Interactive comment on “ Retrieval of aerosol absorption properties using the AATSR satellite instrument : a case study of wildfires over Russia 2010 ”

ning Radiometer (AATSR). The aerosol generated from the major boreal zone burning events that occurred during the summer of 2010 in western Russia are the primary focus of this paper. In general I feel as though the data analysis is somewhat incomplete and also there is a lack of discussion of some of the limitations of the satellite algorithm. For example, the satellite algorithm applied here keeps the fine mode radius constant at geometric radius = 0.07 micron, a relatively small value, and only varies the refractive indices in order to retrieve the AOD and SSA. This approach of fixing the fine mode radius to a constant value is not typical within the satellite aerosol properties retrieval community, see numerous papers by Remer, Levy and Kahn for the MODIS and MISR retrieval algorithms. The issues associated with keeping the fine mode particle radius constant are not adequately discussed in the text. The large biases in retrieved AOD at high AOD levels shown in Figure 5 are very likely a result of the assumed particle radius being too small. For example, in Figure 6, the two AERONET retrievals of SSA shown for Aug 07, 2010 (Moscow_MSU_MO site) when AOD was very high, >3.2 at 440 nm and >2.4 at 675 nm have associated fine mode effective radius for these almucantar scans of ∼0.24 microns, at least twice as large as the assumed radius for the satellite retrieval. This underestimate in fine mode radius by AATSR for some cases is likely to be one of the primary reasons for the overestimate by AATSR of AOD for the high AOD cases, which could also then contribute to the underestimates of SSA retrieved by AATSR. It would be useful to plot the AATSR retrieved SSA as a function of AERONET measured AOD to confirm the large SSA biases of AATSR occur at high AOD. This seems highly likely as it is nearly the only way to achieve the very large overestimates of AAOD made from AATSR retrievals as shown in Figure 9.


Retrieval of aerosol absorption properties
important because of their effects on health and air quality.Aerosol particles originate from human activity and natural sources, by direct generation or by secondary formation from their precursor gases.Effects due to particles generated by human activity (anthropogenic aerosols) need to be considered in the context of effects from particles of natural origin.The assessment of aerosol effects on climate requires information on both the aerosol amount and on other characteristics, such as size, composition and optical properties including absorption (Russell et al., 2010) which is the focus of this paper.Many studies have been conducted on the absorption of aerosols using model simulations, ground-based in situ measurements and different remote sensing techniques utilizing ground based measurements and satellite retrievals (Goto et al., 2011;Satheesh et al., 2009;Medina et al., 2012;Liu et al., 2011).A measure for the aerosol absorption is the single scattering albedo (SSA), defined as the ratio of aerosol scattering to aerosol extinction (the sum of scattering and absorption) which for satellite observations relates to the column properties, i.e. scattering optical depth to the total optical depth (scattering + absorption) of the atmosphere.It is a dimensionless quantity with values between 0 and 1. Satellites provide information on the spatial distribution of aerosols on regional to global scales.Several satellite remote sensing studies have been reported on the retrieval of aerosol absorption properties, represented typically by the SSA.Kahn et al. (2010) presented the latest version (V22) of the MISR algorithm including SSA retrieval, which helps to distinguish between different airmasses.Patadia et al. (2013) presented MISR research retrieval results to evaluate the performance of the MISR V22 Standard aerosol retrieval algorithm.Torres et al. (2013) introduced the combined use of OMI, CALIOP and AIRS observations for the characterization of aerosol properties as an improvement over OMI aerosol retrieval capabilities.Jethva and Torres (2011) showed the improvement in the OMAERUV algorithm by evaluating the OMAERUV products of AOD and SSA against the AERONET measurements over the biomass burning regions of South America, central Africa, and northern India.In Zhu et al. retrieve the SSA during polluted days, using a critical reflectance technique, is applied to MODIS data obtained over biomass to burning regions.In this paper the Advanced Along Track Scanning Radiometer (AATSR) dual view (ADV) aerosol retrieval algorithm is used to provide an estimate of the SSA.The primary parameter retrieved from AATSR data is the aerosol optical depth (AOD) for cloud-free scenarios.The AOD is obtained by minimizing the difference between the computed and measured reflectances at the top of the atmosphere (TOA), simultaneously at three wavelengths, using a least squares method.In this procedure the TOA reflectance is computed using a radiative transfer model in which the mixing ratio of different aerosol components, described below, is continuously varied.The mixing ratio providing the minimum difference between the computed and observed TOA reflectance is used to compute the AOD (de Leeuw et al., 2014;Veefkind et al., 1999;Curier et al., 2009;Kolmonen et al., 2013).However, because the aerosol properties provide the basis for the computation, they could also be used to provide other aerosol properties such as the SSA.In this paper we evaluate how well the SSA retrieval works based on a study of the retrieval of AOD and SSA, using AATSR data, over wildfires over western Russia in the summer of 2010.The SSA and AAOD (AAOD = AOD(1-SSA)) are compared against data obtained from the AERONET level 2.0 inversion product (Dubovik et al., 1998(Dubovik et al., , 2000(Dubovik et al., , 2002)), and further evaluated by comparison with the SSA retrieved from the Ozone Monitoring Instrument (OMI).

Retrieval of the single scattering albedo for fine particles using the AATSR Dual View (ADV) algorithm
The AATSR instrument provides two views of the earth surface, one near-nadir and one at a forward angle of 55 • .For each of these views, the radiances are measured at at the four shortest wavelengths (0.555, 0.659, 0.865 and 1.61 µm) and some of these, together with the longer ones, are used for cloud screening.Over land the surface reflectance, which often overwhelms the aerosol signal at TOA, is accounted for by using both views.The dual view (ADV) algorithm used in this study has been developed for retrieval of aerosol optical properties over land (Veefkind et al., 1999(Veefkind et al., , 2000;;Robles Gonzáles, 2003;Curier et al., 2009;Kolmonen et al., 2013).These properties include the aerosol optical depth (AOD) at three wavelengths (nominally 0.555, 0.659 and 1.61 µm) and the Ångström exponent, but also the mixing ratio of the aerosol components (see below) is available.
In the ADV algorithm, the reflectances measured in the nadir and forward views are used together to eliminate surface reflectance effects and thus retain only the path reflectance.The path reflectance is used to determine AOD for the best fitting combination of aerosol components, as indicated in the introduction.As described in de Leeuw et al. (2014), the four aerosol components used are described by a lognormal size distribution defined by an effective radius and standard deviation, and a complex refractive index (see Table 1).Two of the aerosol components describe fine mode aerosol particles, assumed to be spherical, and the other two describe the properties of coarse mode aerosol particles.One of the fine mode components is weakly absorbing and the other one is strongly absorbing.By mixing these two components the absorbing properties of the fine mode particles can be continuously varied between these two extremes.The coarse mode aerosol particle components represent sea salt aerosol (spherical) and desert dust aerosol (non-spherical).The final aerosol model is determined by first mixing each of the fine and coarse components separately, and finally mixing the ensuing fine and coarse components into a bi-modal lognormal size distribution with associated optical properties.
The TOA reflectances are computed using a radiative transfer method (de Haan et al., 1987) for individual aerosol components.However, these computations are very time-consuming and there look-up tables (LUTs) are created for certain discrete Introduction

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Full sun-satellite geometries and aerosol loads.During the retrieval the weight of each component is varied: where b 1 is the fraction of fine particles, b 2 is the fraction of non-absorbing fine particles, and b dust is the dust fraction.AOD is a function of wavelength λ.The retrieved parameters are the two mixtures (b 1 , b 2 ), and L is the measure of the aerosol loading.
The measure L is used to determine aerosol properties, such as AOD, reflectance, and transmittance due to aerosols, from the LUTs.Generally, the higher L, the higher the aerosol loading.The dust fraction is not retrieved but is provided from a climatology derived as the median of 13 AEROCOM models (cf.de Leeuw et al., 2014).The abbreviations are: wa -weakly absorbing fine component, sa -strongly absorbing fine component, and ss -sea salt coarse component.
During the retrieval, the atmospheric reflectance due to aerosol particles is calculated as indicated above.It is compared to the path reflectance determined from the radiance measured for cloud-free pixels by the AATSR instrument at TOA corrected for surface effects and molecular scattering.The linear mixing of reflectances from LUTs, however, does not correctly take into account the difference of absorbing properties between the aerosol components as shown by Abdou et al. (1997).Instead, a modified linear mixing is used.Adopted for the retrieval algorithm for two arbitrary aerosol components α and β the modified linear mixing of reflectance ρ is given by: Where ω is the single scattering albedo (SSA) and τ is the AOD.Subscript mix refers to the linear mixture of the two aerosol components.In absorption of an aerosol component affects the resulting reflectance ρ aer through the SSA (ω).The dependence of the various terms in Eq. ( 2) on the wavelength and L are excluded from the equation for brevity.
In Table 1 refractive indices for the fine mode particles are listed for the wavelength of 0.555 µm.This value is also applied for the other wavelengths used in this study.However, although the particle size distributions for both fine mode components are identical, the SSA varies with wavelength because of the variation in the particle size parameters (2 πr/λ) with wavelength, which determines the aerosol scattering and absorption, as shown in Table 2 for the two fine particle aerosol components at the AATSR wavelengths.
The different spectral behavior of the SSA of the two fine mode components leads to differences in their TOA reflectance spectra, as illustrated in Fig. 1 where the TOA reflectance for each of the aerosol components is plotted as a function of AOD, for each of the four AATSR wavelengths used in this study.The higher absorption of the strongly absorbing fine component results in lower reflectance values when compared to the weakly absorbing component reflectance.The phenomenon is wavelength dependent and increases with AOD.This, together with the modified linear reflectance mixture in Eq. ( 2), is the basis of the described SSA retrieval.
In the retrieval, the mixture b 2 (and also the fine mode fraction b 1 and the aerosol loading measure L) is sought that minimizes the difference, in a least square sense, between the measured and modelled aerosol reflectances.In practice, surface and Rayleigh reflectance are also taken into account and treated in the retrieval algorithm.The aerosol parameters (e.g., AOD, SSA) are subsequently determined from the aerosol model, i.e. a mixture of the components as determined by b 1 and b 2 , and the value of L. The resulting fine mode SSA ω is given by the linear mixture Table 2 gives the maximum and minimum values of the SSA that can be retrieved.While the retrieved AOD results from the mixture of all four aerosol components, in 9845 Introduction

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Full this study we provide the retrieved SSA for the fine mode particles because our main interest is in testing the possible to retrieve SSA by application to biomass burning aerosol generated by forest fires, which mainly consist of fine mode aerosol particles.Furthermore, the difference in coarse particle reflectance, a mixture of sea salt and dust aerosol particles, is not large at the retrieval wavelengths except for high AOD.
This is illustrated in Fig. 2 where the TOA reflectance for sea salt and dust aerosol components are plotted as function of AOD, for all four wavelengths considered in this study.
One limiting factor of the SSA retrieval is the aerosol concentration.While the modelled aerosol reflectances differentiate effectively at high AOD values, at low AOD levels the reflectance spectra for non-absorbing and absorbing aerosols are almost identical (Fig. 1).Thus, the AATSR measurement error may shadow the aerosol SSA influence.For this reason the SSA retrieval is limited to cases where the retrieved AOD at 0.555 µm has values larger than 0.2.To retrieve the SSA for coarse particles a very high AOD value would be needed to separate the TOA reflectance spectra of the two coarse components properly (Fig. 2).This could, however, be utilized in the detection and retrieval of large dust outbreaks.

OMI aerosol retrieval
OMI retrieval products are used in this work for comparison with the AATSR SSA results.The OMAERUV aerosol algorithm has been designed to produce the optical thickness and single scattering albedo of tropospheric aerosols over ocean and land.OMAERUV considers three major aerosol types: desert dust, carbonaceous aerosols associated with biomass burning, and weakly absorbing sulfate-based aerosols.Each aerosol type is represented by seven aerosol models with varying single scattering albedo, for a total of twenty-one micro-physical models.The extinction optical depth and single scattering albedo are retrieved by examining the variability of the relationship between radiances measured at 0.354 and 0.388 µm (Torres et al., 2002a(Torres et al., , b, 2007)).

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AERONET inversion product
To validate the AOD and compared the AAOD and SSA retrieved from AATSR using ADV as described above, AERONET (Holben et al., 1998) sun photometer measurements are used.AERONET provides the optical properties of aerosols at four wavelengths (440, 670, 870 and 1020 nm) as a retrieval product from the sun photometer sky radiance measurements (almucantar and principal plane) using an inverse code developed by Dubovik and co-authors (Dubovik and King, 2000;Dubovik et al., 2000).The current model version (Dubovik et al., 2006) accounts for particle non-sphericity with a spheroid model and retrieves the portion of non-spherical particles of the aerosol size distribution.The absolute error given by AERONET for the SSA is 0.03 and for the AAOD 0.01.The optical properties such as the SSA are constrained for qualityassurance (level 2.0).This means that the SSA is retrieved only if AOD at 400 nm has a value greater than 0.4 and the solar zenith angle is larger than 50 ( Zvyagintsev et al., 2011).The highest concentrations were observed from 4 to 9 August.The unprecedented intensive heat wave provoked effects over distant areas like Kuopio in Eastern Finland (Mielonen et al., 2012).In view of the high biomass burning aerosol concentrations and expected high SSA values, we selected this period to evaluate the retrieval of SSA using AATSR data and the ADV algorithm.We focus on the August period when the largest AOD values were observed (Mei et al., 2011;Witte et al., 2011;Konovalov et al., 2011;Mielonen et al., 2012).Figure 3 shows the locations of the wildfires over Russia in August 2010.

Results
The concentrations of absorbing particles.The areas with smaller SSA values coincide with areas with high AOD (Fig. 4a and b), i.e. it is likely that the smoke produced by the wild fires is the reason for the low SSA results.The decrease of the effect of forest fire aerosol, resulting in SSA values higher than 0.95, is evident after 19 August (Fig. 4f).

Comparison of AATSR retrieval and AERONET
The AOD and SSA retrieved from AATSR were compared with AERONET level 2.0 products.In this comparison, an area around each AERONET station of 1 • ×1 • was used and the AERONET data were averaged over a time window of ±10 h around the satellite overpass.This large time window was used to collect as many coincidences between the AATSR and AERONET SSAs as possible.The time difference may contribute to the scatter in the results, especially as the local dynamic changes in smoke plume conditions can be large in a very small time period.Scatter plots of AOD and SSA retrieved from AATSR versus AERONET products are shown in Fig. 5.For the AOD, both data sets are in excellent agreement with a correlation coefficient (R) of 0.90, and a small negative bias of −0.09.However, Fig. 5a shows that there are some outliers with AATSR-retrieved AOD values which are substantially higher than AERONET, for AOD 0.7.The mean AOD for this period is similar, 0.25 for AERONET and 0.24 for AATSR (Fig. 5a).
The SSA retrievals from AATSR are compared with the SSA inversion product from AERONET in Fig. 5b.As the AATSR SSA is retrieved at 555 nm, the AERONET values are determined by using linear interpolation between the SSA at 440 and 675 nm.The dotted lines in Fig. 5b indicate the ±0.03 range uncertainty given by AERONET for the SSA inversion.The range of SSA variability for AATSR (0.81−0.97) is slightly different from that for AERONET (0.83−0.98).
A more detailed study on the discrepancy between the AATSR and AERONET SSA values was made using a root mean square error (RMSE) analysis.This parameter measures the difference between the values predicted by a model and the values actually observed from the environment that is being modelled.Here we can divide the 9849 Introduction

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Full SSA retrievals in three cases; the first case includes the total days with SSA coincidences between AATSR and AERONET.Here the RMSE between the measurements is 0.05.The second case contains the SSA values retrieved with AATSR which are lower than 0.90, which includes 20 % of the days where both instruments have results.
For this case the RMSE is 0.08.The last case includes AATSR SSA values larger than 0.90, which has the largest amount of coincidences (80 %) and an RMSE of 0.03.The results show a disagreement between the SSA results when the AATSR SSA is low, but in general we see a reasonable agreement between the retrieved SSA from AATSR and AERONET.
Figure 6 shows the spectral dependence of the AERONET-retrieved SSA on 7 August 2010.The SSA at 555 µm is around 0.95, indicating weakly absorbing particles.
On the other hand, the SSA increases with wavelength in contrast to the expected behavior for fine mode particles (Fig. 1) but in line with a large influence of coarse particles for which the absorption increases with wavelength (Fig. 2).This may provide an explanation for the discrepancy between the SSA retrieved from AATSR and AERONET, since for the AATSR retrieval we only consider the effect of fine particles.

Evaluation of AATSR-retrieved SSA by comparison with OMI
For evaluation of the AATSR-retrieved SSA product, it is compared with OMI SSA.
Here we use a 1 • ×1 • pixel area, with daily resolution.We consider only pixels for which data from both satellites are available.The difference plot, i.e.OMI-AATSR SSA in Fig. 7 shows that there are both positive and negative values, but overall the OMI SSA is higher than that of AATSR.On average the SSA difference over the study area is 0.1.As with the AERONET comparison, AATSR seems to indicate that absorbing aerosol occurs over the whole study area affected by wild fires, whereas OMI often provides higher SSA values, i.e. the occurrence of less absorbing aerosol.It is noted that the maximum SSA value at 0.555 µm which can be retrieved with AATSR is 0.977, for weakly absorbing aerosol (see Table 1) (de Leeuw et al., 2014).This difference may in part explain some of the differences between AATSR and OMI.It is also important 9850 Introduction

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Full to note that there is a difference of three hours between the satellite overpasses due to the diurnal variations in the emissions, which could affect the atmospheric conditions between the two measurements (Krol et al., 2013).The total RMSE value between the retrievals is 0.05.Looking in more detail, similar to the RMSE analysis for comparison with AERONET SSA, we observe that for AATSR SSA lower than 0.90 (16 % of the cases), the RMSE is 0.11.However, for AATSR-retrieved SSA larger than 0.95 (25 % of the cases) the RMSE is 0.03, i.e. good agreement.The remaining 59 % are cases with AATSR SSA values between 0.90 and 0.95.For these cases the RMSE is 0.04.Even though the results show that there is some disagreement between both satellites retrievals as regards how much the aerosol actually absorb, they do agree over areas where the forest fires are.
As an extension of the case study, we present the global SSA retrieved from AATSR for the month September 2010.In Fig. 8 (bottom panel) we show the locations of wild fires in September 2010.A good correspondence is observed between the wild fire locations and the low SSA values in Fig. 8 (top panel).

AAOD Retrievals
The absorbing properties of the aerosols are often expressed in terms of the absorbing aerosol optical depth (AAOD), which is defined as: AAOD = AOD(1−SSA).Figure 9 shows a scatterplot of the AAOD obtained with AATSR versus the AERONET inversion product.The results are similar for low AAOD (< 0.05) but there are some outliers with very high AAOD values retrieved from AATSR.The green lines in Fig. 9 are the estimated uncertainty of the AAOD given for AERONET (0.01).The RMSE between the retrievals is 0.06.When we analyze the cases where the AAOD is lower than 0.05 (74 % of the cases) the RMSE is 0.02 and when the AAOD is larger than 0.05 (26 % of the cases) the RMSE is 0.11.Similar to SSA retrieval, also the AAOD comparison with AERONET data shows underestimation of AATSR retrieved AAOD as compared with the AERONET values.Introduction

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Conclusions
A method to retrieve the SSA with the AATSR satellite instrument has been presented.The AOD and SSA retrievals allow us to estimate the effect of the wildfires over Russia in 2010 on these parameters.The smoke-affected area can be clearly observed both in the AOD and SSA patterns.AOD increased up to 2 during the wild fire period.The SSA values show generally reasonable agreement with both retrieval methods used for comparison, AERONET and OMI, but there are cases for which large discrepancies are observed and hence the method needs further development before it can provide quantitative results.The general results show an RMSE of 0.05 when compared with both AERONET and OMI.
Future improvement is needed in the quantitative estimation of absorbing aerosol properties from satellite data.AATSR can distinguish the area affected by wild fires but cannot give accurate quantitative information about how absorb the aerosol particles actually are.The RMSE decreases to 0.03 in the comparison with AERONET when the low SSA retrievals from AATSR are discarded.In the case of OMI, when the low values are not taken into consideration the RMSE decreases to 0.04.We consider that for both comparisons the agreement is reasonable, taking into account the differences between the retrieval techniques and the temporal and spatial issues in each comparison.
The AAOD retrievals confirm the SSA results, and also point out the good behaviour between the AOD retrievals.The RMSE is 0.02 when the AAOD is low, which represents the 74 % of the cases.It is noted that AAOD is derived from SSA and AAOD and the two quantities are thus not independent.
The global SSA map shows a clear connection between the occurrence of forest fires over Africa and the Amazons and the areas where the SSA is expected to have low values.The retrievals presented here provide a tool for a qualitative indication of  Phys., 11, 9287-9301, doi:10.5194/acp-11-9287-2011Phys., 11, 9287-9301, doi:10.5194/acp-11-9287- , 2011.9847, 9848 Zhu, L., Martins, J. V., and Remer, L. A.: Biomass burning aerosol absorption measurements with MODIS using the critical reflectance method, J. Geophys.Res., 116, D07202, doi:10.1029/2010JD015187, 2011. 9841 Zvyagintsev, A. M., Blum, O. B., Glazkova, A. A., Kotel'nikov, S. N., Kuznetsova, I. N., Lapchenko, V. A., Lezina, E. A., Miller, E. A., Milyaev, V. A., Popikov, A. P Full  Full  Full      fference over the study area is 0.1.As with the AERONET comparison, AATSR seems n the retrievals occur in the areas where absorbing aerosol particles are expected due using the AATSR satellite instrument: a case study of wildfires over Russia 2010 E. Rodríguez 1 , P. Kolmonen 1 , T. H. Virtanen 1 , L. Sogacheva 1 , A.-M. Sundström 2 , and G. de Leeuw 1,2 1 Introduction Aerosol particles have a significant effect on Earth's climate by perturbing the radiation balance both directly, due to scattering and absorption of solar radiation, and indirectly, due to their effect on cloud micro physical properties (IPCC, 2007).Aerosols are also Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

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wavelengths from the visible to the thermal infrared.Aerosol properties are retrieved Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | practice b β = (1 − b α ).Values for ω, τ and ρ come from the corresponding LUTs.It can be seen in Eq. (2) that the Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

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Russian wildfires in the summer of 2010 During the summer of 2010, between the end of July and 18 August, hundreds of wildfires broke out across central Russia, primarily in the Southeastern part and extending to the vicinity of Moscow.Witte et al. (2011); Huijnen et al. (2012); Chubarova et al. (2012) and Mei et al. (2011), among others, give an overview of the weather conditions during this period.In summary, anti-cyclonic conditions persisted around Moscow during the first ten days of August.Heat waves persisted until 14 August, and the relative humidity levels over Moscow were very low with values of 20-40 % (on average in August the humidity is around 77 %, http://meteoweb.ru/cl006-7.php).The fires decreased from 13 August when the first rain arrived, and on 19 August the air cleaned up when the wind turned to the West, which is the most probable wind direction over Moscow.During this period, the PM 10 , CO, and NO x concentrations exceeded almost continuously their maximum permissible concentrations (MPCs) during about 30 days Introduction Discussion Paper | Discussion Paper | Discussion Paper | spatial distributions of the AOD at 555 µm and SSA are shown for three periods in August 2010 in Fig.4a-c.Each map shows values aggregated over the period indicated at the top of each figure.The AOD distributions are combined with 5 days back trajectories at 500 m (in purple) and 100 m (in grey).Figure4ashows 5 day back trajectories arriving at Moscow on 11 August.The airmass at 500 m indicates transport of the forest fire plume during these 5 days to Moscow, where the AOD values were up to 2. On the other hand, the air mass arriving at 100 m indicates that at this level the air was not influenced by the forest fires and hence the atmospheric column was likely stratified with lower aerosol concentrations near the ground than higher up.However, we have no observational data, such as, e.g., lidar measurements, to confirm this.The airmass arriving at Moscow on 12 August at the two altitudes indicate that cleaner air was transported from the west bringing rain (not shown here).Later, the airmass arriving in Moscow on 18 August shows how the air was transported again from the southeast with a high concentration of forest fire aerosol resulting in AOD values of up to 2. On 19 August the airmass originated again from the west and AOD values returned to normal values until the end of the month.The aggregated SSA over the same area and for the same three periods in August 2010 is shown in Fig. 4d-f.The low SSA values in Fig. 4d and e indicate high Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the forest fires location, and especially resulting smoke plume coverage.Discussion Paper | Discussion Paper | Discussion Paper | Excellence (project no.272041), the projects Aerosol-cci (ESA-ESRIN project AO/1-6207/09/I-LG), Globemission (ESA-ESRIN Data Users Element (DUE), project AO/1-6721/11/I-NB), PE-GASOS (EU FP7 ENV.2010.1.1.2-1)and MEGAPOLI (EU FP7 FP7-ENV-2007.1.1.2.1Discussion Paper | Discussion Paper | Discussion Paper | work (AERONET) Sun and sky radiance measurements, J. Geophys.Res., 105, 9791-9806, 2000.9842, 9847 Dubovik, O., Holben, B. N., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanre, D., and Slutsker, I.: Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos.Sci., 59, 590-608, 2002.Discussion Paper | Discussion Paper | Discussion Paper | Witte, J. C., Douglass, A. R., da Silva, A., Torres, O., Levy, R., and Duncan, B. N.: NASA A-Train and Terra observations of the 2010 Russian wildfires, Atmos.Chem.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Calculated TOA reflectances for the weakly (blue) and strongly (red) absorbing fine mode aerosol ponents as function of the reference AOD at 0.500 µm and for the four AATSR wavelengths: star 0.555 µm, e 0.659 µm, plus 0.865 µm, and square 1.610 µm.In this example the geometry is set to a solar zenith angle o , a satellite viewing zenith angle of 5 o (near nadir), and a relative azimuth angle of 10 o .

Figure 1 .
Figure1.Calculated TOA reflectances for the weakly (blue) and strongly (red) absorbing fine mode aerosol components as function of the reference AOD at 0.500 µm and for the four AATSR wavelengths: star 0.555 µm, circle 0.659 µm, plus 0.865 µm, and square 1.610 µm.In this example the geometry is set to a solar zenith angle of 30• , a satellite viewing zenith angle of 5•

Figure 2 .
Figure 2. Modeled top-of-atmosphere (TOA) reflectances for the sea salt (blue) and dust (red) coarse aerosol components as function of the reference AOD at 0.500 µm.The wavelength dependency of reflectance is indicated by symbols: star 0.555 µm, circle 0.659 µm, plus 0.865 µm, and square 1.610 µm.In this example the geometry is set to a solar zenith angle of 30 • , a satellite viewing zenith angle of 5 • (near nadir), and a relative azimuth angle of 10 • .

Figure 4 .
Figure 4. Spatial distributions of AOD and SSA at 0.555 µm over the wildfires region, shown as aggregates for three periods in August 2010.Figure (a-c) presents the AOD during the wild fires (a, b) and after the fires extinguished (c).5 day back-trajectories at 500 m (in purple) and 100 m (in grey) are overlaid on the AOD maps. Figure (d-f) presents the SSA for the same periods.

Figure 5 .
Figure 5.Comparison of AOD and SSA at 555 nm retrieved from AATSR and AERONET.

Figure 6 .
Figure 6.SSA wavelength dependence of the AERONET inversion product for 7 August 2010.

Figure 7 .
Figure 7. SSA difference between the SSA retrieved with OMI and AATSR in August 2010 (OMI-AATSR).Large differences between the retrievals occur in the areas where absorbing aerosol particles are expected due to the occurrence of forest fires.

Figure 9 .
Figure 9.Comparison of AAOD retrievals from AATSR and AERONET over Moscow in August 2010.

Table 1 .
Properties of the aerosol components used in this work(de Leeuw et al., 2014).Listed are the geometric radius r g , standard deviation σ, refractive index n at the wavelength of 0.555 µm, and the aerosol layer height (alh).

Table 2 .
Wavelength dependence of the single scattering albedo SSA for the weakly and strongly absorbing fine mode aerosol components.The values are computed for the optical indices in Table1.