Introduction
Long-time series of UV–visible (UV–vis) satellite measurements are a great
asset for monitoring the distribution and evolution of pollutants such as
NO2, HCHO, or SO2 and aerosol particles in the
troposphere. With the forthcoming new generation of sensors like TROpospheric
Ozone Monitoring Instrument (TROPOMI) on board Sentinel-5-Precursor
, Sentinel-4-UVN and Sentinel-5-UVNS within the Copernicus
programme , they will become an important tool for verifying
the effectiveness of implemented technology to protect the environment and
population against air pollution . While the last generation
of space instruments have had a pixel size of 13 × 24 km2
for the Ozone Monitoring Instrument (OMI) or 80 × 40 km2 for
the Global Ozone Monitoring Experiment (GOME-2), the new generation has
smaller pixel sizes (about 7 × 3.5 km2 for TROPOMI), allowing
air quality mapping of complex urban and city areas. This is also expected to
reduce the probability of cloud contamination. However, the significant
probability of aerosol contamination in areas such as India, China or regions
dominated by biomass-burning episodes will likely remain or may even
increase.
OMI is the Dutch–Finnish push-broom spectrometer flying on the National
Aeronautics and Space Administration (NASA)'s Earth Observation
Satellite (EOS) Aura platform since 15 July 2004. Its Sun-synchronous
orbit has a local equator crossing time of approximately
13:40 LT. The operational tropospheric NO2 product derived from the
visible backscattered spectral light (405–465 nm), such as the OMI
DOMINO-v2 or the very recent Quality Assurance for
Essential Climate Variables (QA4ECV), is nowadays used as a reference. The related
global mapping of tropospheric NO2 concentrations has been used by
many air quality research studies focusing on NOx
emissions and secondary pollutant formation, as well as tropospheric
NOx chemistry and transport, e.g.
, and .
A critical element for an accurate tropospheric NO2 vertical column
density (VCD) retrieval is our capability to reproduce the average light
path along which the photons travelled before being detected by the satellite
sensor at the top of the atmosphere (TOA) in the visible spectral window. In
particular, scattering and absorption induced by atmospheric aerosol
particles over cloud-free scenes are known to lead to very complex light
paths. Because they are emitted by the same sources, high NO2 and
aerosol concentrations are often spatially correlated .
Therefore, aerosol contamination needs to be properly addressed in the
retrieval algorithms. In the frame of tropospheric NO2 retrievals
from visible spectral measurements based the differential optical absorption
spectroscopy (DOAS) approach, the aerosol correction has to be applied to the
air mass factor (AMF): a unitless number representative of the length of the
average light path.
Over cloud-free scenes, a full explicit aerosol correction ideally requires a
comprehensive set of parameters describing aerosols: the single-scattering albedo ω0, scattering phase function, load through the
aerosol optical thickness (AOT) τ, size and vertical distribution
. Among all these variables, many
studies emphasized the importance of the aerosol layer height (ALH) knowledge
. Assuming no aerosol correction, i.e.
an aerosol-free scene , would clearly create large biases in
the OMI tropospheric NO2 retrievals .
There are basically two strategies for achieving the aerosol correction in
the AMF:
either by considering external data, or
by using the available
particle parameters that can be simultaneously derived from the same
UV–visible spectral space-borne measurement.
Studies that reprocessed DOMINO-v2 data set using external data usually relied
either on atmospheric transport model outputs, e.g. GEOS-Chem in the Peking
University OMI NO2 (POMINO) or observations
issued from different satellite platforms, e.g. the Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP) , or even both combined
. Resulting changes mostly occurred in cases of high
aerosol pollution (τ(550nm)>0.8) with increased or decreased
tropospheric NO2 VCDs depending on the geophysical conditions and
aerosol properties and distributions. However, the resulting AMF computation
becomes dependent on these data sources, their quality and the possibility
(or not) to combine them. In general, spatial and temporal
co-registration between the different instruments or due to different
resolutions between the observation pixel and the model grid cell may become
an issue. In the frame of an operational processing, it is generally
preferred to maximize the exploitation of the spectral measurement acquired
by a same instrument representative of the considered observation pixel. One
of the main reasons is the need to have an indication of particle height
representative of the average light path associated with every single OMI
field of view (FOV). Such information is generally not easily and directly
available from an external source. Exploitation of the 477 nm O2-O2
absorption for aerosol retrieval is very promising. It is not only measured
by OMI, but also by GOME-2, TROPOMI, Sentinel-4-UVN and Sentinel-5-UVNS.
Several studies based on ground-based and satellite instruments have
demonstrated its relatively high sensitivity to aerosols, in particular to ALH
.
Because of the difficulty of easily distinguishing clouds from aerosols and
identifying the right aerosol model to use, it has always been preferred to
retrieve effective clouds assuming a Lambertian and opaque reflector model
and consequently to compute
the resulting troposphere NO2 AMF for all the OMI scenes, regardless
of the type of particles present in the scene (clouds and/or aerosols). Such
a correction is historically named an “implicit aerosol correction”
. clearly demonstrated that, in
spite of its implicit nature, the implicit aerosol correction mitigates
biases in the OMI DOMINO NO2 product over cloud-free scenes compared
to an aerosol-free pixel assumption. However, limitations were identified:
A numerical artefact is present due to a too-coarse sampling employed in
the OMI cloud look-up table (LUT), leading to a strong underestimation in
the OMI tropospheric NO2 VCD over scenes with τ(550 nm) ≥
0.6 and aerosols located at high altitude.
The Lambertian cloud model, in
spite of its benefits, somehow remains too simple and likely does not fully
reproduce all the multiple scattering effects inherent to aerosol properties.
The OMI effective cloud algorithm was then updated in order to remove these
numerical artefacts . It also includes many additional
relevant improvements related to the OMI 477 nm O2-O2 measurement.
But its impact on the correction for aerosols has not yet been evaluated.
To move one step further, developed a novel machine-learning
algorithm, based on the neural network (NN) technique, that allows ALH to be
retrieved together with τ from the same OMI 477 nm O2-O2
spectral band over cloud-free scenes. These retrievals were performed in
various cases both over land and sea and compared with reference CALIOP
observations and related climatology . They benefit
from a strong synergy with MODIS on board the NASA Aqua platform flying
together with Aura in the same NASA A-Train constellation in order to
identify the cloud-free scenes and to better constrain the ALH retrieval
quality. For such a purpose, the 477 nm O2-O2 band represents some
advantages compared to the more traditional O2-A band at 760 nm,
which is not measured by OMI:
It is spectrally closer to the NO2,
HCHO, or SO2 absorption features.
It has a wider spectral range
but weaker signal, leading to high sensitivities in the case of high aerosol
loading.
It has fewer radiative transfer challenges arising from
strong absorption lines like in the near-infrared. Moreover, the NN technique
development allows very fast OMI data processing, which is an important
requirement within an operational environment. The aerosol retrievals
performed with this algorithm are expected to lead to an explicit aerosol
correction over cloud-free scenes by using the OMI 477 nm O2-O2
measurement simultaneously acquired with the 405-465 nm NO2 band.
This paper aims to evaluate the benefits of our improved use of the OMI
477 nm O2-O2 band for correcting aerosol effects in tropospheric
NO2 VCD retrieval from the same visible observation. We evaluate the
potential of directly using the OMI NN aerosol ALH (and τ) in view of an
explicit correction. We also assess the expected changes in the implicit
aerosol correction based on the improved OMI effective cloud algorithm. To
compare the different aerosol correction strategies, we reprocessed 2 years
(2006–2007) of the DOMINO-v2 data over different areas and seasons,
dominated by different types of pollution episodes, and thus, NO2 and
aerosol sources:
the large urban and industrialized eastern China region
dominated by a mix of continental polluted fine and weakly absorbing
particles, and dust coarse and scattering particles in summertime
(June–July–August),
the same area essentially dominated by continental
polluted fine and weakly absorbing particles in wintertime
(December–January–February), and
South America during the biomass-burning
season associated with heavy load of strongly absorbing aerosol emission
(August–September).
Given all the aerosol corrections available from our improved use of the
OMI 477 nm O2-O2 band, their comparison in this paper gives an
estimation of the aerosol correction uncertainty in the OMI tropospheric
NO2 VCD retrieval. Sections and
describe the algorithms and the
reprocessing methodology. Section evaluates the
results of the applied aerosol corrections in the reprocessed tropospheric
NO2 retrievals. To complete the analyses,
Sect. includes specific discussions based on
reference simulations to better understand the behaviour of the new OMI
tropospheric NO2 VCD. Similarly to , the advantage of
such simulations is to determine, on well controlled cases, the expected new
biases of the reprocessed OMI tropospheric NO2 VCD and to identify
the key geophysical factors driving them. At the end, in
Sect. , we discuss the benefits and challenges of
each aerosol correction.
The OMI O2-O2 algorithms
O2-O2 DOAS spectral fit
In this paper, both effective cloud and aerosol algorithms are based on the
same OMI 477 nm O2-O2 spectral band. More specifically, they use
the continuum reflectance Rc(475 nm) and the O2-O2
slant column density (SCD) NO2-O2s. These variables
are derived from the DOAS spectral fit approach, which is a prerequisite
to applying either the OMI cloud LUT (see
Sect. ) or the aerosol neural networks (see
Sect. ).
The DOAS method is a specific spectral fit approach following basic principle
of absorption spectroscopy employed for UV and visible absorbing trace gases.
The various DOAS techniques rely on the same key concept: a simultaneous fit
of several trace gas slant column densities from the fine spectral features
due to their absorption (i.e. the high frequency part) present in passive
UV–visible spectral measurements of atmospheric radiation .
The assumed Beer–Lambert (or Bouguer–Lambert) law describes the light
attenuation as a function of the travelled distance in the atmosphere, gas
concentration and its spectral absorption intensity. It is commonly employed
for absorption spectroscopy analyses of NO2, SO2, HCHO
and O3 from the OMI, TROPOMI, GOME, GOME-2 and SCIAMACHY sensors,
e.g. and . The spectral fit is achieved within a
predefined spectral window and the slant column density Ns is
defined as the column density of a trace gas absorber along the average light
path travelled by the detected photons from the Sun through the atmosphere,
surface and back to the satellite sensor.
Here, the OMI 477 nm O2-O2 DOAS fits together the absorption
cross-section spectrum of O2-O2 with a first-order polynomial over
the (460–490 nm) spectral band . Note that the
O3 absorption, also present in this band, is taken into account. The
continuum reflectance Rc at the reference wavelength
λ0 = 475 nm is the reflectance which would be measured in the
absence of O2-O2 in the atmosphere.
In the absence of clouds, both OMCLDO2 and OMI aerosol algorithms rely on how
aerosols affect the length of the average light path along which
O2-O2 absorbs. Rc(475 nm) is known to represent the
enhanced scene brightness due to the additional scattering effects induced by
the particles. In particular, Rc(475 nm) directly increases with
increasing τ. The enhancement magnitude, however, depends on aerosol
properties as well as the surface albedo
. NO2-O2s
is governed by the overall shielding or enhancement effect of the absorption
of the photons by the O2-O2 complex in the visible spectral range
along the average light path. A reduction in the length of the average light
path, i.e. the shielding effect, reduces the absorption by O2-O2. The
aerosol layer height is the primary driver
. An aerosol layer located at high
altitudes causes a large shielding effect on the O2-O2 located in
the atmospheric layers below, by reducing the amount of photons coming from
the top of the atmosphere and reaching the lowest part of the atmosphere,
compared to an aerosol-free scene. As a second-order effect, aerosol
properties such as τ and ω0, and surface reflectance also
contribute to NO2-O2s
.
OMI cloud algorithm OMCLDO2
The OMI cloud algorithm also named OMCLDO2 derives the
effective cloud fraction cf and cloud pressure cp
assuming a single cloud layer as an opaque Lambertian reflector with a
constant albedo of 0.8 and the independent pixel
approximation (IPA) . The measured reflectance R
is formulated as a linear combination of a clear-sky RClear and a
cloudy reflectance RCloud :
R(λ)=cf⋅RCloud+(1-cf)⋅RClear.
An LUT enables the conversion of Rc(475 nm) and
NO2-O2s into cf and cp. It
requires knowledge of the surface reflectance and surface pressure in
addition to the viewing and sun geometry configurations
. Because of the low impact of small clouds on
the O2-O2 band, cp has large uncertainties in the case of
low cf . The term “effective” here means that
these cloud parameters do not represent actual clouds, but our best
explanation of the measured radiance is obtained by combining these variables with the
assumed approximate model . Therefore, the retrieved
cf and cp values of each observed scene match
the measurement summarized by
(Rc(475 nm)-NO2-O2s), such that the
(460–490 nm) radiance budget is comprehensively closed (apart from
instrument noise). For example, true optically thin clouds will be retrieved
as a an opaque and bright Lambertian reflector covering only a small part of
the OMI pixel, mostly because of the large assumed cloud albedo value
.
The main motivation of this cloud retrieval scheme has been the correction of
cloud effects in trace gas retrievals . However, this
algorithm is actually applied both to cloudy and cloud-free scenes with
aerosols, without any prior distinction. Many studies demonstrated that
OMCLDO2 accounts for a large part of aerosol effects in the retrieved
cf and cp
. Under these
conditions, the OMI cloud parameters then become more effective as they
do not represent cloud any more but aerosol effects on the (460–490 nm)
radiance. One could claim that OMCLDO2 then becomes an approximate aerosol
model, independent of those considered in Sect. .
demonstrated how OMCLDO2 responds to aerosols:
cf is mostly driven by Rc(475 nm) and
increases with increasing aerosol load, regardless of its altitude. Its
magnitude is weighted by aerosol properties and surface conditions.
cp represents beforehand the degree of shielding effect applied
by aerosols, which results from a complex combination of ALH as a first
order aerosol load τ and type, surface properties and geometry angles
as a second order. A stronger shielding effect leads to a lower
cp. In general, over scenes with high τ values,
cp correlates well with ALH. Furthermore, regardless of true
aerosol layer altitude, strongly absorbing particles lead to a decrease in
cp, while the presence of more scattering particles increases
cp values .
released a new version of the OMCLDO2 product. The new
algorithm, here named OMCLDO2-New, includes several improvements such as a
better consistency of gas absorption cross sections with the OMI NO2
retrieval algorithm, outlier removal from the spectral fitting, etc. However,
in the context of the implicit aerosol correction, the expected highest
changes come from the higher number of nodes of the OMI cloud LUT. Indeed,
the coarse sampling of the OMI cloud LUT associated with the OMCLDO2-Old
version created a numerical artefact: cp was increasing with
decreasing cf (or aerosol τ) without any physical
explanation . This has strong impacts on the OMI
tropospheric NO2 product, DOMINO v2, in scenes dominated by aerosols
(see Sect. ). Furthermore, a temperature correction is
implemented in OMCLDO2-New to take into account the density-squared
dependence of the O2-O2 absorption. Its impact, however, depends on
the temperature conditions and latitude area .
OMI aerosol neural network
The OMI O2-O2 aerosol algorithm relies on a NN multilayer perceptron
approach to primarily retrieve ALH over cloud-free scenes, but also aerosol
τ(550 nm) as a secondary parameter . Since a
fine characterization of aerosol vertical profiles cannot be retrieved from
OMI UV–visible measurements, they are assumed as one box layer with a
constant pressure thickness (100 hPa). ALH is the middle altitude of this layer
in kilometres over sea level but can also be expressed in pressure. Here, the
strategy differs from the OMI effective clouds of
Sect. . The main motivation is to try to reproduce
aerosol scattering and absorption in the visible spectrum via a more explicit
aerosol model than the assumed opaque Lambertian reflector. Particle
properties in this layer are considered to be homogeneous.
The ALH retrieval requires several input variables, the most critical being
τ(550 nm), as both ALH and τ(550 nm) similarly affect
NO2-O2s and need to be separated .
In theory, τ information may be available from diverse sources (e.g.
atmospheric models, statistical prior guess or observations). In practice,
the MODIS τ product has systematically been preferred due to its good
spatial and temporal collocation with OMI measurements, and its recognized
high quality. Retrieved OMI τ may also be used as they come from a same
spectral measurement (same instrument). However, due to its higher uncertainty
compared to MODIS, its use impacts the quality of OMI ALH .
For OMI τ retrieval, Rc(475 nm) is considered instead of
τ as prior input. Note that, in the next sections, we define
NNMODIS when prior MODIS τ is considered,
NNOMI based on the retrieved OMI τ, and NNTrue
when the true τ value is considered for the synthetic cases (see
Sect. ).
The training data set was generated by full-physical spectral simulations,
assuming explicit aerosol particles without clouds, and with the Determining
Instrument Specifications and Analyzing Methods for Atmospheric Retrieval
(DISAMAR) software by the KNMI . Aerosol scattering phase
function Φ(Θ) was simulated by the Henyey–Greenstein (HG) function
parameterized by the asymmetry parameter g and the average of the cosine of the
scattering angle . Aerosols were specified as standard fine
particles with a typical value of the extinction Ångström exponent
α=1.5 and g=0.7. They are assumed to fully cover the OMI pixel.
To take into account the inaccuracies of the assumed aerosol single-scattering albedo ω0 properties, two training data sets were
generated with a different typical value: one with ω0=0.95 and
one with ω0=0.9 in the visible spectral domain. Therefore, two
separate OMI ALH NN algorithms were developed, one for each aerosol
ω0 value. The rational of these ω0 values relies on
those that are typically identified in the regions dominated by high
NO2 pollution, notably in eastern China .
The HG phase function is known to have some limitations compared to more
physical aerosol scattering models. Nevertheless, it was consciously chosen
in as the main motivation has been as exploratory
development of an ALH retrieval algorithm, using the OMI 477 nm
O2-O2 absorption band, in view of correcting aerosol scattering and
absorption effects in the visible spectral range for tropospheric NO2
retrieval. For such a purpose, quantitatively demonstrated
that τ and ALH are the key needed parameters. Other aerosol parameters,
that are more related to their optical properties, shape and size are of a
secondary importance. This is supported by a significant number of additional
studies . The main reason is that a
comprehensive aerosol correction requires the length of the average light
path in the presence of scattering and absorbing particles. This is primarily
driven by τ and ALH (in addition to the shape of the NO2
vertical profile), much less by the detailed properties of particles.
Consequently, other details describing the shape of the scattering phase
function are of secondary importance, even if they are not negligible. Moreover,
geographical areas impacted by heavy NO2 abundance are generally
dominated by fine spherical particles, weakly absorbing (e.g. sulfate, and
nitrate) or strongly absorbing (e.g. smoke) particles like in eastern China, South
America, and Russia and include urban, industrial and biomass-burning
pollution events . Spheroid particles such as dust
are sometimes mixed but do not dominate.
The HG function is known to be smooth and reproduce the Mie scattering
functions reasonably well with g=0.7 for most aerosol types,
especially for spherical particles . The evaluation results
obtained in showed that this approximation
is not oversimplified for all these cases over eastern China, Russia and South
America. A similar approach is considered for the operational ALH retrieval
algorithms for the Sentinel-4 Precursor and the Sentinel-5 Precursor
and when applying various
explicit aerosol corrections in the tropospheric NO2 AMF calculation
over urban and industrial areas dominated by anthropogenic pollution, for
instance in eastern China .
Similarly to the high cp inaccuracy in the case of low
cf, a high ALH bias is expected below a minimum particle load (i.e.
threshold of τ(550nm)=0.5). This is directly due to the weak
O2-O2 absorption within the 460–490 nm spectral band. Below this
threshold, low aerosol amounts eventually have negligible impacts on
NO2-O2s. The ALH retrieval performance was assessed
over areas in eastern China, South America and Russia with scenes including urban,
industrial and biomass-burning pollution events and for different seasons
. These scenes are mostly dominated by fine
spherical particles, weakly absorbing (e.g. sulfate, and nitrate) or strongly
absorbing (e.g. smoke). Dust particles may sometimes be mixed. Over
cloud-free scenes, OMI ALH has shown consistent spatial patterns with CALIOP
level 2 (L2) ALH over urban and industrial areas in eastern China, with an
uncertainty in the range of [500:700] m and for collocated MODIS scenes with
τ(550 nm) ≥0.5 . Additional analyses showed that
differences between the LIdar climatology of vertical Aerosol Structure for
space-based lidar simulation (LIVAS) and 3-year OMI ALH with MODIS
τ(550m)≥1.0 were in the range of [180:800] m
. Finally, showed the potential
of OMI visible measurements to observe the height of thick and absorbing
aerosol layers released by widespread fire episodes such as in South America.
The aerosol model assumptions, in particular ω0, are the most
critical as they may affect ALH retrieval uncertainty up to a maximum of 660
m. An accuracy of 0.2 is necessary in prior τ(550 nm) information to
limit the ALH bias close to zero over scenes with τ(550 nm) ≥1.0, and
below 500 m for τ(550 nm) values smaller than 1.0. A summary of all
the OMI NN aerosol algorithms as well as related input and output parameters
is given in Table .
Summary of the different OMI
NNO2v mentioned in this paper, and the
configuration of the associated aerosol correction: input OMI Lambertian
equivalent reflectivity (LER) climatology, OMI cloud look-up table (LUT) for
the effective cloud retrievals, aerosol parameters (see for more details
Sects. , and
).
OMI NNO2v data set
Aerosol correction
Configuration details
NO2(DOMINO)
Implicit aerosol correction, OMCLDO2
Coarse OMI cloud LUT,
LER 3-year climatology
No temperature correction of NO2-O2s
NO2(OMCLDO2-Old)
Implicit aerosol correction, OMCLDO2
Coarse OMI cloud LUT
LER 5-year climatology
Temperature correction of NO2-O2s
NO2(OMCLDO2-New)
Implicit aerosol correction, OMCLDO2
Fine OMI cloud LUT
LER 5-year climatology
Temperature correction of NO2-O2s
NO2(NNMODIS,ω0=0.9)
Explicit aerosol correction, OMI Aerosol NN
MODIS τ(550 nm), OMI ALH
LER 5-year climatology
Assumed ω0=0.9
Temperature correction of NO2-O2s
NO2(NNOMI,ω0=0.9)
Explicit aerosol correction, OMI Aerosol NN
OMI τ(550 nm), OMI ALH
LER 5-year climatology
Assumed ω0=0.9
Temperature correction of NO2-O2s
NO2(NNMODIS,ω0=0.95)
Explicit aerosol correction, OMI Aerosol NN
MODIS τ(550 nm), OMI ALH
Assumed ω0=0.95
LER 5-year climatology
Temperature correction of NO2-O2s
NO2(NNOMI,ω0=0.95)
Explicit aerosol correction, OMI Aerosol NN
OMI τ(550 nm), OMI ALH
Assumed ω0=0.95
LER 5-year climatology
Temperature correction of NO2-O2s
From aerosol impacts to aerosol correction – methodology
General methodology
Reprocessing of the OMI tropospheric NO2 product is based on the
DOMINO-v2 data set (see Sect. ) in which the AMF (see
Sect. ) is recomputed with diverse aerosol
correction strategies using the DISAMAR radiative transfer model, over
cloud-free scenes contaminated by aerosols. Recomputed AMF values then replace
the original ones in DOMINO. They are applied to the available
NO2 SCD to derive consequently the tropospheric NO2 VCD.
The tropospheric NO2 AMF computation follows the formulation detailed in
Sect. , which relies either on an implicit or
an explicit aerosol correction: the implicit correction considers the
effective cloud retrievals obtained from OMCLDO2 (see
Sect. ); the explicit aerosol correction employs
aerosol parameters: either OMI ALH and OMI τ from the OMI aerosol NN
(see Sect. ), or OMI ALH and MODIS τ. The
complementary aerosol parameters (i.e. ωo, g, α) follow
those specified in the associated training data set.
The surface albedo is based on the OMI Lambertian equivalent reflectivity
(LER) climatology . In DOMINO-v2, this climatology is
derived from 3-year OMI time series measurements. However, it has evolved since
then with an extended 5-year OMI time series . This evolved
OMI LER is considered for all the reprocessed tropospheric NO2 VCD of
this study. All the other geophysical parameters associated with DOMINO-v2,
such as the NO2 vertical profile, remain identical.
To identify cloud-free OMI observation pixels with aerosols, a similar
strategy to is considered. The DOMINO-v2
NO2 scenes are collected together with the MODIS-Aqua aerosol
τ(550 nm) from the combined Dark Target (DT) and Deep Blue (DB)
products of Collection 6 available at a resolution of 10 km . They
are collocated within a distance of 15 km. The probability of cloud-free OMI
scene is a priori ensured by the availability of the MODIS aerosol product
with the highest quality assurance flag. In this case, MODIS Aqua τ
was then exclusively retrieved when a sufficiently high amount of cloud-free
subpixels was available (i.e. at the MODIS measurement resolution of 1 km)
. However, it is well recognized this may be not completely
representative for the atmospheric situation of the OMI pixel. Therefore, we
added two thresholds for each collocated OMI-MODIS pixel: the geometric MODIS
cloud fraction to be smaller than 0.1, and OMI cf lower than 0.1.
Past experiences showed that OMI cf values in the range of
[0.1:0.2] may still contain clouds (or both clouds and aerosols)
.
Additional synthetic cases analysed in Sects.
and are also based on the DISAMAR model, specified
in a similar way to the NN training data set in Sect. .
Either OMCLDO2 or the OMI NN aerosol algorithms are used to determine the
expected tropospheric NO2 VCD biases.
Air mass factor computations
The computation of tropospheric NO2 AMF
ANO2v is a key step for converting NO2 SCD
NNO2s into tropospheric NO2 VCD
NNO2v, which represents the number of
NO2 moleculescm-2 integrated along the vertical
direction from the surface P0 to the tropopause Ptrop
pressure. The application of ANO2v is crucial to
correct the average light path variability contained in
NNO2s. The ANO2v computation
has generally been recognized as the principal source of errors in
NNO2v determination in areas with a high level of
air pollution . This was emphasized even more by
, who discussed how AMF structural uncertainty is driven by
assumed prior information, and cloud and aerosol correction strategies: up to
42 % over polluted regions and 31 % over unpolluted regions.
In the context of OMI visible spectral measurements,
ANO2v is defined as the ratio of the atmospheric SCD
and VCD :
ANO2v(Ψ,λ)=NNO2s(Ψ,λ)/NNO2v,
with Ψ the list of input parameters prerequired for the radiative
transfer model. Note that before performing this conversion, the
stratospheric and tropospheric contributions to
NNO2s must be separated. Therefore,
ANO2v(Ψ,λ) is only applied to the
tropospheric NO2 SCD. The OMI ANO2v
formulation follows , and the concept of
altitude-resolved AMF a(z) (also named block AMF or BAMF) introduced by
and then generalised by
, and . The ratio of a to the total air mass
factor ANO2 (deduced from the NO2 shape profile) gives
the vertical averaging kernel AK, i.e. the sensitivity of the satellite
measurement to each vertical atmospheric layer .
Overall, ANO2v can then be seen as a unitless number
representative of the length of the average light path followed by the
detected photons in the troposphere. It includes an indication of the
sensitivity to the amount of NO2 in the troposphere, with larger values
indicating a higher sensitivity, assuming no change in vertical NO2
profile. Indeed, in those cases, a change in ANO2v
is directly associated with a change in a at the atmospheric levels where
the trace gas is present. The reference wavelength considered in this paper
is 439 nm, following the OMI NO2 product (see
Sect. ) .
Aerosols may cause either a shielding or an enhancement effect. A shielding
effect occurs when the length of the average light path is reduced leading
to a decrease in ANO2v. Reciprocally, an
enhancement effect results in an increase in ANO2v
. Following Eq. (), any bias in
the ANO2v calculation leads to a direct bias in
NNO2v, with the same value but opposite sign.
Note that, in the case of real OMI tropospheric NO2 retrievals, a
temperature correction is often applied as the temperature of the assumed
NO2 absorption cross section, fixed at 221 K, can differ from the
actual temperature when deriving NNO2s. The
correction term is thus implemented in the computation of
ANO2v such that it represents the ratio of
NNO2s derived with a NO2 cross section at
the real temperature T to the column derived at 221 K. European Centre for
Medium-Range Weather Forecasts (ECMWF) temperature fields are used for this
correction .
The computation of ANO2v requires accurate knowledge
about all the parameters Ψ, affecting the optical properties of the
atmosphere and the length of the average light path. For an aerosol and
cloud-free scene, Ψ generally includes the satellite and solar
geometries, ground pressure and the surface reflectance. In the presence of
clouds and/or aerosols, adequate parameters describing their properties must
be added. Among all these variables, many studies emphasized that ALH and
τ are the most critical aerosol parameters primarily affecting
ANO2v over cloud-free scenes dominated by aerosol
particles . It was clearly
demonstrated that other parameters describing aerosol properties, such as
size, are generally of second order of magnitude for such a purpose. The main
reason is because, to correct aerosol effects, we need the overall length
of the average light path in the presence of scattering and absorbing particles.
This is primarily driven by τ and ALH (in addition to the shape of the
NO2 vertical profile) and much less by the detailed properties of
particles that affect the TOA radiance measurement more .
OMI tropospheric NO2 data set – DOMINO v2
DOMINO v2 is a reference worldwide tropospheric NO2
product derived from the OMI visible measurements and can be downloaded from
the Tropospheric Emissions Monitoring Internet Service (TEMIS) website
(http://www.temis.nl, last access: 12 January 2019).
demonstrated that the implicit aerosol correction in DOMINO-v2 is better than
the clear-sky assumption , with remaining biases between
-20 % and -40 % on tropospheric NO2 VCD, especially in
the presence of absorbing particles and for τ(550 nm) ≥0.5. One of the
main identified limitations has been the coarse sampling of the OMI cloud LUT
nodes used in OMCLDO2 (see Sect. ). The effect of the
OMCLDO2-New version on the implicit aerosol correction has not yet been
analysed.
To our knowledge, no reprocessing has yet been done by applying an explicit
aerosol correction based on (nearly) explicit aerosol parameters retrieved
from the OMI 477 nm O2-O2 spectral band. Thus, the use of OMI ALH
and τ parameters from Sect. is a first attempt to
apply a (nearly) explicit aerosol correction in the
ANO2v computation by using visible spectral
measurements from the same sensor.
DOMINO has recently evolved through the Quality Assurance for Essential
Climate Variables (QA4ECV) project (http://www.qa4ecv.eu, last access: 23 January 2019), which aims to address reliable
and fully traceable quality information on some of the essential climate
variables (ECVs), such as tropospheric NO2, as defined by the
Global Climate Observing System (GCOS) . This reprocessing
contains numerous changes in the complete chain of retrieval, from the
calibrated spectrum, spectral fitting with DOAS, to the AMF computation and
all the ancillary data sets. This new generation of product is expected to
represent one of the best NO2 data sets. Since the reprocessing
products of QA4ECV are still under thorough validation and were not completely
available at the time of this paper (and its technical work), and given our
specific objective focused on the aerosol scattering and absorption
correction by using information from the O2-O2 spectral band, the
last version of DOMINO (v2) is preferred.
The OMI cloud algorithm configuration used at the time of DOMINO-v2 and its
comparison with the other algorithms are summarized in
Table .
Results of reprocessing OMI NO2 and O2-O2 products
All the reprocessed OMI tropospheric NO2 results achieved here are
based on the OMI cloud and aerosol algorithms discussed in the previous
sections and summarized in Table . The main
differences between the different reprocessings are synthesized in
Table for all collocated OMI-MODIS aerosol scenes.
Summary of the changes in the diverse
reprocessing OMI tropospheric NO2 VCD NNO2v
depending on the applied aerosol correction strategy (see
Table ) over all collocated MODIS aerosol scenes
(MODIS τ(550 nm) ≥0.). See more analyses in
Sect. and .
Focus
Comparison reprocessed NO2
Region – season
Changes in NNO2v in percent
Average ± standard deviation
Implicit correction
NO2(OMCLDO2-Old) - NO2(DOMINO)
China – summer
-1.0±9.0
China – winter
-15.6±29.8
South America –
-6.2±16.0
biomass burning
NO2(OMCLDO2-New) - NO2(OMCLDO2-Old)
China – summer
1.3±6.9
China – winter
7.9±19.3
South America –
7.9±14.4
biomass burning
NO2(OMCLDO2-New) - NO2(DOMINO)
China – summer
0.4±10.6
China – winter
-4.0±26.8
South America –
3.1±17.3
biomass burning
Explicit correction
NO2(NNMODIS,ω0=0.95) -NO2(OMCLDO2-New)
China – summer
-2.9±12.5
China – winter
6.8±26.1
South America –
-8.1±16.8
biomass burning
NO2(NNMODIS,ω0=0.9) -NO2(NNMODIS,ω0=0.95)
China – summer
-0.2±7.8
China – winter
-8.2±22.3
South America –
1.3±8.7
biomass burning
NO2(NNOMI,ω0=0.95) - NO2(OMCLDO2-New)
China – summer
6.5±11.9
China – winter
11.2±18.4
South America –
-3.0±14.0
biomass burning
NO2(NNOMI,ω0=0.9) -NO2(NNOMI,ω0=0.95)
China – summer
8.5±13.7
China – winter
-1.9±24.3
South America –
1.0±14.6
biomass burning
Implicit aerosol correction – benefits of the updated OMI cloud algorithm
Among all the main changes that are included in the updated version of
OMCLDO2, the increased sampling of the OMI cloud LUT is expected to show the
most important impacts on the aerosol correction (see
Sect. ). Indeed, the coarse sampling of the OMI cloud
LUT in the former version was clearly identified by as a
limitation regarding the behaviour and the magnitude of cp, and
thus, when deriving NNO2v in the presence of aerosols
(see Sect. ). As depicted by
Fig. , differences in cp are now
quite significant for low τ. On average, cp from OMCLDO2-New
are about 200 hPa lower than from OMCLDO2-Old (with a large standard
deviation) over scenes with MODIS τ(550 nm) ≤ 0.5. Indeed, the low
aerosol load has a very limited effect on NO2-O2s
and does not dominate the measured radiance signal. This results in large
uncertainties in the retrieved cp and a large sensitivity of the
resolution at which the LUT interpolation is performed for these cases. Over
scenes with high aerosol load (MODIS τ(550 nm) ≥1.0), differences
are more minor and may even reverse sign. We attribute the small reverse sign
to the application of the temperature correction of
NO2-O2s (see Sect. ),
which, depending on the temperature difference compared to the assumed
midlatitude summer atmosphere, may apply a positive or negative small
modification of cp in cases of high τ. However, as analysed
by , the impact of the temperature correction of
cp remains minor in cases of high cf and thus aerosol
load, compared to the updated OMI cloud LUT. Overall, all these changes are
consistent with those analysed by over cloudy scenes, with
low and high cf.
Based on synthetic cases, Fig. illustrates
the expected improvements of the implicit aerosol correction of
NNO2v due to the higher OMI cloud LUT sampling.
While remaining NNO2v biases were contained between
-20 % and -40 % with OMCLDO2-Old, they should be now limited to the
range of [0:20] with the use of OMCLDO2-New over scenes with
relatively scattering or weakly absorbing aerosol particles (i.e.
ω0=0.95) and assuming a typical NO2 summer vertical profile
over north-eastern China. Such improvements are particularly good in the case of
aerosols located at an elevated altitude (i.e. more than 1 km). However,
although they were improved, the biases could be higher in the case of strongly absorbing
particles: in the range of [-10:20] % for ω0=0.9.
Additional geophysical parameters, in particular the NO2 profile
shape, may affect these biases and are therefore of high importance (see
further discussions in Sect. ).
Statistics of effective cloud pressure
differences between OMCLDO2-new and OMCLDO2-old (see
Sect. and Table ) in
[hPa] in 2006–2007 as a function of MODIS aerosol optical thickness
(AOT) τ(550 nm). An example over China in summertime
(June–July–August).
Relative
NNO2v biases after application of the implicit
aerosol correction as a function of true aerosol optical thickness (AOT)
τ(550 nm) and based on synthetic cases including different true ALH,
surface albedo =0.05, μ0 = 25∘, μ=25∘ and a typical TM5 NO2 vertical profile for 1 July 2006 at
12:00 over China . True aerosol properties are defined
by α=1.5, ω0=0.95 or 0.9 and g=0.7. Implicit aerosol
correction is derived from the retrievals given by OMCLDO2-Old or
OMCLDO2-New,
which, among other elements, includes the new OMI cloud LUT with a higher
sampling (see Sect. and
Table ). (a) Relative
NNO2v bias resulting from OMCLDO2-Old, true
ω0=0.95, (b) relative NNO2v
resulting from OMCLDO2-New, true ω0=0.95, (c) relative
NNO2v bias resulting from OMCLDO2-Old, true
ω0=0.9 and (d) relative NNO2v
resulting from OMCLDO2-New, true ω0=0.9.
Overall, the future changes when applying the new implicit aerosol
corrections from DOMINO will result from a combination of different
parameters, mainly the higher sampling of the OMI cloud LUT, the temperature
correction of NO2-O2s and the updated OMI surface
albedo database. To quantify the resulting changes in the reprocessed OMI
NNO2v, the results are separated into two categories.
Firstly, Fig. a, c and e illustrate the changes
in reprocessed NNO2v from DOMINO to OMCLDO2-Old. As
indicated in Table , these changes result from two
consequences:
the temperature correction of NO2-O2s and the
new OMI surface albedo, which both directly modify the retrieval of the
effective cloud parameters, and
the direct application of this new albedo when computing
ANO2v.
A higher surface albedo should result in an increased length of the average
light path and therefore an enhanced ANO2v.
However, this can become more complex when combined with the new effective
cloud parameters as they may either enhance, reduce or even counterbalance
this effect. On average, NNO2v is lower (i.e. higher
ANO2v), between -1%±9 % in China in
summertime and -15.6%±29.8 % in China in wintertime. The quality of
these changes, however, depends on the accuracy of the new surface albedo
climatology, which is primarily expected to be more robust due to the longer
time series considered in the OMI reflectance observations (see
Sect. ).
Statistics of relative
NNO2v differences in percentage as a function of MODIS
τ(550 nm) over China and South America in 2006–2007 due to changes in
the applied implicit aerosol correction (see
Sect. ) from DOMINO to OMCLDO2-Old (with the
LER climatology based on a longer time series) and from OMCLDO2-Old to
OMCLDO2-New (OMI cloud LUT with a higher sampling): (a) summer in China
(June–July–August), (b) winter in China
(December–January–February) and (c) South America
(August–September).
Secondly, Fig. b, d and f depict the impacts of
the implicit aerosol correction evolution from OMCLDO2-Old to
OMCLDO2-New. They are directly driven by the improved cp (see
Table ). Over scenes with MODIS τ(550 nm) in
the range of [0.0:0.5], a decreased cp (see
Fig. ) results in a stronger shielding (or
reduced enhancement) effect from particles: NNO2v
generally increases. In contrast, larger cp over scenes with
MODIS τ(550 nm) ≥ 1.0 leads to a lower shielding (or stronger
enhancement) effect: NNO2v decreases. Standard
deviation of these changes is between 15% and 20% in China in wintertime
and South America, and lower than 10% in China in summertime and South
America. Averages are in the range of [1.3%:7.9%]. Regional and
seasonal differences may reflect the implicit dependencies on the aerosol
types, the combined effects on cf-cp, spatially
variable surface albedo and the impacts of seasonal NO2 vertical
profile. All these observed changes are in line with the analyses deduced
from the synthetic cases in Fig. b, d and f,
confirming the improvements thanks to the updated OMI cloud LUT.
Interestingly, these overall changes seem to be in line with the average AMF
uncertainty of 11 % evaluated by due to a different cloud
correction scheme in polluted conditions and assuming cf≤0.2.
Overall, maps in
Figs. c–c
show that the total changes in NNO2v, from DOMINO to
OMCLDO2-New, mostly occur in the eastern part of China, where the NO2
pollution is higher. Spatial patterns of these overall changes mostly result
from a complex combination with MODIS aerosol horizontal distribution as
suggested by Fig. , but also aerosol types and
vertical distribution: a decrease over Beijing areas in summertime and an
increase in the same area in wintertime.
Average maps of MODIS
τ(550 nm), OMI DOMINO NNO2v and differences
after applying the implicit (with OMCLDO2-New) or explicit (with
NNMODIS,ω0=0.95) aerosol correction over China in
summertime (June–July–August) 2006–2007. (a) MODIS
τ(550 nm), (b) OMI DOMINO NNO2v,
(c) OMI NNO2v differences due to changes
between OMCLDO2-New and DOMINO implicit aerosol corrections,
(d) NNO2v differences between explicit
aerosol correction based on the NNMODIS,ω0=0.95
aerosol parameters (i.e. aerosol forward model assuming ω0=0.95,
MODIS τ(550 nm) and retrieved ALH) and implicit aerosol correction
implemented in DOMINO.
Same as
Fig. but over China in wintertime
(December–January–February) 2006–2007.
Same as
Fig. but over South America during
the biomass-burning season (August–September) in 2006–2007.
Explicit aerosol correction results
In this study, there are four possibilities for applying an explicit aerosol
correction from the OMI 477 nm O2-O2 band. Each of them differ
regarding the assumed aerosol properties (i.e. ω0), aerosol τ
observations (i.e. MODIS or OMI) and the consequent fitted ALH (see
Sect. ). All these possibilities were individually
applied when reprocessing the OMI DOMINO product to quantify their overall
differences.
Similarly to the benefits of the new implicit aerosol correction based on
OMCLDO2-New evaluated in Sect. ,
Fig. shows the benefits of the
applied explicit aerosol corrections. Provided that the aerosol model (e.g.
ω0) is in line with the actual aerosol type present in the observed
scene (i.e. ideal scenario), remaining biases in
NNO2v are below 20 % and are slightly dependent on
aerosol parameters (τ, ω0 and ALH) when assuming a NO2
vertical profile representative of a typical summer day over the east of China
. In such a scenario, using either the retrieved
OMI τ value or a more accurate one is not expected to make a major
difference. However, in practice, these results may vary with respect to the
NO2 profile shape and additional errors in the employed aerosol model
(see next subsections).
Relative
NNO2v biases after application of the explicit
aerosol correction as a function of true aerosol optical thickness (AOT)
τ(550 nm) and based on synthetic cases of
Fig. . No bias is included in ω0;
i.e. true and assumed values are identical. (a) True τ and
ω0=0.95, (b) retrieved OMI τ and ω0=0.95,
(c) true τ and ω0=0.9 and (d) retrieved OMI
τ and ω0=0.9.
Figure shows that all reprocessed
NNO2v with implicit or explicit corrections are
larger by [10%:50%] than if no correction was performed, especially
over scenes with MODIS τ(550 nm) ≥1.0. This suggests that both
corrections converge in the same direction (i.e. same sign) in spite of some
different magnitudes of the aerosol correction. Since all the considered
strategies attenuate the NNO2v biases due to
aerosols from an aerosol-free scene assumption, it is worth emphasizing that
all of them, without distinction, are an aerosol correction, regardless of
their implicit or (more) explicit nature.
Statistic of relative
NNO2v differences in percentage over China 2006–2007 in
summer (June–July–August) and winter (December–January–February), after
implicit or explicit aerosol correction compared to no aerosol correction
(i.e. aerosol-free scene assumption): (a) implicit aerosol
correction based on OMCLDO2-New, (b) explicit aerosol correction
based on NNMODIS,ω0=0.95.
Overall, over OMI pixels collocated with MODIS τ(550 nm) ≥0.5,
Fig. depicts that most of the reprocessed
NNO2v values are generally higher with the explicit
aerosol correction than with the implicit aerosol correction from
OMCLDO2-New. This suggests a stronger shielding effect leading to lower
ANO2v(439 nm). The differences increase with
increasing MODIS τ as aerosol effects consequently amplify along the
average light path.
Statistics of relative
NNO2v changes in percentage in 2006–2007 due to
differences between the different explicit aerosol corrections (see
Table ) and the implicit aerosol correction based on
OMCLDO2-New (improved effective cloud parameters, up-to-date version):
(a) China in summertime (June–July–August), (b) China in
wintertime (December–January–February) and (c) South America biomass
burning season (August–September).
In eastern China, by using the explicit aerosol correction with
NNMODIS,ω0=0.95, NNO2v values
are higher than with the implicit aerosol correction, with OMCLDO2-New at
about 12±12.5 % in summer and 40±26.1 % in winter over
scenes with MODIS τ(550 nm) ≥0.5 (see
Fig. ). The larger increase in wintertime
is likely due to different NO2 profile shapes, with NO2
molecules being closer to the surface (see further discussions in
Sect. ). The differences with OMCLDO2-New are
somehow reduced when assuming a lower aerosol ω0. In such a
configuration, the main differences are as follows:
a lower ALH due to an assumed lower ω0 value ,
combined with
a more absorbing aerosol model used to compute
ANO2(439 nm).
In both cases, NNMODIS,ω0=0.9 and NNMODIS,ω0=0.95, prior τ (coming from MODIS) remains unchanged. As
illustrated in Figs. , resulting
NNO2v using NNMODIS, ω0=0.9
over China are smaller (i.e. ANO2v (439 nm) higher)
by about -0.2±7.8% in summer, and -8.2±22.3% in
winter compared to NNO2v with NNMODIS,ω0=0.95. These numbers represent a first evaluation of the
impact of aerosol model uncertainty, assuming one may use a very accurate
prior τ information for both the ALH retrieval and the
ANO2v(439 nm) computation.
Over scenes in South America with MODIS τ(550 nm) ≥ 0.5, the
difference between NNO2v from NNMODIS,ω0=0.95 and from OMCLDO2-New is on average close to zero with a
standard deviation of 16.8 %. The use of NNMODIS,ω0=0.9 reduces NNO2v by about -1.3±8.7%. Interestingly, reported an average
change of 0.6±8% on ANO2v after
reprocessing DOMINO NNO2v over cloud-free scenes
during the biomass-burning season in South America and applying an explicit
aerosol correction based on the OMI near-UV aerosol algorithm (OMAERUV) and
CALIOP aerosol ALH.
When using the retrieved OMI τ as prior input instead of MODIS τ
over eastern China, NNO2v differences with respect
to the use of OMCLDO2-New differ by 5–10 % on average over scenes with
MODIS τ(550nm)≥0.6 (higher in summer but lower in winter).
This suggests a higher sensitivity to the combination of OMI τ and
ALH when used together for the ANO2v computation.
Figures –
show that most of the changes in NNO2v are located
on the eastern part and over areas dominated by heavy NO2 pollution
such as the megacities and the Pearl River delta. The horizontal
distribution of aerosol load adds some complex patterns. Overall, the
quantitative NNO2v differences between the applied
explicit aerosol corrections and the improved implicit aerosol correction can
be considered an average uncertainty related to the choice of an aerosol
correction approach. Similar numbers are reported by , who
indicated an average aerosol correction uncertainty of 45 % over highly
polluted scenes and with large aerosol loading (τ(550 nm) ≥0.5).
Furthermore, it was found that NNO2v from the POMINO
data set over China is 55 % higher (ANO2
smaller) than if no explicit aerosol correction was considered when the aerosol
layer is located above the tropospheric NO2 bulk. The main identified
reason was a reduced shielding effect applied by the effective cloud
parameters resulting from a higher effective cloud pressure (cp=350hPa); i.e. the Lambertian reflector was defined at a lower
altitude. The reduction of this shielding effect may of course be attenuated
when aerosols are mixed with NO2, as their multiple scattering effects
increase, then the average light path length increases and so does the NO2
absorption.
Finally, over scenes with a small amount of aerosol (i.e. MODIS τ(550nm)≤0.2), the difference in NNO2v between the
explicit aerosol correction assuming prior MODIS τ and the implicit
aerosol correction with OMCLDO2-New is systematically lower and non-null:
about an average of -10 % over all the considered regions. This
difference may seem strange as small aerosol amounts are expected to have
an almost negligible effect on the light path and thus on
ANO2v. When OMI τ is instead considered, this
difference becomes positive over China, but is reduced everywhere
(less than 10 %). Note that NNMODIS,ω0=0.95 and
NNMODIS,ω0=0.9 algorithms differ from OMCLDO2 by using
an external geophysical parameter (i.e. MODIS τ). Although it is more accurate
than using the retrieved OMI τ, the combination of an external MODIS
aerosol parameter derived from different assumptions about the scattering
model and surface reflectance may in the end lead to inconsistencies when
combined with the OMI NN model: the 477 nm O2-O2 radiance budget is
likely not closed. This radiance budget is always closed with OMCLDO2 (apart
from the instrument noise), since it simultaneously adjusts both
cf and cp to match the
Rc(475nm)-NO2-O2s combination.
The topic of radiance closure budget and its impacts on
ANO2v are further discussed in
Sect. .
Explicit vs. implicit aerosol correction – main reasons for the differences
As discussed in Sect. , ALH is the first
crucial parameter for the computation of
ANO2v(439 nm). Therefore, as a first assumption, it
is expected that the accuracy of the OMI ALH retrieval, and its difference
with cp, may be one of the first causes (although not unique) of
the difference between the applied implicit and explicit aerosol corrections.
Maps of cp (converted into
cloud height) from OMCLDO2-New and ALH from NNMODIS,ω0=0.95 in kilometres in 2006–2007: (a) cp, China in
summer, (b) ALH, China in summer, (c) cp, China in
winter, (d) ALH, China in winter, (e) cp, South
America and (f) ALH, South America.
Figure compares the average OMI ALH (retrieved with
the NN trained with aerosol ω0=0.95) and cp from
OMCLDO2-New, both converted into metric unit (km) over cloud-free scenes and
for collocated MODIS scenes with τ(550 nm) ≥0.5. Overall, both
variables are quite well correlated, with similar spatial and seasonal
distributions. Values are higher in China in summertime and over South America
and lie in the range of [1.5:5.0] km. They are lower in China in wintertime,
between 0.4 and 2.0 km. Quantitatively, ALH values from NNMODIS,ω0=0.95 show that the fitted aerosol layers are located higher than
the fitted opaque Lambertian clouds; i.e. aerosol pressures are smaller than
cp with average differences in the range of
[-0.49:-50.3] hPa. Standard deviation of the differences are of the
order of 120 hPa. The sign of the differences is reversed when employing
NNMODIS,ω0=0.9 (average differences 12.9–59.3 hPa).
As a first assumption, when ALH values are higher than cp, the
explicit aerosol corrections shall generally apply a stronger shielding
effect to ANO2v. Therefore, the resulting
NNO2v should be larger. However, this element alone
is likely insufficient to explain the differences observed in
Sect. . The combined impacts of the assumed prior
τ value also play a significant role. Furthermore, the assumed
Lambertian cloud and the aerosol Henyey–Greenstein models differ for the
horizontal coverage of the OMI pixel: the opaque Lambertian cloud model only
covers part of the pixel (the fraction coverage is fitted through
cf, optical properties are fixed). The clear pixel fraction
ensures the transmission part of the signal and the related multiple
scattering not present by definition within the Lambertian cloud layer. In
contrast, the aerosol model (and analysed synthetic cases) covers all
pixels (optical properties can be changed, fraction coverage is fixed). The
transmission and multiple scattering properties are included within the
aerosol layer and vary as a function of the optical properties. Therefore,
one can assume that, in the case of optically thick layers, the aerosol model
generally applies a stronger screening effect by fully covering the scene and
thus obstructing the surface transmission signal. By opposition, the surface
transmission signal is more or less always ensured with the Lambertian opaque
model by the non-covered pixel fraction.
Advantages and challenges of an explicit aerosol correction based
on the 477 nm O2-O2 measurement
This section discusses specific elements in order to evaluate the relevancy
of the developed explicit aerosol correction strategy over cloud-free scenes
from OMI, but also in general from all UV–vis satellite measurements. In
particular, we wish to draw the reader's attention to the advantage of using
an explicit aerosol correction based on the exploitation of the 477 nm
O2-O2 spectral band from a satellite measurement, but also the
remaining difficulty of implementing it in practice. The next subsections
focus on the significance of the aerosol model error, the importance of the
NO2 vertical profile, the cases with absorbing particles, the
NO2 vertical averaging kernels and the OMI visible radiance closure
budget issue.
Impact of aerosol model error on tropospheric NO2 air mass factor
When applying an explicit aerosol correction, the accuracy of each variable
describing aerosol properties, once combined with the NO2 vertical
profile (see Sect. ) and surface reflectance,
drives the overall ANO2v(439 nm) computation
uncertainty. The ANO2v uncertainty due to this whole
set of variables, not only ALH, can be defined as the aerosol model
error for OMI NNO2v retrieval.
To understand the quantitative impact of each aerosol model variable
uncertainty, Fig. shows the
ANO2v(439 nm) biases resulting from some aerosol
model inputs. A single bias in ALH of 100 hPa directly affects
NNO2v within the range of [60%:70%] when
absorbing aerosols (ω0=0.9) are located below 0.5 km, assuming
a wintertime NO2 profile and with τ(550nm)=1.4. The
uncertainties are below 50 % for τ(550 nm) ≤0.5 and overall
below 10 % when particles are located at elevated altitudes (i.e. true
ALH≥1.4km). This quantitatively emphasizes how
essential ALH information quality is when particles are actually mixed with
NO2 molecules due to the complexity of reproducing the enhancement of
the average light path caused by scattering effects. A bias of 0.2 in the
assumed τ(550 nm) mostly impacts scenes with a small aerosol load: while
resulting NNO2v uncertainties lie in the range of
[-20%:20%] for τ(550 nm) ≤0.5, they decrease to the
range of [0%:10%] for τ(550nm)=1.4. Finally, an
overestimation of aerosol scattering efficiency (i.e. ω0 bias of
0.05) leads to an underestimation of NNO2v up to
-20 % over scenes with high τ as a consequence of an
underestimation of aerosol shielding effect and therefore a
ANO2v(439 nm) that is too large. Overall, ALH uncertainty is the key
driver of the AMF computation quality. ALH uncertainty must be better than
50 hPa to limit NNO2v bias below 40 %. With
τ uncertainty, they form the most important set of aerosol parameters
prerequired for a high quality of the ANO2(439 nm)
computation. Although it is not negligible, the uncertainty of aerosol model parameters
that are more related to the particle optical and scattering properties, such
as ω0, g and α, is of secondary importance provided that both
the ALH and τ qualities are ensured.
Relative
NNO2v biases due to a direct impact of individual
aerosol parameter biases on the ANO2v(439 nm)
calculation. Synthetic cases are similar to
Fig. , with true aerosol ω0=0.9,
except a typical midlatitude winter NO2 profile is considered
instead: (a) ALHbias=100 hPa, (b)
τ(550nm)bias=0.2 and (c)
ω0bias=0.05.
The importance of the relative layer height
A comprehensive aerosol correction for an accurate ANO2(439 nm)
computation also requires the actual NO2 vertical profile.
Figure a shows the accuracy of the aerosol
corrections in ANO2(439 nm), based on a synthetic case, assuming
the presence of absorbing aerosol particles (ω=0.9) but a typical
NO2 vertical profile of wintertime (1 January, 12:00)
over China. The main difference with Fig.
and Fig. is the presence of a more
abundant tropospheric NO2 bulk closer to the surface and a stronger
decrease rate to higher altitudes . In such a case, relative
NNO2v biases with the implicit aerosol correction
are strongly degraded from [-10%:20%] (summertime) to
[-80%:40%] (winter). As already identified in
Sect. with the summertime NO2
profile, the insufficient shielding effect applied by the effective cloud
parameters from OMCLDO2-New in the case of aerosol layers located at elevated
altitude is severely degraded here (from -10 % to -80 %). The
insufficient enhancement effect when particles are mixed with the
tropospheric NO2 molecules is also amplified here (from 20 % to
40 %).
Relative
NNO2v biases after application of the implicit or
explicit aerosol correction (see Table ) as a function
of true aerosol optical thickness (AOT) τ(550 nm). Synthetic cases are
similar to Fig. , except the summer
NO2 profile was replaced by a typical winter one. Furthermore, only
absorbing aerosols with true ω0=0.9 are considered. Finally, the
impact of a bias on the assumed ω0 through the application of the
explicit aerosol correction is illustrated: (a) implicit aerosol
correction based on OMCLDO2-New, (b) explicit aerosol correction,
true τ and assumed ω0=0.9, (c) explicit aerosol
correction, true τ and assumed ω0=0.95 and (d) explicit
aerosol correction, retrieved OMI τ and assumed ω0=0.95.
When considering an explicit aerosol correction using NNTrue,ω0=0.9, the NNO2v bias is changed to
0 %–40 %. Similarly to summertime, they are lower in the case of particles
at high altitude, suggesting strong benefits of such a correction
scheme in wintertime and/or in the presence of absorbing particles. The cases of
aerosols close to the surface (i.e. lower than 0.5 km) remains an issue due
to the difficulty of distinguishing the scattering effects from the surface and
the adjacent aerosol layer when retrieving ALH. The retrieval seems, in such
a case, to overestimate the aerosol layer altitude.
The observed positive difference of about 40 % between explicit and
implicit aerosol corrections is in line with the analyses over China in
wintertime (see Sect. and
Fig. ). All these elements strongly remind
us and emphasize that the quality of the aerosol corrections and their
differences for NNO2v retrieval are actually more
dependent on the relative height between the particles and the tropospheric
NO2 bulk than the absolute values of ALH or cp
themselves. The evolutions of operational aerosol correction schemes in
present and future air quality UV–visible space-borne sensors, such as
TROPOMI on board Sentinel-5 Precursor, then need to consider a proper joint
characterization of trace gas vertical profiles together with aerosol
vertical distribution and related optical properties.
Explicit vs. implicit aerosol correction – focus on absorbing particles
In this study, the cases with absorbing particles, i.e. aerosol ω0=0.9, brings more challenges to the implicit aerosol correction than scenes
dominated by more scattering aerosols. Such difficulties were also identified
by . As a reminder, the presence of absorbing particles
leads to a reduced cp (increasing the shielding effect) but a
lower cf (lower shielding effect) with a higher
transmittance coming from the clear part of the pixel. Part of the
insufficient shielding effect observed with the implicit correction in
Sect. and
may thus be explained by insufficient coverage of the observation scene by
the Lambertian opaque cloud layer; i.e. a higher fraction of the cloud-free
part of the OMI scene, including the surface, is assumed to lead to a higher
transmittance of the associated atmospheric layers. This last element likely
overall limits the potential of the effective cloud model to apply an
adequate shielding effect to ANO2v. However, these
biases still remain lower than if no aerosol correction was achieved
.
Figures and
a showed the potential of the explicit aerosol
correction but by assuming no bias in the assumed τ and ω0
parameters. To investigate this further, Fig. c–d
depict the impacts of a wrong prior aerosol ω0 (overestimation of
0.05) when applying the explicit aerosol correction through the use of the
OMI aerosol NN. When the true prior τ value for both the ALH retrieval and
the ANO2v computation is conserved, the increases in
NNO2v biases are limited from 0 %–40 %
(see Fig. a) to 0 %–60%. This reflects
how the quality of prior τ knowledge helps to constrain both ALH
retrievals and then limit the perturbation of ANO2v.
However, this may be more severely degraded when inaccurate prior τ is
considered (see Sect. ).
Figure d shows the impacts of replacing true
τ by the retrieved OMI one. NNO2v biases are
higher, about [-40%:60%]. This is a direct result of a
degradation of ALH retrieval due to an overly biased aerosol τ and the
resulting impact on the ANO2v. As discussed in
Sect. , although the ω0 parameter is of secondary
importance for ANO2v(439 nm) itself compared to the
(ALH-τ) combination, its direct impact on τ retrieval consequently
affects the ALH determination accuracy and then indirectly affects
ANO2v.
In spite of these drawbacks, NNO2v biases remain
smaller than if the derived effective cloud parameters were employed through
the implicit aerosol correction (see Fig. a).
Thus, even if imperfect, the explicit aerosol correction based on OMI ALH and
τ retrievals seem to remain advantageous for an efficient aerosol
correction in tropospheric NO2 VCD retrieval from visible satellite
radiance. Nevertheless, true horizontal distributions of aerosols within the
observation pixel may actually be quite heterogeneous. Such problems should
be further investigated with a focus on areas where absorbing particles are
expected, such as in wintertime in China, or during large biomass-burning
episodes. Future studies should also determine how often such an effect occurs
and its overall impact depending on the NO2 vertical profile
variability.
Aerosol model and NO2 vertical averaging kernel
Theoretically, the application of an explicit aerosol model is expected to
simulate more realistic scattering and absorption effects due to particles
when computing ANO2(439 nm). Figure illustrates
the vertical averaging kernel (AK) (see
Sect. ) assuming τ(550 nm) = 1.0 and
from the application of OMCLDO2-New, NNMODIS,ω0=0.95 and
true aerosol conditions, assuming no bias in prior aerosol assumptions. AKs
are very important for estimating the surface NOx
emissions by convoluting the NO2 vertical profiles from the used
atmospheric models to match the OMI NNO2v
observations . AKs based on OMCLDO2-New display a sharp
distinction between the enhanced atmospheric layers located above aerosols
and the shielded layers located below. On the contrary, AKs from
NNMODIS,ω0=0.95 depict a smoother transition, which is
then enhanced at the shielded layers. This transition is more in line with the
actual AKs (Fig. a) and results from the scattering effects
induced by the more or less wide aerosol particle layer. A bright Lambertian
reflector is by nature fully opaque and does not induce multiple scattering
effects. This is partly compensated by the transmission of the clear fraction
of the pixel through the IPA assumption (see Sect. ).
This suggests that applying an explicit aerosol correction leads to the
consideration of more realistic physical assumptions and AK productions.
However, such a suggestion critically depends on an ensemble of other
parameters that contribute to the AK generation: the accuracy of the
retrieved ALH which triggers the location of the enhancement/shielding
transition, the potential aerosol model biases (e.g. ω0, scattering
phase function, etc…) and the difference between actual and assumed
aerosol vertical profiles.
Vertical AK (see
Sects. and ) based on
aerosol τ(550nm)=1.0, NO2 vertical profile of
1 July 2006 at 12:00 over China. Other conditions are similar to
Fig. .
Radiance closure budget issue and potential impacts
The discussions about the model error in the previous subsections
implicitly made the assumption that the whole set of parameters such as
(cf, cp and surface reflectance) on the one hand, or
(τ, ALH, ωo, g, α and surface reflectance) are fully consistent
in the sense that they form one unique particle model. However, when external
data are used to constrain ALH retrieval accuracy, such as MODIS aerosol
τ, one may combine inconsistent model assumptions, leading
to complex artefacts such as the issue of the OMI closure radiance budget.
The radiance closure budget is not only important in the 477 nm O2-O2
band, but also in the NO2 absorption band at the wavelength where the
AMF is computed. Aerosol τ combined with surface reflectance are
expected to drive OMI Rc(475 nm). As discussed in
Sect. , OMCLDO2 simultaneously adjusts both
cf and cp based on the same prior surface
reflectance, such that their combination allows the
(Rc(475 nm)-NO2-O2s) budget to be closed and thus
the OMI 477 nm O2-O2 radiance occurs independently of the accuracy of the
selected model. On the contrary, by using MODIS τ, only OMI
NO2-O2s is exploited, not Rc(475 nm).
If all the prior parameters are accurate and derived from a unique set of
aerosol model and surface reflectance parameters, the
(Rc(475 nm)-NO2-O2s) budget should be
closed. However, any mismatch between the model employed for MODIS τ
determination and the one used in the OMI NN training data set, between MODIS
and OMI instrument radiance, and/or the surface reflectance hypothesis, may
leave this budget open. In particularly, it is worth remembering that the
surface reflectance data set behind MODIS τ, OMI cf and
ANO2v(439 nm) are not identical: for OMI, a
multi-spectral surface Lambertian equivalent reflectance (LER) was
used , while MODIS aerosol retrieval uses a directional
surface spectral reflectance .
Therefore, the strategies of the implicit vs. explicit aerosol correction
analysed in Sect. do not only differ in terms of
assumed particle optical and scattering property model but also on how much
the whole OMI radiance budget is eventually fitted. This last difference
likely explains the strange systematic difference (about -10 %)
identified over OMI scenes with MODIS τ(550 nm) ≤0.2, where aerosol
effects could be assumed to be almost insignificant (see
Fig. ). When. MODIS τ is replaced by
the retrieved OMI τ (see Table ), which is, like
cf, mostly constrained by Rc(475 nm), prior OMI
surface albedo and the same aerosol model used for ALH retrieval and
ANO2v(439 nm) calculation, resulting
NNO2v are higher than with the use of MODIS τ
(see Table ). Moreover, as analysed in
Sect. , the differences between the implicit and
explicit aerosol corrections are smaller everywhere over scenes
with small aerosol load confirming the consistency in the employed models for
each set of parameters and an almost complete closure of the OMI visible
radiance budget (except instrument noise).
At the end, one might wonder what the best option is for an optimal aerosol
correction:
using the best aerosol and surface parameters available for the most
accurate correction at the cost of not closing the satellite radiance budget,
or
applying a less accurate correction but with an ensemble of aerosol
and surface parameters that eventually comprehensively fit the spectral
measurement.
The first option gives more weight to the used auxiliary data, while the
second option maximises the weight of the amount of information contained in
the satellite measurement.
Due to the differences in the OMI-derived LER and the MODIS surface
reflectance, it may be very tempting to select primarily both the OMI τ
and ALH variables to avoid inconsistencies when correcting aerosol
effects. However, in this study, such a choice is not necessarily obvious for
everyone, as the accuracy of the ALH retrieval is strongly dependent on the
requested prior τ . The most accurate OMI ALH retrievals
were obtained with collocated MODIS τ, not with the derived OMI AOT.
This would naturally suggest first that the combination of OMI ALH and MODIS
τ shall give the most accurate tropospheric NO2 AMF. However,
the apparent inconsistencies due to the different algorithms employed for
each physical product are mostly observed in the present study through the
discussion on the TOA radiance closure budget issue. They do not necessarily
mean that the aerosol correction is less accurate.
The answer to such a problem is, in our opinion, not clear at this stage.
But, given the fact that several studies prioritise the application of
multiple parameters from very diverse sources (models, ancillary instruments
with different techniques, etc.) to satellite spectral measurements, we think
that the issue of radiance closure budget should be kept in mind by the
scientific community and further investigated in future research studies. At
the end, an optimal trade-off must be found between the quality of the
NNO2v product and the weight given to the original
satellite measurement.
Conclusions
This paper reprocesses the reference OMI tropospheric NO2 vertical
column density NNO2v for cloud-free scenes over
eastern China and South America for 2006 and 2007. These regions are
dominated by high aerosol loadings. The goal of this study is to evaluate the
benefits of the recently achieved developments during the last years to
improve the aerosol correction in the tropospheric NO2 air mass
factor ANO2v, a crucial parameter when retrieving
NNO2v from visible air quality satellite
backscattered measurements. In particular, the tested aerosol correction
strategies rely on our recent experiences with the 477 nm O2-O2
spectral band: a key spectral band present in current and future air quality
UV–vis satellite instruments such as OMI, GOME-2, TROPOMI, Sentinel-4-UVN
and Sentinel-5-UVNS. The use of this band is important in view of operational
data processing, as it allows parameters to be derived that, in principle,
could reproduce the particle scattering and absorption effects on the average
light path in the visible spectral window and represent the sensor field of
view. Regardless of the type of algorithm employed here, these parameters can
all be derived using computationally inexpensive methods, are directly
representative of the OMI pixel in terms of spectral measurement, spatial
coverage and temporal acquisition, and can directly be used for the
ANO2v computation.
The two tested algorithms are the OMI cloud software OMCLDO2
and the OMI aerosol neural network (NN) approach
. The most important difference between these
methods is the assumed aerosol model, not only for the retrieval of the
aerosol parameters in the O2-O2 band, but also how they represent
aerosol effects in the
tropospheric NO2 air mass factor. The OMCLDO2 represents the aerosols
as effective cloud parameters and thus implicitly correct the aerosol
effects, whereas the NN approach explicitly models and corrects for aerosols.
The most recent OMCLDO2 update from includes, among many
elements, an increased number of nodes in the LUTs and the necessary
temperature correction of the O2-O2 slant column density.
For both methods, the reprocessed NNO2v shows
smaller biases over cloud-free scenes dominated by aerosol pollution,
compared to the standard DOMINO retrieval and its related aerosol correction.
Previous studies showed an underestimation of the OMI
NNO2v between -20 % and -40 % over
scenes with MODIS aerosol optical thickness τ(550 nm) ≥ 0.6,
assuming scattering aerosol particles with aerosol single-scattering albedo
ω0=0.95 and summertime NO2 vertical profile. In similar
conditions, these biases are expected to be contained in the limit of
[0%:20%] for both the most recent OMCLDO2 method and the NN
method. On average, the applied explicit NN correction leads to higher
NNO2v values compared to the implicit OMCLDO2
correction up to 40% depending on the seasons, regions and aerosol
pollution episodes. This represents our best estimate of the aerosol
correction uncertainty for the OMI NNO2v retrieval
due to the possible choices of the algorithm correction. They are attributed
to the model differences and the associated output variables.
Although both the implicit OMCLDO2 and explicit NN methods are a significant
improvement over the current OMI DOMINO retrieval, we also found limitations
that need to be further studied:
If absorbing aerosols (i.e. single-scattering albedo ω0≤0.9)
are present, the implicit aerosol correction still leads to substantial
biases in NNO2v due to an insufficient applied
shielding effect: between -80 % and 20 % assuming a typical
NO2 wintertime profile. This is most severe when particles are
located at high altitude (above 1.4 km) and with heavy aerosol load. In
similar conditions, the explicit aerosol correction allows us to mitigate the
biases to the range of [0%:40%] if accurate τ and aerosol
model are available, or [-40%:60%] with inaccurate τ (e.g.
retrieved OMI τ) and aerosol model.
Biases remain high when particles are located close to the
surface, regardless of the aerosol correction methodology. The distinction
between aerosol scattering and surface reflectance is challenging under such
conditions. Since particles are close to the tropospheric NO2 bulk
(either mixed or slightly above), small errors in the retrieved aerosol
height or effective cloud pressure have a significant impact on the derived
NO2 column.
All aerosol correction methodologies are sensitive to the NO2
vertical profile and the employed surface albedo or reflectance. Future
improvements need to address both these parameters together with any
evolution that can still be done in the aerosol or effective cloud retrievals
from the 477 nm O2-O2.
Although the OMI aerosol NN algorithms lead to a promising ALH retrieval,
its use for an explicit aerosol correction is not straightforward. Indeed,
ALH is only one variable (although a key one) of a set of aerosol parameters
that need to be applied for the required explicit aerosol correction. Its
combination with other aerosol parameters (size, ω0, τ) can lead
either to model errors and/or to the risk of not closing the OMI radiance
budget if any of these parameters were issued from an external source (model
or instrument) that is not consistent with the OMI ALH retrieval.
Based on its estimated performances, we recommend an operational
processing of OMI data, where no distinction is made between cloudy,
cloud-free and aerosol-contaminated scenes, to first use the implicit
aerosol correction based on OMCLDO2. It allows both
cloud and aerosol particle effects to be adequately corrected on the average light path. If cloud-free
scenes can be carefully identified and collected in the same way as has been
done here, then the explicit aerosol correction based on the OMI NN aerosol
algorithms should be considered. We demonstrated in this study, for the first
time, its high performance and the realism of the simulated physical effects.
Moreover, the developed NN approach can ensure a fast cloud-free data
processing.
Overall, the considered aerosol corrections can in principle be transposed to
the future generation of air quality UV–vis satellite sensors, such as
TROPOMI, Sentinel-4 and Sentinel-5-UVNS. It can also be considered for other
trace gases of interest, e.g. SO2 or HCHO. However, such an
approach has to be adapted to the specificities of this new generation of
instruments.