Satellite instruments provide a vantage point for studying aerosol
loading consistently over different regions of the world. However, the
typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors
should be considered. Discrepancies exist between aerosol optical depth
(AOD) products due to differences in their information content, spatial and
temporal sampling, calibration, cloud masking, and algorithmic assumptions.
Users of satellite-based AOD time-series are confronted with the challenge
of choosing an appropriate dataset for the intended application. In this
study, 16 monthly AOD products obtained from different satellite sensors and
with different algorithms were inter-compared and evaluated against Aerosol
Robotic Network (AERONET) monthly AOD. Global and regional analyses
indicate that products tend to agree qualitatively on the annual, seasonal
and monthly timescales but may be offset in magnitude. Several approaches
were then investigated to merge the AOD records from different satellites
and create an optimised AOD dataset. With few exceptions, all merging
approaches lead to similar results, indicating the robustness and stability
of the merged AOD products. We introduce a gridded monthly AOD merged
product for the period 1995–2017. We show that the quality of the merged
product is as least as good as that of individual products. Optimal
agreement of the AOD merged product with AERONET further demonstrates the
advantage of merging multiple products. This merged dataset provides a
long-term perspective on AOD changes over different regions of the world,
and users are encouraged to use this dataset.
Introduction
Interactions of atmospheric aerosols with clouds and radiation are the
largest source of uncertainty in modelling efforts to quantify current
climate and predict climate change (IPCC, 2018). To reduce such
uncertainties, we need observations to constrain climate models. However,
these observations must be accurately calibrated and validated, have
consistent or at least well-characterised uncertainties, and provide
adequate temporal and spatial sampling over a long period of time. With
their ability to cover the globe systematically, satellites provide this
global and temporal perspective. Satellite observations have produced major
advances in our understanding of the climate system and its changes,
including quantifying the spatio-temporal states of the atmosphere, land and
oceans, and aspects of the underlying processes. However, as the typical
lifetime of a single satellite platform is on the order of 5–15 years, a
single sensor data record may not be long enough to discern a climate signal
(WMO, 2017). Moreover, aerosol products from different satellites and
algorithms all have limitations regarding their spatial and temporal
coverage and vary in their accuracies depending on environmental conditions
(aerosol loading and type, surface brightness, and observation geometry), often
leading to regional differences (e.g. Li et al., 2014b). Thus, the
application of satellite observations for climate change studies requires
using products from multiple sources to derive consistent regional
conclusions.
The key parameter used for aerosol-related studies to date is the aerosol
optical depth (AOD), which is the vertical integral of extinction by aerosol
particles through the atmospheric column. Over the last several decades, AOD
remote sensing has been performed from space using a wide variety of sensors
that have different characteristics, including being passive or active, operating in ultraviolet (UV) to
thermal infrared (TIR) spectral regions, being single-view to multi-view, being
single-pixel to broad swath, having a sub-kilometre to tens-of-kilometres resolution, being intensity-only or polarimetric, and having different orbits and observation time(s). Table 1 lists
the datasets used in the current study, together with key references. Aside
from the Earth Polychromatic Imaging Camera (EPIC; orbiting at L1 Lagrange
point directly between the Earth and the sun on the Deep Space Climate Observatory (DSCOVR) satellite), all sensors are in polar-orbiting, sun-synchronous low-earth orbits
(∼600–800 km). Only a few of these sensors were optimised for
accurate aerosol property retrieval, and for many, AOD at one or more
visible wavelengths is the only quantitatively reliable aerosol parameter
they provide. Table 1 is not exhaustive for available AOD products. Other
AOD products such as those from active sensors such as the Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP) and imaging radiometers on geostationary
satellites are not considered here, as they have very different sampling
characteristics (e.g. CALIOP profiles a curtain swath, with areas either
viewed twice daily and twice during the night during a month or not at all;
geostationary sensors sample a constant disc, typical at a frequency of 10 min to 1 h); thus their monthly mean products are conceptually very
different from polar-orbiters.
No two datasets provide identical results, whether applying the same
algorithm principles to multiple similar sensors (Sayer et al., 2017,
2019; S. Li et al., 2016; Levy et al., 2013) or even between “identical”
sensors, such as the Moderate Resolution Imaging Spectroradiometers (MODISs) on
Terra and Aqua (Sayer et al., 2015; Levy et al., 2018) for which calibration and
time of day differences remain. Using different retrieval algorithms for
products retrieved from the same instruments introduces additional
discrepancies, such as those found by de Leeuw et al. (2015), Popp et al. (2016)
for three Along Track Scanning Radiometer (ATSR) datasets.
Differences can become larger when comparing products from different sensors and
algorithms (Kokhanovsky and de Leeuw, 2009; Kinne, 2009; Li et al., 2014b).
One other important factor contributing to differences is related to the
approach to cloud masking, which affects the pixels selected for processing
by retrieval algorithms and propagates into different levels of clear-sky
bias in daily and monthly aggregates (Sogacheva et al., 2017; Zhao et al.,
2013; Li et al., 2009). Escribano et al. (2017) estimated the impact of
choosing different AOD products for a dust emission inversion scheme and
concluded that the large spread in aerosol emission flux over the Sahara and
Arabian Peninsula is likely associated with differences between satellite
datasets. Similarly, Li et al. (2009) concluded that differences in
cloud-masking alone could account for most differences among multiple
satellite AOD datasets, including several for which different algorithms
were applied to data from the same instrument.
There is no single “best” AOD satellite product globally. For example, the MODIS Deep
Blue (DB) AOD product shows better performance than MODIS Dark Target
(DT) in most regions, besides bright surfaces (i.e. deserts and
arid/semi-arid areas) (Wei et al., 2019a). However, despite the differences
between satellite products and the fact that none is uniformly most
accurate (Sayer et al., 2014; de Leeuw et al., 2015, 2018), the application
of statistical techniques such as principal component or maximum covariance
analysis (Li et al., 2013, 2014a, b) shows that there are key similarities
among the AOD products tested.
Merging multi-sensor AOD products holds the potential to produce a more
spatially and temporally complete and accurate AOD picture. With multiple
observational datasets available, it is important to examine their
consistency in representing aerosol property variability in these
dimensions. This is useful for constraining aerosol parameterisations in
climate models (Liu et al., 2006), in the study of aerosol climate effects
(Chylek et al., 2003; Bellouin et al., 2005) and for verifying global
climate models (e.g. Kinne et al., 2003, 2006; Ban-Weiss et al., 2014)
in which satellite-retrieved AOD monthly aggregates are used.
However, such an integration into a coherent and consistent climatology is a
difficult task (Mishchenko et al., 2007; Li et al., 2009). There are only a
few studies where an AOD record was merged from different satellites.
Chatterjee et al. (2010) describe a geostatistical data fusion technique
that can take advantage of the spatial autocorrelation of AOD distributions
retrieved from the Multi-angle Imaging Spectroradiometer (MISR) and MODIS,
while making optimal use of all available datasets. Tang et al. (2016)
performed a spatio-temporal fusion of satellite AOD products from MODIS and
Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) using a Bayesian maximum
entropy method for eastern Asia and showed that, in the regions where both
MODIS and SeaWiFS have valid observations, the accuracy of the merged AOD is
higher than those of the MODIS and SeaWiFS AODs individually. Han et al. (2017) improved the AOD retrieval accuracy by fusing MODIS and CALIOP data.
Sogacheva et al. (2018b) combined ATSR and MODIS AOD to study the trends in
AOD over China between 1995 and 2017. Naeger et al. (2016) combined daily
AOD products from polar-orbiting and geostationary satellites to generate a
near-real-time (NRT) daily AOD composite product for a case study of
trans-Pacific transport of Asian pollution and dust aerosols in mid-March
2014. J. Li et al. (2016) constructed a monthly mean AOD ensemble by combining
monthly AOD anomaly time series from MODIS, MISR, SeaWiFS, Ozone Monitoring
Instrument (OMI) and POLarization and Directionality of the Earth's
Reflectances (POLDER) and applying an ensemble Kalman filter technique to
these multi-sensor and ground-based aerosol observations to reduce
uncertainties. Penning de Vries et al. (2015) examined relationships between the
monthly mean AOD, Ångström exponent (AE) from MODIS, UV Aerosol
Index from the Global Ozone Monitoring Experiment–2 (GOME-2) and trace gas
column densities and showed the advantage of using multiple datasets with
respect to characterising aerosol type. Boys et al. (2014) combined SeaWiFS
and MISR AODs with the GEOS-Chem global model to create and study trends in a
15-year time series of surface particulate matter levels.
A meaningful merge should account for the strengths and limitations of each
constituent record. The spread of satellite AOD records also adds to the value of
constraining their uncertainty; whereas a lack of diversity among datasets
does not mean that they have converged on the true value, the existence of
unexplained diversity does imply that they have not.
To assess their consistency, the products should be compared during
overlapping periods, because interannual and shorter-term variability in
atmospheric aerosols can be significant in some parts of the world (e.g.
Lee et al., 2018). In the current study, AOD monthly aggregates from 16
different satellite products were evaluated with ground-based measurements
from the Aerosol Robotic Network (AERONET; Holben et al., 1998). Note that,
as with all measurements, even the AERONET spectral AOD has limitations as
to where it can be informative. For example, AERONET includes
∼450 active stations in 2019, offering far more spatial
coverage than in 1993 when the network was founded, yet even now AERONET
spatial sampling is particularly limited in remote areas which are often
those where aerosol gradients are large, e.g. near sources (e.g. Shi et
al., 2011; J. Li et al., 2016).
Based on the comparison with AERONET, we estimate how well the satellite AOD
monthly aggregates reproduce the AERONET AOD climatology. We considered
areas with different aerosol types, aerosol loading and surface types, which
are the dominant factors affecting AOD product quality. This allows users to
choose the AOD product of a better quality, depending on the area and research
objective. A verification of open-ocean monthly data using the Maritime
Aerosol Network (MAN; Smirnov et al., 2009) is not possible in this way,
because MAN data are acquired during cruises on ships of opportunity rather
than as regular, repeating observations at specific locations.
Different approaches for merging the AOD products (median, weighted
according to the evaluation results) are introduced in the current paper.
AOD evaluation results are used to merge the L3 gridded monthly AOD data and
AOD time series for the period 1995–2017, using different methodologies. The
resulting AOD merged products are evaluated against AERONET and compared
against one another.
This study grew out of discussions at annual AeroSat (https://aerosat.org,
last access: 9 May 2019) meetings about how to move forward on the
difficult topic of combining distinct aerosol data records. AeroSat is a
grass-roots group of several dozen algorithm developer teams and data users.
Meeting in person around once a year in concert with its sibling AeroCom
group of aerosol modellers (https://aerocom.mpimet.mpg.de, last access:
9 May 2019) allows an active discussion between data providers and data users
to highlight developments, discuss current issues and open questions in the
field of satellite aerosol remote sensing and aerosol modelling.
The paper is organised as follows. In Sect. 2, the AOD products and
regions of interest are introduced. The main principles and results for the
statistical evaluation of individual monthly AOD retrievals are presented in
Sect. 3. Alternative methods for merging are discussed in Sect. 4. AOD
merged products are introduced, evaluated and inter-compared with individual
products in Sect. 5. Annual, seasonal and monthly regional AOD time series
are presented and discussed in Sect. 6. A brief summary and conclusion are
given in the final section.
Regions of interest, instruments and AOD productsRegions of interest
There are huge regional differences in AOD loading types (composition and
optical properties), seasonality and surface reflectance (Holben et al.,
2001; Dubovik et al., 2002; Pinty et al., 2011). Retrieval quality
(accuracy, precision and coverage) varies considerably as a function of these
conditions, as well as whether a retrieval is over land or ocean. Therefore,
this study focuses on surface-specific (land or ocean) and regional
evaluation of these diverse aerosol products.
In addition to evaluating AOD products AOD over land, over ocean and globally
(note that not all sensor–algorithm combinations retrieve over both
surfaces), we chose 15 regions that seem likely to represent a sufficient
variety of aerosol and surface conditions (Fig. 1 and Table S1 in the Supplement). These include
11 land regions, two ocean regions and one heavily mixed region. The land
regions represent Europe (denoted by Eur), Boreal (Bor), northern, eastern
and western Asia (AsN, AsE and AsW, respectively), Australia (Aus), northern
and southern Africa (AfN and AfS), South America (AmS), and eastern and western Northern America (NAE and NAW). The Atlantic Ocean is represented as two
ocean regions, one characterised by Saharan dust outflow over the central
Atlantic (AOd) and a second that includes burning outflow over the southern
Atlantic (AOb). The mixed region over Indonesia (Ind) includes both land
and ocean. Due to documented large changes in AOD during the last 25 years
(Sogacheva et al., 2018a, b), we also considered the south-eastern China
(ChinaSE) subset of the AsE region.
The main body of the paper focuses on two regions, Europe and ChinaSE, and the
big-picture results (global, all land and all ocean). The two regions, Europe and ChinaSE,
were chosen because they are often the focus of aerosol studies. Results
from the remaining regions are presented in the Supplement.
Fifteen land and ocean regions defined in this study: Europe (Eur),
Boreal (Bor), northern Asia (AsN), eastern Asia (AsE), western Asia (AsW),
Australia (Aus), northern Africa (AfN), southern Africa (AfS), South America
(SA), eastern North America (NAE), western North America (NAW), Indonesia
(Ind), Atlantic Ocean dust outflow (AOd) and Atlantic Ocean biomass burning
outflow (AOb). In addition, south-eastern China (ChinaSE), which is part of the
AsE region, marked with a blue frame, is considered separately. Land, ocean
and global AOD were also considered.
Instruments, algorithms and AOD products
An overview of the instruments and AOD products included in this study is
presented in Table 1. AOD products from the same instruments retrieved with
different algorithms are named in the paper with the instrument and
retrieval algorithms, e.g. ATSR dual-view (ADV), ATSR Swansea University (SU), Terra Dark Target (DT) &
Deep Blue (DB) and Terra MAIAC (multi-angle implementation of atmospheric correction). When both Terra and Aqua are considered, we
call them together as MODIS DT&DB or MODIS MAIAC. Note that we used the
merged MODIS Dark Target and Deep Blue product (Sayer et al., 2014; denoted
“DT&DB”), rather than the results of the individual DB and DT
algorithms, as this merged dataset was introduced into the product for
similar purposes as the one explored in this work. An ensemble ATSR product
(ATSR_ens) was generated from the three ATSR products (ATSR
ADV, ATSR SU and ATSR ORAC – ATSR with the optimal retrieval of aerosol and cloud algorithm) in order to combine the strengths of several
algorithms and to increase the coverage of the combined product (Kosmale et
al., 2020). The ensemble was calculated per pixel as
the weighted mean of the individual algorithm values with weights given by the
inverse of the individual pixel level uncertainty values. The ensemble
algorithm required as a minimum for each pixel to have valid results from at least two
of the contributing algorithms. The uncertainties in each algorithm were
first corrected in their absolute values to agree on average with the mean
error.
For some products, AOD data are available for wavelengths other than 0.55 µm. Specifically, Total Ozone Mapping Spectrometer (TOMS) and OMI
products include AOD at 0.50 µm, Advanced Very-High-Resolution
Radiometer (AVHRR) NOAA includes AOD at approximately 0.63 µm (with slight
variation between the different AVHRR sensors), and EPIC AOD is available at
0.44 µm (in the dataset used in the current study). If the wavelength
is not mentioned specifically, 0.55 µm is implicit.
In most cases the official AOD monthly products (typically referred to as
Level 3 or L3 data), which correspond to arithmetic means of daily mean data
aggregated onto (typically) a 1∘× 1∘ grid, have been used
without further processing. The first exceptions are for AVHRR NOAA and
POLDER, which provide very high AOD values poleward of ca. 60∘ and
over Hudson Bay (50–70∘ N, 70–95∘ E), respectively. The values are unrealistic, a likely a
consequence of cloud and/or sea ice contamination. To eliminate those
unrealistic values, AOD values of >0.7 have been removed over
the mentioned-above areas. Applying that limit decreased the offset between
the AVHRR NOAA product and other products but did not eliminate it (see
Sect. S2 in the Supplement for details). Additionally, MISR standard (0.5∘× 0.5∘ resolution) and AVHRR NOAA (0.1∘× 0.1∘
resolution) L3 AOD products were aggregated by simple averaging to
1∘ to match the other datasets.
Due to differences in instrument capabilities and swath widths (Table 1),
the spatial and temporal data sampling available for calculating monthly
averages varies considerably among the satellite products. The ATSR products and
MISR have narrow swaths and generally provide only a few days with
retrievals per month, whereas most of the rest see the whole planet roughly
every day or two so that their coverage is mostly limited by, e.g. the
persistence of cloud cover. As mentioned previously, EPIC is a special case,
as it provides moving snapshots of the day-lit portion of the Earth, up to
several times per day, as distinct from overpasses at only specific local
solar equatorial crossing times for the sensors on polar-orbiting
satellites. Further, TOMS and OMI have a notably coarser pixel resolution
than the others, so their coverage and quality are more sensitive to cloud
masking decisions. Some datasets provide measures of internal diversity
(e.g. standard deviation), but none currently provides estimates of the
monthly aggregate uncertainty against some standard, which would be a
combination of (both systematic and random) retrieval uncertainties and
sampling limitations. This is an area currently being investigated by
AeroSat due to the wide use of L3 products.
For the intercomparison between AOD products, we chose three “reference” years:
2000, when the AOD products from TOMS, AVHRR NOAA, SeaWiFS, ATSR-2, MODIS
Terra and MISR are available (for the full year, except for MISR and MODIS
Terra, which were available from March to December);
2008, when the AOD products from Advanced ATSR (AATSR), MODIS Terra and Aqua, MISR, AVHRR
NOAA, AVHRR DB/SOAR (Satellite Ocean Aerosol Retrieval), SeaWiFS and POLDER are available; and
2017, when the AOD products from MODIS Terra and Aqua, MISR, VIIRS (Visible Infrared Imaging Radiometer Suite) and
EPIC are available.
For products with no coverage over ocean (TOMS, OMI and MAIAC products) or
land (AVHRR NOAA), global AOD was not considered.
Overview of the sensors, data records and AOD algorithms discussed
in this paper. For the products availability, see Table 4.
Sensor(s)Coverage and L3 grid sizeAlgorithm versionAlgorithm principlesReferencesTotal Ozone Mapping Spectrometer (TOMS) (UV spectrometer)1979–1993 and 1996–2001; 3100 km swath; 1∘; daily and monthlyNimbus-7/TOMS: N7AERUV v. 0.4.3; EP/TOMS: EPAERUV v. 0.1.3.Enhanced sensitivity of TOA (top of the atmosphere) spectral reflectance in the UV to aerosol extinction and absorption.Torres et al. (1998, 2005)Advanced Very-High-Resolution Radiometer (AVHRR)1981–2017; 2900 km swath; 0.1∘; daily and monthlyAVHRR NOAASingle-channel retrieval of aerosol optical depth; over ocean only.Ignatov and Stowe (2002) Heidinger et al. (2002), Zhao et al. (2008)(bispectral, single-view, broad-swath radiometer)1989–1991 (NOAA7), 1995–1999 (NOAA14) and 2006–2011 (NOAA18); 0.5 and 1∘; daily and monthlyDeep Blue/SOAR, V. 4Land: surface modelled using database or NDVI (normalised difference vegetation index). Ocean: bispectral simultaneous retrieval.Hsu et al. (2017), Sayer et al. (2017)Along Track Scanning Radiometer (ATSR-2) and Advanced ATSR (AATSR), both called as ATSR1995–2003 (ATSR-2) and 2002–2012 (AATSR); 512 km swath; 1∘; daily and monthlyADV/ASV v2.31Land: spectral constant reflectance ratio. Ocean: modelled reflectance.Flowerdew and Haigh (1995), Veefkind et al. (1998), Kolmonen et al. (2016), Sogacheva et al. (2017)(dual-view radiometer in the visible and near-infrared; thermal infrared for cloud)SU v4.3Iterative model inversion for continuous retrieval of AOD and FMF. Land: retrieval of BRDF parameters. Ocean: prior reflectance model.North et al. (1999), North (2002), Bevan et al. (2012)ORAC v4.01 (in current paper, as a part of the ATSR ensemble only)Optimal estimation. Land: SU surface parametrization. Ocean: sea surface reflectance model.Thomas et al. (2009), Sayer et al. (2010)ATSR ensemble v. 2.7Uncertainty-weighted mean of ATSR2/AATSR baseline algorithms ADV, ORAC and SU.Kosmale et al. (2020)Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (multispectral, single-view, broad-swath radiometer)1997–2010; 1502 km swath; 0.5 and 1∘; daily and monthlyDeep Blue/SOAR V.1Land: surface modelled using database or NDVI. Ocean: multispectral simultaneous retrieval.Sayer et al. (2012a, b), Hsu et al. (2004, 2013a)Multi-angle Imaging SpectroRadiometer (MISR) (multispectral, with four bands, visual–near-infrared, multi-angle, i.e. nine angles, radiometer)2000–present; 380 km swath; 0.5∘; daily and monthlyStandard algorithm (SA) V23Land: surface contribution estimated by empirical orthogonal functions and assumption of spectral shape invariance. Ocean: two-band (red, NIR) retrieval using cameras not affected by sun glint. Both: lookup table with 74 mixtures of 8 different particle distributions.Martonchik et al. (2009), Garay et al. (2017, 2019), Witek et al. (2018), Kahn et al. (2010)Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua (multispectral, single-view, broad-swath radiometer)Terra: 2000–present; Aqua: 2002–present; 2300 km swath; 1∘; daily, 8 d and monthlyDT&DB C6.1DT: surface is function of wind speed (ocean) or parameterized spectral relationship (land–vegetation/dark soil). DB: database and spectral relations of surface reflectance.DT: Levy et al. (2013, 2018), Gupta et al. (2016) DB: Hsu et al. (2013a, 2019) DT&DB: Levy et al. (2013), Sayer et al. (2014)MAIAC V6Simultaneous retrieval of surface and aerosol from time series of observations.Lyapustin et al. (2018)Ozone Monitoring Instrument (OMI) (UV spectrometer)2004–2016; 2600 km swath; 1∘; daily and monthlyOMAERUV v. 1.8.9.1Enhanced sensitivity of TOA spectral reflectance to UV aerosol extinction and absorption.Jethva and Torres (2011), Torres et al. (2007, 2013, 2018)
Continued.
Sensor(s)Coverage and L3 grid sizeAlgorithm versionAlgorithm principlesReferencesPolarization and Directionality of the Earth's Reflectances (POLDER) 3 (multispectral, multi-angle polarimeter)Dec 2004–Dec 2013 2100km×1600km swath; 1∘; daily, monthly and seasonallyGRASP v.1Simultaneous retrieval of surface and aerosol in frame of multi-pixel approach: statistically optimised fitting of large pixels groups (aggregated in time and space); the aerosol is assumed as an external mixture of several predefined aerosol components.Dubovik et al. (2011, 2014, 2019)Visible Infrared Imaging Radiometer Suite (VIIRS) (multispectral, single-view, broad-swath radiometer)2012–present; 3040 km swath; 1∘; daily and monthlyDeep Blue/SOAR, V.1Land: surface modelled using database or spectral relationship. Ocean: multispectral simultaneous retrieval.Sayer et al. (2018a, b, 2019), Hsu et al. (2019)Earth Polychromatic Imaging Camera (EPIC) (multispectral radiometer orbiting at Lagrange point)2015–2016; 1∘; daily and monthlyMAIAC V1Simultaneous retrieval of surface and aerosol from time series of observations.Huang et al. (2020)AOD products intercomparison and evaluation with AERONET
The AOD deviations of the individual products from the median AOD (Figs. S1
and S2 in the Supplement) are discussed in detail in the Supplement (Sect. S2). These show
regional differences, even for products retrieved from the same instruments
with similar algorithm. Both negative and positive deviations are observed
in regions with high AOD; both aerosol optical model assumptions and surface
type are also likely to influence the AOD retrieval. High AOD might, in turn,
be wrongly screened as cloud, and thus the resulting lack of high AOD
retrieval leads to a low bias in monthly AOD. To further reveal differences
among the AOD products retrieved with different algorithms and applied to
different satellites, the diversity of the satellite annual mean AOD for
years 2000, 2008 and 2017 is discussed in Sect. S3 (Figs. S3 and S4). The
diversity is lower in 2017, when only MODIS, MISR, EPIC and VIIRS AOD
products are available.
Evaluation of monthly AOD
To evaluate the quality of any AOD product, the verification of the product
against more accurate reference measurements, where possible, is obligatory.
Ground-based measurements such as those from AERONET (cloud screened and quality
assured Version 3 Level 2.0; Giles et al., 2019) provide highly accurate
measures of AOD that are widely used as ground truth for the validation of
satellite AOD data. Extensive L2 AOD validation has been performed for
different aerosol products.
However, climate model evaluation is often performed on monthly scales.
Thus, climate analysis begs for evaluation of satellite AOD monthly
aggregates (Nabat et al., 2013; Michou et al., 2015; S. Li et al., 2016). Only
a few attempts have been made to evaluate AOD monthly aggregates retrieved
from satellites (e.g. Li et al., 2014b, Wei et al., 2019b). This is because
verification of the L3 monthly aggregate satellite AOD is not a true
validation (and note the use of “evaluation” and “verification” here
instead of “validation”). AERONET provides AOD at a single point and is
not necessarily representative of AOD in a 1∘× 1∘ grid.
While AERONET samples during all cloud-free daylight hours, a given
polar-orbiting sensor will only report once per day and at the same time each
day (e.g. 13:30 LT for sensors in the A-Train). The possible
spatial representativity issues associated with this latter point are a
topic of current investigation (e.g. J. Li et al., 2016; Virtanen et al.,
2018; Schutgens, 2019). Nevertheless, AERONET's instantaneous AOD
uncertainty (around 0.01 in the mid-visible; Eck et al., 1999) is
significantly lower than most satellite products, and its temporal sampling
is much more complete. As such, it remains a useful source for evaluating
these L3 products, and for this purpose we compare AOD monthly aggregates of
all available data from both AERONET and each satellite product. Deviations
between satellite and AERONET monthly aggregates are expected, e.g. due to
differences in satellite spatial and temporal sampling (Sect. 2.2, Table 1),
particularly for those satellites with lower coverage.
Results from this comparison have limitations. As mentioned previously,
AERONET provides data over certain locations within a grid cell, whereas
satellites cover a larger fraction of the area of a grid cell (depending on
sampling and cloud cover). So, for example, if AERONET is likely to miss
extreme high values (localised plumes missing an AERONET station), that will
result in AERONET showing lower AOD than from a satellite. Conversely, if a station
happens to be directly under an aerosol plume and the satellite algorithm
filters as a cloud, the AERONET value would be higher.
Neither AERONET nor satellite monthly AOD aggregates are true monthly
AOD values. When we refer to “AOD monthly aggregate” we mean the daytime,
cloud-free AOD monthly aggregated from whatever data are available. How the
aggregate is calculated is also important; AOD distributions on monthly
scales are often closer to lognormal than normal, which suggests that the
arithmetic monthly mean may not be the most appropriate summary metric
(O'Neill et al., 2000; Sayer and Knobelspiesse, 2019). The discrepancies
between different statistics can be exacerbated when a dataset provides
poor sampling of the extreme conditions. Nevertheless, as it is the most
widely used statistic within the community and is the standard output of
current L3 products, monthly means are presented in this analysis. The
general framework could be applied to other AOD summary statistics (e.g.
monthly median or geometric mean, advocated by Sayer and Knobelspiesse, 2019)
if these L3 outputs become more widely available in the future.
In the evaluation exercise, AERONET monthly mean AOD and AE (which describes
how AOD depends on wavelength and is sometimes used as a proxy for aerosol
type) were calculated from AERONET daily means. AOD verification was
performed for all available AERONET monthly data and separately for
different aerosol types, which were defined with AOD and AE thresholds.
Although these thresholds are subjective, we consider “background aerosol”
to be cases where AOD<0.2, “fine-dominated” to be where
AOD>0.2 and AE>1, and “coarse-dominated” to be
cases where AOD>0.2 and AE<1 (e.g. Eck et al., 1999).
This classification has also been used by e.g. Sayer et al. (2018b) and
Sogacheva et al. (2018a, b). The annual and seasonal maps of prevailing
aerosol type for AERONET locations, calculated from the AERONET data
available for the period of 1995–2017, are shown in Fig. S5. Such a
classification differentiates major aerosol scenarios. The biomass burning
seasons over the Amazon and South Africa are clearly identified by a domination of
the fine aerosol particles in JJA (June, July, August) and SON (September,
October, November), and the Asian dust transport season in MAM (March,
April, May) is clearly coarse dominated. As the deviation of each satellite
product from the median has regional components (Figs. S1 and S2). Even
though we tried to choose regions with (somewhat) homogeneous aerosol
conditions during a given season, AOD conditions (and thus algorithm
performance) might vary within the regional AERONET stations, which may
represent different aerosol/surface conditions within one study regions, may
have different record lengths. To keep similar weighting for each station in
a region, we first calculated statistics for each AERONET station
separately and then calculated the regional median validation statistics
from all available stations.
To reveal how retrieval quality depends on AOD loading, offsets between
AERONET AOD and satellite product AOD were estimated for binned AERONET AOD,
and the number of observations in each AOD bin is reported. Correlation
coefficient (R, Pearson correlation), offset (satellite product-AERONET), root-mean
square error (RMSE) and fraction of points that fulfil the Global Climate
Observing System (GCOS) uncertainty goals (GE) of the larger of 0.03 or
10 % of AOD (GCOS, 2011) are also reported.
These monthly AOD verification results are used to calculate weights for
each satellite dataset in one of the merging approaches later in Sect. 4.2.
Binned offset global evaluation
As an example, AOD-binned evaluation results are shown in Fig. 2. for Terra
DT&DB and in Fig. S6 for all products. A general tendency towards
positive satellite-retrieved AOD offsets is observed for most products under
background conditions. On average, 70 %–80 % of monthly AODs fall into class
“background” (AOD≤0.2), so total AOD mean biases are expected
to have similar behaviour. TOMS and OMI have the highest positive offsets
globally, which is in line with the results from the dataset spatial
intercomparison (Sect. S2). Offsets close to 0 for background AOD are
observed for the MODIS MAIAC products.
For most products, except MODIS DT&DB, AOD offsets become negative for
AOD>0.2 (fine- and coarse-dominated aerosol types) with
increasing amplitude (up to 0.2–0.5) towards highest AOD values. MODIS
DT&DB show the lowest offsets for 0.2<AOD<1. Offsets
for VIIRS are close to 0 for AOD<0.5 and reach ca. 30 % of AOD at
AOD≈1. For the current MISR standard product, AOD is systematically
underestimated for AOD>∼0.5; this is largely due
to treatment of the surface boundary condition at high AOD (Kahn et al.,
2010) and is addressed in the research aerosol retrieval algorithm (Garay
et al., 2019; Limbacher and Kahn, 2019). Except for TOMS and Terra MAIAC,
offsets are smaller for coarse-dominated AOD.
Difference between Terra DT&DB and AERONET monthly AOD for
selected AOD bins: median bias (circles), bias standard deviation (error
bars) for all AOD types (purple), background aerosol (purple;
AOD≤0.2), fine-dominated AOD (blue) and coarse-dominated
AOD (green) The fraction (F) of points in each bin is represented by orange bars. For all
individual products see Fig. S6.
AOD products retrieved from satellites having better coverage show a better
agreement with AERONET monthly aggregates. Thus, sampling differences (swath
and pixel selection) are critical in evaluation of monthly products, as
expected but are not the only factor influencing the evaluation results.
AOD evaluation over selected regions
Due to differences in instrument specifications and retrieval approaches,
the performance of retrieval algorithms depends largely on aerosol type,
aerosol loading and surface properties at certain locations (e.g. Sayer et
al., 2014). In this section we show the evaluation results for AOD products
in two selected regions: Europe and ChinaSE (Fig. 3). Results for all
regions are shown in Fig. S7. For each region, statistics (R, % of points
in GE, offset and RMSE) for all 16 products are combined into one subplot.
The merged AOD product M is introduced in Sect. 5.2; evaluation results for
that product are summarised in Sect. 5.2.1.
Algorithm performance over Europe is similar for most products, with an R of
0.55–0.65, 45 %–55 % of the pixels in the GE, an offset of 0.05–0.1 and RMSE
of ∼0.1. For TOMS and OMI, the performance of each is slightly worse
than for other products in Europe. In ChinaSE, the offset (0.1–0.2) and RMSE
(0.2–0.3) are considerably higher than in Europe, and fewer pixels fit
within the GE (15 %–30 %). This is likely due to a combination of high AOD
loading and accompanying high uncertainty in the products, indicated by high variability
in aerosol composition and surface properties. In Indonesia and for the
biomass burning outflow over the Atlantic, the MODIS and MISR products show
a better agreement with AERONET than the ATSR-family products.
Several products which use different surface treatment (ATSR SU,
MODIS-family and MISR) show a similarly higher R over AfN, an area of high
surface reflectance. However, a high R does not imply that performance is
better, only that variations in AOD are captured better. Other statistics
(number of pixels within GE, offset and RMSE) in AfN are worse compared with
those in Europe.
Overall, no single product has the best statistics for all metrics and
regions. Retrievals tend to perform well in areas with darker (more
vegetated) surfaces and where aerosol type is less variable over time. In
these cases, biases are small and retrieval uncertainties are often better
than the GE, tracking temporal AOD variability well but with a tendency to
underestimate high-AOD events. In more complex tropical environments, data
should be used with greater caution, as there is a greater tendency to
underestimate AOD. However, correlation often remains high, suggesting a good
ability to identify monthly AOD variations, despite this underestimation.
AERONET evaluation statistics for Europe and ChinaSE: correlation
coefficient R, bar, and fraction of pixels satisfying the GCOS requirements, GE,
⊕; offset (satellite product-AERONET), Δ, and root-mean-square
error RMSE, *. Shown for AOD monthly aggregates for each product (1:16; legend for
products below the plot) and the L3 merged product (M; approach 2 with RM2 for all aerosol types; for details see Sect. 4.2) with corresponding colours
(legend) for the selected regions (as in Fig. 1). N is the number of matches
with AERONET. Note, for products that do not provide the global coverage
(e.g. no retrieval over oceans), the results are missing. For all studied
regions, see Fig. S7.
AOD time series
In order to move towards consistency in regional and global AOD records
derived from multiple satellites using different sensors and retrieval
techniques, this section examines annual regional AOD time series obtained
from the different products.
Besides the positive offset for TOMS and OMI (Figs. S1, S2, S6 and S7),
consistent temporal patterns are observed, and similar interannual AOD
variability is tracked by all datasets (Figs. 4 and S8). AOD peaks in
Europe in 2002, in ChinaSE in 2006/2007, 2011 and 2014, (possibly related to
changes in anthropogenic emissions; Sogacheva et al., 2018a, b).
Relative AOD peaks over the Atlantic dust area in 1998, 2012 and 2015
(Peyridieu et al., 2013), and obvious AOD peaks in Indonesia related to the
intensive forest fires in 1997, 2002, 2006 and 2015 (Chang et al., 2015; Shi
et al., 2019) are clearly seen.
Annual AOD time series from different products (see legend) for
Europe and ChinaSE. For all selected regions see Fig. S8.
However, significant regional offsets between products exist, which are
largest in regions with high aerosol loading. Over ChinaSE, MODIS-family
products show higher monthly AOD compared to all others. Over AfN, ATSR SU and ATSR_ens reach higher monthly
aggregated AOD than the MODIS-family products, whereas comparisons with
AERONET are similar for ATSR and MODIS (with slightly higher RMSE for ATSR
by 0.05); differences are likely tied to the small number of stations in
this region. A large offset between MODIS and ATSR is revealed over
Australia (Fig. S8).
AOD annual cycles for individual products for the year 2008 are discussed in
Sect. S8. As in the annual time series (Figs. 4 and S8), the annual AOD cycles
are similar between the products (Fig. S9), with more pronounced deviations
in areas of high AOD.
AOD merging approaches
Here, 12 AOD products (all available at 0.55 µm) were used to create
a merged AOD product for the period of 1995–2017. The temporal availability
of the AOD products is shown in Table 2 (counting cases of partial coverage
of a dataset during a year as available).
Availability and coverage of the AOD products for merging for each
year in the period 1995–2017. N: annual number of available products.
We tested two broad approaches for merging, summarised in Fig. 5. In the
first, the median AODs from the available (10 globally and two over land)
individual uncorrected and offset-adjusted (shifted to a common value)
products were calculated (approach 1, Sect. 4.1 for details). In the second
approach, AOD-weighted means were created where the weights for individual
products were derived from the evaluation with the AERONET through two
different ranking methods (see approach 2 in Sect. 4.2 for details). The same
merging scheme was applied to the L3 uncorrected products (Sect. 2.2) and
regional time series (Sect. 3.1) yielding 10 merged AOD products and 10
merged regional time series.
Scheme for the merging approaches; applied for L3 products or
regional time series.
To achieve best estimates of the regional AOD by merging multi-sensor
monthly AOD data, the systematic and random components of uncertainties
within each product should be considered explicitly. However, this cannot
yet be done; only some of the L2 products used to create the L3 monthly
products contain pixel-level propagated or estimated uncertainties, and
their associated propagations to L3 products (together with other
contributions from e.g. sampling limitations) have not yet been quantified
robustly. The analysis herein therefore represents an initial effort in the
absence of a full uncertainty budget. Uncertainties for the chosen merged L3
product (details are discussed in Sect. 5.2.2) were estimated as the root-mean-squared sum of the deviations between the chosen merged product and
either the median from the all uncorrected products (approach 1) or each of
the other seven merged products (approach 2).
Approach 1: AOD median for uncorrected and offset-adjusted (shifted) AOD
products
The mean (arithmetic average) value, although commonly used in climate
studies, is not generally equal to the most frequently occurring value (the
mode) and may not reflect the central tendency (the median) of strongly
asymmetrical distributions such as those that can be found for AOD (O'Neill et al.,
2000; Sayer and Knobelspiesse, 2019). Although the central limit theorem
implies that this should be less of an effect when making an estimate of the
mean AOD from a cluster of AOD datasets (i.e. a merged time series), in
practice this is unlikely to be fully the case because the different datasets are not independent estimates of the underlying AOD field. This is
because they are made with sensors and techniques which are not independent
(i.e. typically similar spectral/spatial bands and sampling limitations)
and may have different bias characteristics. Further, by itself, the mean
does not provide any information about how the observations are scattered,
whether they are tightly grouped or broadly spread out. Thus, we study the
median (which is more robust in the presence outliers which might be caused by a
poorly performing algorithm in a certain region) and standard deviations (as
a metric of diversity) between the products chosen for merging.
As shown in Sect. 3, the AOD time series of different products display
highly consistent temporal patterns, albeit with spatiotemporally varying
offsets (Figs. 4, S8 and S9). We use the Terra DT&DB product as a
reference to estimate the average offsets between products, because its time
period overlaps with each AOD product considered in the current study.
Means and standard deviations of the offsets for all individual products
from the Terra DT&DB AOD are shown in Fig. 6 for Europe and ChinaSE and
in Fig. S10 for all selected regions. Offset magnitudes and their variations
depend on AOD loading; offsets are typically higher for high AOD. Over land,
ocean and thus globally, the offset is negative relative to Terra DT&BD
for most of the products. This includes Europe and ChinaSE. However, over
the bright surface area in northern Africa, AVHRR DT/SOAR, VIIRS, ATSR SU
and ATSR ensemble show high (0.05–0.1) positive bias. Also, all ATSR
products are biased high in Australia and South America. Thus, the median
for the offset-adjusted product is expected to be positive biased. For
details, see Sect. 5.1, where evaluation results for the AOD products merged
with different approaches are discussed.
Regional annual average AOD offset between each dataset and the
Terra DT&DB dataset. GCOS requirement of ±0.03 is shown as a
background colour. For all selected regions, see Fig. S10.
With the shifted median merging approach, each AOD product was shifted on a
regional basis, based on its regional offset with respect to Terra DT&DB
(Sect. 5.2). The median and standard deviation of AOD time series were then
derived from these 10 shifted and Terra DT&DB data records.
Approach 2: weighted AODMethod
As shown in Sect. 3.1, the products differ in the degree to which each
represents the AERONET values on the monthly scale. Our second approach is a
weighted mean AOD, where the weights are assigned based on the agreement of each dataset with monthly AERONET averages. This represents an initial attempt
to adjust the level of confidence assigned to each product on a regional
basis; better-comparing products are given more weight in the calculation of
a combined product.
An AOD-weighted mean was calculated, with a ranking approach based on the
statistics from the AERONET comparison for AOD: R, bias, RMSE, GE (Figs. 4
and S8) and median bias of the binned AOD in the range [0.45, 1] (Figs. 3 and
S7). The last criterion was added to specifically consider algorithm
performance for higher AOD.
Two ranking methods were tested. For the first ranking method (RM1) based on
best statistics, the 12 products were ranked from 1 (worst) to 12 (best) for
each statistic (R, GE, RMSE, bias and binned bias) separately. The five separate
ranks were then summed, so the maximum possible rank is 12⋅5=60. A
downside of this method is that when several products have similar
statistics small variations in statistics can produce large spread in
ranking. Note that no product received a perfect (60) rating.
To overcome this potential downside, the second ranking method (RM2)
considers statistics falling into binned ranges (rather than the absolute
evaluation statistics). For each statistic, the following windows, [0.5, 1]
for R, [0, 0.5] for GE, [0, 0.2] for bias, [0, 0.15] for RMSE and [-0.5, 0]
for the binned bias, were divided into 10 bins, and a rank (from 1 to 10)
was assigned depending on which bin a particular statistic falls for a
particular product. As a result, several algorithms can be ranked equally
for certain statistics if their statistics fell within the same bin. For
example, if R for three products is between 0.8 and 0.85, all three receive
a rank score of 8 for that statistic.
The sum of the five ranks (R, GE, RMSE, bias and binned bias), w, for
each product i was calculated and transformed to a weight of each product (as
a fraction of total sum for the product from the total sum of ranking for
all products) to calculate the AOD-weighted mean, AOD‾, as follows:
AOD‾=∑i=1n(wi⋅AODi)∑i=1n(wi).
As shown in Sect. 3.1, the performance of the retrieval algorithms often
depends on the aerosol conditions (aerosol type and loading; Fig. 2) and
surface properties. Accordingly, weights for the different AOD products were
calculated separately for each region for different aerosol types
(background, fine-dominated or coarse-dominated) separately and “all”
aerosol types together considering the corresponding regional statistics
from the AERONET comparison. However, aerosol types often change in time and
space within the same region (Fig. S5). Thus, those weights for each aerosol
type were applied globally to merge both L3 monthly products and time
series. As a result, eight merged AOD products were obtained, which include the following: the product of two ranking approaches (RM1 and RM2) and four sets of statistics (all points
and the background and the fine- and coarse-dominated subsets).
Ranking results (weights) for individual products
The weighting of the contribution of each product to the merged data product is
shown in Fig. 7 (Europe and ChinaSE) and Fig. S11 (all selected regions) for
three aerosol types (background, fine-dominated and coarse-dominated) and
all aerosol types together (all). With some exceptions (e.g. in AOb,
where the RM2 weight of Aqua DT&DB is ca. 15 % higher for
coarse-dominated type, and in Australia, where the RM2 weight of SeaWiFS and
Aqua MAIAC is 10 %–15 % higher for coarse-dominated type; Fig. S11), the
difference in weights obtained with RM1 and RM2, if they exist, does not
exceed 5 %–10 %. Thus, the ranking methods RM1 and RM2 introduced in the
current study produce similar results. Some products show a better performance
for certain aerosol types (Figs. 4 and S4). Thus, the weight of the product
depends on which aerosol type is favoured for merging. For example, in
Europe VIIRS has lower weight for fine-dominated aerosols, whereas the
corresponding weight for ATSR SU is higher for that aerosol type. In
ChinaSE, Terra DT&DB performs worse than Terra MAIAC for background
aerosols, so for that aerosol type the weight for Terra MAIAC is higher.
As with the results discussed in Sect. 3, none of the algorithms
consistently outperforms the others in all regions. There is no clear leader
over Europe, a region with low AOD, indicating a similar performance of all
algorithms under background conditions. Over land globally, also a region
with low AOD, the ranks are similar for EOS (electro-optical-system) sensors and ATSR, with somewhat
higher number for VIIRS. Over ocean globally, the ranks are similar for all
existing products. One likely reason that the VIIRS and MODIS ranks are
often higher is their better coverage, which enables them to better
represent AERONET monthly means over land as they sample the variations more
fully. However, MODIS is ranked lower over the Atlantic dust region. The
lowest ranks are obtained consistently for TOMS, OMI and POLDER, due to
their high biases.
Ranks for the different aerosol classes (all, background, fine-dominated and
coarse-dominated) are different, which raises another aspect of using
multiple products. Over land, MODIS MAIAC often has a higher rank for
background AOD, whereas MODIS DT&DB is better for other aerosol types.
(a) Weights of each product obtained with RM1 and RM2 for Europe and (b) ChinaSE for different aerosol types (all, background, fine-dominated
and coarse-dominated). For all regions, see Fig. S11.
Merged L3 AOD products
As a recap, 10 merged products are created, which include the following: shifted and unshifted medians
from approach 1 and eight (two ranking methods times four aerosol type
classes) from approach 2. In this section these products are evaluated
against AERONET.
Evaluation of the all merged L3 AOD products with AERONET
Evaluation results (using the same method as in Sect. 3.1) reveal
similarities in the accuracy of products merged with different approaches.
The AOD binned bias of the merged products (Fig. S12) shows a similarly
small deviation from AERONET (±0.03) for AOD<0.5 (positive
for AOD<0.3 and negative for 0.3<AOD<0.5). The
offset is slightly higher for the median of the shifted AOD product
(approach 1), because as discussed earlier, Terra DT&DB has a positive
bias relative to most of the other individual products; this results in
slightly elevated AOD compared to the others. For AOD>0.5, where
the number of cases is very low, the underestimation increases as AOD
increases. As for individual products, the coarse-dominated merged products
have the smallest offset with AERONET.
Correlation coefficient, number of the pixels in the GE, offset and RMSE for
the AOD merged product are shown in Fig. 8 for Europe and ChinaSE and in
Fig. S13 for all regions. The merged products have the best temporal
coverage and the number of points used for validation (N) is higher than for
any individual product. The correlation coefficients and the number of the
pixels matching within the GE are as high as for the one or two best ranked
products in the corresponding regions, except for the product merged with
approach 2. The offset is close to the average offset, and the RMSE tends to
be lowest. Thus, the quality of the merged products, except for the shifted
AOD product, is as good as that of the most highly ranked individual AOD
products in each region.
AERONET comparison statistics: correlation coefficient R, bar, and
fraction of pixels satisfying the GCOS requirements, GE, ⊕; offset, Δ, and root-mean-square error RMSE, *. Shown for AOD products merged
with different approaches, median, shifted median, RM1 and RM2 for different
aerosol types for Europe and ChinaSE. For all regions, see Fig. S13.
Final merged product evaluation and intercomparison with individual
products
The agreement of the RM1 and RM2 approaches is encouraging, as we can
conclude from the big-picture analysis (Sect. 5.1) that the details of the
methodology do not matter much. As there is no significant difference in the
evaluation results for products merged with approaches 1 and 2, we choose
the RM2 approach for all aerosol types as the main merged product. We
use this for further intercomparison with individual products to reveal the
regional and seasonal differences between the products. If not specifically
stated, the merged product mentioned below is the one obtained with RM2 for
all aerosol types (RM2 for all).
Summarised evaluation results
The difference between the L3 merged product and the median of all
individual products used for merging (Table 2) was calculated for the year 2008
(Fig. 9a, as Fig. S1 for individual products). The difference is within GCOS
requirements over both land and ocean (0.009 and 0.007, respectively) and
globally (0.008). High latitudes contribute most to the positive bias over
oceans, whereas a positive bias is observed over land mostly over bright
surfaces.
The evaluation statistics for the L3 merged product against AERONET
extracted from Figs. S12 and S13 are combined in Fig. 9b, c, d for all 15
regions, as well as for land, ocean and globally. For most regions, R is
between 0.75 and 0.85, 20 %–60 % fall
within the GE, and the RMSE and offset are between 0.05 and 0.1, though
somewhat higher for the regions with potentially high AOD loading (Indonesia, AOd, AsW and AsE). Statistics for the merged product
(M) are also shown in Figs. 3 and S7 for comparisons with individual
products.
(a) L3 merged (approach 2 with RM2 for all) AOD product deviation from the annual median AOD calculated from individual products used for merging
(Table 2) for the year 2008 (as Fig. S1 for individual products), (b) L3 monthly
merged AOD product evaluation with AERONET: binned AOD bias for all
(purple; background (AOD<0.2; purple), fine-dominated
(blue) and coarse-dominated (green) aerosol types. (c,d) Regional
statistics (c: correlation coefficient R, bar, and fraction of pixels that
fulfil the GCOS requirements, GE, circle; d: offset, Δ; RMSE, *).
Uncertainties
Uncertainties (unc, meaning 1-σ of the uncertainty distribution) for
the merged L3 products (monthly, seasonal and annual) were estimated as the
root-mean-squared sum of the deviations between the chosen merged product M
(RM2 for all), the median from the all uncorrected products (approach 1) and
each of the other seven merged products (approach 2, with RM1 for all aerosol types and RM1
and RM2 each applied for background, fine-dominated and coarse-dominated particles).
unc=1N∑1N(mi-M)2,
where mi is AOD from alternative merged product i, M is AOD from
the chosen merged product (RM2 for all), and N is the number of the
alternative merged products. Note that this is a structural uncertainty
(i.e. a sensitivity to diversity and decisions in dataset merging) rather
than a total uncertainty for the merged product. Seasonal and annual
uncertainties for the year 2008 are shown in Fig. 10. These uncertainties show
artefacts at regional boundaries because the merging was done according to
regional statistics.
Seasonal and annual structural uncertainties between the L3
merged product (M; approach 2 with RM2 for all) and other L3 merged products
calculated with the approaches 1 and 2 for the year 2008.
The estimated annual and seasonal structural uncertainties are low,
0.005–0.006 globally. They show seasonal dependence, reaching 0.008 and
0.009 on average over land in MAM and JJA, respectively. The uncertainties
are larger (0.01–0.03, on average, up to 0.05) in regions with high AOD
(e.g. ChinaSE, India in JJA, AfN in MAM and JJA, AfS in JJA and SON). This
means that the uncertainties introduced through the choice of merging strategy
often fulfil the requirements calculated by Chylek et al. (2003) for an AOD
uncertainty of 0.015 over land and 0.010 over ocean, in order to estimate
the direct aerosol radiative effect to within 0.5 Wm-2. The fact that
this merging uncertainty estimate is smaller than the previously discussed
GCOS goal uncertainties implies that reasonable merging method decisions may
be of secondary importance in terms of meeting those goals. It is cautioned,
though, that since many of the algorithms are susceptible to the same error
sources and subject to similar sampling limitations, the uncertainty
estimates calculated here are likely to be a lower bound on the true
uncertainty in the merged datasets. And it should be remembered that these
uncertainties cover only the aspect of choosing the merging method but not
the entirety of the uncertainties in the merged datasets versus AERONET.
Spatial and temporal intercomparison with other products
The deviation between individual products and the merged product for the year 2008 is shown in Fig. 11. Among the products used for merging, POLDER has
largest positive offset (0.026), and SeaWiFS has the highest largest negative
offset (-0.026) on global average. Over land, POLDER has the highest
positive offset (0.031); the offsets for ATSR SU and Terra DT&DB are also
high (0.024 and 0.023, respectively). The highest negative offsets relative to
the merged product are for MAIAC (-0.046 and -0.041 for Terra and Aqua,
respectively). Over ocean, POLDER, Terra DT&DB and ATSR ADV are offset
high by 0.022–0.024, whereas ATSR SU and SeaWiFS are offset low (-0.030 and
-0.027, respectively) compared to the merged AOD product. Most of the
observed global, land and ocean AOD offsets (except for Aqua MAIAC over
land) are within the GCOS requirement of ±0.03. VIIRS agrees best
with the merged product globally (0.003) and over ocean (-0.003); AVHRR
DT/SOAR and AQUA DT&DB agree best with the merged product over land,
showing opposite-in-sign offsets of -0.011 and 0.009, respectively. Regional
biases between the individual products and the merged product are similar to
regional biases shown in Fig. 2, where the individual products were compared
with median AOD calculated from all individual products available at 0.55 µm.
Regional annual offsets between individual AOD products and the merged AOD
product are shown in Fig. S14 (cf. with those for the median AOD product in
Figs. 6 and S10). For AsE, which includes ChinaSE and AfN, the AOD offset
is higher than 0.03 (GCOS requirement in low-AOD conditions) for some
products. However, those areas are characterised by high AOD loading
(annual AOD is between 0.4 and 0.8) that is related to e.g. anthropogenic
pollution and/or dust events. If the GCOS requirement of 10 % of AOD is
also applied here, then most of the offsets are within the GCOS
requirements. The highest regional offsets relative to the merged AOD
dataset are associated with products which provide AOD at wavelengths other than 0.55 µm – TOMS (0.50 µm), OMI (0.50 µm) and EPIC
(0.44 µm) – and thus are not used for merging.
In some regions, AOD offsets between individual products and the merged
product show seasonal behaviour (Fig. S15). In ChinaSE, the negative offsets
for AVHRR NOAA, SeaWiFS and VIIRS are most pronounced in JJA. In AsW, the
ATSR ADV positive offset is higher for that season. In AfN, most products
have their largest negative offsets in JJA, whereas ATSR SU and ATSR_ens (which includes the ATSR SU product) have their highest positive
biases. In SA, offsets are lower in JJA for all products. In AOb offsets are
lower in MAM, and in AOd offsets are lower in SON for all products.
AOD deviation of the individual products relative to the merged
AOD product for the year 2008. Global, land and ocean AOD mean differences are
shown for each product, when available.
Mean offset and standard deviation (in parentheses) between time
series obtained with different approaches for three time periods, determined
based on products availability.
1995–19992000–20112012–2017Time series from merged L3 to median time series0.009 (0.009)0.007 (0.005)0.011 (0.006)Merged time series to median time series0.011 (0.010)0.004 (0.002)0.009 (0.006)Time series from merged L3 to merged time series0.010 (0.014)0.004 (0.004)0.004 (0.004)Merged AOD time series
As the L3 AOD merged products (Sect. 5), the AOD time series from the
individual products (Figs. 4 and S8) were merged, using approach 1 (median
for uncorrected AOD) and approach 2 (RM1 and RM2 for different aerosol
types). The shifted AOD median (approach 1 for shifted products) has clear
limitations when the product chosen as a reference (Terra DT&DB, in our
case) deviates considerably from other products over most of the regions
(except for Aus, AfN and SA; Fig. S8). Thus, the median for shifted products
is not discussed here. However, the median-shifted AOD approach allows an
extension of the time series back to 1978–1994, where only the TOMS AOD
(over land) and AVHRR NOAA (over ocean) long-term products currently exist
and the merging approaches introduced in the current study are not
applicable.
Annual AOD time series merged with two different approaches (red and
light blue for approaches 1 and 2, respectively) and AOD time series from
the L3 merged data (approach 2; olive) for the selected regions. In each, ±1σ of the AOD from all uncorrected AOD products is shown as light
blue shadow (often small, thus not visible). TOMS over land and AVHRR NOAA
over ocean products shifted to the merged time series are also shown with dashed grey
and purple lines, respectively, when available.
(a) Seasonal and (b) monthly AOD median time series (red), merged
time series (blue) and time series from the merged L3 product (olive) for
Europe and ChinaSE. AOD±1σ for the merged time series and
for the time series from the merged L3 products are shown as light blue and
light olive shadows, respectively. Note the different scale. For all
selected regions, see Figs. S16 and S17.
The two merging approaches (approach 1 for uncorrected products and approach 2 for weighted AOD) tested here agree well (Fig. 12). The offsets between
time series calculated with different approaches are again low
(0.004–0.011). Spatial consistency is indicated by high correlation (similar
positions of peaks) in AfN and its Atlantic dust outflow region. Interannual
variation as well as the standard deviations are highest for regions with
the largest AOD, e.g. over ChinaSE (anthropogenic emissions) and Indonesia
(biomass burning). The time series of ChinaSE follows the known patterns
caused by stepwise regional emission reductions in the last 25 years
(Sogacheva et al., 2018b). AOD time series merged with different approaches
show a good agreement for all timescales: annual (Fig. 12), seasonal and
monthly (Fig. 13a and b, respectively, for Europe and China and Figs. S16
and S17 for all studied regions).
The offsets between the merged time series and time series calculated from
the merged L3 product have a regional component and, as, discussed above,
depend on the availability of the products (Table 2). The offsets between
the time series merged with different approaches (Table 3) are slightly
higher for all regions for the periods 1995–1999 and 2012–2017, when fewer
products are available for merging (Table 2). The deviation up to 0.05
(AODapproach1>AODapproach2) is observed over
Indonesia and North America before 2002, when both MODIS satellites become
operational. For other regions, the deviation is considerably lower (below
0.03). By adding MISR and both MODIS products in 2000/2002, the offset
between the time series is reduced. ATSR products are not available starting
in 2012, when the VIIRS product became available. In 1995–1999, the mean
offset is similar for all three time series. The offsets are higher for
regions with high AOD loading (e.g. Asia and northern Africa, Fig. S18). In
2000–2011 and 2012–2017, the offset is lowest (0.004) between the merged and
the median time series, as well as between the merged time series and the
time series calculated from the merged L3 product. The agreement in the time
series obtained with different approaches supports the conclusion made based
on the evaluation results that, for the big-picture analysis of overall
trends, details of the methodology do not matter very much.
Annual, seasonal and monthly time series from the merged L3 monthly AOD show
slightly higher deviation of both signs compared to the merged time series
discussed above. Interestingly, seasonality is observed in the deviation. In
AfN, the AOD from the monthly merged L3 is higher in autumn for the period
of 1995–1999. In Bor and AsN (Figs. S16 and S17), the deviation is higher in
spring for the period of 1997–1999. A possible explanation might be the
sparser coverage in those areas (due to restrictions in retrieval algorithms
to retrieve bright surfaces, e.g. desert or snow). Regional offsets between
the annual, seasonal and monthly AOD merged time series and the time series
from the merged L3 monthly product are summarised for three timescales in
Fig. S19. The offset is lower for annual data and generally increases with
the time resolution. As the previous analysis showed, the offset is bigger
in high-AOD regions (e.g. Asia, AfN and SA).
Overall, good agreement exists between the time series calculated using
different merging approaches and different orders of the processing steps.
There is a general consistency, and similar temporal patterns are observed
between the time series merged with two approaches and the time series from
merged L3 AOD product, despite small differences, which are more pronounced
at the beginning of the period, when less products are available. With only
few exceptions, the offsets between the AOD time series calculated with
different approaches are within the GCOS requirement of ±0.03 or
10 % of AOD.
A separate study is planned where regional and global trends in this merged
AOD L3 product will be analysed.
Instrument, archive, URL and DOI (last access: 17 February 2020, for all), name and creator of the products used in the current study (if available).
InstrumentArchiveName of the product, link, creatorMerged AODSodankyläFMI_SAT_AOD-MERGED:(product introduced in the current paper)NSDCURLhttp://nsdc.fmi.fi/data/data_aodTOMSNASAURLL2 daily:GES DISChttps://disc.gsfc.nasa.gov/datasets?page=1&subject=Aerosols&measurement=Aerosol%20Optical%20Depth%2FThicknessL3 monthly AOD data available by requestOMINASAURLL2 daily: https://aura.gesdisc.eosdis.nasa.gov/data/Aura_OMI_Level2/OMAERUV.003/GES DISC(Torres, 2006)L3 monthly AOD data available by requestAVHRRNOAAURLAOT_AVHRR_v003r00_monthly_avg: https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00977DOI10.7289/V5BZ642P(Zhao and NOAA CDR Program, 2017)AVHRRNASAURLDBAER_avhrrnoaa11_monthly_1deg:NCCShttps://portal.nccs.nasa.gov/datashare/AVHRRDeepBlueSeaWiFSNASAURLDeepBlue-SeaWiFS-1.0_L3M:GES DISChttps://measures.gesdisc.eosdis.nasa.gov/data/DeepBlueSeaWiFS_Level3/SWDB_L3M10.004/DOI10.5067/MEASURES/SWDB/DATA304(Hsu et al., 2013b)VIIRSNASAURLAERDB_M3_VIIRS_SNPP: https://search.earthdata.nasa.gov/search/granules?p=C1561219905-LAADS&m=36.67372044211211!10.125!0!1!0!0%2C2&ac=true&tl=1566140492!4!!&fs10=Aerosol%20Optical%20Depth/Thickness&fsm0=Aerosols&fst0=AtmosphereLAADSDOI10.5067/VIIRS/AERDB_M3_VIIRS_SNPP.001(via EarthData)(Sayer et al., 2018a)ATSRADV ICAREURLESACCI_L3C_ATSR_ENVISAT_ADV:http://www.icare.univ-lille1.fr/archive(Kolmonen et al., 2016)ATSR SUICAREURLESACCI_L3C_ATSR_ENVISAT_SU:http://www.icare.univ-lille1.fr/archiveATSR ensembleICAREURLESACCI_L3C_ATSR_ENVISAT_ENS:http://www.icare.univ-lille1.fr/archiveMODIS DT&DB*NASA LAADSURLTerra: MOD08_M3: https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD08_M3Aqua: MYD08_M3: https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MYD08_M3DOITerra:10.5067/MODIS/MOD08_M3.006Aqua:10.5067/MODIS/MYD08_M3.006(Platnick et al., 2015)
Continued.
InstrumentArchiveName of the product, link, creatorMODIS MAIACLP DAASURLL2 daily:https://lpdaac.usgs.gov/data_access/data_poolL3 monthly AOD data available by requestMISRURLMISR_AM1_CGAS:http://eosweb.larc.nasa.gov/project/misr/misr_tableDOI10.5067/Terra/MISR/MIL3MAE_L3.004POLDERURLhttps://www.grasp-open.comEPICURLL3 monthly AOD data available by requestAERONETURLhttps://aeronet.gsfc.nasa.gov/(Holben et al., 1998)
This study has analysed the consistency of regional time records of monthly
AOD from 16 different satellite products. These were obtained from a wide
range of different instruments – TOMS, AVHRR, SeaWiFS, ATSR-2, AATSR,
MODIS, MISR, POLDER, VIIRS and EPIC – with largely varying information
content and sampling and with different algorithms based on different
remote sensing approaches, quality filtering, cloud masking and averaging.
Differences between those 16 data records in a set of regions with different
characteristics across the globe were demonstrated and verified against a
ground-based AERONET monthly mean dataset in order to answer the question
how well a satellite dataset can reproduce monthly gridded mean AERONET
values in a region.
AOD time series (monthly, seasonal and annual) from the products show a good
consistency of temporal patterns but significant regional biases due to all
those differences. In many cases the more pronounced differences were
between different algorithms applied to the same sensor, rather than between
similar algorithms applied to different sensors. This is encouraging in that
it implies that algorithmic uncertainties (either retrieval assumptions or
pixel selection criteria) can be similar to or larger than sensor ones
(e.g. calibration quality and sampling limitations), and as such, refining
individual algorithms can still make meaningful steps towards providing
better L3 products.
To build an AOD product merged from 12 individual satellite products, two
different approaches were introduced and tested. In approach 1, a simple
median of the 12 uncorrected and shifted to Terra DT&DR product time
records was conducted. In approach 2, the AOD evaluation results (for
different aerosol types) against AERONET were used to infer a ranking which
was then used to calculate a weighted AOD mean. Two different ranking
methods, RM1, simple ranking based on better statistics, and RM2, ranking
based on binned statistics, were tested in approach 2. In addition, the
order of the processing steps in approach 2 was interchanged (L3 dataset
merging or regional merging) to test the stability of the results.
Ten merged L3 AOD monthly products were created and evaluated with AERONET.
The evaluation shows that the quality of the merged products (except for one
created with the approach 1 for shifted AOD) is as good as that of the most
highly ranked individual AOD products in each region. One of the merged
products (approach 2 with RM2 for all) was chosen as a final merged product
(http://nsdc.fmi.fi/data/data_aod, last access: 20 January 2020),
based on slightly better evaluation results. Structural uncertainties for
the final merged product were estimated.
All merged regional AOD time series show a very high consistency of temporal
patterns and between regions, and the time records with their uncertainties
(standard deviations shaded around the median values) clearly illustrate the
evolution of regional AOD. With few exceptions all merging methods lead to
very similar results, which is reassuring for the usefulness and stability
of the merged products.
There are of course caveats to these rather simple and straightforward
merging approaches, which do not consider in much detail the differences in
sampling and sensitivity to different conditions (e.g. surface brightness or
number of independent observables) of the different instruments and
algorithms. It is well known that monthly, seasonal or annual gridded mean
values can carry large uncertainties, whether inferred from a few
ground-based stations meant to represent a full grid cell or from satellite
images containing large gaps due to limited swath, clouds or failed
retrievals. Pixel-level uncertainties are becoming available for a growing
number of satellite products, and it would be highly beneficial if these
estimated errors could be propagated consistently to those gridded monthly
products. However, this requires deeper insight and new methods to take into
account correlation patterns among parts of the uncertainties and to
estimate practically the sampling-based uncertainties in light of
approximated AOD variability. Altogether, as frequently requested from a
user point of view, the stability and consistency of regional, merged AOD
time series should be seen as strengthening our confidence in the
reliability of satellite-based data records. Recent, ongoing and future
work to improve the Level 3 uncertainty budget of the satellite products –
as well as assessment of spatio-temporal uncertainties in time-aggregated
AERONET data – will benefit the creation and assessment of merged time
series. The corresponding time series can be used in regional and global AOD
trend analyses and for comparison with (climate and reanalysis) model AOD
fields. Aside from the merged dataset itself, some key main outcomes of
this research have been a quantification of the diversity between monthly
satellite AOD products and their comparability with monthly averages from
AERONET and the sensitivity of the merged time series to some sensible
decisions which must be made in creating it. Merged AOD product will be extended as satellite missions continue and new data versions are released.
Data availability
URL and DOI (if available) of the products used in the current study, as
well as of the merged AOD product (FMI_SAT_AOD-MERGED), are summarised in Table 4.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-2031-2020-supplement.
Author contributions
The exercise on AOD merging has been initiated and widely discussed by the
AeroCom/AeroSat community. The work has been performed by LS, who
collected data, developed the methodology, performed the analysis and wrote the extended draft of the
paper. The evaluation results were widely discussed with the AOD data
providers, who coauthor the paper. TP, AMS and RAK also considerably contributed to writing. All authors contributed to
reviewing the drafts.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank attendees of AeroCom/AeroSat workshops over the past
several years for lively and informative discussions, which helped provide
the impetus for and shape this analysis. AeroCom and AeroSat are unfunded
community networks which participants contribute to within the remit and
constraints of their other aerosol research.
Financial support
The work presented is partly supported by the Copernicus Climate Change
Service (contracts C3S_312a_lot5 and
C3S_312b_Lot2) which is funded by the
European Union, with support from ESA as part of the Climate Change
Initiative (CCI) project Aerosol_cci (ESA-ESRIN projects
AO/1-6207/09/I-LG and ESRIN/400010987 4/14/1-NB) and the AirQast 776361
H2020-EO-2017 project.
Review statement
This paper was edited by Stelios Kazadzis and reviewed by three anonymous referees.
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