Long-term variation of aerosol optical properties associated with aerosol types over East Asia using AERONET and satellite (VIIRS, OMI) data (2012–2019)

Eom, Sujin; Kim, Jhoon; Lee, Seo-Young; Holben, B. N.; Eck, T. F.; Park, Sung-Bin; Park, Sang Seo

doi:10.1016/j.atmosres.2022.106457

Cited by 7 publications

(3 citation statements)

References 97 publications

(178 reference statements)

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“…AERONET is a global aerosol optical property monitoring network established by the National Aeronautics and Space Administration. This network uses the CIMEL automatic sun-photometer as the basic observing instrument [40], which can observe aerosol characteristics at eight wavelengths from visible to near-infrared every 15 min, and the network consists of more than 1500 sites around the world [41]. These sites have been providing researchers with long-term, stable, and easily accessible aerosol data for decades.…”

Section: Aerosol Robotic Network (Aeronet) Datamentioning

confidence: 99%

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Jiang,

Liu,

Jiao

2023

Atmosphere

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Atmospheric aerosol significantly affects the climate environment and public health, and Aerosol Optical Depth (AOD) is a fundamental optical characteristic parameter of aerosols, so it is important to develop methods for obtaining AOD. In this work, a novel AOD retrieval algorithm based on a Convolutional Neural Network (CNN) method that could provide continuous and detailed aerosol distribution is proposed. The algorithm utilizes data from Sentinel-2 and Aerosol Robotic Network (AERONET) spanning from 2016 to 2022. The CNN AOD data are consistent with the AERONET measurements, with an R2 of 0.95 and RMSE of 0.049 on the test dataset. CNN demonstrates superior performance in retrieving AOD compared with other algorithms. CNN retrieves AOD well on high reflectance surfaces, such as urban and bare soil, with RMSEs of 0.051 and 0.042, respectively. CNN efficiently retrieves AOD in different seasons, but it performs better in summer and winter than in spring and autumn. In addition, to study the relationship between image size and model retrieval performance, image datasets of 32 × 32, 64 × 64 and 128 × 128 pixels were created to train and test the CNN model. The results show that the 128-size CNN performs better because large images contain rich aerosol information.

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Section: Aerosol Robotic Network (Aeronet) Datamentioning

confidence: 99%

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Jiang,

Liu,

Jiao

2023

Atmosphere

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“…The aforementioned factors, particularly those related to aerosol properties, govern at a large degree aerosol-radiation interactions, thus highlighting the importance of a reliable and accurate aerosol typing. Ground-based measurements, acquired using instrumentations deploying passive or active remote sensing techniques, have provided critical insight on aerosol-speciated intensive and extensive radiative properties [18][19][20][21][22][23]. Spaceborne observations, providing near-global coverage, have effectively overcome the spatial constraints of ground-based stations [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Assessing Lidar Ratio Impact on CALIPSO Retrievals Utilized for the Estimation of Aerosol SW Radiative Effects across North Africa, the Middle East, and Europe

Moustaka,

Korras-Carraca,

Papachristopoulou

et al. 2024

Remote Sensing

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North Africa, the Middle East, and Europe (NAMEE domain) host a variety of suspended particles characterized by different optical and microphysical properties. In the current study, we investigate the importance of the lidar ratio (LR) on Cloud-Aerosol Lidar with Orthogonal Polarization–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIOP-CALIPSO) aerosol retrievals towards assessing aerosols’ impact on the Earth-atmosphere radiation budget. A holistic approach has been adopted involving collocated Aerosol Robotic Network (AERONET) observations, Radiative Transfer Model (RTM) simulations, as well as reference radiation measurements acquired using spaceborne (Clouds and the Earth’s Radiant Energy System-CERES) and ground-based (Baseline Surface Radiation Network-BSRN) instruments. We are assessing the clear-sky shortwave (SW) direct radiative effects (DREs) on 550 atmospheric scenes, identified within the 2007–2020 period, in which the primary tropospheric aerosol species (dust, marine, polluted continental/smoke, elevated smoke, and clean continental) are probed using CALIPSO. RTM runs have been performed relying on CALIOP retrievals in which the default and the DeLiAn (Depolarization ratio, Lidar ratio, and Ångström exponent)-based aerosol-speciated LRs are considered. The simulated fields from both configurations are compared against those produced when AERONET AODs are applied. Overall, the DeLiAn LRs leads to better results mainly when mineral particles are either solely recorded or coexist with other aerosol species (e.g., sea-salt). In quantitative terms, the errors in DREs are reduced by ~26–27% at the surface (from 5.3 to 3.9 W/m2) and within the atmosphere (from −3.3 to −2.4 W/m2). The improvements become more significant (reaching up to ~35%) for moderate-to-high aerosol loads (AOD ≥ 0.2).

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“…For example, background aerosols like clean continental and maritime aerosols often have an AOD value from 0 to 0.2, while anthropogenic and biomass-burning aerosols have much higher AOD, which ranges from 0.3 to 1.0, or even over 2 in some extremely polluted situations [8]. AOD is quite useful in identifying background aerosols, which are characterized by relatively low aerosol loading emitted from local sources [9,10]. Many previous studies have determined the BAOD threshold value when identifying aerosol types (e.g., urban industry, dust) from clean or background types.…”

Section: Introductionmentioning

confidence: 99%

Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution

Chen,

Huang,

Dong

et al. 2024

Remote Sensing

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Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.

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Long-term variation of aerosol optical properties associated with aerosol types over East Asia using AERONET and satellite (VIIRS, OMI) data (2012–2019)

Cited by 7 publications

References 97 publications

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Assessing Lidar Ratio Impact on CALIPSO Retrievals Utilized for the Estimation of Aerosol SW Radiative Effects across North Africa, the Middle East, and Europe

Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution

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