We present a new and improved version (V4.0) of the NASA standard nitrogen
dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the
Aura satellite. This version incorporates the most salient improvements for
OMI NO2 products suggested by expert users and enhances the NO2
data quality in several ways through improvements to the air mass factors
(AMFs) used in the retrieval algorithm. The algorithm is based on
the geometry-dependent surface Lambertian equivalent reflectivity (GLER)
operational product that is available on an OMI pixel basis. GLER is
calculated using the vector linearized discrete ordinate radiative transfer
(VLIDORT) model, which uses as input high-resolution bidirectional
reflectance distribution function (BRDF) information from NASA's Aqua
Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land
and the wind-dependent Cox–Munk wave-facet slope distribution over water,
the latter with a contribution from the water-leaving radiance. The GLER
combined with consistently retrieved oxygen dimer (O2–O2)
absorption-based effective cloud fraction (ECF) and optical centroid
pressure (OCP) provide improved information to the new NO2 AMF
calculations. The new AMFs increase the retrieved tropospheric NO2 by
up to 50 % in highly polluted areas; these differences arise from both
cloud and surface BRDF effects as well as biases between the new MODIS-based
and previously used OMI-based climatological surface reflectance data sets.
We quantitatively evaluate the new NO2 product using independent
observations from ground-based and airborne instruments. The new V4.0 data
and relevant explanatory documentation are publicly available from the NASA
Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/, last access: 8 November 2020),
and we encourage their use over previous versions of OMI NO2 products.
Introduction
The Dutch–Finnish-built Ozone Monitoring Instrument (OMI) has been operating
onboard the NASA EOS Aura spacecraft since July 2004 (Levelt et al., 2006,
2018). The primary objectives of OMI's mission are to continue the long-term
record of total column ozone and to monitor other trace gases relevant to
tropospheric pollution worldwide. Observations of sunlight backscattered
from the Earth over a wide range of UV and visible wavelengths
(∼ 260–500 nm) made by OMI allow for the retrieval of various
atmospheric trace gases, including nitrogen dioxide (NO2). NO2 is
a critically important short-lived air pollutant originating from both
anthropogenic and natural sources. It is the principal precursor to
tropospheric ozone and a key agent for the formation of several toxic
airborne substances such as nitric acid (HNO3), nitrate aerosols, and
peroxyacetyl nitrate. Satellite-based observations yield a global,
self-consistent NO2 data record that can complement field measurements.
During more than 16 years of operation, OMI has provided a unique,
practically uninterrupted daily NO2 data record that has been widely
used for atmospheric research and applications, accentuating demands for
accurate NO2 data products. The power of OMI to track NO2
pollution is demonstrated through observations of enhanced column amounts
over polluted industrial areas (e.g., Boersma et al., 2011; Lamsal et al., 2013;
Krotkov et al., 2016; Kim et al., 2016; Cai et al., 2018; Montgomery and
Halloway, 2018), weekly patterns with significant reduction on weekends
following energy usage (e.g., Ialongo et al., 2016), and seasonal patterns
(e.g., van der A et al., 2008) that reflect changes in NOx emissions
and photochemistry (e.g., Shah et al., 2020). Exploiting the close relationship
between NOx emissions and tropospheric NO2 columns, OMI NO2
data have been used to detect and quantify the strength and trends of
NOx emissions from power plants (Duncan et al., 2013; de Foy et al., 2015;
Liu et al., 2019), ships (e.g., Vinken et al., 2014a), lightning (e.g., Pickering
et al., 2016), soil (e.g., Vinken et al., 2014b), oil and gas production (e.g.,
Dix et al., 2020), forest fires (Schreier et al., 2014), and other area
sources such as cities in the US (Lamsal et al., 2015; Lu et al., 2015; Kim et al., 2016),
Europe (e.g., Zhou et al., 2012; Castellanos et al., 2012; Vinken et al., 2014a),
Asia (Ghude et al., 2013; Goldberg et al., 2019a), and other world urban
areas (Krotkov et al., 2016; Duncan et al., 2016; Montgomery and Halloway, 2018). OMI
NO2 observations have frequently been used to evaluate chemical
transport models (CTMs) (e.g., Herron-Thrope et al., 2010; Han et al., 2011;
Hudman et al., 2012; Pope et al., 2015; Rasool et al., 2016), to study
atmospheric NOx chemistry and lifetime (e.g., Lamsal et al., 2010; Beirle
et al., 2011; Canty et al., 2015; Tang et al., 2015; Laughner and Cohen,
2019), and to infer ground-level NO2 concentrations (Lamsal et al.,
2008; Gu et al., 2017), NO2 dry deposition (Nowlan et al., 2014; Geddes
and Martin, 2017), and emissions of co-emitted gases including carbon
dioxide (CO2) (Konovalov et al., 2016; Goldberg et al., 2019b; Liu et
al., 2020).
Over the last decade, there have been considerable efforts to improve
NO2 data quality from OMI and other satellite instruments (e.g.,
Boersma et al., 2018). Special emphasis has been placed on improving
auxiliary information (e.g., a priori NO2 vertical profiles, surface
reflectivity), particularly with respect to spatial and temporal resolution.
For instance, the global OMI NO2 products are based on a priori
NO2 profiles from relatively coarse-resolution (> 1.0∘× 1.25∘) global CTM simulations (Boersma
et al., 2011; Krotkov et al., 2017; Choi et al., 2020). Many regional
studies suggest a general low bias in the global tropospheric NO2
column products, particularly over polluted areas, that can be partially
mitigated by using a priori information from high-resolution CTM simulations
(Russell et al., 2011; McLinden et al., 2014; Lin et al., 2014, 2015;
Goldberg et al., 2017; Choi et al., 2020). Current global NO2
retrievals are based on a low-resolution (0.5∘× 0.5∘) static climatology of the surface Lambert equivalent
reflectivity (OMLER) product (Kleipool et al., 2008), which is likely biased
high due to insufficient cloud and aerosol screening. This bias in surface
reflectivity can lead to an underestimation of tropospheric NO2
retrievals (Zhou et al., 2010; Lin et al., 2014; Vasilkov et al., 2017). In
addition, the OMLER data do not account for the significant day-to-day
(orbital) variability in surface reflectance caused by changes in
sun–satellite geometry, a phenomenon often expressed by the bidirectional
reflectance distribution function (BRDF). Zhou et al. (2010) demonstrated
the impact of both the spatial resolution and the BRDF effect on OMI
tropospheric NO2 retrievals over Europe by using high-resolution
surface BRDF and albedo products from the Moderate Resolution Imaging
Spectroradiometer (MODIS). Taking advantage of the MODIS high-resolution
data, albeit neglecting the BRDF and atmospheric effects, Russell et al. (2011) and McLinden et al. (2014) created improved NO2 products from the
NASA standard product (Bucsela et al., 2013; Lamsal et al., 2014) over the
continental US and Canada, respectively. While these and subsequent studies
(e.g., Kuhlmann et al., 2015; Laughner et al., 2019) addressed the
limitation of climatological LER data for NO2 retrievals, they did not
account for the surface BRDF effect on the OMI cloud products (cloud
pressure and fraction), which are also inputs to the NO2 algorithm.
Applying the MODIS BRDF data consistently to both the NO2 and cloud
retrievals demonstrably improves the quality of OMI NO2 retrievals over
China (Lin et al., 2014, 2015; Liu et al., 2019). However, this approach is
computationally expensive and is applicable to land surfaces only. Our
previous work (Vasilkov et al., 2018) proposed an approach appropriate for
satellite NO2 data processing on a global scale (a) by using MODIS BRDF
information consistently in the cloud and NO2 retrievals (b) for both
land and water and (c) in an efficient way. Here, we apply the approach
globally for the first time in the standard NASA OMI NO2 algorithm.
In this paper we describe various updates made in the version 4.0 (V4.0)
NASA OMI NO2 algorithm, discuss their impact on the retrievals of
tropospheric and stratospheric NO2 column amounts, and provide an
initial quantitative assessment of NO2 data quality. Section 2
describes the OMI NO2 algorithm and various auxiliary data used by the
algorithm. We present validation results in Sect. 3. Section 4 summarizes
the conclusions of this study.
OMI and the NO2 standard product
OMI is an ultraviolet–visible (UV–Vis) spectrometer on the polar-orbiting
NASA Aura satellite (Levelt et al., 2006,
2018). Aura, launched on 15 July 2004, follows a sun-synchronous orbit with
an Equator crossing time near 13:45 local time. OMI employs two-dimensional
charge-coupled device (CCD) detectors and operates in a push-broom mode, registering spectral data
over a 2600 km cross-track spatial swath. The broad swath enables global
daily coverage within 14–15 orbits. In the OMI visible channel used for
NO2 retrievals, each swath, measured every 2 s, comprises 60
cross-track fields of view (FOVs) varying in size from ∼ 13 km × 24 km near nadir to ∼ 24 km × 160 km for
the FOVs at the outermost edges of the swath. Each orbit consists of
∼ 1650 swaths from terminator to terminator. OMI's full daily
coverage has been affected by data loss due to an anomaly presumably caused
by material on the spacecraft outside the instrument that results in reduced
coverage to about half of its original swath, as discussed in Sect. 2.4.
The OMI NO2 standard product (OMNO2) algorithm provides retrievals of
NO2 column (total, tropospheric, and stratospheric) amounts by
exploiting Level-1B calibrated radiance and irradiance data from the visible
channel (350–500 nm with 0.63 nm spectral resolution). The algorithm employs
a multi-step procedure that consists of (1) a spectral fitting algorithm to
calculate NO2 slant column densities (SCDs) as discussed in Sect. 2.1, (2) determination of air mass factors (AMFs) to convert SCDs to vertical
column densities (VCDs) as discussed in detail in Sect. 2.2, (3) a scheme
to remove cross-track-dependent artifacts or stripes, and (4) a
stratosphere–troposphere separation scheme to derive tropospheric and
stratospheric NO2 VCDs. The AMF depends upon a number of parameters
including optical geometry (solar and viewing azimuth and zenith angles),
surface reflectivity, cloud pressure and fraction, and the shape of the
NO2 a priori vertical profile.
Since the first release of OMNO2 in 2006 (Bucsela et al., 2006; Celarier et
al., 2008), there have been significant conceptual and technical
improvements in the retrieval of NO2 from space-based measurements.
Prior versions developed a new scheme for separating stratospheric and
tropospheric components in version 2.1 (V2.1) (Bucsela et al., 2013; Lamsal
et al., 2014) and a new algorithm for improved NO2 SCD retrievals in
V3.0 (Marchenko et al., 2015; Krotkov et al., 2017), including improved
cloud products (Veefkind et al., 2016) in V3.1 (Choi et al., 2020). The
current version, V4.0, further improves on the retrievals in a number of
significant ways for NO2 AMF and VCD calculations. Figure 1 shows a
schematic diagram of the retrieval algorithm, and Table 1 summarizes the
differences and similarities between previous (V3.1) and current (V4)
versions. Some of the approaches in the V4 algorithm are similar to those
used in V3.1, but there are several important changes as discussed in detail
in Sect. 2.1 and 2.2.
Schematic diagram of the NASA OMI NO2 algorithm version 4.0,
which is coupled with the cloud- and geometry-dependent surface Lambertian
equivalent reflectivity (GLER) algorithms that ultimately produce
stratospheric (strat) and tropospheric (trop) NO2 vertical column
densities (VCDs). Acronyms used here are described in the relevant sections
below. VLIDORT: vector linearized discrete ordinate radiative transfer;
MODIS: Moderate Resolution Imaging Spectroradiometer; BRDF: bidirectional
reflectance distribution function; DEM: digital elevation model; NISE:
near-real-time ice and snow extent; AMSR-E: Advanced Microwave Scanning
Radiometer for Earth Observing System (EOS); SSMIS: Special Sensor Microwave
Imager–Sounder; GEOS-5: Goddard Earth Observing System version 5; Ps:
surface (terrain) pressure over OMI pixel; ECF: effective cloud fraction;
CRF: cloud radiance fraction; OCP: optical centroid pressure; Sw: scattering
weight; LUT: lookup table; GMI: Global Modeling Initiative; AMF: air mass
factor; SCD: slant column density.
Summary of algorithms and approaches used in the NASA NO2
algorithms versions 3.1 and 4.0.
Algorithm component Version 3.1 (released 2018)Version 4.0 (released 2019)Spectral fitNO2Modified DOAS fit (Marchenko et al., 2015)Same as in V3.1O2–O2DOAS fit from KNMI (Veefkind et al., 2016)Modified DOAS fit (Vasilkov et al., 2018)AMFTerrain reflectivityMonthly climatology (Kleipool et al., 2008)Daily GLER data (Vasilkov et al., 2017; Qin et al., 2019; Fasnacht et al., 2019)Terrain pressureAt pixel center (calculated from terrain height and GMI terrain pressure)Average over pixel (calculated from terrain height and GMI terrain pressure)Cloud pressure and fractionOperational O2–O2 cloud product (OMCLDO2) v2.0 (Veefkind et al., 2016)New O2–O2 cloud product (OMCDO2N) derived using the GLER product (Vasilkov et al., 2018)Cloud radiance fractionCalculated at 440 nm from OMCLDO2v2.0 cloud fraction using VLIDORT-based lookup tableCalculated at 440 nm from OMCDO2N cloud fraction using VLIDORT-basedlookup tableScattering weightsTOMRAD-based lookup tableSame as in V3.1A priori NO2 profilesGMI-derived yearly varying monthlymean profiles at 1∘× 1.25∘Same as in V3.1Stripe correction Based on data from 30∘ S–5∘ N of five orbitsSame as in V3.1Stratosphere–troposphere separation Spatial filtering and interpolation (Bucsela et al., 2013), but with minor changes in box sizesSame as in V3.1NO2 and O2–O2 spectral fittingNO2 spectral fitting algorithm
The spectral fitting algorithm for the operational standard OMI NO2
product is described in detail in Marchenko et al. (2015). Briefly, the
algorithm retrieves NO2 slant column densities (SCDs) by using a
differential optical absorption spectroscopy (DOAS) approach (e.g., Platt
and Stutz, 2006). In the DOAS approach, laboratory-measured spectra of
NO2 (Vandaele et al., 1998) and glyoxal (Volkamer et al., 2005),
HITRAN08-based water vapor spectra (Rothman et al., 2009), and rotational
Raman (RR; Ring effect) filling-in are sequentially fitted to the
OMI-measured reflectance spectrum in the 402–465 nm wavelength range. The
slant column represents the integrated abundance of NO2 along the
average photon path from the sun through the atmosphere to the satellite.
The Ring spectra are calculated as a linear combination of the atmospheric
(Joiner et al., 1995) and liquid water (Vasilkov et al., 2002) RR spectra,
convolved with the wavelength- and cross-track-dependent OMI transfer
function (Dirksen et al., 2006). The algorithm employs a multi-step,
iterative retrieval procedure for removal of the Ring and spectral
under-sampling (Chance, et al., 2005) patterns as well as a low-order
polynomial smoothing prior to estimation of SCDs for all interfering
species. This is in contrast to the conventional DOAS approach that treats
the Ring effect as a pseudo-absorber and fits all absorbers simultaneously
with the polynomial functions. For accurate wavelength shifts (radiances vs.
irradiances), the standard product algorithm splits the entire fitting
window into seven carefully selected, partially overlapping micro-windows,
iteratively evaluates the RR spectrum amplitudes, performs wavelength
adjustments for each segment, and then iteratively retrieves the NO2,
H2O, and glyoxal in the windows best suited for a particular trace gas
species.
The OMI NO2 SCDs from the standard product were compared with improved
SCD retrievals from the Quality Assurance for Essential Climate Variables
(QA4ECV; http://www.qa4ecv.eu/, last access: 18 May 2020), BIRA-IASB's (Royal Belgian
Institute for Space Aeronomy) QDOAS software (http://uv-vis.aeronomie.be/software/QDOAS/, last access: 18 May 2020), and the latest KNMI
retrievals (van Geffen et al., 2015) and are shown to agree within 2 %
(Zara et al., 2018). The typical NO2 SCD uncertainties amount to
∼ 0.8 × 1015 molec. cm-2, or 5 %–7 % in
high-SCD areas and 15 %–20 % in low-SCD areas (Marchenko et al., 2015).
O2–O2 spectral fitting algorithm
The oxygen dimer (O2–O2) slant column fitting algorithm shares
many features of the NO2 fitting algorithm and is described in detail
in Vasilkov et al. (2018). It consists of a multi-step, iterative retrieval
approach with three carefully selected micro-windows sampling the flanks and
the core of the broad O2–O2 feature centered at 477 nm. The
algorithm exploits OMI-measured reflectance spectra in the 451–496 nm range
to determine the wavelength shifts and RR amplitudes. The Ring patterns are
removed from the original OMI reflectances during the iterative adjustments
for differences in the wavelength registration of radiances and irradiances.
The O2–O2 slant columns are retrieved after removal of the
NO2 and H2O absorptions estimated by the algorithm discussed in
the previous section and of the ozone absorption using total ozone data
from Veefkind et al. (2006). After removal of the interfering signals, the
477 nm O2–O2 absorption profile is carefully normalized to the
adjacent O2–O2 absorption-free reflectance levels accounting for
very different wavelength dependencies of surface reflectances over various
geographical sites (e.g., the open-ocean and desert area), as described in
Vasilkov et al. (2018). The normalized O2–O2 absorption profiles
are then iteratively fitted with the temperature-dependent cross sections
from Thalman and Volkamer (2013) over the 463–488 nm range to derive
O2–O2 SCDs. These are used to estimate the cloud properties as
discussed below in Sect. 2.2.2.
Improved air mass factor calculations
The AMF, which is defined as the ratio of SCD to VCD, is needed to calculate
the retrieved NO2 VCD. Details of the AMF and its calculation are given
in Palmer et al. (2001). The AMF for each FOV is calculated by combining
altitude-dependent (z-dependent) scattering weights (w) computed with a radiative
transfer model and a local a priori vertical NO2 profile shape (S),
taken from a chemistry transport model:
AMF=∫z1z2wzSzdz.
For the tropospheric AMF, the integral extends from the surface to the
tropopause, whereas the integral from the tropopause to the top of the
atmosphere provides the stratospheric AMF. The scattering weight at a given
altitude describes the sensitivity of the backscattered radiation to the
abundance of the absorber at that altitude. For an optically thin absorber
like NO2, scattering weights are a function of atmospheric scattering
and are considered to be independent of the species' vertical distribution
(Palmer et al., 2001). Factors affecting scattering weights include
wavelength, optical geometry (solar and viewing azimuth and zenith angles),
surface reflectivity, and cloud pressure and fraction. The wavelength
dependence of scattering weights is accounted for by creating an average of
scattering weights derived from the values at multiple wavelengths within
the NO2 spectral fitting window. To compensate for the effect of the
assumed constant NO2 temperature (220 K) in the NO2 SCD
retrievals, the scattering weights are corrected for the atmospheric
temperature effect using local climatological monthly temperature profiles
as discussed in Bucsela et al. (2013). These profiles are based on the
meteorological field from the Modern-Era Retrospective Analysis for Research
and Applications (MERRA-2) (Gelaro et al., 2017).
The a priori NO2 profile shapes are computed from a monthly mean
climatology of vertical NO2 profiles constructed from the Global
Modeling Initiative (GMI) CTM simulation (Douglass et al., 2004; Strahan et
al., 2007; Strode et al., 2015) driven by MERRA-2 meteorology. The spatial
resolution of the model is 1.25∘ in longitude and 1.0∘
in latitude, and the atmosphere is divided into 72 pressure levels extending
from the surface to 0.01 hPa. The model output is sampled between 13:00 and 14:00 local time, consistent with the OMI overpass time. The use of monthly
NO2 profiles helps capture the seasonal variation in the NO2
vertical distribution (Lamsal et al., 2010). The simulation is based on
yearly varying NOx emissions, as discussed in Strode et al. (2015);
this is necessary to account for the effect of rapidly changing NOx
emissions (e.g., Tong et al., 2015; Duncan et al., 2016; Miyazaki et al.,
2017) on local NO2 profile shapes (Lamsal et al., 2015; Krotkov et al., 2017).
For each FOV, AMFs are computed for clear (AMFclr) and cloudy
(AMFcld) conditions. The AMF of a partially cloudy scene is
calculated by assuming the independent pixel approximation:
AMF=1-fr×AMFclr+fr×AMFcld,
where fr is the cloud radiance fraction (CRF), defined as the fraction
of the measured radiation that comes from clouds and scattering aerosols,
and it is computed at 440 nm from the retrieved effective cloud fraction (ECF),
fc, using Eq. (8) (see below). AMFclr is calculated for the
ground reflectivity of Rs and at terrain pressure Ps, whereas
AMFcld is calculated assuming a Lambertian surface of reflectivity
0.8 at the retrieved cloud pressure. Below we provide a detailed discussion
of each of these input parameters that are incorporated in the OMNO2 V4.0
algorithm.
New surface reflectivity product for NO2 and cloud retrievals
Surface reflectivity is an important input parameter for UV–Vis satellite
retrievals of trace gases and cloud information. The surface reflectance
over both ocean and land depends upon viewing and illumination geometry and
can be accurately described by the bidirectional reflectance distribution
function (BRDF). This effect is, however, neglected by most currently
available trace gas and cloud algorithms, which use a climatological
Lambert equivalent reflectivity (LER) for the surface. To account for
surface BRDF effects in the NO2 and cloud retrievals, here we use the
geometry-dependent surface LER (GLER) product derived using the Moderate
Resolution Imaging Spectroradiometer (MODIS) BRDF data and the vector
linearized discrete ordinate radiative transfer (VLIDORT) calculation
(Vasilkov et al., 2017; Qin et al., 2019; Fasnacht et al., 2019). The GLER
allows for a computationally efficient approach that does not require major
changes to the existing trace gas and cloud algorithms.
We derive GLER by inverting the top-of-atmosphere (TOA) radiance (I) of a
Rayleigh atmosphere over a non-Lambertian surface for each specific FOV and
sun–satellite geometry within the Lambertian framework, i.e.,
I=I0+GLER×T/(1-GLER×Sb),
where I0 is the TOA radiance calculated for a black surface, T is the
total (direct + diffuse) solar irradiance reaching the surface converted
to the ideal Lambertian-reflected radiance (by dividing by π
steradians) and then multiplied by the transmittance of the reflected
radiation between the surface and TOA in the direction of a satellite
instrument, and Sb is the diffuse flux reflectivity of the atmosphere
for the case of its isotropic illumination from below (Dave, 1978). The
values of I0, T, and Sb are pre-computed with VLIDORT and stored in a
lookup table. The GLER values are calculated at wavelengths relevant for
both NO2 (440 nm) and cloud (466 nm) retrievals.
Over land, the BRDF is calculated using the Ross-thick Li-sparse kernel
model (Lucht et al., 2000) in VLIDORT (Spurr, 2006):
BRDF=aiso+avolkvol+ageokvol,
where the coefficients aiso, avol, and ageo come from the
Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5
gap-filled seasonally snow-free BRDF product MCD43GF (Schaaf et al., 2002,
2011) for band 3 (459–479 nm) available at 30 arcsec spatial resolution
and 8 d temporal resolution. The term aiso is the isotropic
contribution describing the Lambertian part of light reflection from the
surface; the volumetric kernel (kvol) describes light reflection from a
dense leaf canopy, and the geometric kernel (kgeo) describes light
reflection from a sparse ensemble of surface objects casting shadows on the
background assumed to be Lambertian. The kernels are the only
angle-dependent functions, the expressions of which are given in Lucht et al. (2000). The band 3 BRDF coefficients spatially averaged over an actual
satellite FOV are used to calculate TOA radiance and GLER at 466 nm. To
calculate GLER at 440 nm, we apply a scaling method using the ratio of
OMI-derived Lambert equivalent reflectivity (LER) data at 440 and 466 nm:
GLER440=GLER466×fs.
The value of fs=LER440LER466 is taken from the
gridded monthly LER ratio data at 1∘× 1∘ or
coarser resolution. The LER is determined from OMI TOA radiance measurements
as discussed in Vasilkov et al. (2017, 2018). We use clear-sky (effective
cloud fraction < 0.02) and aerosol-free (OMI UV aerosol index, < 0.5; Torres et al., 2007) OMI LER data to create the monthly
gridded data. The cloud and aerosol screening is necessary because the
spectral dependence of surface features differs from that of clouds and
aerosols.
Over water, the surface reflectance is calculated at the two wavelengths,
440 and 466 nm, using VLIDORT. To calculate TOA radiance, we include
light specularly reflected from a rough water surface and diffuse
light backscattered by water bulk. We also account for contributions from
oceanic foam that can be significant for high wind speeds. Reflection from
the water surface is described by the Cox–Munk slope distribution function,
which depends on both the wind speed and the wind direction (Cox and Munk,
1954). Polarization at the ocean surface is accounted for by using a full
Fresnel reflection matrix as suggested by Mishchenko and Travis (1997).
We use wind speed data from a pair of satellite microwave imagers that
include the Advanced Microwave Scanning Radiometer–Earth Observing System
(AMSR-E) instrument onboard the NASA Aqua satellite (Meissner and Wentz,
2004) for 2004–2011 and the Special Microwave Imager–Sounder (SSMIS) onboard
the Air Force Defense Meteorological Satellite Program (DMSP) Satellite F16
(Wentz et al., 2012) afterwards. Wind direction data are taken from the
Global Modeling Assimilation Office (GMAO) Goddard Earth Observing System
Model Forward Processing for Instrument Teams (GEOS-5 FP-IT) near-real-time
assimilation.
Diffuse light from the ocean is described by a Case 1 water model with a
single input parameter of chlorophyll concentration (Morel, 1988) taken from
the monthly Aqua MODIS data. The common Case 1 water model developed for the
visible channel (Morel, 1988) was extended to the UV using data from Vasilkov et al. (2002, 2005). To calculate water-leaving radiance, we require the
downwelling irradiance at the surface (i.e., atmospheric transmittance).
Since the transmittance and the water-leaving contribution are coupled, we
develop a simple coupling scheme in VLIDORT to ensure that the value of
water-leaving radiance used as an input at the ocean surface will correspond
to the correct value of the downwelling flux reaching the surface interface
(Fasnacht et al., 2019).
For OMI ground pixels covering land and water surfaces, the TOA radiance (I)
is calculated as an average of radiance for land (IL) and water
(Iw) weighted by the pixel land fraction (f):
I=fIL+(1-f)Iw.
The value of f is determined by converting various surface categories in
the MODIS data (note that these are of much higher spatial resolution than
the OMI data) into a binary land–water mask (e.g., treating all shorelines
and ephemeral water as the land category and classifying all other water
subcategories simply as water). The areal fraction of land (or water) for
each OMI pixel is then computed as the statistics of the binary categories.
Figure 2 shows an example of changes in surface reflectivity used in the
previous (V3.1) and the current (V4.0) version of the OMI NO2
algorithm. The GLER data computed for OMI observations as discussed above
for 20 March 2005 differ considerably from the OMI-derived climatological
monthly LER data (Kleipool et al., 2008) for March. As shown in Figs. 2
and 3a, the GLERs are generally lower than climatological LER data except
at swath edges with large viewing angles and over areas affected by sun glint
that correspond to higher values of GLER. Changes over the sun-glint areas
are rather large, reaching up to 0.3. The climatological LER data derived by
analyzing histograms of 5 years of OMI-based LER data likely overestimate
the actual surface reflectivity due to residual cloud and aerosol
contamination and underestimate over sun-glint areas as the procedure ignores
sun-glint-affected observations. In contrast, the GLER data over land are
based on atmospherically corrected radiances from high-resolution MODIS
observations, minimizing the impact of both cloud and aerosols.
Surface reflectivity at 440 nm (a) derived using MODIS BRDF data with OMI geometry (GLER) on 20 March 2005 compared with (b) OMI-based monthly LER climatology
(OMLER) for the month of March (Kleipool et al., 2008). The bottom panel (c) shows the difference between MODIS-based and climatological surface reflectivity data.
Differences (V4.0–V3.1) in (a) surface reflectivity, (b) cloud
radiance fraction, and (c) cloud optical centroid pressure for 20 March 2005, as used in the V3.1 and V4.0 algorithms and binned by the values of
corresponding parameters from V4.0. Data are separated for land (blue) and
ocean surfaces, as well as by sun-glint (green) and non-sun-glint (orange) geometry
over ocean. The vertical bars represent the standard deviation for each bin
of those parameters.
Improved cloud product retrieval
We develop a new algorithm that provides cloud parameters, namely cloud
radiance fraction (CRF) and cloud optical centroid pressure (OCP), and use
them in the OMNO2 algorithm. Similar to the standard OMCLDO2 algorithm
(Veefkind et al., 2016), our cloud algorithm exploits the O2–O2
absorption to retrieve O2–O2 SCD as discussed in Sect. 2.1.2,
but it derives the two cloud parameters using the GLER and other ancillary data
that are used in the NO2 algorithm, maintaining inter-algorithm
consistency. The OMCLDO2 algorithm retrieves these parameters using the
climatological LER data from Kleipool et al. (2008). In the following, our
new cloud product is referred to as OMCDO2N.
The derivation of CRF and OCP is based on a simple cloud model called the
mixed Lambertian equivalent reflectivity (MLER) model (Joiner and Vasilkov,
2006; Veefkind et al., 2016). The MLER model treats cloud and ground as
horizontally homogeneous, opaque Lambertian surfaces and mixes them using
the independent pixel approximation (IPA). According to the IPA, the
measured TOA radiance, Im, is a sum of the clear-sky (Ig) and
overcast (Ic) subpixel TOA radiances that are weighted with an
effective cloud fraction (ECF), fc (e.g., Stammes et al., 2008):
Im=Ig1-fc+Icfc.
We choose the wavelength of 466 nm that is not substantially affected by
rotational Raman scattering (RRS) or atmospheric absorption to derive
fc. The parameters Ig and Ic are a function of the ground and
cloud LERs, respectively, and are calculated using VLIDORT (Spurr, 2006) and
obtained with an interpolated lookup table. We use GLER discussed above for
ground reflectivity and a uniform cloud reflectivity of 0.8 (Koelemeijer et
al., 2001; Stammes et al., 2008). The value of fc is calculated by
inverting Eq. (7). Note that aerosols are implicitly accounted for in
the determination of fc, as they are treated (like clouds) as
particulate scatters. CRF (fr) defines the fraction of TOA radiance
reflected by cloud:
fr=fc×IcIm.
We use pre-computed lookup tables of the TOA radiances generated using
VLIDORT. Due to its wavelength dependence, we calculate CRF at 466 nm for
OCP at 440 nm for NO2 retrievals.
The MLER model compensates for photon transport within a cloud by placing
the Lambertian surface somewhere in the middle of the cloud instead of at
the top (Vasilkov et al., 2008). The pressure of this surface corresponds to
OCP, which can be modeled as a reflectance-averaged pressure level reached
by backscattered photons (Joiner et al., 2012). We retrieve cloud OCP from
the O2–O2 SCD discussed above (Sect. 2.1.2). The cloud OCP, Pc, is estimated by inversion using the MLER method to compute the
appropriate O2–O2 AMFs:
SCD=AMFg×VCDg×1-fr+AMFc×VCDc×fr,
where VCD (SCD / AMF) is the vertical column density of O2–O2 over ground
(VCDg) and cloud (VCDc). The clear-sky (AMFg) and
overcast or cloudy (AMFc) subpixel AMFs are calculated at 477 nm with
ground (GLER) and cloud (0.8) reflectivity, respectively. Lookup tables for
the AMFs were generated using VLIDORT. Temperature profiles needed for
estimation of VCD and AMF are taken from the GEOS-5 global data assimilation
system (Rienecker et al., 2011).
In addition to OCP, we retrieve the so-called scene pressure. The scene
pressure is derived from Eq. (9) assuming that fr=1 and cloud
reflectivity equals the scene LER. The scene LER is determined from the measured
TOA radiance using the equation (Eq. 3) that defines TOA radiance in the
Rayleigh atmosphere over a Lambertian surface. In the absence of clouds,
aerosols, and any major gas absorptions, the scene pressure should be equal
to the surface pressure. The scene pressure is therefore an important
diagnostic tool for evaluation of the performance of cloud pressure
algorithms.
Figure 4 shows an example of cloud products retrieved with our algorithm
compared with those retrieved from the standard OMCLDO2 algorithm (Veefkind
et al., 2016). The retrieved OCP and CRF from the two algorithms exhibit
broadly consistent spatial patterns in both cloud altitude and amount. The
values of OCP generally range from 370 to 1001 hPa in OMCDO2N versus 150 to 1011 hPa in OMCLDO2N. For both products, CRF varies from 0 for
clear-sky to 1 for overcast conditions. A systematic difference is evident,
with generally higher values in OMCDO2N for OCP by 147 hPa and CRF by 0.01
compared to OMCLDO2. For OCP, there is a general pattern in difference,
with OMCDO2N OCP higher for low-altitude clouds (> 700 hPa) and
lower values for high-altitude clouds (< 300 hPa) (Fig. 3c). The
largest OCP differences occur for cases in which cloud pressures in OMCLDO2 are
clipped to 150 hPa. For CRF, larger differences occur for partially cloudy
scenes, with higher CRF values in OMCDO2N by 0–0.1 for both land and water
surfaces (Fig. 3b). Exceptions are over sun-glint areas where CRF in
OMCDO2N is lower by 0–0.3 with a mean difference of 0.13.
Cloud optical centroid pressure at 477 nm (a, c, e) and cloud
radiance fraction at 440 nm (b, d, f) retrieved for 20 March 2005 with the OMNO2
V4.0 (a, b) and V3.1 (c, d) algorithms, respectively. Panels (e, f) show
their differences. Gray represents the OMI pixels with retrieved
cloud pressure equal to terrain pressure in V4.0 on the left and over
snow and ice surface identified by the NISE flag on the right.
Treatment over snow and ice surfaces
Over ice and snow surfaces, identified by near-real-time ice and snow
extent (NISE) flags (Nolin et al., 2005) in the OMI Level-1b data, the
following treatments are made for surface reflectivity. In the case of permanent
ice and snow surfaces, the MCD43GF product provides BRDF parameters,
allowing us to calculate GLER. Over seasonal snow area usually with data
gaps in MCD43GF, we calculate OMI-derived LER capped by a constant snow
albedo of 0.6 following Boersma et al. (2011). In rare cases of pixels not
flagged by NISE and gaps in MODIS data, we use OMI LER climatology (Kleipool
et al., 2008) regardless of whether the surface is either snow- and ice-covered but
missed by NISE or snow- and ice-free.
The OMI-derived scene reflectivity and scene pressure are used for NO2
and cloud retrievals over seasonally snow-covered areas. If the NISE flags are
set as true, the following assumptions are made in our CRF, OCP, and
NO2 retrievals. Over bright surfaces (scene reflectivity > 0.2), we consider the scenes to be snow- or cloud-covered and assign the scene
pressure to OCP. In addition, if the difference between the surface pressure
and scene pressure is smaller than 100 hPa, the scene is considered to be
either cloud-free or covered by optically thin clouds following the cloud
over snow classification by Vasilkov et al. (2010), and CRF for the pixel is
set to zero. If the difference between the surface pressure and scene
pressure exceeds 100 hPa, the scene is considered to be overcast by
optically thick (shielding) clouds (Vasilkov et al., 2010), and CRF for the
pixel is set to 1. To avoid a possible NISE misclassification (Cooper et
al., 2018) for low-reflectivity scenes (scene reflectivity < 0.2),
we consider such scenes to be snow- and ice-free and calculate CRF, OCP, and
NO2 AMF using the standard procedure with GLER for those scenes.
Improved terrain height and pressure calculation
Terrain pressure is a critical parameter for the AMF in NO2 and cloud
algorithms as well as for the total optical depth of the Rayleigh atmosphere
in the GLER algorithm. Prior studies have shown that errors in terrain
pressure can introduce over 20 % errors in retrieved NO2 VCD,
especially in areas of complex terrain (Zhou et al., 2010; Russell et al., 2011).
Here, we use a 2 arcmin global relief model of global land–water surface
data (ETOPOv2; National Geophysical Data Center, 2006) to derive terrain
height for each individual OMI ground pixel. We derive the pixel-average
terrain height by collocating and averaging the high-resolution data as
discussed in Qin et al. (2019). The corresponding terrain pressure for each
OMI pixel (Ps) is calculated from the terrain pressure–height
relationship established based on MERRA-2 monthly terrain pressure
(Ps_GMI) at a spatial resolution of 1∘
latitude × 1.25∘ longitude used in the GMI model
discussed above:
Ps=Ps_GMIe-(ΔzH),
where Δz (=z-zGMI) represents the difference between the
average terrain height for an OMI pixel (z) and the terrain height at GMI
resolution (zGMI). The parameter H=kTMg represents the
scale height, where k is the Boltzmann constant, T is the temperature at the
surface, M is the mean molecular weight of air, and g is the acceleration
due to gravity.
Impact of the changes on AMF
Figure 5 shows an example of how changes in each individual input parameter
affect tropospheric AMFs, which, in turn, translate inversely to tropospheric
NO2 column retrievals. Replacing climatological LER from OMLER with
daily GLER data affects scattering weight profiles in the lower troposphere,
resulting in lower values of tropospheric AMF almost everywhere, except over
sun-glint areas where the use of GLER enhances scattering weights and
tropospheric AMF (Fig. 5a). The changes in tropospheric AMF with GLER
usually range from -50 % to 25 %, occasionally reaching up to -100 %.
The effect is small (-6 % to 1 %) for overcast scenes (CRF > 0.9), increases (-28 % to 17 %) over clear and partially cloudy
scenes (CRF < 0.5) for unpolluted regions, and surges (-62 % to
3 %) over polluted areas (>5×1015 molec. cm-2). Figure 6a shows GLER-driven changes in clear-sky (CRF < 0.5) tropospheric AMF for different surface and scene types, separated by
tropospheric NO2 column amounts. For 80 % of cases over land, 97 %
over water outside sun-glint areas, and 98 % over sun-glint areas,
tropospheric NO2 columns are <1.5×1015 molec. cm-2, and the average GLER-driven differences are small at -6.6±17.3 %, -3.8±7.1 %, and 4.0±12.9 %, respectively. The
differences increase gradually with column amount over NOx source
regions (e.g., cities and highly polluted coastal areas), with binned (of
size 1×1015 molec. cm-2) average differences ranging
from -10±20.1 % to -30±19.7 %. Over snow and ice surfaces,
changes are rather large, reaching up to a factor of 2. The impact of
change in the surface reflection data on stratospheric AMFs is negligible
(< 2 %).
Impact on tropospheric AMF (i.e., V4.0–V3.1) from changes in
(a) surface reflectivity, (b) cloud and surface treatment, (c) terrain
pressure, and (d) their combination on 20 March 2005. The panel (c) inset
shows a zoomed view of the impact over complex terrain in the western US.
Figures 5b and 6b show how changes in the cloud parameters (CRF and OCP)
affect tropospheric AMF. Replacing OMCLDO2-based cloud parameters with those
from OMCDO2N changes scattering weight profiles in a complicated way. Higher
values of OCP in OMCDO2N will include additional portions of scattering
weights between the OMCDO2N- and OMCLDO2-based OCPs, especially in the lower
troposphere, thereby reducing the tropospheric AMF. On the other hand, the
higher CRF values lead to an increased contribution of the cloudy AMF in the
calculation of tropospheric AMF, thereby increasing its value. Their
combination causes a wide range of scenarios and large variation in
the AMF effect. Overall, the change in cloud parameters causes enhancement
of tropospheric AMFs for partially cloudy and overcast scenes and reduction
for clear-sky scenes, especially over polluted areas. The AMF differences
are generally large for low AMF values that are driven by enhanced
differences in either OCP, CRF, or both as discussed in Vasilkov et al. (2017). The changes in tropospheric AMF with the OMCDO2N-based cloud
parameters usually range from -17 % to 28 %, with larger variation over
land (-34 % to 40 %) compared to water (-12 % to 25 %) and for
low (< 1) AMF (-47 % to 41 %) compared to high (> 3) AMF (-4 % to 18 %). The largest changes in AMF (-96 % to 62 %)
occur over snow and ice surfaces that result from the difference in the
treatment of snow and ice for cloud and NO2 retrievals as discussed in
Sect. 2.2.3. For clear-sky and partially cloudy scenes with CRF < 0.5, the effect of the changes in cloud parameters differs between land and
water surfaces as well as sun-glint and non-sun-glint geometries and becomes
more pronounced over polluted land and coastal areas (Fig. 6b). As in the
case of surface reflectivity, the impact of the change in cloud parameters
on stratospheric AMF is < 1 %.
The impact on tropospheric AMF (i.e., V4.0–V3.1) from changes
in (a) surface reflectivity, (b) cloud, and (c) their combination for clear
and partially cloudy scenes (CRF < 0.5) on 20 March 2005. Percent
differences in tropospheric AMF are sorted by tropospheric NO2 columns,
separating them by land (blue) and ocean, as well as by sun-glint (green) and
non-sun-glint (orange) geometry over ocean. The vertical bars represent the
standard deviations for the tropospheric NO2 column bins.
Figure 5c presents an example of changes in tropospheric AMF differences
between the previous approach of using terrain pressure at OMI pixel centers
and the pixel-average terrain pressure implemented in the current version
(V4.0). In general, the AMF changes driven by the changes in terrain
pressure are within ±1 % over ocean and ±3 % over land,
although at times they can reach up to 30 %, especially for observations
over complex terrain such as mountainous regions (Fig. 5c inset).
Figures 5d and 6c show the AMF differences arising from the combined effect
of changes in all parameters discussed above. The effect arising from the
replacement of the climatological OMLER with GLER is partially compensated for
by the effect arising from the change in cloud parameters in places where
the two parameters exhibit opposite trends. Exceptions are over polluted land
and coastal areas; the GLER effect on AMF is augmented by the cloud effect.
The average AMF changes arising from all parameters (2 %) are lower than
the changes arising from either GLER (-2.3 %) or cloud parameters
(4.1 %), although the combined effect leads to a wider range of variation
in AMF changes (-100 % to 57 %) compared to the effect from
individual parameters. The changes arising from all parameters are somewhat
smaller (-21 % to 34 %) for overcast scenes (CRF > 0.9)
compared to (-47 % to 29 %) clear and partially cloudy scenes
(CRF < 0.5) and are substantial (-137 % to 30 %) over highly
polluted areas (>5×1015 molec. cm-2) and
over snow and ice surfaces (-126 % to 99 %). Differences in the AMF effect
are evident among land, water, and sun-glint areas (Fig. 6c). The impact of
the changes is below 1 % for the stratospheric AMF.
Row anomaly and removal of stripes
The retrieved NO2 SCDs have persistent relative biases in the 60
cross-track FOVs and show a pattern of stripes running along each orbital
track. This instrumental artifact is corrected using the “de-striping”
procedure described in detail in Bucsela et al. (2013). Briefly, the
de-striping algorithm estimates the mean cross-track biases using
measurements obtained at latitudes between 30∘ S and 5∘ N and from orbits within
two orbits of the target orbit. These correction values, one for each cross-track
position, are then subtracted from the retrieved SCDs to derive the
de-striped SCD field.
Starting 25 June 2007 and presumably even earlier, OMI experienced a more
severe form of anomaly that affects the quality of radiance data in certain
rows at all wavelengths (Dobber et al., 2008; Schenkeveld et al., 2017).
This effect, called the “row anomaly” (RA), has developed and changed over
time. Currently, the RA has affected approximately half of the OMI's FOVs,
resulting in OMI's global coverage now being 2 d instead of 1 d before the
onset of the RA.
The quality of radiance data for the RA-affected FOVs is sufficiently poor
as to prevent reliable NO2 retrievals. Therefore, we abandon retrieval
calculations for all measurements that are flagged by the RA-detection
algorithm used in the Level-1 processing. We found that this RA-detection
algorithm may not be sufficiently sensitive to the relatively small (but
important for our purposes) RA changes. Figure 7 shows an example of
anomalous rows not flagged by the RA-detection algorithm but observed in the
NO2 retrievals. Shown are time series of average NO2 SCDs
normalized by geometric AMFs over the Pacific Ocean for the RA-unaffected
row of 20 (0-based) compared with three rows that show significant
degradation in the quality of SCD retrievals. These particular rows are in
immediate proximity to the main RA area, thus showing the gradual RA
evolution: in the present epoch the RA slowly shifts towards the
high-numbered rows – note the sequential timing of the big drops in the
retrievals in rows 44–46. While the data from the three rows start
deviating from row 20 beginning from summer 2016, the data quality degrades
further for rows 44, 45, and 46 from September of 2017, 2018, and 2019,
respectively, to the extent that they cannot be sufficiently corrected by
the de-striping algorithm. In such cases, we implement additional
RA flagging for those rows that start showing anomalous behavior and
exclude those data from Level-2 and higher-level NO2 products.
The time series of OMI NO2 SCD normalized by the geometric
AMF for clear-sky and partially cloudy conditions (CRF < 0.5) over
the Pacific Ocean. The data are separated by cross-track scan position,
comparing the presumably RA-free row 20 (black) with rows 44 (red), 45
(orange), and 46 (green). The row numbers are 0-based.
Calculation of stratospheric and tropospheric NO2 columns
We use an observation-based stratosphere–troposphere separation scheme to
estimate the stratospheric NO2 field, as discussed in detail in Bucsela
et al. (2013), and the algorithm remains unchanged in the current version.
Briefly, the stratospheric field for an orbit is computed by creating a
gridded global field of initial stratospheric NO2 VCD estimates
(Vinit) with data assembled from within ±7 orbits of the target
orbit:
Vinit=SstratAMFstrat=S-StropapAMFstrat.
Here, Sstrat and AMFstrat represent stratospheric SCD and AMF,
respectively. A priori estimates of the tropospheric contribution
(Stropap) are subtracted from the measured de-striped
SCDs (S), and grid cells wherein this contribution exceeds 0.3×1015 molec. cm-2 are masked. This masking ensures that the
model contribution to the retrieval is minimal, especially in polluted
areas. The residual field of the initial stratospheric VCDs measured outside
the masked regions mainly over unpolluted or cloudy areas is smoothed by a
boxcar average and a two-dimensional interpolation, yielding an estimate for
stratospheric NO2 VCD (Vstrat) for an individual ground pixel.
The estimation of the stratospheric NO2 VCD allows for the computation
of the tropospheric NO2 VCD (Vtrop) from the de-striped NO2
SCD (S) and the tropospheric AMF (AMFtrop):
Vtrop=StropAMFtrop=S-SstratAMFtrop,
where stratospheric NO2 SCD (Sstrat) is calculated from
stratospheric AMF (AMFstrat) and Vstrat computed in the previous step.
With the updates in surface and cloud treatments as discussed in Sect. 2.2, the current version has made significant improvements, particularly in
tropospheric AMFs and consequently in VCD estimates. Further improvement to
the retrievals is possible by enhancing the quality of a priori NO2
profiles through improvements in model resolution, emissions, and chemistry,
which remain unchanged in the current version. If improved a priori NO2
profiles become available, one can first use Eq. (1) to readily recalculate
AMFtrop by combining them with scattering weights (wz) archived in the data files and then use Eq. (12) together with other
supplied parameters to recalculate Vtrop. The same approach can be
applied to remove the effect of a priori profiles used in retrievals
altogether, while comparing NO2 columns from a model simulation with
retrievals (Eskes and Boersma, 2003; Lamsal et al., 2014).
Figure 8 shows a comparison of tropospheric and stratospheric NO2 columns retrieved from the V3.1 and V4.0 algorithms for 20 March 2005. As
expected, the updates implemented in V4.0 yield higher (∼ 10 %–40 %)
tropospheric NO2 columns in polluted areas, with less-pronounced
(±10 %) differences in background and low-column areas. These
results are consistent with the observed differences in the tropospheric AMF
as discussed above in Sect. 2.2.4 and with other previous regional
studies over land surfaces (Zhou et al., 2010; McLinden et al., 2014; Lin et
al., 2014, 2015; Laughner et al., 2019; Liu et al., 2019) that implemented
one or more of the changes applied in V4.0. In contrast to changes in
tropospheric NO2 retrievals, changes in stratospheric NO2
estimates range between -3.6×1014 molec. cm-2 and
3.2×1014 molec. cm-2 and are close to the range of
expected uncertainties of stratospheric NO2 estimates (Bucsela et al.,
2013). The relative differences in the stratospheric NO2 column between the
two versions are close to 0 % on average, usually ranging between -2.5 %
and 2.0 % and occasionally reaching up to ±13 %. This difference in
stratospheric NO2 estimates is much larger than the difference in
stratospheric AMFs and is caused by differences in tropospheric AMFs that
influence NO2 observations over unpolluted and cloudy areas used by the
stratosphere–troposphere separation scheme.
Tropospheric (a) and stratospheric (b) NO2 VCD from V4.0 and
their differences (c, d) with respect to V3.1 data (V4.0–V3.1) for 20 March 2005.
Gray in the tropospheric NO2 maps represents cloudy areas
(CRF > 0.5). Bottom panels show the average (black circles) and
standard error (vertical bars) of the relative difference, 100 × (V4.0–V3.1) / V3.1, for tropospheric (e) and stratospheric (f) NO2
VCDs plotted as a function of the respective NO2 column amounts. The green
symbols represent the logarithm of the number of samples.
Figure 9 shows the seasonally averaged tropospheric NO2 columns over
the selected domains of North America, Europe, southern Africa, and Asia for
the months of June, July, and August in 2005. These domains contain highly
polluted areas with significant NOx emissions where the impact of
changes in surface reflectivity and cloud parameters on
tropospheric NO2 retrievals becomes increasingly important. The use of
more accurate pixel-specific information for surface and cloud parameters in
V4.0 results in significantly enhanced tropospheric NO2 column
retrievals almost everywhere. The effect, however, varies with the vertical
distribution of NO2, with the largest effects in high-column areas.
The 3-month (June, July, August) average tropospheric NO2
columns for low cloud conditions (CRF < 0.5) in 2005 over North
America (first row), Europe (second row), southern Africa (third
row), and Asia (fourth row) from V4.0 (first column), V3.1 (second
column), and their difference (V4.0–V3.1).
Figure 10 shows the seasonal average tropospheric NO2 columns for
December through February. While seasonal differences in NO2 columns
are evident owing to changes in NOx lifetime and boundary layer depth,
the impact of algorithm changes in V4.0 remains similar. There are two
notable exceptions specifically related to observations over snow and ice
surfaces. First, there are significant data gaps in V3.1 but nearly none in
V4.0. In V3.1, retrievals over snow and ice areas were considered to be
highly uncertain and therefore discarded, following the recommendation of
Boersma et al. (2011). As discussed above in Sect. 2.2.3, V4.0
incorporates changes in surface and cloud treatment in the NO2 algorithm
that allows us to retain more observations that we determine to be our
acceptable level of cloudiness. Next, these algorithm changes led to
profound changes in the calculated tropospheric AMFs and resulting NO2
column amounts. The reduction in tropospheric NO2 retrievals in V4.0
over snow- and ice-covered surfaces arises from a combined effect of enhanced
values of surface reflectivity, their impact on the CRF and OCP retrievals,
and an inconsistent number of samples used in the calculation of the
seasonal average. Nevertheless, due to inferiority in the quality of BRDF
data and complexities in separating snow from clouds, caution is
needed when interpreting wintertime data at high latitudes.
Same as Fig. 9, but for December, January, and February. The
gray areas represent a lack of good observations as determined by data
quality flags.
Figure 11 shows some examples of how changes in the algorithm from V3.1 to
V4.0 affect monthly domain average tropospheric NO2 columns over areas
affected by various NOx sources. In contrast to minor changes over the
pristine Pacific Ocean, month-to-month changes over source regions vary
considerably. The differences in tropospheric NO2 columns between V4.0
and V3.1 range from -11 % to 15 % over Beijing, China, and from 0 % to 29 %
over the Ruhr area in Germany, suggesting variations in relative differences
among cities and industrial areas. The changes over a major biomass burning
area in the Democratic Republic of Congo, Angola, and Zambia range 13 %–56 %
during the biomass burning season of May through August but are < 5 % in other months. Differences between the two versions are small over
areas influenced by lightning NOx emissions.
Monthly average tropospheric NO2 columns in 2006 calculated
from V3.1 (black) and V4.0 (red) data over selected 5∘
latitude × 5∘ longitude boxes from locations that
are dominated by either anthropogenic (Beijing, China, and the Ruhr area,
Germany), biomass burning (Democratic Republic of Congo – DRC, Angola, and
Zambia), lightning (DRC), or no significant (Pacific) NOx sources. The
vertical bars show the monthly standard deviation. The blue symbols that
correspond to the right y axis show the monthly relative difference (in percent)
between V4.0 and V3.1.
In Fig. 12, we examine the monthly variation of tropospheric NO2 columns
from the two versions over five highly populated and polluted cities that
vary in terrain types ranging from coastal (e.g., Shanghai, Tokyo) to
mountainous (e.g., Mexico City). NO2 columns in V4.0 are generally
higher than V3.1 by 0 %–30 %, but the difference can occasionally reach up
to 50 % in some months. Changes of that order of magnitude in highly
polluted areas have implications for the estimation of NOx emissions and
trends using these data.
Same as Fig. 11, but for a 1∘ latitude × 1∘ longitude box over five highly populated and
polluted cities.
Assessment of OMI NO2 product
In this section, we compare OMI NO2 columns with total column
retrievals from ground-based Pandora measurements and integrated
tropospheric columns from aircraft spirals at several locations of the
DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn
and VERtically Resolved Observations Relevant to Air Quality) field campaign
held between 2011 and 2014.
Comparison between OMI and Pandora total column NO2
Here, we compare the total column NO2 retrievals from OMI and the
ground-based Pandora spectrometer. Pandora is a compact sun-viewing remote
sensing instrument that provides estimates of NO2 column amounts from
the surface to the top of the atmosphere (Herman et al., 2009, 2018). The
NO2 retrieval approach for Pandora is similar to that of OMI and
consists of the DOAS spectral fitting procedure to derive NO2 SCD and
its conversion to VCD using AMFs. However, the details differ due to the
lack of top-of-atmosphere radiance measurements for the spectral fitting and
simplicity in the AMF calculation for Pandora due to its direct-sun
measurements.
To compare with the OMI observations, we use Pandora data for sites listed
in the Pandonia Global Network (https://www.pandonia-global-network.org/, last access: 10 May 2020). Out of 22 sites, we select 18
sites that we determined to be suitable for comparison. Data from some of
the sites (e.g., Rome, Italy) are consistently higher than OMI by over a
factor of 2, suggesting that the sites may be in close proximity to local
sources that cannot be resolved by OMI. Although some of the selected sites
have sporadic and short-term measurements (e.g., Ulsan, South Korea), we
consider them for improved sampling and coverage. The collocation criteria
include spatial and temporal matching between OMI and Pandora observations
by selecting the OMI pixels that encompass the Pandora site and using
Pandora 80 s total NO2 column data averaged over ±10 min
of OMI observations. We use high-quality data obtained under clear-sky
conditions with the root mean square of spectral fitting residuals <
0.05 and NO2 retrieval uncertainty < 0.05 DU (∼1.3×1015 molec. cm-2) for Pandora and with CRF < 0.5 for OMI.
Figure 13 shows a comparison of OMI total NO2 columns (sum of
tropospheric and stratospheric columns) to coincidently sampled Pandora
direct-sun NO2 column retrievals at a clean site in Izaña on
Tenerife, Spain, and a more polluted site in Greenbelt (Maryland,
USA). The Izaña Atmospheric Observatory is located on the top of a
mountain plateau, with an elevation of 2373 m above sea level. Since
the site is free of local anthropogenic influences, Pandora observations
likely provide stratospheric and free tropospheric NO2 amounts. In
contrast, the Greenbelt site in a suburban Washington DC area has traffic
and air quality typical of polluted US cities. As shown in Fig. 13a and b, OMI NO2 retrievals from the two versions are highly consistent
(r>0.92), with somewhat higher values in V4.0 compared to
V3.1 by 13 % on average in Greenbelt and just 1 % in Izaña. The
variations of OMI NO2 from both versions are also broadly consistent
with the Pandora measurements. The OMI and Pandora NO2 columns are
fairly correlated (r=0.32, N=232) at Izaña and moderately
correlated (r=0.51, N=123) at Greenbelt; often the differences
between each individual OMI and Pandora observation are significant.
Overall, the total column NO2 data from OMI are higher than Pandora,
with an average difference of < 16 %. Occasional large
discrepancies between OMI and Pandora reflect a combination of spatial
heterogeneity, differences in spatial and temporal sampling, differences in
the vertical sensitivity of satellite- and ground-based observations, and errors
in OMI and Pandora retrievals.
The time series of NO2 total columns retrieved from Pandora
(black circles) and OMI at (a) Izaña, Spain, and (b) Greenbelt, Maryland,
USA, with the OMI retrievals represented by the filled blue (V4.0) and open
purple (V3.1) circles. Right panels show the monthly variation of NO2 total
columns at (c) Izaña for 2016–2019 and (d) Greenbelt for 2018–2019, as
calculated from Pandora (black line with filled circles) and OMI
measurements (bars). OMI NO2 total columns retrieved with V4.0 (blue)
and V3.1 (purple) are separated into tropospheric and stratospheric
components. The vertical lines represent the standard deviation from the
average.
Figure 13c and d show the multiyear monthly mean variation of OMI
and Pandora NO2 columns. The seasonal variation in Pandora and OMI
NO2 columns is highly consistent and exhibits a summer maximum and a
fall minimum at Izaña and a winter maximum and summer minimum in
Greenbelt. The seasonal variation in the total column reflects that of the
stratosphere for Izaña and of the troposphere in Greenbelt. For
Izaña, the monthly mean differences between OMI and Pandora range from
8.2 % in June to 38 % in October for V4.0 and from 7.0 % in June to
37 % in October for V3.1. This discrepancy is likely due to the large
aerial coverage of OMI pixels including nearby cities, unlike the point
measurements made by Pandora at the mountaintop. The average tropospheric
NO2 column observed by OMI is 8.9×1014 molec. cm-2,
suggesting significant NO2 amounts in the troposphere with 20 %–32 %
contributions to total column NO2 on a monthly scale. For Greenbelt,
the monthly mean differences between OMI and Pandora are within ±12 % for the majority of the cases for both versions, with V4.0 improving
agreement for February, April, May, and December and worsening somewhat in
other months, especially in September and November when the two versions
exhibit larger differences in tropospheric NO2 retrievals.
Figure 14 shows average total NO2 columns measured by Pandora and OMI
at the 18 selected sites. Although there is a wide range of differences
between individual sites, Pandora and OMI observations exhibit a good
spatial correlation, with slightly improved correlation for V4.0 (r=0.65,
N=1082) compared to V3.1 (r=0.62). The site-specific average values
generally agree within ±35 % for columns < 1016 molec. cm-2. For more polluted sites, OMI retrievals tend to be lower than the
Pandora data. Although the relationship between Pandora and OMI has not
changed appreciably with the updates made in the OMI V4.0 product, the
corrections are in the right direction for a majority of the sites. The
observed differences should not be interpreted as biases in retrievals but
rather as the combined effect of differences in spatial coverage,
heterogeneity in the NO2 field, preferential placement of Pandora
instruments, and potentially a lack of site-specific profile shapes assumed
in OMI retrievals.
The scatter plot of Pandora versus OMI V4.0 (black) and V3.1
(green) average total column NO2 for 18 Pandora sites. The vertical and
horizontal lines represent the standard deviations for Pandora and OMI,
respectively. The dotted line represents the 1:1 relationship.
Assessment using DISCOVER-AQ observations
We also use NO2 observations from the DISCOVER-AQ field program to
assess OMI NO2 retrievals. The DISCOVER-AQ campaign was composed of
four field deployments: the Baltimore–Washington area in Maryland (MD) in July 2011; the San Joaquin Valley in California (CA) in January–February 2013;
Houston, Texas (TX), in September 2013; and Denver, Colorado (CO), in
July–August 2014. An observing strategy of the campaign was to carry out
systematic and concurrent in situ and remote sensing observations from a
network of ground sites and research aircraft that spiraled over each site
two to four times a day. The payload of the P-3B research aircraft included in situ
measuring instruments to measure NO2 profiles in the 0.3–5 km altitude
range. Each campaign hosted ground-based networks of surface monitors to
provide in situ NO2 observations and Pandora spectrometers to
measure NO2 column amounts.
We use Pandora NO2 column observations and in situ NO2 spiral data
spatially and temporally matched to OMI on clear and partially cloudy (cloud
radiance fraction < 0.5) days. Airborne measurements were carried
out using the four-channel chemiluminescence instrument from the National
Center for Atmospheric Research (Ridley and Grahek, 1990) and the thermal
dissociation laser-induced fluorescence instrument from the University of Berkeley
(Thornton et al., 2000). Despite differences in the measurement technique
and sampling strategy, NO2 measurements from the two instruments are
highly consistent and generally agree within 10 %, with the exception of
∼ 32 % difference for Houston (Choi et al., 2020). Here, we
use the 1 s merged data from the chemiluminescence instrument only,
taking advantage of its high-frequency measurements. The spiral data are
extended to the ground by using coincident in situ surface NO2
measurements sampled over the duration of spiral (∼ 20 min). To account for NO2 amounts in the missing portion from the
highest aircraft altitude to the tropopause, we use NO2 from the GMI
simulation. Like the surface data, the Pandora total column NO2 data
are averaged over the duration of each aircraft spiral. For OMI, we include
data from all cross-track positions that are not subject to the row anomaly.
Site average total (circles) and tropospheric (bars) NO2
column data from the P-3B spiral (white bars), Pandora (green circles), and OMI
(orange and red). The OMI tropospheric columns are derived using
GMI-simulated (OMIGMI, orange) and P-3B (OMIobs, red) NO2
profiles. The vertical bars for sites with over two observations represent the
standard deviations.
Figure 15 and Table 2 show a summary of the comparison of OMI V4.0 and V3.1
NO2 columns to vertically integrated tropospheric columns from the
P-3B aircraft at 20 spiral locations. Overall, tropospheric NO2 columns
from OMI and aircraft spirals suggest poor agreement but a good
correlation (r=0.74, N=100), with slightly improved agreement for V4.0
compared to V3.1. The agreement and correlations between OMI and P-3B
observations vary by campaign locations (e.g., r=0.4 for MD to r=0.81
for CA for V4.0). The level of improvements from V3.1 to V4.0 also varies from
1.2 % in TX to 9.8 % in CA. OMI retrievals are usually lower than the
aircraft data, with larger differences for sites with larger NO2
gradients and columns (e.g., Denver–LaCasa, CO; Fresno, CA). OMI is rarely
higher than the aircraft data as this usually happens over relatively
cleaner sites (e.g., Fairhill, MD). This alternating nature of the variation
in results in polluted versus clean areas suggests that OMI's large
footprint size and the narrow spiral radius (∼ 4 km) of the
aircraft are likely the primary causes of the observed differences. This was
demonstrated in Choi et al. (2020) by using high-resolution Community
Multi-scale Air Quality Model (CMAQ) simulations. Additional contributions
to the observed differences could come from OMI retrieval errors arising
from the use of coarse-resolution GMI-based a priori NO2 profile
shapes in the AMF calculation. Such profile-related retrieval errors can be
partially accounted for by replacing GMI profiles with the aircraft-observed
NO2 profiles (OMIobs). The use of observed profiles in the OMI
retrievals from both versions leads to a slight change in correlation but a
20 %–35 % reduction in the mean difference between OMI and aircraft
observations, highlighting the role of a priori profiles in NO2
retrievals as suggested by previous studies (Russell et al., 2011; Lamsal et
al., 2014; Goldberg et al., 2017; Laughner et al., 2019; Choi et al., 2020).
The campaign average difference between OMI and aircraft observations is
-38.8 % in V3.1 and -23.1 % in V4.0, resulting in a net improvement of
15.7 % with V4.0. We note here that the aircraft-observed profiles can be
very different from the actual profiles over OMI's FOVs (pixels) due to a
difference in the sampling domains for the two measurements.
Comparison of OMI V3.1 and V4.0 NO2 retrievals based on a
priori NO2 profiles from GMI (OMI) and P-3B aircraft observations
(OMIobs) to P-3B observations during the DISCOVER-AQ field campaign.
Shown here are the correlation coefficient (r) and mean difference, which is
calculated as OMI minus validation data.
Figure 15 and Table 3 also show the comparison between the OMI and Pandora
total column retrievals at the 20 DISCOVER-AQ sites. The correlations
between collocated OMI V4.0 and Pandora observations for individual campaign
locations vary from fair (r=0.13 for MD) to good (r=0.70 for CO), with a
moderate correlation (r=0.56, N=83) for all observations from the four
locations. The changes in correlation from V3.1 to V4.0 are generally small,
with a minor improvement in CA and deterioration in MD. Compared to the
aircraft observations, the OMI data generally show better agreement with the
Pandora retrievals, with the smallest difference in MD and the largest
difference in CO. Compared to V3.1, the agreement is improved for V4.0 by
up to 9 %. The use of aircraft-observed NO2 profiles in AMF
calculations leads to higher OMI column retrievals than those from Pandora
for MD and TX and lower columns than Pandora for CA and CO. Overall, total
column retrievals from OMI V3.1 and V4.0 are respectively 33.5 % and
16.3 % lower than Pandora; this change is consistent with the comparison
between OMI and P-3B observations. The observed discrepancy between the OMI,
aircraft spiral, and Pandora data points to general difficulties in
comparing observations of different spatial resolutions for a short-lived
trace gas like NO2 that has large spatial gradients, especially in the
boundary layer.
Same as Table 2, but with Pandora NO2 column observations.
We have described a series of significant improvements made to the
operational OMI NO2 standard product (OMNO2) algorithm. The new
version, version 4.0 (V4.0), of the OMNO2 product, recently released to the
public at the NASA Goddard Earth Sciences Data and Information Services
Center (GES DISC), mainly relies on improved methods and high-resolution
inputs for a more accurate determination of air mass factors (AMFs). Major
improvements include the following: (1) a new O2–O2 cloud algorithm to estimate
cloud radiance fraction (CRF) and cloud optical centroid pressure (OCP),
both required for the AMF calculation; (2) new MODIS BRDF-derived
geometry-dependent surface Lambertian equivalent reflectivity (GLER) input
data used in both the NO2 and cloud retrievals; (3) improved terrain
pressure calculated for OMI's footprint; and (4) improved surface and cloud
treatments over snow and ice surfaces. Over open-water areas, inputs to the
GLER calculations include chlorophyll concentrations from MODIS, wind
speed data from the Advanced Microwave Scanning Radiometer–Earth Observing
System (AMSR-E) and the Special Microwave Imager–Sounder (SSMIS)
instruments, and wind direction data from the NASA GEOS-5 model. The
following algorithmic steps remain unchanged: the scheme for separating
stratospheric and tropospheric components, first implemented in version 2.1
(Bucsela et al., 2013; Lamsal et al., 2014); an optimized spectral fitting
algorithm used for NO2 slant column density retrievals (Marchenko et
al., 2015); and the use of annually varying monthly mean inputs derived from the Global Modeling
Initiative (GMI) (e.g., NO2 vertical profile shapes), as
implemented in version 3.0 (Krotkov et al., 2017).
The changes in inputs result in substantial changes in tropospheric AMFs (and
thus VCDs) in V4.0 relative to the previous version (V3.1). The
geometry-dependent GLER data computed for OMI observations used in V4.0
differ considerably from the OMI-derived climatological LER data (Kleipool
et al., 2008) used in V3.1. The data from GLER (a unitless value with
0.0–1.0 range) are generally lower, by < 0.05, than the
climatological LER data over land and ocean outside sun-glint areas; GLER
is much higher over sun-glint areas and reaches more than 0.3 due to the
geometry-dependent Fresnel reflection. The cloud parameters (OCP and CRF)
retrieved from the new O2–O2 cloud algorithm described here and
those from the operational cloud algorithm (Veefkind et al., 2016) used in
V3.1 exhibit significant differences, with generally larger values for both
parameters in V4.0 compared to V3.1 but noticeable exceptions over
sun-glint areas where CRFs in V4.0 are lower than V3.1 by < 0.3.
Over snow and ice surfaces, identified by near-real-time ice and snow
extent (NISE) flags in the OMI L1b data, various adjustments are made in
V4.0 for GLER, OCP, and CRF by using other diagnostic parameters (e.g.,
scene pressure) retrieved by the new cloud algorithm. The scattering weights
and tropospheric AMFs for NO2 respond to the changes in these input
parameters in a complicated way. Typically, tropospheric AMFs decrease with
the use of GLER and increase with the use of the new cloud parameters, with
exceptions over water surfaces affected by sun glint, for which we observe the
opposite effect. Over highly polluted areas, the effect from GLER is
augmented by the effect from the new cloud parameters, resulting in a
considerable decrease in the tropospheric AMF. Changes in tropospheric AMFs
resulting from updates in the treatment of snow- and ice-covered areas
are also significant. Changes in the adopted terrain pressure (V4.0 vs. V3.1)
can also have a sizable effect on tropospheric AMFs, particularly over areas
with a complex terrain. In contrast, for stratospheric AMFs the combined
impact of all of these algorithmic updates is negligible.
The changes in tropospheric AMFs translate directly into changes in
tropospheric NO2 retrievals and indirectly into stratospheric NO2
estimates. Over background and low-column NO2 areas, tropospheric
NO2 column estimates have not changed appreciably from V3.1 to V4.0.
Over more polluted areas, the tropospheric NO2 retrievals have
typically increased by 10 %–40 % from V3.1 to V4.0, mostly in direct
proportion to the pollution level. Most of the increase in highly
polluted areas is driven by the change in the surface reflectivity data used
in the AMF calculation, with additional increases due to changes in the cloud
parameters. Changes in the stratospheric NO2 estimates are usually
within ±2.5 %, which is close to the range of estimated
uncertainties of stratospheric NO2 estimates.
A global assessment of V4.0 tropospheric and stratospheric NO2 products
was performed by a thorough evaluation of their consistency with the data
from V3.1, which was carefully evaluated in our previous works (e.g.,
Krotkov et al., 2017; Choi et al., 2020). In addition, we use
NO2 measurements made by independent ground- and aircraft-based
instruments to evaluate the V4.0 product. The comparison of OMI total column
NO2 data to collocated Pandora observations at its 18 global network
and 20 DISCOVER-AQ locations suggests that OMI and Pandora are generally
highly consistent, exhibit similar seasonal variation, and agree within
their expected uncertainties of 2.7×1015 molec. cm-2 for Pandora
(Herman et al., 2009) and ∼ 30 % for OMI under clear-sky
conditions (Boersma et al., 2011; Bucsela et al., 2013). Individual data
points differ considerably, and OMI tends to be lower than Pandora over
highly polluted areas with spatially inhomogeneous NO2. The comparison
of OMI tropospheric NO2 column retrievals to columns derived from the
aircraft spirals and surface data during the DISCOVER-AQ campaign also
suggests general agreement in spatial variation, but OMI values are about a
factor of 2 lower in polluted environments. This difference is partly due
to inaccurate a priori assumptions but primarily to OMI's relatively large
pixels. The use of observed NO2 profiles as a priori information
reduces the bias from ∼ 50 % to 23 % on average. A
multi-axis differential optical absorption spectrometer (MAX-DOAS) (e.g.,
Chan et al., 2019) or high-spatial-resolution measurements from aircraft
(e.g., Nowlan et al., 2016; Lamsal et al., 2017; Judd et al., 2019) would
provide a more comprehensive validation by mapping the NO2
distributions over the complete areas of aircraft spirals and the satellite FOVs.
In this study, we focused on improving the surface and cloud parameters in
the NASA standard NO2 product retrievals. To further improve the
retrieval accuracy, it is important to incorporate improved retrieval
methods and auxiliary information, such as high-resolution a priori NO2
profiles. For instance, current cloud algorithms based on the MLER model
treat aerosols implicitly by providing effective (cloud + aerosol) CRF and
effective cloud OCP, both necessary inputs for AMF calculations. Cloud
effects on trace gas retrievals can be compromised by unknown aerosol
effects, which lead to errors in AMF calculations. Therefore, the use of the
GLER product in the NO2 algorithm will greatly benefit from an explicit
accounting for aerosol effects, particularly over polluted regions. We have
recently developed an explicit and consistent aerosol correction method
that can be applied consistently in both cloud and NO2 retrievals
(Vasilkov et al., 2020); it uses a model of aerosol optical properties
from a global aerosol assimilation system paired with radiative transfer
calculations. This approach allows us to account for aerosols within the OMI
cloud and NO2 algorithms with relatively small changes and will be used
in the next version of the NO2 algorithm.
Code and data availability
The Level-2 swath-type column NO2 product (OMNO2; 10.5067/Aura/OMI/DATA2017; Krotkov et al., 2019a) is available from
the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) website
(10.5067/Aura/OMI/DATA2018, Krotkov et al., 2019b).
Other OMNO2-associated NO2 products, such as the Level-2 gridded column
product, OMNO2G, and the Level-3 gridded column product, OMNO2d, both
sampled at regular 0.25∘ latitude × 0.25∘ longitude
grids, are distributed through the NASA GES DISC (10.5067/Aura/OMI/DATA3007, Krotkov et al., 2019c) and
GIOVANNI (https://giovanni.gsfc.nasa.gov/giovanni/, NASA, 2020a) websites.
An additional high-spatial-resolution (0.1∘× 0.1∘
latitude–longitude grid) OMNO2d product (OMNO2d_HR) is also
made available through the NASA AVDC website (https://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/OMI/V03/L3/OMNO2d_HR/, NASA, 2020b). The AVDC website also hosts overpass files for several hundred sites
around the globe (https://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/OMI/V03/L2OVP/OMNO2/, NASA, 2020c).
Author contributions
LNL, NAK, JJ, and AV designed the data analysis. WQ, ZF, NAK, DH, and AV
developed and evaluated the GLER product. EY, SM, AV, NAK, JJ, and BF
developed and evaluated the cloud product. LNL, NAK, SM, WHS, and EB have
developed and evaluated the NASA NO2 standard product. LNL and SC
conducted validation of the OMI NO2 products using Pandora and other
independent observations. LNL, AV, SM, and ZF wrote the paper with
comments from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the NASA Earth Science Division for funding OMI NO2
product development and analysis. The Dutch–Finnish-built OMI is
part of the NASA EOS Aura satellite payload. KNMI and the Netherlands Space
Agency (NSO) manage the OMI project. We acknowledge the NASA Pandora,
ESA Pandonia, and NASA DISCOVER-AQ projects for free access to the data.
We thank the two anonymous reviewers for their helpful comments.
Financial support
This research has been supported by the NASA Earth Science Division (grant no.
80NSSC18M0086 for partial support).
Review statement
This paper was edited by Dominik Brunner and reviewed by two anonymous referees.
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