range is significantly reduced compared to the
data set without time-matched observations. Figure 22a shows a slight
decreasing trend in Version 3 AOD for increasing Version 2 AOD and most of
the larger AOD deviations are for sites in Asia where the impact of the OMI
NO2 corrections may be contributing to the slight shift of up to 0.02 for a
few months and sites.
Statistics corresponding to Figs. 21 and 22 for AOD interpolated to
500 nm, Ångström exponent at 440–870 nm, precipitable water (cm), and the
number of days. Version 3 Level 2.0 and Version 2 Level 2.0 data are compared
for the same multiyear monthly averages when sites have a total of more than
1000 days for all months and more than 30 days in each month. Data represented
as “matched” indicates the further condition that the exact observations were
matched in Version 2 and Version 3 Level 2.0 multiyear monthly average data
sets. Note that PW values for the “matched” data set are approximately the
same as the unmatched data set.
ParameterAOD500nmAE440-870nmPW (cm)DaysAOD500nmAE440-870nm(V3–V2)(V3–V2)(V3–V2)(V3–V2)(V3–V2)(V3–V2)unmatchedunmatchedunmatchedunmatchedmatchedmatchedAverage0.002-0.01-0.02-0.4-0.002-0.03Standard deviation0.0220.100.0624.80.0040.10Maximum0.2470.290.341500.0150.35Minimum-0.166-1.54-0.45-130-0.029-1.63Number of months295329532953295325142514
Long-term multiyear (1993–2016) monthly average comparisons of the
Version 3 and Version 2 Level 2.0 data sets at the NASA Goddard Space Flight
Center (GSFC), Maryland, USA. Panel (a) provides the AOD interpolated to
500 nm for each version on the primary y axis and differences on the
secondary y axis. Panels (b) and (c) are plotted similarly for
the AE440-870nm and the number of days in the multiyear monthly
average, respectively.
For unmatched or time-matched data sets in Table 5, the PW
climatology changed on insignificantly average. The multiyear monthly
overall day difference (Table 5) for the unmatched PW data
set was near zero and the standard deviation was near 25 days while the
maximum of +150 and minimum of -130 days indicate significant
variability due to the differences in quality controls among the
algorithms. Overall, the changes from Version 2 to Version 3 in PW are generally negligible in terms of the contribution to the
calculation of the AOD.
Similar to Fig. 23 except for Lulin, Taiwan (lat 23.47,
long 120.87), from 2006 to 2017.
The multiyear monthly overall average difference between Version 3
and Version 2 for unmatched data is +0.002 and time-matched data
is -0.002, indicating remarkable consistency among the long-term average
quality-assured data sets. For example, the NASA GSFC AERONET site
multiyear monthly average (Fig. 23) located 20 km north of Washington,
D.C., shows minor variations in the AOD and an increase in AE due to removal of
cirrus clouds during the winter months and increasing AOD in the summer
months due to the greater abundance of cloud-processed or near-cloud
aerosols (Eck et al., 2014).
Similar to Fig. 23 except for Xianghe, China (lat 39.75,
long 116.96), from 2001 to 2017, except 2009.
Comparison of AE440-870nm in Figs. 21b and 22b shows significantly
lower values for Version 3 than Version 2 Level 2.0 at low optical depth. An
analysis of long-term average data at Lulin, Taiwan (lat 23.47,
long 120.87), identified significant reduction of Version 3
AE relative to Version 2 AE at very low AOD due to temperature characterization
that resulted in improved AOD spectral dependence (Fig. 24). The Lulin site
is a high-altitude mountain station located in south central Taiwan, and
this site is affected episodically by trans-boundary aerosol plumes from
East and Southeast Asia (Lin et al., 2013; Wang et al., 2013). In eastern
China, multiyear monthly averages from the Xianghe site (lat 39.75,
long 116.96) show a significant Version 3 AOD increase of 0.2,
while maintaining nearly the same AE and increasing the number of days up to
near 40 % for the multiyear monthly average in July and August (Fig. 25).
The Xianghe site is located to the east of Beijing and is routinely impacted
by urban pollution and episodically by biomass burning and desert dust
events (Li et al., 2007). The significant increase in the AOD for Xianghe is
likely due to the retention of highly variable fine-mode aerosol events
particularly at very high AOD, which were removed by the Version 2 cloud-screening wavelengths utilizing large triplets of less than 675 nm (Eck et al.,
2018). Additionally, some very high AOD events at Xianghe were previously
removed by the Version 2 mid-visible low signal threshold but are now
retained in Version 3, but often only for wavelengths longer than 675 nm, so
the statistics for these days are not accounted for in the 500 nm data shown in Fig. 25.
Similar to Fig. 23 except for Mongu, Zambia (lat -15.25,
long 23.15), from 1997 to 2010.
Similar to Fig. 23, except for IER Cinzana, Mali (lat 13.28,
long -5.93), from 2004 to 2017.
At the Mongu (lat -15.25, long 23.15) site (Fig. 26), the
biomass burning smoke typically occurs during the dry season from April
through November due to biomass fuel cooking and agricultural burning (Eck
et al., 2003). Comparisons of multiyear monthly averages for the Mongu site
show small deviations for AOD up to ±0.01 with slight increases in
Version 3 AE during December through March due to enhanced cirrus cloud
removal from the solar aureole check. Notably, the number of days for the
Mongu multiyear monthly averages significantly decreased by 10 % to
25 % in Version 3 due to improved cloud screening and sensor head
temperature anomalies affecting instrument performance. In Cinzana, Mali
(Fig. 27), the aerosol loading is dominated by background dust aerosol with
episodic contributions to the aerosol loading from biomass burning smoke
from November to March (Cavalieri et al., 2010). The AERONET IER Cinzana
site (lat 13.28, long -5.93) multiyear monthly averages
show generally 0.03 lower AOD for Version 3 than Version 2 and nearly the
same AE for both versions. The number of days for each month is 7 % to
25 % lower in Version 3 when compared to Version 2, mainly due to improved cirrus cloud screening.
Summary
The Aerosol Robotic Network (AERONET) has adopted a new automated quality
assurance algorithm called Version 3. The significant impacts of the
Version 3 algorithm are updated and improved cloud screening and quality control
methods, which are powerful tools in quality assuring the Sun photometer AOD
data. Comparisons between the quality-assured data sets of Version 3 and
Version 2 show excellent agreement. Deviations can be explained by known
algorithm differences such as changes in the cloud-screening triplet
variability, cirrus cloud detection and removal, implementation of
temperature characterization, updates to NO2 climatology, modification
of site coordinates and elevation, and identification of instrument
anomalies such as aerosol optical depth (AOD) diurnal dependence, AOD
spectral dependence, and instrument electrical and temperature stability.
The high statistical agreement in multiyear monthly averaged AOD
substantiates Version 3 algorithms and suggests that the Version 3 database
will validate most Version 2 research conclusions but exceptions can exist.
For example, the Version 3 algorithm permitted AOD measurements of thick
biomass burning smoke in Indonesia during the strong 2015 El Niño event
during which Version 2 AOD data were not available (Eck et al., 2019). As a result,
MODIS satellite retrieval modifications have been identified to capture more
high-optical-depth events rather than masking them as clouds (Shi et al.,
2019). Given AERONET algorithm enhancements, we recommend the Version 3 AOD
database for scientific investigations.
Major highlights of this work include (not listed in priority) the following.
An automatic quality control algorithm significantly reduces the necessity
of analysts to inspect millions of AERONET measurements. The AERONET Version 3
algorithm applied in near-real-time provides high-quality AOD for data
assimilation applications. The Version 3 Level 2.0 data are provided within
30 days of the post-field calibration evaluation after the instrument
deployment, improving the timeliness of quality-assured data.
Improvements to the total AERONET database cloud screening result in about
60 % removal of clouds from the complete Sun photometer database and this
value is similar to the coverage of clouds globally of about 68 % (Rossow
and Schiffer, 1999). Autonomous Cimel Sun photometers can view gaps and
nearby regions of the clouds and become inactive during rain periods due to
wet sensor activation, and AERONET sites are dominated by land locations,
which generally have lower cloud cover on average; therefore, these factors
would reduce the difference between total AERONET cloud removal percentage
and global satellite observations. Over 36 % of the total data were
removed by the four-quadrant solar tracker sensitivity check due to less
accuracy in tracking the Sun in cloudy conditions, while about 23 % of the
removal was due to the variability in clouds with respect to more
homogeneous aerosol loading.
Utilizing the shape of the solar aureole radiances with scattering angle, a
cirrus detection algorithm was developed by leveraging MPLNET lidar cloud
detection capabilities. The solar aureole cirrus algorithm eliminates
∼5 % of the Level 1.0 AOD data to reduce the bias of
optically thin cirrus clouds in the AERONET database.
Spectral temperature correction has been implemented for all AERONET
instruments using the sensor head temperature sensor reading. The
temperature characterization shows significant AOD deviation ± 0.01
variation between -25 and +50∘C for the silicon at
1020 nm since this wavelength is on the edge of the silicon detector
sensitivity range. Other wavelengths in the 440 to 1640 nm range have weak
temperature dependence from -25 to +30∘C with a
few wavelengths having greater temperature dependence at higher temperatures.
New automated instrument anomaly screening provides a systematic and
objective scheme to remove entire measurements or individual wavelengths
from the AERONET AOD database. Importantly, obstructions to the instrument
optics are now removed automatically using an AOD diurnal dependence
algorithm based on the optical air mass. The AOD diurnal dependence
technique employs several conditions that were developed to mitigate the
removal of true diurnal dependence conditions while maximizing the removal
of data significantly impacted by anomalies affecting the instrument optics.
Bias and uncertainty estimates for near-real-time AOD are computed by using
the difference of the pre-field calibration AOD minus the interpolated
calibration AOD. The near-real-time AERONET data have an estimated bias of up
to +0.02 and 1σ uncertainty of up to 0.02; these values have slightly
higher uncertainty for shorter wavelengths and slightly lower uncertainty
for longer wavelengths.
The AERONET Version 3 and Version 2 AOD quality-controlled databases are
analyzed to have a long-term monthly average difference of +0.002 with
±0.02 SD and greater agreement for time-matched
observations with an average difference of -0.002 with ±0.004 SD.
The high statistical agreement in multiyear monthly averaged AOD
validates the advanced automatic data quality control algorithms and
suggests that migrating research to the Version 3 database will corroborate
most Version 2 research results and likely lead to some more accurate results.
Examination of long-term sites in various aerosol source regions indicates
mainly subtle changes in AOD, AE, and the number of days available; however,
in some months, improved cloud screening, high aerosol loading retention,
and improved instrument anomaly screening not attained by Version 2 explain
larger deviations in these parameters.
AERONET Version 3 has evolved into a database with unparalleled presence in
Sun photometry. Future algorithms could include improvements to the
detection of cirrus clouds in polar environments, where the ice crystal size
is approaching the size of large non-cloud aerosols, the determination of
anomalies in high-aerosol-loading conditions, and the identification of true
AOD diurnal dependence versus one generated by an instrument anomaly. Cimel
radiometers will also measure the moon to derive lunar AOD (Berkoff et al.,
2011; Barreto et al., 2013, 2016; Li et al., 2016). For example, current
lunar measurement protocols do not include lunar aureole measurements
analogous to the solar aureole measurements; hence the lack of these
measurements potentially reduces the ability of the algorithm to remove
cirrus clouds at night, and thus a variation of the quality control
methodology may need to be developed. Other surface-based remote-sensing
networks such as MAN (Smirnov et al., 2009), SKYNET (Takamura et al., 2004),
GAW-PFR (Kazadzis et al., 2018), and PANDORA (Herman et al., 2009)
may benefit by implementing applicable quality control methods established by AERONET.
Version 3 AOD data are available from the AERONET web site
(https://aeronet.gsfc.nasa.gov, last access: 1 August 2018) and the web site provides these data freely
to the public. Data may be acquired by utilizing several download mechanisms
including site-by-site download tools and web service options for near-real-time data acquisition.
For 5 years, the AERONET staff (listed from DG to BH) worked
individually and collaboratively drawing on their decades of
scientific, engineering, and programming expertise to develop and assess the
Version 3 AOD processing system presented herein. Traditional assignment of
co-authorship is not possible. Aside from the first author, contributing
AERONET staff are listed in reverse chronological order based on their start
date with the project. JL, JC, and EW provided lidar data for development of
the cirrus curvature methodology. SK and AL provided gaseous and water vapor
absorption coefficients based on radiative transfer models.
The authors declare that they have no conflict of interest.
Acknowledgements
The AERONET and MPLNET projects at NASA GSFC are supported by the
Earth Observing System Project Science Office cal–val, Radiation
Sciences
Program at NASA headquarters, and various field campaigns. NCEP Reanalysis
data are obtained routinely from the US National Weather Service Climate
Prediction Center. We would like to thank Edward Celarier for several
discussions and providing the OMI NO2 monthly climatology. Fred Espenak
and Chris O'Byrne (NASA GSFC) provided solar and lunar eclipse predictions
and the Eclipse Explorer software.
We thank the MPLNET PIs for their effort in establishing and maintaining the
sites: Arnon Karnieli (SEDE_BOKER), Sachi Tripathi (Kanpur),
Greg Schuster (COVE), Margarita Yela Gonzalez (Santa Cruz de Tenerife), and
John Barnes (Trinidad Head).
The authors thank the AERONET calibration facilities in the USA (NASA GSFC,
NOAA Mauna Loa Observatory, and NEON), France (PHOTONS), and Spain (RIMA and
Izana). We thank the following AERONET PIs and their staff for maintaining
the sites and contributing aerosol data: Norm O'Neill, Ihab Abboud, and
Vitali Fioletov (PEARL, Toronto, Bratt's Lake); Itaru Sano (Osaka); Paulo Artaxo
(Rio Branco); Neng-Huei Lin (Lulin); Pucai Wang and Xiangao Xia
(Xianghe); Mikhail Panchenko (Ussuriysk); Arnon Karnieli (Sede Boker);
Emilio Cuevas-Agullo (Santa Cruz Tenerife); Joseph Prospero (Ragged Point);
Soo-Chin Liew and Santo Salinas Cortijo (Singapore); Sachchida Nand Tripathi
(Kanpur); Francisco Reyes (Màlaga); and Jean Rajot and Beatrice Marticorena
(IER-Cinzana). A special acknowledgement is given to the AERONET principal
investigators and their site staff around the world, who participate in
monitoring aerosols to expand our scientific understanding of the Earth.
Edited by: Vassilis Amiridis
Reviewed by: two anonymous referees
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