Aerosol–cloud interaction continues to be one of the leading
uncertain components of climate models, primarily due to the lack of
adequate knowledge of the complex microphysical and radiative processes of
the aerosol–cloud system. Situations when light-absorbing aerosols such
as carbonaceous particles and windblown dust overlay low-level cloud decks
are commonly found in several regions of the world. Contrary to the known
cooling effects of these aerosols in cloud-free scenario over darker
surfaces, an overlapping situation of the absorbing aerosols over the cloud
can lead to a significant level of atmospheric absorption exerting a positive
radiative forcing (warming) at the top of the atmosphere. We contribute to this
topic by introducing a new global product of above-cloud aerosol optical
depth (ACAOD) of absorbing aerosols retrieved from the near-UV observations
made by the Ozone Monitoring Instrument (OMI) onboard NASA's Aura platform.
Physically based on an unambiguous “color ratio” effect in the near-UV
caused by the aerosol absorption above the cloud, the OMACA (OMI above-cloud
aerosols) algorithm simultaneously retrieves the optical depths of aerosols
and clouds under a prescribed state of the atmosphere. The OMACA algorithm
shares many similarities with the two-channel cloud-free OMAERUV algorithm,
including the use of AIRS carbon monoxide for aerosol type
identification, CALIOP-based aerosol layer height dataset, and an OMI-based
surface albedo database. We present the algorithm architecture, inversion
procedure, retrieval quality flags, initial validation results, and results
from a 12-year long OMI record (2005–2016) including global climatology of
the frequency of occurrence, ACAOD, and aerosol-corrected cloud optical
depth. A comparative analysis of the OMACA-retrieved ACAOD, collocated with
equivalent accurate measurements from the HSRL-2 lidar for the ORACLES Phase
I operation (August–September 2016), revealed a good agreement
(
Aerosol–cloud interactions continue to be the most significant source of uncertainty in estimating the role of aerosols and clouds in Earth's changing radiation budget (IPCC, 2013). One of the main hurdles is the lack of adequate knowledge of the complex microphysical and optical processes of an aerosol–cloud system that govern the resultant impact on the regional and global climate. An important aspect of the problem is when the light-absorbing aerosols such as biomass burning generated carbonaceous particles and windblown mineral dust overlay low-level cloud decks. Such situations are commonly observed from satellites over several oceanic and continental regions of the world on daily to seasonal scales (Devasthale and Thomas, 2011; Alfaro-Contreras et al., 2016). The transoceanic transport of elevated fine mode aerosols from biomass burning and coarse mode aerosols from dust storms observed from the ground and satellites is a well-known phenomenon (Prospero et al., 2002; Kaufman et al., 2005; Chand et al., 2008; Torres et al., 2012). Aerosols in the cloud-free scenario over dark surfaces are known to produce a net cooling effect (negative radiative forcing) on climate. In contrast, an overlapping situation of the absorbing aerosols over cloud can lead to a significant level of atmospheric absorption and thus exert a positive radiative forcing at the top of the atmosphere (TOA) (Keil and Haywood, 2003; Chand et al., 2009; Meyer et al., 2013; Feng and Christopher, 2015; Zhang et al., 2016). The magnitude of direct radiative effects of aerosols above cloud depends directly on the amount of aerosol loading, the microphysical–optical properties of the aerosol layer (Eswaran et al., 2015) and the underlying cloud deck (Meyer et al., 2013), geometric cloud fraction, and cloud optical depth (Chand et al., 2009), as well as the diurnal cycle of clouds (Min and Zhang, 2014). The resultant aerosol-driven atmospheric heating can have a significant influence on atmospheric stability, cloud formation and lifetime, and the hydrological cycle (Wilcox, 2012; Lu et al., 2018). Different climate models treat aerosol–cloud interaction processes differently, which results in significant inter-model discrepancies in aerosol forcing assessments, especially over the absorbing aerosol–cloud overlap regions, i.e., the southeastern Atlantic Ocean and Southeast Asia (Schulz et al., 2006). To accurately quantify the direct and semi-direct effects of aerosols in cloudy skies, it is imperative that a measurement-based global database is made available to the community.
In the past decade, the development of several independent algorithms that quantify aerosol loading above cloud from satellite-based active, as well passive, sensors has been a breakthrough. These techniques have shown the potential to retrieve above-cloud aerosol optical depth (ACAOD) using measurements from different A-train sensors (Jethva et al., 2014). The depolarization ratio (Hu et al., 2007) and color ratio methods (Chand et al., 2008) applied to the CALIPSO/CALIOP active lidar measurements of two-way transmittance (Chand et al., 2008) directly provide ACAOD and the corresponding Ångström exponent. Waquet et al. (2009) make use of polarized radiances measured by PARASOL/POLDER to retrieve ACAOD. An operational version of this algorithm capable of retrieving ACAOD globally has also been developed (Waquet et al., 2013). Taking advantage of enhanced aerosol absorption sensitivity in the near-UV, Torres et al. (2012) introduced a technique to retrieve ACAOD and underlying aerosol-corrected cloud optical depth (COD), simultaneously. Jethva et al. (2013) have extended the color ratio method to the visible and shortwave IR (SWIR) channel measurements of MODIS to deduce a pair of ACAOD and aerosol-corrected COD. Using an iterative optimal estimation approach applied to the MODIS observations, Meyer et al. (2015) and Sayer et al. (2016) have developed multispectral algorithms to simultaneously retrieve ACAOD, COD, and cloud effective radius for scenes with absorbing aerosols over clouds.
Satellite-based passive and active sensors can observe the aerosols in the entire atmospheric column above the cloud deck. Conventional satellite retrievals have provided a global distribution of aerosols only over cloud-free regions, leaving the vast cloudy region unmonitored regarding the presence of aerosols. Likewise, field campaigns in the past mainly focused on the measurement and characterization of aerosol properties in cloud-free conditions to evaluate and improve satellite-based retrievals as well as model simulations. The lack of a measurement-based aerosol database in the cloudy atmosphere severely limits our understanding of the aerosol's effects on cloud radiative forcing and microphysical properties.
Simulation of UVAI (y axis) vs. reflectance at 388 nm
(x axis) for the different pairs of ACAOD and COD (both at 388 nm) for the
carbonaceous
Torres et al. (2012) introduced a remote sensing technique to simultaneously retrieve ACAOD and aerosol-corrected COD using the near-UV observations made by the Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite. The method is physically established on an unambiguous “color ratio” effect produced by the absorbing aerosols above clouds in the two near-UV channels, i.e., 354 and 388 nm. Under a prescribed state of the atmosphere, the aerosol-led changes in the cloud radiances measured at the top of the atmosphere (TOA) relate to a pair of ACAOD and COD. The technique was successfully tested over the case studies of carbonaceous aerosols above the cloud deck found over the southeastern Atlantic Ocean. Furthermore, ACAOD derived using the near-UV color ratio method was found to be consistent when inter-compared against those inferred from other research-based algorithms applied to the different A-train sensors (MODIS, CALIOP, POLDER) (Jethva et al., 2014).
In this paper, we apply the near-UV color ratio algorithm on a global scale to produce a Level 2 orbital dataset of ACAOD using OMI observations. First, we describe different components of the algorithm (Sect. 2), followed by the theoretical estimates of the expected uncertainties (Sect. 3). Initial validation results of the OMACA aerosol retrieval using airborne HSRL-2 measurements are presented in Sect. 4. The results of the frequency of occurrence analysis of the aerosols above clouds are discussed in Sect. 5. A global 12-year long record of ACAOD and aerosol-corrected COD derived from OMACA,along with a quantitative analysis of the impact of aerosol absorption on COD retrievals, are presented in Sect. 6. The paper is summarized and concluded in Sect. 7.
Light absorbing aerosols such as carbonaceous aerosols (Kirchstetter et al., 2004) and dust particles (Wagner et al., 2012) exhibit strong wavelength dependence in absorption, particularly in the UV region of the spectrum. On the other hand, clouds show minimal to no spectral dependence in reflectance when measured from the space. When absorbing aerosols overlay the cloud deck, the spectral contrast in the UV is further enhanced, producing a strong “color ratio” effect in the two-channel TOA reflectance measurements (Torres et al., 2012). This effect is often referred to as “cloud darkening” caused by the aerosol-led spectral absorption. The UV aerosol index (UVAI) measured by OMI is an excellent indicator of the presence of absorbing aerosols in the clear (Torres et al., 1998) as well as the cloudy atmosphere (Torres et al., 2012). Radiative transfer simulation shows that for a prescribed state of the atmosphere, the reduction in the spectral reflectance and UVAI between a pair of wavelengths depends on the optical depth of both aerosols and cloud, single-scattering albedo, and aerosol layer height. Founded on this unambiguous detection of absorbing aerosols above the cloud, Torres et al. (2012) introduced a technique that delivers the simultaneous retrieval of ACAOD and aerosol-corrected COD from OMI's two near-UV observations (354 and 388 nm). Figure 1 shows the two-dimensional domain of simulated reflectance at 388 nm (x axis) and UVAI (y axis) for several pairs of ACAOD and COD for the carbonaceous aerosols (left panel) and spheroidal dust particles (right panel). Under a prescribed set of assumptions, i.e., aerosol layer height (ALH), aerosol single-scattering albedo (SSA), surface albedo, and geometry, the two-channel measurements of OMI can be related to a pair of ACAOD and COD.
We use OMI Level-1b calibrated and geolocated radiance measurements at 354 and 388 nm as the primary input to the OMACA algorithm. First and foremost, the observed radiances are used to calculate the UVAI (Mie), as described by Torres et al. (2018), which is a residual quantity resulting from the comparison between measured and calculated radiances between 354 and 388 nm. Compared to the previously adopted Lambert equivalent reflector (LER) based method for calculating UVAI (Herman et al., 1997; Torres et al., 1998), the new approach offers a better characterization of clouds by accounting for the angular dependence of cloud reflectance (phase function) of liquid water clouds.
A general flowchart of the OMACA algorithm.
The aerosol-type identification scheme for OMACA has been directly adopted from
the operational cloud-free OMI/OMAERUV (version 1.8.9.1) two-channel
algorithm. It uses Aura/OMI UVAI coincident with the real-time Aqua/AIRS
retrievals of carbon monoxide (CO) to distinguish three major aerosol types,
i.e., carbonaceous aerosols, dust particles, and urban and industrial aerosols.
OMACA considers two absorbing aerosol types, i.e., carbonaceous aerosols and
desert dust. The aerosol type identification scheme adopts a threshold of 0.8
in UVAI to assign either smoke or dust aerosol type, which subsequently depends
on the columnar amounts of CO retrieved by AIRS. Since the CO is a primary
gaseous component of open-field biomass burning, it constitutes a reliable
tracer of carbonaceous aerosol. For the Northern Hemisphere (NH), the
threshold in CO is set to 2.0
The straightforward way of discerning the absorbing aerosol type works efficiently in most cases; however, it may break down under certain situations, i.e., when dust aerosols are present over regions characterized by high CO levels associated with pollution episodes other than the biomass burning smoke for which the scheme would assign absorbing aerosol type as smoke. Note that the aerosol type identification scheme does not account for the mixture of aerosols for which either smoke or dust aerosol type is assigned depending upon the threshold values of UVAI and CO. The reader is recommended to refer to Torres et al. (2013) for obtaining a detailed description of the scheme and its implementation within the OMAERUV algorithm.
The representation of aerosol vertical profile relies on an averaged aerosol
layer height (ALH) dataset derived using the 30-month long combined and
colocated measurements of CALIOP vertical backscatter and OMI UVAI (Torres
et al., 2013). The aerosol profile is assumed to follow a quasi-Gaussian
distribution around mean ALH given by the CALIOP-OMI dataset. For the surface
characterization, we use a near-UV surface albedo database derived using the
multiyear OMI LER observations. The method adopts a minimum LER approach
which ensures minimal or no contamination from the atmosphere, i.e., aerosols
and clouds, in the measured reflectivity. Afterward, the minimum LER dataset
derived from the OMI observations was scaled in the temporal domain to the
seasonality of surface albedo retrieved in the visible wavelengths from
MODIS. The dataset contains surface albedo values at 354 and 388 nm at a
grid resolution of 0.25
The aerosol single-scattering albedo (SSA) is one of the most critical
assumptions that can govern the accuracy of ACAOD retrieved from OMI (Torres
et al., 2012). A perturbation of
We have used the existing cloud-free OMAERUV SSA product to generate a daily
database to prescribe SSA required in OMACA. The world is split up into
14 regions, based on the patterns of absorbing aerosols above cloud inferred
from the frequency of occurrence analysis (given in Sect. 5). Figure 2a
shows the geographical boundaries of the selected regions. For each region
and each day of OMI observation, the daily, region-specific value of
above-cloud SSA was estimated for the carbonaceous and dust aerosol types
separately using the respective cloud-free SSA retrieval weighted by the
corresponding UVAI observations (> 0.8). Since the OMAERUV
algorithm assigns a fixed aerosol type, i.e., smoke, dust, or background
aerosol, to each valid cloud-free pixel of OMI, it is possible to estimate
daily regional SSA value separately for smoke and dust aerosol types. In the
case of missing daily regional SSA due to cloud cover or no OMI orbital data,
the method relies on the availability of SSA values on nearby days with a
sequential preference, i.e., weekly (
Using ground-based AERONET inversion, Eck et al. (2013) showed that the absorption properties of the smoke aerosols over central and southern Africa exhibit a seasonal trend where the monthly mean aerosol SSA (440 nm) increases from 0.84 in July to 0.93 in November. An analysis of the OMI-retrieved SSA (388 nm) in the same paper also showed a similar trend as that observed by AERONET and suggests that the seasonal change in SSA is widespread over much of southern Africa. The present approach of assigning the above-cloud aerosol SSA captures the daily variability of aerosol absorption for each region and thus eliminates the need to assume a time-invariant value of SSA for the retrievals of above-cloud aerosols.
OMACA considers two major partially absorbing aerosol types, i.e., carbonaceous and dust aerosols. The microphysical and optical properties of these aerosol types are adopted from the current operational OMAERUV aerosol models (Torres et al., 2013). Each aerosol type consists of seven distinct sub-models that differ in their imaginary part of the refractive index. The tables included in Appendix list the model properties of both types of aerosol models. Carbonaceous aerosols are assumed as spherical particles with a wavelength-dependent imaginary refractive index in the near-UV region (relative spectral dependence of 20 %) that accounts for the presence of organics in the biomass burning generated aerosols (Kirchstetter et al., 2004; Jethva and Torres, 2011). Dust aerosols are considered spheroidal in shape with a fixed distribution of the spheroidal axis ratio adopted from Dubovik et al. (2006) and applied to the near UV observations (Torres et al., 2018).
OMACA is essentially a look-up-table (LUT) based algorithm. To generate LUTs, we employ the vector discrete ordinate radiative transfer model VLIDORT (Spurr, 2006). Clouds are assumed to be liquid in phase and follow the standard C1 size distribution (Deirmendjian, 1969). Aerosol size distribution is assumed to follow a bimodal, log-normal distribution with parameters adopted from the standard OMAERUV aerosol models (see Appendix A). Carbonaceous aerosols are assumed to be spherical with associated scattering phase functions calculated following Mie theory. Dust particles, on the other hand, are treated as a mixture of randomly oriented spheroids with a fixed distribution of axis ratios (Dubovik et al., 2006). The phase matrix elements of each spheroidal dust aerosol model of OMACA (Appendix A) were calculated using a set of pre-calculated kernels and an associated software package made available by Oleg Dubovik (personal communication, 2015). The extracted phase matrix elements of each dust model were ingested into VLDIORT to simulate TOA radiances. More details on the treatment of spheroidal dust in the OMI aerosol retrieval framework are given in Torres et al. (2018).
LUTs were generated for carbonaceous and dust aerosol models. Each aerosol type consists of seven discrete aerosol SSA (388 nm) ranging from 0.75 to 1.00, for the 354 and 388 nm wavelengths for a total of seven node values in ACAOD, eight nodes in COD, at different geometries of solar zenith angle, viewing zenith angle, and relative azimuth angle. The simulations were carried out for two surface pressure levels, for four different ALH referenced at respective surface pressure levels, and for five nodes in surface albedo. The node values for each variable are listed in Appendix A. The LUT radiances interpolated at observed geometry, pressure level, ALH, and SSA are matched with the OMI-observed radiance in 2-D retrieval domain (Fig. 1) to find a pair of ACAOD and COD at 388 nm. The retrieved values at 388 nm are converted to 354 and 500 nm wavelengths following the spectral dependence of extinction associated with the assumed model in the retrieval process. Figure 3 illustrates a general flow diagram of the OMACA algorithm.
We adopt a bi-parametric approach to identify the presence of absorbing aerosols above the cloud. The Lambertian equivalent reflectivity or LER measured at a near-UV wavelength is proportional to the brightness of the scene. LER represents the reflectivity of the scene when Rayleigh scattering is taken out from the TOA radiance measurements. On the other hand, UVAI is an excellent indicator of the presence of absorbing aerosols in both cloud-free and overcast skies (Torres et al., 2012) over all surfaces. Radiative transfer simulations show that while LER is directly proportional to COD, the layers of absorbing aerosols above cloud produces higher magnitudes of UVAI that depend on the above-cloud AOD, aerosol model, and cloud brightness (COD). Thus, higher values of LER and UVAI potentially represent scenes of absorbing layers of aerosols over low-level cloud deck.
OMI offers a spatial resolution of 13
A description of the OMACA algorithm retrieval quality flags.
Theoretical simulations of the retrieved ACAOD for two discrete values of UVAI under different combinations of SSA and ALH assumptions. Threshold values of 0.8 and 1.3 in UVAI correspond to the algorithm quality flags of “0” (best) and “2” (less confidence).
Each qualified OMI retrieval of the above-cloud aerosols is assigned with an appropriate algorithm quality flag. Table 1 describes the algorithm quality flags attached to each valid retrieval and their associated observed conditions. Retrievals with the quality flag equal to “0” are expected to be the best in quality as they are associated with the larger magnitudes of UVAI (> 1.3) and LER388 (> 0.25) with both providing high confidence in the detection of absorbing aerosols above the cloud. An analysis using the OMMYCLD product over the southeastern Atlantic Ocean for the period of June–July–August 2007 revealed a well-constrained non-linear relationship between the MODIS-derived COD multiplied by the geometric cloud fraction, and LER388. A threshold for LER388 of 0.25, adopted for the best quality retrievals, compares to the COD times geometric cloud fraction of 3–4. Thus, given the geometric cloud fraction of unity the minimum COD retrieved by OMACA would be in the range 3–4.
Theoretical error (%) in ACAOD (388 nm) due to the uncertainty in the assumption of SSA at 388 nm. The reference value of SSA (388 nm) assumed in the calculation was 0.89; the error in SSA (leftmost column) represents perturbation from the reference value. The cloud optical depth underneath the aerosol layer was assumed to be 10.
N/R: not retrieved.
Lower magnitudes in both parameters might result in less confidence in the
detection of either overcast pixels
(0.20 < LER388 < 0.25, quality flag
Theoretical error (%) in ACAOD (388 nm) due to the uncertainty in the assumption of aerosol layer height (ALH). The reference value of ALH assumed in the calculation was 4.0 km; the error in ALH (leftmost column) represents perturbation from the reference value. The cloud optical depth underneath the aerosol layer was assumed to be 10.
N/R: not retrieved.
Retrievals assigned with the algorithm quality flag “3” are considered to
be the lowest in confidence as they represent spurious non-aerosol related
enhancement in UVAI (up to 2.0) at certain extreme geometries. However, if
the observed UVAI exceeds a value of 2.0, then the retrievals are assigned
with the quality flag “0”, “1”, or “2” depending upon the observed LER
and UVAI. The OMACA algorithm operates over both ocean and land pixels with
terrain pressure greater than 800 hPa, which encompasses the majority of the
regions of frequent aerosol–cloud overlap (see Sect. 5). Retrievals over
oceanic cloud pixels are performed at all Sun glint angles if measured LER
exceeds 0.30; for the 0.20 < LER < 0.30 condition
retrievals are performed with pixels having Sun glint
angle > 20
The OMACA algorithm relies on the presumptions about the atmosphere and surface properties. Therefore, it is imperative to estimate the sensitivity of the OMACA retrievals to the departure of the actual state of the atmosphere from the one assumed in the inversion. Earlier, Torres et al. (2012) described the theoretical uncertainties in the near-UV based retrieval of ACAOD and aerosol-corrected COD. However, the analysis was confined to a narrow range of input conditions. Here, we reperform the same exercise by considering an extended range of perturbation in each assumed parameter.
The approach to calculating the uncertainties in the OMACA retrievals follows a perturbation-based method. The errors were calculated by contrasting the retrievals derived assuming a reference state and perturbed state of a particular input parameter. For example, given a fixed set of aerosol size distribution, ALH, and surface albedo, OMACA retrievals are derived assuming a range of aerosol SSA. The retrievals are then compared with those derived considering a reference value of SSA, which is the center value of the prescribed range. The errors in the retrievals then can be calculated given both underestimated and overestimated values of an assumed parameter.
Table 3 lists the percent error in ACAOD (388 nm) caused by varying
uncertainty in the aerosol SSA for an above-cloud smoke situation. The
reference value of SSA (388 nm) was assumed to be 0.89; ALH and surface
albedo were referenced at 3.0 and 0.05 km, respectively. Errors were
calculated for a range of uncertain SSA, i.e.,
Theoretical error (%) in ACAOD (388 nm) due to the uncertainty in the assumption of aerosol absorption Ångström exponent (AAE). The reference value of AAE (354–388 nm) assumed in the calculation was 2.65; the error in AAE (leftmost column) represents perturbation from this reference value. The cloud optical depth underneath the aerosol layer was assumed to be 10. Simulations marked with N/R (not retrieved) represent the retrieval failure due to the out-of-domain issue.
Theoretical error (%) in aerosol-corrected COD (388 nm) due to the uncertainty in the assumption of aerosol SSA and layer height (ALH).
Theoretical error (%) in aerosol-corrected COD (388 nm) due to the uncertainty in the assumption of cloud effective radius.
Earlier, an intercomparison analysis of OMI-AERONET SSA retrievals, based on
the previous public version of the OMAERUV product, from over 269 AERONET
sites distributed globally, showed an agreement within
Since its public release in 2012, the OMAERUV algorithm has been upgraded
with several major changes, including better treatment of dust particles
assuming realistic spheroidal shape distribution, accounting for angular
scattering effects of clouds in the calculation of UVAI, use of new minimum
surface LER dataset using the synergy of multi-year OMI and MODIS
observations, and updated cloud screening and retrieval flagging scheme. The
regional, daily SSA dataset used in the OMACA product has been derived from
the latest version of the OMAERUV (version 1.8.9.1) product, released in 2017. A new comparative analysis of OMI-AERONET SSAs over global
AERONET locations demonstrated that the agreement between the two independent
sets of retrievals improved significantly for scenes identified as smoke and
dust aerosol type. Quantitatively, about 59 % (83 %), 65 %
(88 %), and 72 % (91 %) of the matchups are found to be within
the expected limits of
Relative to the errors in ACAOD due to the uncertain SSA, departures of ALH
from the assumed state results in lower errors (Table 4). For assigning the
mean ALH, OMACA relies on a global, monthly mean dataset derived from the
30-month collocated CALIOP and OMI observations (Torres et al., 2013). The
OMI-CALIOP climatology of ALH was developed using mostly clear-sky
observations in both datasets with maximum LER in OMI dataset restricted to
0.25. The threshold in LER (0.25) largely avoided above-cloud aerosols scenes
that are assigned with the best quality OMACA retrievals (Table 1). The
transport of aerosols over the cloud in hotspot regions is known to occur
above the boundary layer and between the altitudes 3 and 6 km, as observed from
CALIOP lidar. Furthermore, the time series of fractional AOD shown in Fig. 9
(right-hand axis in blue ink) later in this paper demonstrated that a large
fraction of the total column AOD, about 80 %–100 %, was retrieved as
AOD above the clouds during the seasonal biomass burning and dust episodes
over the hotspot regions. For these reasons, we expect that the OMI-CALIOP
ALH database derived from cloud-free observations is also representative
over the hotspot regions of above-cloud aerosols. The expected uncertainty in
the derived ALH dataset is about
The OMACA 2-D retrieval domain shown in Fig. 1 suggests that the magnitudes of UVAI primarily modulate the retrieved value of ACAOD. Several parameters including ACAOD, COD, ALH, SSA, and AAE can influence the magnitudes of UVAI. For instance, given a fixed set of spectral AODs at 354 and 388 nm wavelengths, ALH, and SSA at 388 nm, the magnitude of derived UVAI strongly varies with the assumed value of AAE (Fig. 4 of Jethva and Torres, 2011). In other words, for a given value of observed UVAI, different assumptions of AAE would result in different values of the retrieved AOD. The “smoke” and “dust” aerosol models adopted in the OMACA algorithm assume an invariant spectral dependence of the imaginary part of the refractive index. For the carbonaceous and dust aerosol models, the relative spectral dependence in the imaginary index is assumed to be 20 % (Kirchstetter et al., 2004; Jethva and Torres, 2011) and 39 %, respectively, between the 354 and 388 nm wavelengths, at which the imaginary index at 354 nm is higher than that at 388 nm. For a fixed set of size distribution parameters and range of SSA (388 nm), this results in AAE in the range 2.5–3.0 for the carbonaceous aerosol models, and 2.0–4.0 for the dust models (see Appendix A). Theoretical error (%) in ACAOD (388 nm) due to the uncertainty in the assumption of aerosol AAE (354–388 nm range) is listed in Table 5. The reference value of AAE was assumed to be 2.65 corresponding to the moderately absorbing smoke model, and COD underneath the aerosol layer was assumed to be 10. AAE was perturbed in steps of 0.5 in both directions from the reference value. Similar to the uncertain SSA simulations, errors in ACAOD behave asymmetrically to the perturbations in AAE with larger (relatively lower) magnitudes of errors are associated with the underestimated (overestimated) AAE.
The corresponding simulated errors in the retrieval of aerosol-corrected COD
resulting from uncertain SSA and ALH are shown in Table 6. The overall errors
in COD are found to be much smaller compared those in the ACAOD retrievals.
An uncertainty of
The errors in COD retrievals due to the uncertainty in effective radius
follow asymmetric behavior to the perturbation around the assumed state.
While a large underestimation of
The sensitivity of ACAOD and aerosol-corrected retrievals to the uncertainty
in three major assumptions made in OMACA considered a broad range of
perturbation. However, we anticipate that the prescribed values of SSA, ALH,
and AAE are accurate to within
Comparison of coincident and collocated spectral ACAODs measured and
retrieved from HSRL-2 and OMI/OMACA for a total of seven ER-2 flights
operated during ORACLES phase I operation over the southeastern Atlantic
Ocean in August–September 2016.
Unlike the validation exercise of satellite-based aerosol retrievals in cloud-free skies, for which the ground-based direct measurements of AOD are amply available from hundreds of sites worldwide, such an assessment of ACAOD retrieved from the satellite is a challenging task due to the lack of reference aerosol measurements above the clouds. This is because the field campaigns in the past mainly focused on the measurements and characterization of aerosol properties in cloud-free conditions leaving vast cloudy areas unmonitored regarding the aerosol measurements. However, the airborne lidar such as high spectral resolution lidar (HSRL) when flying above the top of the aerosol layer can make direct measurements of aerosol extinction and thus provides AOD above the cloud. Also, airborne sunphotometers can make such measurements by flying above the cloud and below the aerosol layer. Using a limited dataset of the direct measurements of AOD above the cloud carried out by the NASA Ames Airborne Tracking Sunphotometer (AATS) and Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) sensors during different field campaigns, Jethva et al. (2016) have validated ACAOD retrieved using the “color ratio” method (Jethva et al., 2013), similar to the one presented here, but applied to the TOA visible-near infrared reflectances measured by the MODIS.
The monthly mean distribution of cloudy-sky frequency of occurrence of above-cloud absorbing aerosols deduced from the 12-year record (2005–2016) of OMI's near-UV observations.
NASA's ORACLES (observations of aerosols above clouds and their interactions)
(
Figure 4 shows the comparison of spectral ACAODs measured by HSRL-2 and
retrieved from OMI/OMACA for a total of seven ER-2 flights conducted during
August–September of 2016 (26 August, 12, 16, 18, 20, 22, 24 September).
HSRL-2 measurements falling within the boundaries of each OMI pixel, as
defined in the OMPIXCOR product, were averaged and compared with the ACAOD
value of the corresponding OMI pixel. The spatially collocated
aircraft–satellite matchups were grouped according to the three different
time windows, i.e., no time constraints (in hours), and
The remaining discrepancies in the comparison could be primarily attributed to the inherent uncertainties associated with both types of measurements, particularly in the satellite retrievals of ACAOD as discussed in the previous section, and spatiotemporal heterogeneity in aerosol fields unresolved by the collocation method. Despite these uncertainties, a reasonable agreement of OMACA-retrieved ACAOD with more accurate measurements from HSRL-2 for the ORACLES campaign provided the credibility and confidence in the product and allowed us to use it for the regional and global analyses presented in the rest of the paper. While the primary focus of the paper is to highlight the ACAOD product, its spatiotemporal distribution, and initial validation against ORACLES/HSRL-2 observations, we are working with the ORACLES team to perform a detailed validation of the OMACA product, both ACAOD and COD, using airborne in situ and remote sensing measurements. The results of the validation analysis will be covered in a dedicated follow-up publication.
The regional and global climate impact of absorbing aerosols above cloud depends on the total aerosol loading above the cloud, the microphysical and optical properties of aerosols and underlying cloud deck, as well as the spatial and temporal extent of the aerosol–cloud overlap scenes. We have carried out a global frequency of occurrence of absorbing aerosols above the cloud (FOACA) to identify the regions of frequent aerosol–cloud overlap. We adopt a bi-parametric approach to identify the scenes of absorbing aerosols overlaying the low-level cloud decks, as described in Sect. 2.2.3. FOACA is referenced to the cloudy-sky observations and defined as the ratio of the total number of days with an ACA condition (LER > 0.25 and UVAI > 0.8) to the total number of days with cloudy-condition (LER > 0.25). Additionally, we take advantage of the OMMYDCLD product to calculate the geometric cloud fraction for each qualifying OMI pixel. For the FOACA analysis, we adopt a less strict threshold of cloud fraction of 0.5. Since the main purpose of this analysis was to identify the presence of absorbing aerosols and cloud in the same atmospheric column and not to quantitatively retrieve ACAOD, a less strict value cloud fraction should adequately represent both aerosols and clouds in the corresponding pixel.
Figure 5 shows the monthly averages of FOACA derived from the 12-year record (2005–2016) of OMI following the above-described method. This analysis reveals several important regions of the world where the overlap of absorbing aerosols over clouds are frequently observed. During July through September, carbonaceous aerosols generated from agricultural burning over the central and southern Africa are mobilized over the semi-permanent low-level stratocumulus water clouds in the southeastern Atlantic Ocean (Torres et al., 2012; Alfaro-Contreras et al., 2016; Meyer et al., 2015). With more than 80 % of the cloudy-sky observations identified as aerosols above cloud during the Northern Hemisphere summer, the southeastern Atlantic Ocean is considered to be one of the prime study regions the world and also a natural laboratory to study the aerosols above cloud phenomenon. The springtime biomass burning activities such as burning of forest, savanna/grassland, and crop residue over Southeast Asia (SEA) countries, including Thailand, Myanmar, Laos, Cambodia, and Vietnam release significant amounts of trace gases and carbonaceous aerosols into the atmosphere (Elvidge and Baugh, 1996; Streets et al., 2003). Natural color images from the satellite show that smoke particles emitted from these activities were mobilized under the influence of winds over the widespread cloud deck over southern China, creating the appearance of the darker cloud deck. FOACA results show that about 20 %–40 % of the cloudy days are marked with smoke aerosols overlying bright cloud deck. Additionally, the smoke–cloud overlap seen in the OMI data is not merely confined to over land, but also extended over the western Pacific Ocean, albeit less often.
Dust storms originated over the Sahara Desert in northern Africa during the summer are often transported across the tropical Atlantic Ocean (Prospero et al., 2002; Kaufman et al., 2005; Huang et al., 2010; Yu et al., 2015). A substantial part of the dust transport occurs over the low-level stratocumulus clouds. The FOACA analysis shows that the presence of mineral dust aerosols above the cloud accounts for 40 %–80 % of the total cloudy-sky OMI observations. Similarly, mineral dust particles originated from the Arabian Peninsula, Iran and Pakistan often mobilized over the Arabian Sea during the active months (July–August) of the southwest Indian summer monsoon (Moorthy et al., 2005; Satheesh et al., 2006). The transported dust frequently overlies the low-level clouds such that the dust-cloud overlap accounts for the 40 %–60 % of the cloud-sky days.
Monthly time series of the regional cloudy-sky frequency of occurrence (in percentages with respect to the total cloudy-sky observations) of absorbing aerosols above cloud derived using OMI anomaly-free observations (Rows 1 to 23). Solid lines represent the linear fit to the respective time-series data.
Global distribution of monthly mean above-cloud AOD (388 nm) deduced from the 12-year (2005–2016) OMI observations.
The springtime Asian outflow of air pollutants, both anthropogenic and
mineral dust, across the Pacific Ocean is documented in various studies (Liu
et al., 2003; Huebert et al., 2003). The FOACA maps for the April and May
months revealing the transport pattern encompasses the entire northern
Pacific basin from the eastern coast of Asia to the western coast of North
America with 10 %–30 % of the cloudy-sky scenes identified as
aerosols over the clouds. One of the salient features of the FOACA analysis
is the smoke transport from southern Africa to the Indian Ocean. Carbonaceous
aerosols emitted from the fires in southeastern Africa during the late
biomass burning season (September) are often taken away from the continent
along the eastward flow and advected above the clouds over the southern
Indian Ocean (Sinha et al., 2004). The FOACA map for the month of September
highlights that the transport of smoke over the cloud is confined to within
the latitude range 35 to 20
Prior to our study, Devsthale and Thomas (2011) and Zhang et al. (2016) have studied the cloud-sky FOACA over the global ocean using 4 years and 8 years of CALIOP observations, respectively. The results presented in these papers are largely consistent with the ones derived from OMI observations over oceanic regions that are influenced by long-range transport of smoke and dust aerosols from continents as described above.
Since mid-2007, OMI observations have been affected by a possible external obstruction that perturbs both the measured solar flux and Earth radiance. This obstruction affecting the quality of radiance at all wavelengths for a particular viewing direction is referred to as “row anomaly” since the viewing geometry is associated with the row numbers on the charge-coupled device detectors. The row anomaly issue was detected for the first time in mid-2007 for a few rows, which over the period of operation expanded to other rows in 2008 and later. Figure 6a shows the current status of the row anomaly as identified by the anomaly algorithm developed for the NASA OMI total ozone product OMTO3 (Schenkeveld et al., 2017). At present, about half of the total 60 rows across the track are identified and flagged as row anomaly affected positions for which no physical retrievals are being performed.
Above it was examined and concluded that for the derivation of a meaningful trend in the global FOACA, only those rows or positions of the OMI instrument should be considered that are mostly unaffected by the row anomaly throughout the OMI operation period (Jethva, 2015). This is because UVAI exhibits a dependency on the scan angle in which the westward viewing geometry (left side of the scan, row numbers 1–30) shows higher values than those measured for the eastward-looking geometry (right side of the scan, row number 31–60). The row-averaged UVAI for the OMI operation period shown in the middle panel of Fig. 6b reflects the asymmetry in UVAI. OMI lost its most rows on the right side of the scan post-2007 operation due to the row anomaly. The remaining rows on the left side of the scan, where the UVAI values are larger than those on the right side of the scan, introduces a positive shift in the absolute values of UVAI, which leads to an overall positive trend in the FOACA. Therefore, the global trend (%) in FOACA calculated using all rows of OMI gives a positive trend (0.178 per year) as shown in the bottom panel of Fig. 6c. However, when the global trend is calculated using observations from rows which are mostly free of row anomaly throughout the OMI operation period, i.e., row number 1–23, the FOACA shows a negligible trend (0.022 per year). Regionally, we find similar results of positive trends in FOACA when all rows of OMI were used in the calculations (not shown here) against the statistically negligible trend when observations from rows 1 to 23 were considered, as shown in Fig. 7. Based on the present findings and also according to the results published in an interactive comment (Jethva, 2015), we strongly recommend the users of the OMACA product to use only those observations that are free of row anomaly throughout the OMI operation (2004 to present) for the trend related analysis.
Figure 8 shows the global distribution of ACAOD (388 nm) derived from OMACA
algorithm applied to the 12-year long record of OMI observations. The OMACA
Level 2 retrievals with algorithm quality flag “0”, “1”, and “2” were
averaged on every 0.5
During the Northern Hemisphere summer, larger magnitudes of ACAOD
(> 0.7) are retrieved over the southeastern Atlantic Ocean along
the coast of Namibia and Angola. Noticeably, the area coverage of ACAOD
expands substantially as the season progresses with retrieval coverage
confined to within 1500 km from the western coast of Africa in June to
encompassing almost the entire Atlantic Ocean basin (
During the Northern Hemisphere summer months, ACAOD in the range 0.3–0.5 is
observed over the tropical Atlantic Ocean where the transport of dust takes
place from the Saharan desert to over the oceanic clouds. The area coverage
of the retrievals is at its maximum in July, spanning half of the tropical Atlantic
Ocean basin with maximum ACAOD (
The OMACA product also captures springtime (April and May), long-range trans-Pacific transport of dust aerosols originated over the Gobi and Taklamakan Deserts possibly mixed with urban pollution and smoke along the transport pathways and over the clouds. The magnitude of retrieved ACAOD of the above-cloud Asian outflow ranges from 0.4 to 0.5 near the eastern coast of northeastern Asia, reducing to 0.2–0.3 along the transport over the mid-Pacific and up to the western coast of North America.
Time series of regional monthly mean above-cloud AOD at 388 nm (left y axis, color: red) and the ratio of above-cloud AOD and cloud-free AOD (right y axis, color: blue), both at 388 nm, derived using the OMACA and OMAERUV products, respectively.
Time series of regional, monthly mean aerosol-corrected (left y axis, color: red) COD (388 nm) derived using a 12-year long OMACA record. The y axis on the right side (color: blue) depicts a time series of the % difference between simultaneous retrievals of apparent/non-corrected and aerosol-corrected COD for the same period and regions. Only anomaly-free observations of OMI (Rows 1 to 23) were used in the calculations.
Percent difference in COD (aerosol-corrected minus non-corrected) as
a function of above-cloud absorption AOD (388 nm) for the smoke
Figure 9 (left y axis, color: red) shows the regional, monthly mean time series of ACAOD (388 nm) for the five prominent regions of the world where the frequent overlap of absorbing aerosols above the cloud is observed. The regional monthly mean ACAODs were calculated in the following way. For each region and month, an averaged value of ACAOD was calculated, and a number of Level 2 observations that went into the averaging was also stored. Subsequently, a set of 12-monthly averaged values scaled by multiplying them with a fraction that is defined as the ratio Level 2 observations for the individual months to the maximum number of Level 2 observations found over the 12-year period (2005–2016) over the same region. Scaling the monthly averaged value with the calculated fraction ensures the representativeness of ACAOD statistics over the spatial and temporal domains and thus facilitates the intercomparison. For instance, a comparison of the two averaged values derived from two different set of statistics, i.e., under-populated and adequately populated, likely results in the misinterpretation of monthly time-series data. The procedure described above was applied to the OMACA observations for the five prominent aerosol–cloud overlap regions to produce the time series shown in Fig. 9.
The temporal evolution of ACAOD (388 nm) over the southeastern Atlantic Ocean exhibits a repetitive seasonal cycle with monthly mean values reaching up to 0.4 during the dry biomass burning season. Although an inter-annual variation is apparent in the time series, e.g., lower and higher ACAOD during the burning season of 2012 and 2015, no significant trend is noticed over the 2005–2016 OMI record. Over the tropical Atlantic Ocean, the monthly ACAOD values fall in the range 0.2–0.3 during summer months when dust aerosols from northern Africa are mobilized over the low-level oceanic stratocumulus clouds. The inter-annual variations of ACAOD over the Arabian Sea are found to be significant with the monthly value of 0.4 during the first 2 years of the record (July and Aug of 2005 and 2006) followed by a drastic reduction in the aerosol loading above the cloud during the middle part of the record. The springtime biomass burning and resulting smoke aerosols above cloud over Southeast Asia (4th panel) is evident in the time series where the peak values of monthly ACAOD vary from 0.2 to 0.5 depending on the year.
It is important to quantify the fraction of the total columnar aerosol loading, situated above the cloud, for several applications. For instance, the cloud-free aerosol retrieval represents the aerosol in the entire vertical column while avoiding the cloudy-sky scenes. Therefore, the statistics of cloud-free retrievals over the regions with frequent aerosol–cloud overlap becomes restrictive, leading to partially incomplete representation of aerosol properties on a regional and temporal scales. This kind of scenario affects the calculations of regional climatology, radiative forcing assessments, and aerosol transport. However, the availability of above-cloud (OMACA) and cloud-free total columnar (OMAERUV) AODs from OMI allows us to estimate the fractional aerosol loading above the cloud. The ratio of monthly mean ACAOD to the total columnar AOD (both at 388 nm) displayed on the right-side y axis of Fig. 9 shows that the fractional aerosol loading over the cloud can be as large as 80 %–100 % during the peak months of biomass burning and dust transport over the respective regions. A significant fraction of the aerosol column above clouds indicates that the long-range transport of partially absorbing aerosols occurs in the free troposphere and over the low-level clouds. However, the above-cloud and total column AOD are comparable in magnitude during peak aerosol activities,
Figure 10 displays the monthly mean evolution of aerosol-corrected COD (left y axis, color: red) for the five prominent regions of aerosol–cloud overlap. The monthly mean values were calculated following the procedure described in the previous section. The seasonal cycle of COD over the southeastern Atlantic Ocean exhibits repetitive behavior with the maximum value of COD reaching up to 8.0 during the peak burning period. On the other hand, the monthly cycle of COD over the tropical Atlantic Ocean (Arabian Sea) during the same season shows more considerable interannual variations with COD in the range 6 to 14 (4 to 10). The magnitudes of aerosol-corrected COD over Southeast Asia during the springtime biomass burning season (March–April) are found to be the largest among five regions considered here with values ranging between 16 and 20 except for the years 2008 and 2013 when COD was less than 12.
The right-side y axis of Fig. 10 (color: blue) depicts the concurrent monthly time series of the percent difference between apparent (non-corrected) and aerosol-corrected CODs the magnitudes of which represent the error in the retrieval of COD at 388-nm wavelength with reference to the corrected COD if the presence of aerosols is ignored in the inversion. Note that only those COD retrievals are considered here for which the absorbing aerosols are identified above the clouds. While there is a significant variation in the magnitudes of the percentage difference between the two CODs over different regions, generally larger errors are associated with the higher aerosol-corrected COD and ACAODs (shown in Fig. 9). More discussion on the impact of aerosol absorption on cloud retrievals is presented in the next section.
The presence of absorbing aerosols above cloud obstructs the light reflected
by the cloud top, and thus reduces cloud-reflected upwelling UV (Torres et
al., 2012), VIS, and NIR radiation (Jethva et al., 2013; Meyer et al.,
2015) reaching the TOA. Therefore, cloud retrievals of COD derived from
passive sensors such as OMI are expected to be biased low if absorbing
aerosols are not accounted for in the inversion. The OMACA product reports two
sets of COD, one corrected for the presence of absorbing aerosols overlying
cloud deck, and one retrieved assuming no aerosols above the cloud, which is
termed as the apparent COD. The magnitudes of bias in the apparent COD depend
on the strength of aerosol absorption and backscattering, as well as on the
actual value of COD. Note that OMACA does not directly retrieve the aerosol
absorption optical depth (AAOD) but retrieves ACAOD assuming an a priori
value of SSA (see Sect. 2.2.2.3). Therefore, the AAOD can be readily
calculated using these two pieces of information as
AAOD
For both aerosol types, increasing the magnitude of negative bias in the
retrieval of apparent COD is related to AAOD, suggesting the impact of aerosol
absorption on the retrieved COD when the presence of absorbing aerosols is
ignored in the inversion. Retrievals identified with the “smoke” aerosol
type, predominantly found over the biomass burning regions of the southeastern
Atlantic Ocean and Southeast Asia show a much larger range of AAOD and
associated bias in COD than that observed with “dust” aerosols found in the
regions of dust transport over the tropical Atlantic Ocean and the Arabian
Sea. Noticeably, the magnitudes of negative bias in the cloud retrievals are
also codependent on the absolute values of COD (here it is aerosol-corrected
COD). For instance, for an AAOD of 0.1, the bias in COD is
We have developed a global above-cloud aerosol algorithm, formally named
OMACA (OMI above-cloud aerosols), to simultaneously derive the columnar
optical depth of absorbing aerosols above the cloud and underlying
aerosol-corrected cloud optical depth from the near-UV observations made by
Aura/OMI. Physically based on the enhanced spectral contrast in the near- UV
region (354–388 nm) caused by aerosol absorption above the cloud, OMACA
relates the TOA observations in the two channels to a pair of ACAOD and COD
under a prescribed set of assumptions. The architect of the OMACA algorithm
in terms of the ancillary datasets (CALIOP-OMI based ALH, OMI-based near-UV
surface albedo, and use of AIRS CO for the aerosol type identification),
aerosol models (smoke and dust), and retrieval approach (two-channel
inversion) is analogous to the OMI's two-channel, cloud-free OMAERUV aerosol
algorithm. OMACA was applied to the entire record of OMI observations
(October 2004 to present) to deduce a global research product of AOD above
the cloud. Currently, the Level 2 orbital data product is stored on a freely
accessible Aura Validation Data Center webpage
(
An analysis of the frequency of occurrence of the above-cloud absorbing aerosols reveals several important regions of the world where the overlap of absorbing aerosols and cloud are frequently observed on a monthly to seasonal scales. These regions include the southeastern Atlantic Ocean and Southeast Asia, where layers of smoke aerosols produced from the seasonal agricultural biomass burning spread thousands of kilometers over the regional low-level stratocumulus cloud deck; the tropical Atlantic Ocean and the Arabian Sea, where dust aerosols transported from Sahara and Arabian deserts, respectively, found over low-level clouds; the northern Pacific Ocean, where dust particles originated from Asian deserts, possibly mixed with the pollution haze, along the eastward transport pathways are found to overlie clouds; and the southern Indian Ocean, where the smoke produced from agricultural burning over southeastern Africa drifts along the easterly winds and overlies the oceanic cloud deck. Globally, as well as on a regional scale, no significant trend in the frequency of ACA was noted when only those observations of OMI instrument (Rows 1 to 23) that are free of row anomaly throughout the OMI operation period (2004 to 2016) were used in the calculation. The climatology maps of the retrieved ACAOD (388 nm) derived from a 12-year long OMACA record show moderate (0.3 < ACAOD < 0.5, away from the sources) to higher aerosol loading (ACAOD > 0.8 in the proximity to the sources) above the cloud over these prominent aerosol–cloud overlap regions. When compared with the cloud-free, columnar aerosol loading distribution retrieved from the OMI/OMAERUV two-channel algorithm, ACAOD accounts for as large as 60 %–100 % of the total columnar loading over different regions during peak biomass burning and dust transport seasons.
The aerosol-corrected CODs retrieved at the near-UV wavelength (388 nm) are found to be noticeably higher than those retrieved assuming no aerosols above the cloud. The percent bias in COD with reference to the aerosol-corrected COD is found to strongly correlate with AAOD as well as the magnitude of COD. For instance, carbonaceous aerosols above cloud found over the southeastern Atlantic Ocean and Southeast Asia during respective biomass burning seasons result in a significant negative bias in apparent COD, the magnitudes of which increase with increasing aerosol absorption as well as the cloud brightness.
A direct comparison of coincident and collocated ACAODs derived from
OMI
Conventional aerosol remote sensing algorithms provide distribution of aerosols in the cloud-free areas leaving behind vast cloudy regions unmonitored regarding the co-presence of aerosols and clouds. The OMACA aerosol product offers a quantitative characterization of aerosol loading above cloud on a global scale. Several observational and modeling studies have shown that an overlap of absorbing aerosols above cloud leads to significant atmospheric warming, which can affect cloud lifetime and the hydrological cycle. The magnitudes of these effects depend upon the amount of aerosol loading above the cloud, cloud brightness (COD), optical and microphysical properties of aerosols and clouds, and cloud fraction. The OMACA aerosol product from OMI presented in this paper offers a crucial missing piece of information of the aerosol loading above cloud that will help us to quantify the radiative effects of aerosols above the cloud and its resultant impact on clouds and thus climate. A global above-cloud aerosol product, in conjunction with the standard cloud-free aerosol product, provides us with an unprecedented all-sky aerosol distribution from space. This can substantially enhance our knowledge of how aerosols affect cloud radiative forcing and microphysical properties, and aerosol transport.
The
OMI/OMACA Level-2 dataset presented in this paper is stored at a freely
accessible Aura Validation Data Center web page
(
Optical and microphysical properties of the OMACA dust aerosol model.
n/a: Not applicable. Real refractive
index
Axis ratio distribution for the spheroidal dust particles (Dubovik et al., 2006).
Optical and microphysical properties of the OMACA carbonaceous aerosol model.
Real refractive index
Nodes of OMACA look-up table.
Content of OMACA HDF-EOS5 data file.
HJ developed the global OMACA algorithm, conducted the analysis, prepared graphics, and wrote the paper. CA developed an initial version of the research version of OMACA code, which was then revised, updated, and operationalized by HJ. OT ensured the integrity of various stages of the development and supervised the overall flow of the work. All authors contributed to editing the paper.
The authors declare that they have no conflict of interest.
Authors acknowledge the support of the OMI SIPS team
(