Biomass burning is an important and uncertain source of aerosols and
NO
Satellite observations of backscattered radiation have been vital in measuring and monitoring global-scale air pollution, consisting of a mixture of aerosols and reactive gases that are either directly emitted or formed through various chemical and physical processes. These global data sets of atmospheric composition contain important information on the chemistry of the atmosphere (e.g., Stavrakou et al., 2013), trends in air quality (e.g., Castellanos and Boersma, 2012), as well as emissions from fossil fuel burning (e.g., Jaeglé et al., 2005), biogenic hydrocarbon sources (e.g., Marais et al., 2012), lightning (e.g., Bucsela et al., 2010), and biomass burning (e.g., Castellanos et al., 2014). However, the retrieval of tropospheric column amounts of trace gases from satellite observations is complicated and remains a challenge.
In the retrieval of NO
In the Dutch OMI NO
The DOMINO retrieval does not directly take into account the effect of
aerosols on the AMF, but instead uses an implicit correction by assuming
that the cloud parameters retrieved by the OMI (Ozone Monitoring Instrument)
cloud algorithm (OMCLDO2) (Acarreta et al., 2004; Stammes et al., 2008)
account for the effect of the aerosols on the light path. The DOMINO
retrieval takes the approximation that the effects of aerosols on the
tropospheric AMF can be represented as the fractional coverage of a
Lambertian reflector that yields a top-of-atmosphere (TOA) reflectance that best agrees with the
observed reflectance, i.e., a radiometrically equivalent, or effective, cloud
fraction. Previous work has shown that for OMI the effective cloud fractions
retrieved in the O
For a few synthetic cases of assumed aerosol type and optical depth, Boersma et al. (2004) showed that the tropospheric AMF could increase by as much as 40 % when aerosol radiative effects are directly accounted for. This raises the following question: to what extent can the implicit correction via the retrieved cloud parameters mimic the effects of different observed aerosol concentrations, vertical aerosol distributions, and physical aerosol properties?
Compared to a pure molecular scattering atmosphere, the presence of a scattering component, whether aerosol or cloud, can change light paths as well as their contributions to the TOA reflectance. In some aspects, the radiative effects of scattering aerosols and clouds are comparable. Both aerosols and clouds decrease the sensitivity to an absorber at lower altitudes, as more photons will be scattered back to the satellite before reaching the surface, a shielding effect. Moreover, clouds and aerosols also increase the sensitivity to an absorber above the scattering layer, by increasing the contribution of these light paths to the TOA reflectance, i.e., an albedo effect. While these effects can be approximated by the effective cloud model, an opaque Lambertian surface with high albedo (Koelemeijer and Stammes, 1999) (i.e., the altitude-dependent AMFs (Eskes and Boersma, 2003) or scattering weights (Palmer et al., 2001) below a cloud are zero), aerosols can modify the radiative transfer in ways that may not be adequately covered by this model.
Because aerosols and trace gases are often well mixed near the surface, aerosols can increase the sensitivity to an absorber as a result of multiple scattering, which increases the light path and thus trace gas absorption in the pollution layer compared to a Rayleigh atmosphere. The effect of aerosols and clouds will also differ in the case of absorbing aerosols, which will decrease the sensitivity to an absorber by decreasing the number of photons that return to the satellite from within and below the aerosol layer. Finally, due to their different characteristic sizes (cloud particles being larger), aerosol and cloud particles have different phase functions. Thus, an accurate estimate of the height and physical properties of an aerosol layer with respect to the vertical distribution of the absorber is essential for accurate air mass factor calculations for trace gas retrievals (Leitão et al., 2010).
Lin et al. (2014) studied the effect of aerosols on OMI NO
In this work, we investigated the properties of the implicit aerosol
correction for tropospheric NO
In our analysis we compared NO
OMI is a nadir viewing imaging spectrometer aboard the EOS Aura satellite
that measures backscattered radiation in the UV–Vis from 270 to 500 nm (Levelt et al., 2006). During the first 3 years of operation starting in
2004, OMI provided daily global coverage at a nominal resolution of 13 km
The O
The retrieval spectral window includes the collision-induced absorption
feature of oxygen (O
A DOAS fit of the OMI reflectance spectrum is used to derive the continuum
reflectance at the reference wavelength of 475 nm and the O
O
In the Dutch OMI NO
In DOMINO v2 (Boersma et al., 2011), the cloudy-sky
The altitude-resolved AMFs in the LUT are represented as a function of six
forward model parameters
In deriving the altitude-resolved AMF LUT with DAK, surface reflectivity was assumed to be Lambertian, and the atmosphere plane-parallel, but polarization was accounted for. The temperature and pressure vertical profiles corresponded to the AFGL mid-latitude summer profile.
Irie et al. (2012) and Ma et al. (2013) have shown that DOMINO v2
NO
The OMAERUV algorithm retrieves aerosol extinction optical depth (AOD) and single scattering albedo (SSA) at 388 nm, for cloud-free scenes (Torres et al., 2013, 2007). AODs at 354 and 500 nm converted from 388 nm are also reported. Clear-sky conditions are required to reliably retrieve AOD and SSA, because reflectance from clouds causes errors in the retrieved aerosol parameters. Thus, strict cloud filtering is implemented in the algorithm (see Appendix A for details regarding the cloud filtering criteria).
The retrieval algorithm makes use of the relationship between the 354–388 nm spectral contrast and the 388 nm reflectance to derive the AOD and SSA at 388 nm, while the 354 and 500 nm products are obtained by converting the 388 nm product using the spectral dependence of the prescribed aerosol type and particle size distribution. The OMAERUV algorithm assumes that the column aerosol can be represented by one of three main aerosol types: dust, carbonaceous aerosol associated with biomass burning, or weakly absorbing sulfate based aerosol. The microphysical properties of the three types are based on long-term statistics from AERONET (Aerosol Robotics Network; Holben et al., 1998). The algorithm uses a LUT of reflectances at 354 and 388 nm that were calculated for each aerosol model using the University of Arizona radiative transfer model (Caudill et al., 1997). The LUT has nodal points in AOD, SSA, aerosol layer height (ALH), surface pressure, and viewing geometry.
In a recent improvement to the OMAERUV algorithm, a new scheme was implemented to prescribe the aerosol type based on collocated AIRS CO observations, UVAI, and geographical location. Depending on the aerosol type, a best guess ALH is also prescribed. For the case of carbonaceous aerosols with aerosol index greater than 0.5, the ALH is inferred from a multiyear climatology of ALH that was developed from CALIOP backscatter vertical profile measurements (Torres et al., 2013); otherwise the ALH is assumed to be 1.5 km. The vertical profile of aerosol extinction is modeled as a Gaussian distribution that peaks at the ALH and has a 1 km half-width. For sulfate-based aerosols, the algorithm assumes that the aerosol concentration decreases from the surface in an exponential decay with 2 km scale height. The approximations for the shapes of the aerosol extinction vertical profiles are based ground-based lidar observations (Torres et al., 1998).
The OMAERUV standard level 2 data product consists of a final estimate for AOD and SSA consistent with the prescribed best guess ALH described above. The level 2 data product also provides the AOD and SSA that would have been retrieved at the five ALH nodal points (0, 1.5, 3.0, 6.0, and 10 km) of the LUT. Thus, one can interpolate the AOD and SSA to an ALH other than the best guess ALH if better information on the ALH is available, such as (instead of the climatology) simultaneous observations from CALIOP.
In a comparison to AOD observations at 44 AERONET sites around the world,
Ahn et al. (2014) found that for 65 % of the observations, the difference
between AERONET and OMAERUV AOD was less than 30 %, the expected
uncertainty of the retrieval. Overall, for carbonaceous aerosols, the slope
and
CALIOP is a dual-wavelength polarization lidar on board the CALIPSO
satellite that measures attenuated backscatter at 532 and 1064 nm at a
vertical resolution of 30 m below 8.2 km, and 60 m up to 20.2 km (Winker et
al., 2013). Along the orbital track, CALIOP has a horizontal resolution of
335 m. Observations are available from mid-June 2006. The CALIOP level 2
products include a vertical feature mask that characterizes atmospheric
layers as containing cloud, aerosol, or clean air. Cloud and aerosol are
detected with a threshold technique (Vaughan et al., 2009), and a
discrimination algorithm (Liu et al., 2009) assigns a cloud–aerosol
discrimination (CAD) score to each layer. The CAD score is a percentile
between
The 2006–2008 fire season (July–November) average DOMINO v2 NO2
tropospheric columns, OMAERUV 500 nm AOD, MODIS-Aqua active fires, and CALIOP
lv2 532 nm AOD. Pixels were selected and re-gridded to 0.25
For this work, we used daytime CALIOP level 2 532 nm aerosol extinction
vertical profiles that were collocated with DOMINO and OMAERUV retrievals.
Although the algorithm accounts for signal attenuation above a layer, strong
absorption by black carbon at 532 nm can diminish the sensitivity to
aerosols near the surface (Torres et al., 2013) adding uncertainty to the
retrieved aerosol extinction in these layers. However, in our analysis of
NO
A comparison of CALIOP observations to ground-based lidar showed that the top and base height of aerosol and cloud layers of the two measurements generally agreed to within 0.1 km, indicating that the CALIOP cloud–aerosol discrimination algorithm can provide reliable information on the vertical profile of aerosols (Kim et al., 2008). In general, analysis of co-located MODIS and CALIOP AOD retrievals indicates that CALIOP AOD is higher than MODIS, but the two observations are roughly within the combined expected uncertainty (Winker et al., 2013).
As we are interested in retrievals affected by biomass burning emissions, we
filtered the OMI observations within our South American domain
(36
We created a data set of OMI-CALIOP collocated pixels by averaging together
all daytime CALIOP extinction vertical profiles within 0.5
In Fig. 1 we show DOMINO v2.0 NO
The probability distributions of the prescribed aerosol layer height (ALH) in the OMAERUV retrieval, and the effective ALH (Eq. 7) derived from CALIOP 532 nm observed aerosol extinction vertical profiles. The mean and standard deviation of the CALIOP effective ALH are 1.5 and 0.62 km, respectively.
Because OMAERUV-retrieved AOD and SSA is sensitive to the prescribed ALH, we derived new estimates of AOD and SSA that reflect the CALIOP-observed vertical distribution of aerosols. Figure 2 shows the probability distributions of the OMAERUV-prescribed ALH and the CALIOP effective ALH over South America for biomass burning aerosols. For pixels where OMAERUV assigns an ALH equal to zero, this corresponds to an aerosol vertical profile with a maximum at the surface that decays exponentially with a 2 km scale height. In Fig. 2 this is depicted as an ALH equal to 1.88, which is the effective ALH for such a profile. The mean CALIOP effective ALH is 1.5 km, the same default value that is utilized for carbonaceous aerosols in the OMAERUV retrieval. However, there is substantial variability in the daily observations, which show that 50 % of the observations have an ALH less than 1.5 km.
The shape of the observed CALIOP aerosol extinction vertical profile
and the collocated simulated TM4 NO
Figure 3 shows the average shape of the observed CALIOP aerosol extinction
vertical profile and the collocated simulated TM4 NO
The change in the OMAERUV 388 nm AOD and SSA from replacing the standard retrieval prescribed aerosol layer height (ALH) with the CALIOP-observed effective ALH.
We replaced the OMAERUV-prescribed climatological ALH with observed CALIOP effective ALHs to obtain an estimate of the SSA and AOD that better reflects the daily variability of the aerosol vertical profile. To do this we interpolated the OMAERUV AOD and SSA given on the five altitude nodal points to the CALIOP ALH (Fig. 4). On average, the AOD interpolated to the effective ALH was 7 % higher than the AOD derived from the OMAERUV-prescribed ALH. There was on average no change in the SSA. Small increases in AOD are expected because although the OMAERUV assumed aerosol layer heights are generally consistent with the CALIOP observations, CALIOP observations indicate that more profiles have enhanced aerosol extinction closer to the surface (Fig. 2).
When collocated daily measurements are compared, the slope between CALIOP 532 nm AOD and OMAERUV 500 nm AOD is 0.65 and the correlation coefficient is 0.57. Figure 5 shows daily OMAERUV AOD measurements after adjusting for the CALIOP ALH. The slope increases to 0.70, comparable to the slope from the OMAERUV-AERONET evaluation discussed in Sect. 2.3, but there is no change in the correlation coefficient. For both choices of ALH, 68 % of the OMAERUV AOD observations were within 30 % of the CALIOP observations.
Because the CALIOP footprint samples only a fraction of the OMI pixel, we can expect scatter in the comparison of the OMAERUV and CALIOP AOD, and that the AOD derived from CALIOP will likely not be as representative of the AOD for the DOMINO viewing scene as the OMAERUV AOD. The OMAERUV observations also provide the spectral information needed to calculate the AOD at the DOMINO reference wavelength. For these reasons, we scaled the CALIOP aerosol extinction vertical profiles to the OMAERUV AOD in our analysis. Thus, in our analysis CALIOP observations provide the aerosol vertical profile shape, but the AOD and SSA are based on OMI observations.
Comparison of daily OMAERUV 500 nm AOD and collocated CALIOP lv2 532 nm AOD for the 2006–2008 fire season (July–November) over South America. The gray solid line represents the least squares fit through the origin. See Fig. 1 for the pixel and layer selection criteria.
Altitude-resolved AMFs were computed with the DISAMAR (Determining Instrument Specifications and Analyzing Methods for Atmospheric Retrieval) radiative transfer model (de Haan, 2011). DISAMAR was designed to simulate retrievals of properties of atmospheric trace gases, aerosols, clouds, and the ground surface for passive remote-sensing observations. Similar to the DAK radiative transfer model used to derive the DOMINO LUT, DISAMAR computes the reflectance and transmittance in the atmosphere using the polarized doubling–adding method (de Haan et al., 1987). This method calculates the internal radiation field in the atmosphere for an arbitrary number of layers, in which Rayleigh scattering, gas absorption, and aerosol and cloud scattering and absorption can occur. A key difference between DAK and DISAMAR is that DISAMAR utilizes a separate altitude grid for the radiative transfer calculations that is independent of the grid used for specifying the atmospheric properties. This is important for simulating strong vertical gradients in the radiation field, e.g., near the top of clouds.
The probability distribution of the differences in AMF retrieved by DISAMAR and DOMINO for all pixels in which MODIS-Aqua reported an active fire between July and November in 2006–2008. The DOMINO tropospheric AMF data were filtered for tropospheric quality flag equal to zero and surface albedo less than 0.3.
Comparison between DISAMAR tropospheric AMFs calculated with the standard retrieval using parameters from DOMINO v2.0 (DISAMAR-standard), and DISAMAR tropospheric AMFs calculated with explicit aerosol effects (DISAMAR-aerosol). The AOD and SSA for these retrievals are determined by the OMAERUV retrieval, and aerosol extinction profiles were taken from the CALIOP lv2 retrieval.
In Fig. 6, we show the comparison of NO
The differences between the DOMINO and DISAMAR tropospheric AMFs in Fig. 6
represent the errors that arise from interpolating the LUT in the DOMINO
retrieval, and numerical differences that arise from higher-resolution
vertical layering in the DISAMAR radiative transfer calculations. On
average, tropospheric AMFs calculated in DOMINO v2.0 using the LUT approach
are nearly equivalent to online radiative transfer modeling with DISAMAR,
as the differences in tropospheric AMF derived from the two methods are on
average
In order to model aerosol absorption and scattering effects explicitly, we
took SSA and AOD from OMAERUV retrievals, and aerosol extinction vertical
profiles from co-located CALIOP observations. In the retrievals with
explicit aerosol effects, for each pixel we used the DOMINO viewing
geometry, surface albedo, and temperature, pressure, and NO
On the left are altitude-resolved AMFs from the DISAMAR-standard and
DISAMAR-aerosol calculations. The tropospheric AMF is given next to each
label in the legend, and the DOMINO tropospheric AMF is given for reference.
On the right are the NO
On the left are altitude-resolved AMFs from the DISAMAR-standard and
DISAMAR-aerosol calculations. The tropospheric AMF is given next to each
label in the legend, and the DOMINO tropospheric AMF is given for reference.
On the right are the NO
We do not expect residual cloud contamination to significantly affect our
results because we limited our analysis to pixels where active fires are
detected by MODIS-Aqua, and clear skies facilitate favorable conditions for
open burning. An additional check was made by comparing the CALIOP measured
cloud
In DISAMAR, the Ångström exponent calculated from the OMAERUV AOD at 388 and 500 nm gives the spectral dependence of the AOD, while the SSA was linearly interpolated to 439 nm from the retrieved SSA at 388 and 500 nm. Aerosol scattering was modeled by the Henyey–Greenstein phase function with an asymmetry parameter of 0.7, consistent with the biomass burning aerosol models used in the OMAERUV retrieval, as well as long-term statistics from AERONET observations in Brazil (Dubovik et al., 2002). We will refer to these retrievals as DISAMAR-aerosol.
In Fig. 7 we show the comparison of tropospheric AMFs calculated with the
DISAMAR-standard and DISAMAR-aerosol retrievals for all 13 356 OMI-CALIOP
collocated pixels over South America. Tropospheric AMFs are on average
11 % higher when OMAERUV and CALIOP aerosol characteristics (instead of
effective O
Figure 8 shows typical altitude-resolved AMFs, CALIOP aerosol extinction
profiles, and simulated TM4 NO
In Fig. 8, the difference in the tropospheric AMFs is small because the
implicit aerosol correction reasonably approximates the shape of the
altitude-resolved AMFs calculated with observed aerosol parameters. The
difference in the tropospheric AMF is primarily driven by the discontinuity
in the altitude-resolved AMF introduced by the effective cloud (Eq. 1). The
altitude-resolved AMF represents the change in the logarithm of the TOA
reflectance when a unit amount of NO
The ratio of the DISAMAR-aerosol tropospheric AMF to the
DISAMAR-standard tropospheric AMF with respect to
The difference in the CALIOP effective aerosol layer pressure (ALP)
and the O
Ranges of parameters that are observed by OMI for which less than 20 % biomass burning aerosol-related average error in the AMF can be expected.
Comparing Figs. 8 and 9, the factors that distinguish retrievals
with large differences from those with small differences in tropospheric
AMFs are lower effective cloud pressure, higher effective cloud fraction,
and higher AOD. Also, in pixels where the effective cloud correction fails
(Fig. 9), the O
In Fig. 10, we binned the differences in tropospheric AMF (DISAMAR-aerosol
– DISAMAR-standard) for all 13 356 pixels considered according to the
difference between the O
Figure 10b and d show that for the approximately 70 % of pixels where
the AOD was less than 0.6 and the effective cloud radiance fraction was less
than 30 %, the difference in AMF was on average also less than
The largest mean differences between the DISAMAR-aerosol and
DISAMAR-standard retrieval occur for O
Simulations of differential optical thickness for an aerosol layer centered at 850 hPa and extending for 300 hPa (solid red line). In each figure the differential optical thicknesses of Lambertian clouds with continuum reflectance equal to that of the aerosol layer simulation (i.e., equal cloud fraction) are shown for different cloud pressures (dashed lines).
Uncertainties in the observed aerosol parameters used in the DISAMAR-aerosol
tropospheric AMF calculations can account for only part of the 30–50 %
average difference between the DISAMAR-standard and DISAMAR-aerosol
calculations for high AODs (> 0.6) (see Appendix B); the upper
limit of the combined uncertainties in retrieved aerosol parameters is
25–30 %. The remaining difference between the DISAMAR-standard and
DISAMAR-aerosol calculations stems from a combination of misrepresenting the
height of the aerosol layer (i.e., the DISAMAR-standard retrieval predicts
decreased sensitivity to NO
Several factors could lead to an O
Figure 12a shows that the differential optical thickness of the aerosol layer with SSA equal to 1.00 corresponds to a Lambertian cloud between 850 and 900 hPa. When the SSA decreases to 0.90, the differential optical thickness for the aerosol layer is reduced and corresponds to a Lambertian cloud at 750 hPa (Fig. 12b).
Aerosol absorption would be enhanced if a strongly absorbing layer were elevated above a more optically thick scattering layer or equivalently if the surface albedo increased. This is shown in Fig. 12c, where the surface albedo for the simulation of an aerosol layer with AOD equal to 1.5 and SSA equal to 0.90 is increased from 0.04 to 0.07. The 477 nm differential optical thickness now corresponds to a Lambertian cloud at 650 hPa. Figure 13 shows the comparison of surface albedo and the difference between the observed effective cloud pressure and the observed effective aerosol layer pressure. The figure indeed indicates that negative differences between observed effective cloud pressure and observed effective aerosol layer pressure are associated with larger surface albedos, particularly when the AOD exceeds 0.7.
Another mechanism through which larger observed surface albedos could lead
to lower effective cloud pressures is if the surface albedo climatology is
biased high due to (1) cloud or smoke contamination (Kleipool et al., 2008),
or (2) short-term darkening of the surface by biomass burning. If a surface
albedo larger than the actual scene albedo is used in the forward Lambertian
cloud model, the expected O
Comparison of the surface albedo utilized in the O
Aerosol absorption also significantly affects the retrieved O
In this paper we analyzed the properties of the implicit aerosol correction
in the DOMINO tropospheric NO
Comparison of the OMAERUV-retrieved 388 nm AOD and observed effective cloud fraction binned by the OMAERUV-retrieved SSA.
From our analysis we identified the ranges of retrieved O
Retrievals with effective cloud pressure less than 800 hPa tend to have the largest differences in tropospheric AMF because typically these cloud pressures were lower than the collocated effective aerosol layer pressure observed by CALIOP. When observed aerosol parameters were included in the radiative transfer calculations, tropospheric AMFs were on average 20–40 % larger than the tropospheric AMFs derived using effective cloud parameters. These situations correspond with overestimated shielding in the implicit aerosol correction approach because the assumption of an opaque cloud underestimates the altitude-resolved AMF below the effective cloud.
Simulations of O
Above an effective cloud fraction of 0.3 or an AOD of 0.60, tropospheric
AMFs calculated with observed aerosol parameters were on average 30–50 %
larger than the tropospheric AMFs derived using effective cloud parameters.
These differences cannot be accounted for by the uncertainties in the
retrieved aerosol parameters. This implies that for large fires or
smoldering fires that release significant amounts of aerosols, the DOMINO
NO
In our analysis we compared AMFs from the DOMINO retrieval calculated by
interpolating a look-up table with radiative transfer calculations from
DISAMAR; the mean and standard deviation of the difference was
Our analysis holds promise for a strategy to include the effect of aerosols on tropospheric AMF calculations for clear-sky pixels based on globally available satellite observations. Although, on average, the differences in tropospheric AMFs calculated with effective cloud parameters versus observed aerosol parameters are small, tropospheric AMFs can differ by more than a factor of 2.
In the presence of actual clouds, the effect of aerosols on the tropospheric AMF may be offset or enhanced depending on the amount and height of the clouds (Lin et al., 2014). As aerosol optical depth from OMI is not observable in the presence of clouds, further work is needed to exploit data from high spatial resolution aerosol sensors that can resolve scene heterogeneity, as well as global atmospheric simulations of aerosols.
In order to include aerosol data in the retrieval, online radiative transfer modeling would be required. Currently, this is computationally prohibitive for a near-real-time retrieval, although in the future enhanced computational techniques as well as using more and faster processors may alleviate this problem, particularly for offline regional retrievals. We suggest that for applications where spatial and temporal averaging is impossible, such as short-term validation campaigns, these effects should be considered.
The change in the calculated tropospheric AMF as a result of a
decrease from 0.7 to 0.6 in the aerosol asymmetry parameter (
In the OMAERUV retrieval pixels are labeled as cloud free if one of the
following three conditions occurs: (1) carbonaceous aerosol has been
identified and the measured reflectivity at 388 nm is less than 0.16, (2) the
difference between the measured scene reflectivity and the assumed surface
albedo (
The UVAI is a measure of the deviation of the observed UV spectral contrast from a pure Rayleigh scattering atmosphere. UVAI will be negative for scattering aerosols (Penning de Vries et al., 2009), positive for absorbing aerosols, and will increase with the height, the optical depth and the single scattering co-albedo of the absorbing aerosol layer (de Graaf, 2005; Torres et al., 1998).
The change in the calculated tropospheric AMF as a result of a 30 % or 0.1 decrease (whichever is larger) in the AOD used in the DISAMAR radiative transfer model.
The change in the calculated tropospheric AMF as a result of a 0.05 decrease in the SSA used in the DISAMAR radiative transfer model.
The following sensitivity analysis shows how the uncertainties in the
observed aerosol parameters used in the DISAMAR-aerosol tropospheric AMF
calculations can account for only part of the 30–50 % average difference
between the DISAMAR-standard and DISAMAR-aerosol calculations for high AODs
(> 0.6). Figures A1–A3 show the change in the DISAMAR-aerosol AMF
when (a) the SSA is reduced by 0.05, the threshold for agreement for 75 %
of OMAERUV SSA retrievals with AERONET observations, (b) the AOD is reduced
by 30 % or 0.1 (whichever is greater), the estimated uncertainty of the
OMAERUV AOD, and (c) the asymmetry parameter is reduced from 0.7 to 0.6, the
approximate lower limit for the absorbing aerosol models used in the OMAERUV
retrieval. AERONET observations during the dry season in South America show
that the average and standard deviation of the asymmetry parameter at 440 nm is 0.68
A 0.1 decrease in the asymmetry parameter resulted in an approximately 5 %
(maximum 10 %) increase in AMF that is weakly correlated with AOD above
AOD equal to
For large (> 0.6) optical depths, the uncertainty in the SSA
contributes the most to uncertainties in the DISAMAR-aerosol AMF calculation
(Fig. A3). In general, the DISAMAR-aerosol AMF decreased when the SSA was
reduced by 0.05, as increased light absorption by aerosols reduces the
sensitivity to NO
The authors thank the CALIOP project for producing and making available the data sets used in this analysis. We also thank the AERONET project and the principal investigators of the sites used in this work, and Maarten Sneep for the development of py-DISAMAR. Patricia Castellanos acknowledges Guido van der Werf and funding from the Netherlands Space Office (NSO), project ALW-GO-AO/10-01. Folkert Boersma acknowledges receiving funding for this research from NWO Vidi Grant 864.09.001 and from the European Community's Seventh Framework Programme under grant agreement no. 607405 (QA4ECV). Edited by: M. Van Roozendael