Multi-satellite aerosol observations in the vicinity of clouds

Várnai, Tamás; Marshak, Alexander; Yang, Weidong

doi:10.5194/acp-13-3899-2013

Cited by 39 publications

(51 citation statements)

References 39 publications

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“…The opposite behavior arises because in our dataset, particle size decreases with the increase of cloud fraction, as cloudier air coming from North America or Europe contains smaller pollution particles than the more clear air containing larger sea salt or desert dust particles. The differences between results for our study region and the region around the Azores confirm the earlier finding that near-cloud behaviors display significant variability across the globe [34].…”

Section: Discussionsupporting

confidence: 79%

Effect of Cloud Fraction on Near-Cloud Aerosol Behavior in the MODIS Atmospheric Correction Ocean Color Product

Várnai

Marshak

2015

Remote Sensing

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Abstract:Characterizing the way satellite-based aerosol statistics change near clouds is important for better understanding both aerosol-cloud interactions and aerosol direct radiative forcing. This study focuses on the question of whether the observed near-cloud increases in aerosol optical thickness and particle size may be explained by a combination of two factors: (i) Near-cloud data coming from areas with higher cloud fractions than far-from-cloud data and (ii) Cloud fraction being correlated with aerosol optical thickness and particle size. This question is addressed through a statistical analysis of aerosol parameters included in the MODIS (MODerate resolution Imaging Spectroradiometer) ocean color product. Results from ten Septembers (2002-2011) over part of the northeast Atlantic Ocean confirm that the combination of these two factors working together explains a significant but not dominant part (in our case, 15%-30%) of mean optical thickness changes near clouds. Overall, the findings show that cloud fraction plays a large role in shaping the way aerosol statistics change with distance to clouds. This implies that both cloud fraction and distance to clouds are important to consider when aerosol-cloud interactions or aerosol direct radiative effects are examined in satellite or modeling studies.

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Section: Discussionsupporting

confidence: 79%

Effect of Cloud Fraction on Near-Cloud Aerosol Behavior in the MODIS Atmospheric Correction Ocean Color Product

Várnai

Marshak

2015

Remote Sensing

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“…First, Várnai et al . [] found strong stray light contamination only within 1 km from clouds. Second, the aerosol retrievals rely greatly on long wavelengths where 3‐D radiative effects are much weaker [e.g., Wen et al ., ]: Aerosol properties are estimated by an iterative procedure that first estimates 869 nm AOT and 443–869 nm AE based on reflectances at 749 nm and 869 nm (where the ocean is very dark), and then refines these estimates using 443 nm reflectances, which help accounting even for the small ocean reflectance at the longer wavelengths [ Gordon and Wang , ; Wang and Shi , , and the introduction to atmospheric correction at http://oceancolor.gsfc.nasa.gov/DOCS/SeaDAS/seadas_training.html].…”

Section: Causes and Radiative Consequences Of Near‐cloud Changesmentioning

confidence: 99%

“…We note that 3‐D effects, causing about a third of near‐cloud increases in 550 nm reflectance in Várnai et al . [], may still impact the retrieval procedure through the 443 nm reflectance, but their magnitude is unclear at this point. Overall, we expect that the true values of optical thickness and AE are somewhere between the blue and red curves and are likely closer to the blue ones.…”

Section: Causes and Radiative Consequences Of Near‐cloud Changesmentioning

confidence: 99%

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Near‐cloud aerosol properties from the 1 km resolution MODIS ocean product

Várnai

Marshak

2014

JGR Atmospheres

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This study examines aerosol properties in the vicinity of clouds by analyzing high-resolution atmospheric correction parameters provided in the MODIS (Moderate Resolution Imaging Spectroradiometer) ocean color product. The study analyzes data from a 2 week long period of September in 10 years, covering a large area in the northeast Atlantic Ocean. The results indicate that on the one hand, the Quality Assessment (QA) flags of the ocean color product successfully eliminate cloud-related uncertainties in ocean parameters such as chlorophyll content, but on the other hand, using the flags introduces a sampling bias in atmospheric products such as aerosol optical thickness (AOT) and Angstrom exponent. Therefore, researchers need to select QA flags by balancing the risks of increased retrieval uncertainties and sampling biases. Using an optimal set of QA flags, the results reveal substantial increases in optical thickness near clouds-on average the increase is 50% for the roughly half of pixels within 5 km from clouds and is accompanied by a roughly matching increase in particle size. Theoretical simulations show that the 50% increase in 550 nm AOT changes instantaneous direct aerosol radiative forcing by up to 8 W/m 2 and that the radiative impact is significantly larger if observed near-cloud changes are attributed to aerosol particles as opposed to undetected cloud particles. These results underline that accounting for near-cloud areas and understanding the causes of near-cloud particle changes are critical for accurate calculations of direct aerosol radiative forcing.

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“…Data holes are also much more closely aligned with the presence of clouds; while this will not affect the L2 retrievals themselves (as only cloud-free sensor pixels are used), it does improve the utility of the data for those wishing to assess the dependence of aerosol properties as a function of distance from clouds (e.g. Bar-Or et al, 2011;Várnai et al, 2013) or collocate aerosol and cloud data for other purposes.…”

Section: A M Sayer Et Al: Modis Bow-tie Effect and Aerosols 5285mentioning

confidence: 99%

Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation

2015

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Abstract. The scan geometry of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, combined with the Earth's curvature, results in a pixel shape distortion known as the "bow-tie effect". Specifically, sensor pixels near the edge of the swath are elongated along-track and across-track compared to pixels near the centre of the swath, resulting in an increase of pixel area by up to a factor of ∼ 9 and, additionally, the overlap of pixels acquired from consecutive scans. The Deep Blue and Dark Target aerosol optical depth (AOD) retrieval algorithms aggregate sensor pixels and provide level 2 (L2) AOD at a nominal horizontal pixel size of 10 km, but the bow-tie distortion means that they also suffer from this size increase and overlap. This means that the spatial characteristics of the data vary as a function of satellite viewing zenith angle (VZA) and, for VZA > 30• , corresponding to approximately 50 % of the data, are areally enlarged by a factor of 50 % or more compared to this nominal pixel area and are not spatially independent of each other. This has implications for retrieval uncertainty and aggregated statistics, causing a narrowing of AOD distributions near the edge of the swath, as well as for data comparability from the application of similar algorithms to sensors without this level of bow-tie distortion. Additionally, the pixel overlap is not obvious to users of the L2 aerosol products because only pixel centres, not boundaries, are provided within the L2 products. A two-step procedure is proposed to mitigate the effects of this distortion on the MODIS aerosol products. The first (simple) step involves changing the order in which pixels are aggregated in L2 processing to reflect geographical location rather than scan order, which removes the bulk of the overlap between L2 pixels and slows the rate of growth of L2 pixel size vs. VZA. This can be achieved without significant changes to existing MODIS processing algorithms. The second step involves additionally changing the number of sensor pixels aggregated across-track as a function of VZA, which preserves L2 pixel size at around 10 km × 10 km across the whole swath but would require algorithmic quality assurance tests to be re-evaluated. Both of these steps also improve the extent to which the pixel locations a user would infer from the L2 data products represent the actual spatial extent of the L2 pixels.

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Multi-satellite aerosol observations in the vicinity of clouds

Cited by 39 publications

References 39 publications

Effect of Cloud Fraction on Near-Cloud Aerosol Behavior in the MODIS Atmospheric Correction Ocean Color Product

Effect of Cloud Fraction on Near-Cloud Aerosol Behavior in the MODIS Atmospheric Correction Ocean Color Product

Near‐cloud aerosol properties from the 1 km resolution MODIS ocean product

Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation

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