Aerosol remote sensing over land: A comparison of satellite retrievals using different algorithms and instruments

Kokhanovsky, Alexander A.; Bréon, François‐Marie; Cacciari, Alessandra; Carboni, Elisa; Diner, David J.; Nicolantonio, W. Di; Grainger, R. G.; Grey, W.; Höller, Robert; Lee, Kwon-Ho; Li, Z.; North, Peter; Sayer, A. M.; Thomas, G. E.; Hoyningen‐Huene, W. von

doi:10.1016/j.atmosres.2007.02.008

Cited by 214 publications

(112 citation statements)

References 45 publications

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“…In addition to their cloud microphysical effects, aerosols also modulate cloud formation and convective behavior through their radiative effects, for which aerosol optical thickness (AOT) is a commonly used metric. AOT has the advantage of being readily observed by remote sensing, and AOT measurements are now done routinely from space by several sensors (Kaufman et al, 2002;Yu et al, 2003;Kahn et al, 2007;Kokhanovsky et al, 2007) as well as by ground-based sunphotometer networks Kim et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions

2009

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Abstract.A large number of published and unpublished measurements of cloud condensation nuclei (CCN) concentrations and aerosol optical thickness (AOT) measurements have been analyzed. AOT measurements were obtained mostly from the AERONET network, and selected to be collocated as closely as possible to the CCN investigations. In remote marine regions, CCN 0.4 (CCN at a supersaturation of 0.4%) are around 110 cm −3 and the mean AOT 500 (AOT at 500 nm) is 0.057. Over remote continental areas, CCN are almost twice as abundant, while the mean AOT 500 is ca. 0.075. (Sites dominated by desert dust plumes were excluded from this analysis.) Some, or maybe even most of this difference must be because even remote continental sites are in closer proximity to pollution sources than remote marine sites. This suggests that the difference between marine and continental levels must have been smaller before the advent of anthropogenic pollution.Over polluted marine and continental regions, the CCN concentrations are about one order of magnitude higher than over their remote counterparts, while AOT is about five times higher over polluted than over clean regions. The average CCN concentrations from all studies show a remarkable correlation to the corresponding AOT values, which can be expressed as a power law. This can be very useful for the parameterization of CCN concentrations in modeling studies, as it provides an easily measured proxy for this variable, which is difficult to measure directly. It also implies that, at least at large scales, the radiative and microphysical effects of aerosols on cloud physics are correlated and not free to vary fully independently. While the observed strong empirical correlation is remarkable, it must still be noted that thereis about a factor-of-four range of CCN concentrations at a given AOT, and that there remains considerable room for improvement in remote sensing techniques for CCN abundance.

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Section: Introductionmentioning

confidence: 99%

Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions

2009

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“…The optimum value of AOT and aerosol model is selected on the basis of best-fit of surface reflectances to the model implemented in the algorithm (North et al, 1999;North 2002), and adapted for efficient processing of global data by Grey et al (2006a). Global validation with AERONET and other satellite sensors was presented by Grey (2006b) and Kokhanovsky et al (2007). The algorithm has also been adapted to use the 5-angle sampling of the CHRIS-PROBA instrument (Davies et al, 2010).…”

Section: Swansea University Algorithmmentioning

confidence: 99%

“…The advances and problems related to aerosol remote sensing from space have recently been reviewed (Kokhanovsky and de Leeuw, 2009;Lee et al, 2009). In particular, it was found that different algorithms/instruments produce somewhat different results for AOT absolute values and spatial distributions even if the same location at the same time is observed Kokhanovsky et al, 2007;Liu and Mishchenko, 2008;Mishchenko et al, 2009;Kahn et al, 2009). Li et al (2009) list the following possible reasons for the discrepancy: (1) cloud masking; (2) instrument calibration; (3) treatment of the underlying surface; (4) assumptions on the aerosol model.…”

Section: Introductionmentioning

confidence: 99%

The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light

et al. 2010

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Abstract. Remote sensing of aerosol from space is a challenging and typically underdetermined retrieval task, requiring many assumptions to be made with respect to the aerosol and surface models. Therefore, the quality of a priori information plays a central role in any retrieval process (apart from the cloud screening procedure and the forward radiative transfer model, which to be most accurate should include the treatment of light polarization and molecular-aerosol coupling). In this paper the performance of various algorithms with respect to the of spectral aerosol optical thickness determination from optical spaceborne measurements is studied. The algorithms are based on various types of measurements (spectral, angular, polarization, or some combination of these). It is confirmed that multiangular spectropolarimetric measurements provide more powerful constraints compared to spectral intensity measurements alone, particularly those acquired at a single view angle and which rely on a priori assumptions regarding the particle phase function in the retrieval process.

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“…Hence, spaceborne platforms are necessary to observe aerosol properties over global or continental scale. A considerable progress has been made to estimate AOD from space-borne platforms in past two decades (Martonchik et al, 1998;Ignatov et al, 2004;Remer et al, 2005Remer et al, , 2008Kokhanovsky et al, 2007;Laszlo et al, 2008;Livingston et al, 2009;Kahn et al, 2010;Tanré et al, 2011;Hsu et al, 2012). However, only few satellite sensors are able to provide AOD over land and the retrieval of other aerosol properties like size distribution, single scattering albedo or refractive-index remains to be a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Role of Coarse and Fine Mode Aerosols in MODIS AOD Retrieval: a case study over southern India

et al. 2014

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Abstract. In the present study we compare the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in southern India (Gadanki, 13.45 • N, 79.18 • E) from April 2008 to March 2011. While the comparison between total (coarse mode + fine mode) AODs shows correlation coefficient (R) value of about 0.71 for Terra and 0.77 for Aqua, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R value of 0.44 for both Terra and Aqua. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R value of 0.74 for Terra and 0.66 for Aqua. The seasonal variation is also well captured by both ground-based and satellite measurements. It is shown that both the total AOD and fine mode AOD are significantly underestimated with slope of regression line 0.75 and 0.35 respectively, whereas the coarse mode AOD is overestimated with a slope value of 1.28 for Terra. Similar results are found for Aqua where the slope of the regression line for total AOD and fine mode AOD are 0.72 and 0.27 whereas 0.95 for coarse mode. The fine mode fraction derived from MODIS data is less than one-half of that derived from the sky-radiometer data. Based on these observations and comparison of single scattering albedo observed using sky-radiometer with that of MODIS aerosol models, we argue that the selection of aerosol types used in the MODIS retrieval algorithm may not be appropriate particularly in the case of southern India. Instead of selecting a moderately absorbing aerosol model (as being done currently in the MODIS retrieval) a more absorbing aerosol model could be a better fit for the fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively absorbing type, usage of coarse sea-salt particles which is less absorbing is more appropriate. However, not all the differences could be accounted based on aerosol model, other factors like errors in retrieval of surface reflectance may also be significant in causing underestimation of AOD by MODIS.

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Aerosol remote sensing over land: A comparison of satellite retrievals using different algorithms and instruments

Cited by 214 publications

References 45 publications

Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions

Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions

The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light

Role of Coarse and Fine Mode Aerosols in MODIS AOD Retrieval: a case study over southern India

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