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

Abstract

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.

Mapping the Twilight Zone—What We Are Missing between Clouds and Aerosols

“…An analysis by Cho et al [8] suggests that some of these may be explained by spectral characteristics outside the algorithm definitions. One explanation is the presence of cloud halo pixels in Lost A, which are characterized by increasing reflectance with increasing proximity to a pure cloud pixel [1,7,9,24]. Indeed, the analysis of class reflectivities as a function of distance from cloud and aerosol features as shown in Figure 8 supports this: Most of the values with the highest reflectivities in "Lost A" are close to the nearest cloud, as shown in Figure 8a, with opposite conclusions for the aerosol pixels with increasing reflectivity with larger distances (Figure 8b).…”

“…These transition zones have been called a "continuum" [5] or a "twilight zone" [1], and are the focus of this study. For a better understanding of aerosol-cloud interactions, it is necessary to comprehend the extent and spatial patterns of this transition zone between aerosols and clouds and its impact on the Earth's energy balance [1,6,7]. Accordingly, the transition zone between aerosols and clouds has received increasing attention, and is influenced radiatively by the presence of activated aerosols and nearby optically thick clouds [1,5].…”

Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning

“…We first sample local patches in each region using a dense sampling strategy, and then extract local features. Within each patch, we extract histograms of each local feature with three scales , i.e., (P, R) = (8, 1), (16,2) and (24,3). After each patch is represented as a histogram, we apply max pooling strategy on all local histograms for each region, i.e., reserving the maximum response of each histogram bin among all histograms.…”

“…Specifically, we extracted LBP feature with (P, R) equal to (8,1), (16,2) and (24,3), and then concatenated histograms of the three scales to form a feature vector for each cloud image. So the final feature vector of each cloud image has 10 + 18 + 26 = 54 dimensions.…”

“…Clouds are one of the most vital macroscopic parameters in the research of climate change and meteorological services [1,2]. Nowadays, clouds are studied in satellite-based and ground-based manners.…”