We provide an analysis of the commonly used Ozone Monitoring
Instrument (OMI) aerosol index (AI) product for qualitative
detection of the presence and loading of absorbing aerosols. In our
analysis, simulated top-of-atmosphere (TOA) radiances are produced
at the OMI footprints from a model atmosphere and aerosol profile
provided by the NASA Goddard Earth Observing System (GEOS-5)
Modern-Era Retrospective Analysis for Research and Applications
aerosol reanalysis (MERRAero). Having established the credibility of
the MERRAero simulation of the OMI AI in a previous paper we
describe updates in the approach and aerosol optical property
assumptions. The OMI TOA radiances are computed in cloud-free
conditions from the MERRAero atmospheric state, and the AI is
calculated. The simulated TOA radiances are fed to the OMI near-UV aerosol retrieval algorithms (known as OMAERUV) is compared
to the MERRAero calculated AI. Two main sources of discrepancy are
discussed: one pertaining to the OMI algorithm assumptions of the
surface pressure, which are generally different from what the actual
surface pressure of an observation is, and the other related to
simplifying assumptions in the molecular atmosphere radiative
transfer used in the OMI algorithms. Surface pressure assumptions
lead to systematic biases in the OMAERUV AI, particularly over the
oceans. Simplifications in the molecular radiative transfer lead to
biases particularly in regions of topography intermediate to surface
pressures of 600 and 1013.25
The direct radiative effect of atmospheric aerosols changes the energetics of the atmospheric column by scattering and absorption of incident solar and outgoing long-wave radiation, generally cooling the underlying surface and possibly warming elevated layers depending on the aerosol absorption properties (e.g., Ångström, 1929; McCormick and Ludwig, 1967; Chýlek and Coakley, 1974; Charlson et al., 1990, 1991, 1992; Chýlek et al., 1995; Hansen et al., 1997; Haywood et al., 1997). Modification of the temperature profile by this aerosol direct effect has impacts on atmospheric stability and hence clouds (the so-called semi-direct effect; Hansen et al., 1997), and can also feedback on dynamics and so affect the winds and distributions of trace species including water and aerosol and chemical pollutants (e.g., Mulcahy et al., 2014, and see Haywood and Boucher, 2000). The aerosol direct effect depends on the vertical profiles of aerosol loading (usually represented by the aerosol extinction profile, which integrated over the vertical column provides the aerosol optical depth, or AOD), aerosol scattering properties (represented by the scattering-phase function or more simply by the asymmetry parameter), and the absorption (represented as the single-scattering albedo, or SSA). Obtaining these properties on a global scale is a considerable challenge owing to the spatial, temporal, and compositional (e.g., chemical speciation, size) variability of aerosols. There has been considerable progress in the last 15 years in characterizing the global column-integrated AOD both from ground-based and space-based remote sensing platforms (e.g., King et al., 1999; Chin et al., 2009). Information on the aerosol vertical profile has also become available in recent years (Welton et al., 2000; Campbell et al., 2003; Winker et al., 2010; McGill et al., 2015), albeit with lesser spatial coverage owing to the active sensor techniques required (i.e., single-beam profiling from ground-based or orbiting lidars). Determination of aerosol-phase function is not generally available from remote sensing platforms, although there is some information possible from multi-angle sensors such as the Multi-angle Imaging Spectroradiometer (MISR; Diner et al., 1998) and the potential for more as multi-angular polarimeters are being developed for future missions (e.g., NASA ACE Science Working Group, 2016, and the European Space Agency 3MI instrument manifested for launch on METOP SG-A in mid-2021). The MISR instrument additionally provides estimates of aerosol height for optically thick layers by exploiting its stereo viewing capabilities (e.g., Mims et al., 2010). Determination of absorption remains, however, a significant challenge, as most satellite remote sensing platforms are only weakly sensitive to this parameter, although work done with the recent space-based Polarization and Directionality of Earth Reflectances (POLDER; Waquet et al., 2016) and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL; Lacagnina et al., 2015) instruments and polarimetry gives some insight into this, as it does also for aerosols above clouds (Peers et al., 2015). A recent analysis of estimates of the global direct aerosol radiative forcing highlights aerosol absorption as the largest contributor to overall uncertainty in the direct aerosol radiative effect (Loeb and Su, 2010; see also Kahn, 2011).
One technique for determining column aerosol absorption properties is the near-UV method pioneered in the 1990s with measurements from the space-based Total Ozone Mapping Spectrometer (TOMS; Herman et al., 1997; Torres et al., 1998). The approach determines a qualitative aerosol index (AI) using the observed spectral contrast in two channels where ozone absorption is weak. The AI is a measure of the deviance of the observed spectral contrast from what would be expected in a purely molecular atmosphere. In the absence of clouds, the AI signal has sensitivity to the aerosol loading (i.e., AOD, including its spectral dependence), altitude, and spectral contrast in single-scattering albedo (Torres et al., 1998; Hsu et al., 1999). The AI approach for detection of absorbing aerosols has been applied to other sensors, including GOME (de Graaf et al., 2005), SCIAMACHY (de Graaf and Stammes, 2005; Penning de Vries et al., 2009), and OMPS on the Suomi NPP satellite (after, e.g., McPeters et al., 1998).
The AI is a fundamental intermediate parameter used in the retrieval
of aerosol properties derived from measurements taken with the Ozone
Monitoring Instrument (OMI; Levelt et al., 2006), as well as its TOMS
predecessor (Torres et al., 2002). OMI is the successor to the TOMS
series, a joint Dutch–Finnish hyperspectral (270–500
In this work, we focus on the aerosol index produced in the OMI
near-UV aerosol algorithm, called OMAERUV (Torres et al., 2007), which
follows from the aerosol index introduced above and is described more
completely in Sect. 2.3. The ultimate objective of our work here is to
provide a critical evaluation of the OMAERUV aerosol algorithm using
new capabilities for simulating the OMI signals from aerosol model
simulations. Colarco et al. (2002) described a simulator for near-UV
aerosols based on the previous TOMS data. Using a chemical transport
model with aerosol loading, altitude, and particle size distributions
constrained by primarily aircraft and ground-based observations they
were able to use their simulator to derive the absorption of Saharan
dust aerosols, retrieving the imaginary component of the dust
refractive index needed to reproduce the observed AI
Our approach for evaluating AI follows B15. Using results from the GEOS-5-produced MERRAero aerosol reanalysis (Sect. 2.1) we simulate the OMI radiances (Sect. 2.2) and compute a MERRAero-based version of the OMI aerosol index (Sect. 2.3). The simulated radiances can also be fed into a stand-alone version of the operational OMAERUV aerosol retrieval algorithms, from which we obtain a retrieval of aerosol properties based on the synthetic radiances, followed by an extensive analysis of the algorithm performance in the context of an observing system simulation experiment (OSSE). In this paper, we concentrate especially on the calculation of the AI, leaving the retrieval of AOD and AAOD (or, equivalently, SSA) from the synthetic radiances and the OSSE study for a follow-up paper. Unlike B15 where the main goal was to evaluate aerosol absorption properties in MERRAero, here we examine the accuracy of the AI reported by the OMAERUV product, performing a detailed calculation that uses MERRAero aerosol and meteorological fields as our “nature run”. One important simplification we are making is that we assume an entirely cloudless atmosphere. That is, the synthetic radiances at the root of our study include only the impacts of scattering from the surface, the molecular atmosphere, and aerosols.
As described in B15, the MERRAero aerosol reanalysis arises from a “replay”
of the GEOS-5 model driven by meteorology from the MERRA atmospheric
reanalysis (Rienecker et al., 2011), followed by assimilation of
550
The MERRAero-produced atmospheric state and aerosol mass mixing ratio
distributions used here are identical to those described in B15. MERRAero was
produced for the time period 2002–2015 at a global
As in B15 we focus our study on the year 2007, specifically the period June–September 2007, and we note that the analysis is performed assuming a cloud-free atmosphere.
We simulate the OMI radiances at 354 and 388
In summary, MERRAero provides the vertical profiles of aerosol mixing ratio and relative humidity needed to compute the aerosol optical properties, and the vertical pressure profile needed to simulate molecular scattering. The GEOS-5 interface to VLIDORT translates the aerosol and molecular vertical profiles to the profiles of AOD, SSA, and phase scattering matrix input to VLIDORT. All of this information from MERRAero is sampled from the global model at the OMI pixel footprint using the OMI Level 2 product for the same day, which additionally provides needed information on the OMI viewing geometry associated with each pixel (i.e., solar zenith angle, sensor zenith angle, and relative azimuth angle) and the spectral surface reflectivity. In B15, the OMAERUV quality flags were used to discard cloudy pixels; here we are computing the synthetic radiances at the OMI footprints and providing them to the OMAERUV algorithms as if the atmospheric column is cloud-free.
Fundamental to calculating the aerosol index from the radiances is the
computation of the so-called Lambertian equivalent reflectivity (LER), which
is the surface reflectance that would be needed under a purely molecular
atmosphere to explain the actual radiance (observed or simulated) at the top
of atmosphere for the actual atmospheric profile. The LER at wavelength
In this version of the OMAERUV algorithm, the LER is corrected by the
spectral dependence of the surface as in B15, but here with two
modifications:
Finally, the definition of the aerosol index is as in B15:
For the remainder of this paper we focus on using MERRAero as a representation of a “true” nature state. That is, MERRAero is assumed to provide a sufficiently realistic simulation of aerosol distributions and composition so that we can simulate from those fields the radiances OMI would have observed under its viewing conditions. In the following, we refer to the MERRAero AI as the aerosol index calculated in our OMI simulator using those radiances. Additionally, we propagate those same radiances through the OMAERUV retrieval algorithms, and then recover from the retrieval the aerosol quantities (e.g., AI, AOD, and SSA) that can be compared to the known state MERRAero provided in the first place. We do not assume any errors in the simulated radiances provided as input to the OMAERUV retrieval algorithms. While inclusion of random, realistic errors in the simulated radiances would further the characterization of the OMAERUV algorithmic performance, the focus of this paper is rather on the algorithmic choices and their impacts on the retrieved aerosol quantities. Wind et al. (2013, 2016) performed similarly spirited observation simulation analyses with GEOS-5 based on the MODIS aerosol and cloud algorithms. We focus in this paper only on the AI. In the following, we refer to the OMAERUV AI as the aerosol index returned by the OMAERUV algorithms using the MERRAero-computed radiance inputs. Our objective is to identify and then attempt to resolve major areas of discrepancy between the MERRAero AI and OMAERUV AI. To do this we simulate MERRAero radiances for the full OMI swath assuming clear-sky conditions. We do this for all OMI orbits for the months June–September 2007.
From Eqs. (1) and (3) we see that the AI depends on a calculation of
the atmospheric molecular scattering, which in turn depends on an
assumption of the atmospheric pressure profile. The assumption of the
atmospheric pressure profile is handled differently in OMAERUV vs. in
MERRAero. In OMAERUV, the surface pressure is nominally assumed to
have a fixed value of 1013.25
Figure 1a shows the MERRAero AI on 5 June 2007, showing major
absorption features across Saharan Africa, the Arabian Peninsula, and
much of southern Asia. Other absorption features are present over
southern Africa, in the Pacific Ocean immediately west of Mexico, and
near Beijing and across the northern Pacific. Note the wide areas,
mainly over the ocean, that are shaded grey. Because the OMAERUV
algorithms rely on pre-computed lookup tables of the atmospheric
radiance profile bounded by surface pressures of 600 and
1013.25
For Fig. 1c, the OMAERUV AI is computed with the algorithm using its
default assumption of the surface pressure. Thus the assumed surface
pressure in the OMAERUV algorithm is constant over the oceans and
static in time, and so differs from the actual surface pressure at any
given moment, where spatial and temporal variability in the surface
pressure in the MERRAero simulation is assured by the MERRA
meteorology (i.e., surface pressure in the real atmosphere changes
with weather patterns). Figure 1b shows this OMAERUV–MERRAero
difference in the surface pressures on 5 June 2007, and it is clear
that differences of tens of hPa are possible. Negative pressure
differences (red shading) in Fig. 1b are apparent over the land,
mainly in southern Australia and northern Europe, and so coincident
with the negative AI differences shown in Fig. 1c. (It is worth noting
here that most of the greyed-out region over the oceans occurs in
places where MERRA has surface pressures greater than
1013.25
In Fig. 1d we show the OMAERUV AI–MERRAero AI difference where the
OMAERUV calculation was performed using the MERRAero grid-box mean
surface pressure, and indeed we see that much of the AI difference
apparent in Fig. 1c is reduced in magnitude in Fig. 1d. The negative
bias over the land is much reduced, as is the noise near topographic
features. Residual positive discrepancies in the AI as high as
Figure 2 presents a similar analysis for the entire month of June 2007. In Fig. 2a we present the joint histogram of the OMAERUV AI–MERRAero AI difference for the case of OMAERUV calculating AI with its own surface pressure assumption vs. the OMAERUV–MERRAero surface pressure difference for all OMI pixels during the month (again, excluding those where the MERRA surface pressure is out of bounds of the OMAERUV lookup tables). It is clear in Fig. 2a that most of the pixels are near the origin, where the differences on both axes are nearly zero. The excursions toward high surface pressure differences are relatively infrequent (ten to hundreds of points vs. the hundreds of thousands of points with near-zero pressure differences) and are mainly associated with the difference of the MERRAero resolved surface pressure from the OMAERUV values in highly variable topography (i.e., the noise in the mountainous regions shown in Fig. 1b). Also clear from Fig. 2a is the sense in which a difference in the assumed surface pressure propagates to a difference in the derived AI. Where OMAERUV assumes a higher surface pressure than MERRAero it also systematically derives a higher AI. The opposite is also true: OMAERUV derives a lower AI for pixels in which its surface pressure is assumed lower than MERRAero provides.
Figure 2b presents the same results sorted differently, showing the AI difference as a function of the MERRAero AI. Colored points are for the case where the OMAERUV AI is calculated using the OMAERUV surface pressure, with the color indicating the OMAERUV–MERRAero surface pressure difference. Grey points are for the case where OMAERUV AI is calculated using the MERRAero surface pressures. It is immediately apparent that much of the scatter in the AI difference is reduced when OMAERUV calculates the AI using the MERRAero surface pressure. Most of the discrepancy in the OMAERUV AI calculation where OMAERUV uses its own surface pressure occurs for low positive values of the MERRAero AI, with low AI values indicating either low aerosol loading, low aerosol altitude, or the presence of non-absorbing aerosols. The discrepancy is also clearly a function of the surface pressure discrepancy, with the greatest negative AI differences occurring at pixels where the surface pressure difference is most negative, and vice versa for the high AI differences. On the other hand, at high values of the MERRAero AI the AI difference is nearly zero. It follows that as aerosol loading decreases, the radiative transfer becomes more sensitive to the molecular profile, and so especially to differences in the pressure profile implied by the OMAERUV surface pressure relative to what is provided by MERRAero. For the baseline case of OMAERUV using its own surface pressure, 18 % of the pixels have an absolute difference in the simulated AI from MERRAero greater than 0.2. When OMAERUV uses the MERRAero surface pressure only 5 % of the pixels have an absolute difference in the AI greater than 0.2. The results for the full month shown in Fig. 2 are thus very similar to the results for the single day shown in Fig. 1.
Figure 3 continues this analysis, showing maps of the monthly mean
OMAERUV AI–MERRAero AI difference for June 2007 for both the cases
where OMAERUV calculates AI using its own surface pressure (Fig. 3a)
and where OMAERUV uses the MERRAero grid-box mean surface pressure
(Fig. 3b). Over the ocean there are large regions that are again
excluded (grey shading) because MERRAero persistently has surface
pressure
OMAERUV–MERRAero AI differences for
June 2007.
Section 3.1 identified the surface pressure assumptions as a significant driver of the MERRAero AI and OMAERUV AI differences given the same input radiances. When OMAERUV was forced to use the MERRA-provided surface pressure the residual AI differences were greatly reduced, as shown in Figs. 3 and 4, particularly over the ocean. As shown in Figs. 3b and 4, however, the AI residual over land in cases where OMAERUV used the MERRAero surface pressure remains and is a persistent, stationary feature, apparently spatially correlated with topographic features. We explore here the nature of this residual difference. Our hypothesis is that this difference results largely from differences in the treatment of the molecular atmosphere scattering between MERRAero and OMAERUV, as discussed above in Sect. 2.3.
As in Fig. 3b, but showing the OMAERUV–MERRAero AI
difference for OMAERUV using the MERRAero surface pressure for
We examine this here for a region in the central United States,
extending from roughly western Illinois to western Nevada (120–90
Results of sensitivity study exploring pressure interpolation
of radiative transfer calculation results provided by
OMAERUV.
Having identified a high bias in the OMAERUV AI over land with respect
to MERRAero and finding an apparent correlation with surface
elevation, we explore further with a simple sensitivity analysis. We
construct a synthetic orbit of the OMI Level 2 data in which
a spectrally invariant surface albedo of 0.05 (a typical value for the
surface reflectivity) is prescribed and we define a range of viewing
geometries that encompasses typical OMI viewing angles (solar zenith
angle between 8 and 96
Figure 6 presents the results of this sensitivity analysis as applied
to the OMAERUV calculation. The LER as derived in Eq. (1) depends on
the “observed” TOA radiance (i.e., what MERRAero provides) and
properties of the molecular atmosphere:
We have updated and expanded on the capabilities of the OMI radiance
simulator described in B15. The OMI TOA radiances at 354 and
388
The OMI TOA radiances were simulated for the period June–September 2007, and the MERRAero AI was computed from the model results. The radiances were provided to the OMAERUV aerosol retrieval algorithms, which returned their own calculation of the AI, which was subsequently compared to those derived from MERRAero. Two major discrepancies were identified:
These results allow us to make two recommendations with respect to the OMAERUV algorithms. First, the surface pressure assumptions used in the operational algorithms should be revisited, and consideration should be given to using readily available surface pressures from meteorological analyses from any modern weather prediction system (i.e., from the GEOS-5 near-real-time prediction system). A caveat to that recommendation is that the surface pressure from the analysis model must be modulated by a high-spatial-resolution topography data set in order to provide surface pressures consistent with the actual viewing conditions over variable terrain. This was not done in our study, where we used the grid-box mean surface pressure values. The second recommendation is that the molecular atmosphere calculation should be revisited to either incorporate a more realistic, exact radiative transfer solution with respect to the actual surface pressure and atmospheric profile, or else to at least include more intermediate nodal points in the lookup table solutions to improve accuracy in the returned AI. Both of these recommendations are under consideration for future versions of the OMAERUV aerosol products.
It should be noted that our analysis was performed for a single season (June–September 2007) under simulated aerosol loadings expected to be valid in that season. Differences in the aerosol loading, composition, and vertical distribution at other times of the year may have some effect on the conclusions presented here, although we expect the main points to hold. A possible seasonal dependence in the over-ocean residual AI difference following the surface pressure correction was found in Fig. 4 and should be explored further.
The analysis presented in this paper has demonstrated the use of a state-of-the-art aerosol modeling system to simulate radiances observed by real observing systems. We used those simulated fields to interrogate the aerosol retrieval algorithms applied to the data from those observing systems. It should be noted that the magnitude of the AI discrepancies found in this study are typically small, generally less than 0.1 over the ocean, but can be larger (often 0.5 or higher) over land. While these discrepancies may not be large in terms of the semi-quantitative way in which the AI is often used in the research community, we point out that the AI is used to threshold certain algorithmic choices in the OMAERUV retrievals of AOD and AAOD, and so we expect these discrepancies to have non-negligible impact on those products. A subsequent study will follow up on our approach further and critically examine the retrieved AOD and AAOD from the OMAERUV algorithms. Enhancement of these capabilities will facilitate development of new observing systems and algorithms by revealing important sensitivities of algorithm and observation choices.
The OMAERUV products used in this study are an intermediate
research version (version 1.6.2.1) of the standard data products and are
available upon request. The current version of the OMAERUV products is
available from the NASA Goddard Earth Sciences Data and Information Services
Center (
The authors declare that they have no conflict of interest.
This work was carried out under the auspices of a NASA Aura ROSES proposal, NNH13ZDA001N-AURA, Ken Jucks (program manager). We thank an anonymous reviewer and Marloes Penning de Vries for their comments on the manuscript. Calculations of the MERRAero simulated atmospheric radiances were performed at the NASA Center for Climate Simulation (NCCS) computing cluster. The intermediate version of the OMAERUV research algorithms used in this study are version 1.6.2.1. Edited by: Marloes Penning de Vries Reviewed by: Marloes Penning de Vries and two anonymous referees