Dataset Information

Observation-based study on aerosol optical depth and particle size in partly cloudy regions.

ABSTRACT: This study seeks to help better understand aerosol-cloud interactions by examining statistical relationships between aerosol properties and nearby low-altitude cloudiness using satellite data. The analysis of a global dataset of MODIS (Moderate Resolution Imaging Spectroradiometer) observations reveals that the positive correlation between cloudiness and aerosol optical depth (AOD) reported in earlier studies is strong throughout the globe and during both winter and summer. Typically, AOD is 30-50% higher on cloudier-than-average days than on less cloudy days. A combination of satellite observations and MERRA-2 global reanalysis data reveals that the correlation between cloud cover and AOD is strong for all aerosol types considered: sulfate, dust, carbon, and sea salt. The observations also indicate that in the presence of nearby clouds, aerosol size distributions tend to shift toward smaller particles over large regions of the Earth. This is consistent with a greater cloud-related increase in the AOD of fine mode than of coarse mode particles. The greater increase in fine mode AOD implies that the cloudiness-AOD correlation does not come predominantly from cloud detection uncertainties. Additionally, the results show that aerosol particle size increases near clouds even in regions where it decreases with increasing cloudiness. This suggests that the decrease with cloudiness comes mainly from changes in large-scale environment, rather than from clouds increasing the number or the size of fine mode aerosols. Finally, combining different aerosol retrieval algorithms demonstrated that quality assessment flags based on local variability can help identifying when the observed aerosol populations are affected by surrounding clouds.

SUBMITTER: Varnai T

PROVIDER: S-EPMC7380075 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Observation-based study on aerosol optical depth and particle size in partly cloudy regions.

Várnai T T Marshak A A Eck T F TF

Journal of geophysical research. Atmospheres : JGR 20170904 18

This study seeks to help better understand aerosol-cloud interactions by examining statistical relationships between aerosol properties and nearby low-altitude cloudiness using satellite data. The analysis of a global dataset of MODIS (Moderate Resolution Imaging Spectroradiometer) observations reveals that the positive correlation between cloudiness and aerosol optical depth (AOD) reported in earlier studies is strong throughout the globe and during both winter and summer. Typically, AOD is 30- ...[more]

PMID: 32724904

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Project description:Satellite-based PM(2.5) monitoring has the potential to complement ground PM(2.5) monitoring networks, especially for regions with sparsely distributed monitors. Satellite remote sensing provides data on aerosol optical depth (AOD), which reflects particle abundance in the atmospheric column. Thus AOD has been used in statistical models to predict ground-level PM(2.5) concentrations. However, previous studies have shown that AOD may not be a strong predictor of PM(2.5) ground levels. Another shortcoming of remote sensing is the large number of non-retrieval days (i.e., days without satellite data available) due to clouds and snow- and ice-cover. In this paper we propose statistical approaches to overcome these two shortcomings, thereby making satellite imagery a viable method to estimate PM(2.5) concentrations. First, we render AOD a robust predictor of PM(2.5) mass concentration by introducing an AOD daily calibration approach through the use of mixed effects model. Second, we develop models that combine AOD and ground monitoring data to predict PM(2.5) concentrations during non-retrieval days. A key feature of this approach is that we develop these prediction models separately for groups of days defined by the observed amount of spatial heterogeneity in concentrations across the study region. Subsequently, these methodologies were applied to examine the spatial and temporal patterns of daily PM(2.5) concentrations for both retrieval days (i.e., days with satellite data available) and non-retrieval days in the New England region of the United States during the period 2000-2008. Overall, for the years 2000-2008, our statistical models predicted surface PM(2.5) concentrations with reasonably high R(2) (0.83) and low percent mean relative error (3.5%). Also the spatial distribution of the estimated PM(2.5) levels in the study domain clearly exhibited densely populated and high traffic areas. The method we have developed demonstrates that remote sensing can have a tremendous impact on the fields of environmental monitoring and human exposure assessment.

Dataset Information

Observation-based study on aerosol optical depth and particle size in partly cloudy regions.

Publications

Observation-based study on aerosol optical depth and particle size in partly cloudy regions.

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