Global Aerosol Climatology from ICESat-2 Lidar Observations
This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite I...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2240 |
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| author | Shi Kuang Matthew McGill Joseph Gomes Patrick Selmer Grant Finneman Jackson Begolka |
| author_facet | Shi Kuang Matthew McGill Joseph Gomes Patrick Selmer Grant Finneman Jackson Begolka |
| author_sort | Shi Kuang |
| collection | DOAJ |
| description | This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models. |
| format | Article |
| id | doaj-art-6d72788ec5284b4dba49d59d0f76646f |
| institution | Matheson Library |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6d72788ec5284b4dba49d59d0f76646f2025-07-11T14:42:29ZengMDPI AGRemote Sensing2072-42922025-06-011713224010.3390/rs17132240Global Aerosol Climatology from ICESat-2 Lidar ObservationsShi Kuang0Matthew McGill1Joseph Gomes2Patrick Selmer3Grant Finneman4Jackson Begolka5Iowa Technology Institute, The University of Iowa, Iowa City, IA 52242, USADepartment of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA 52242, USADepartment of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA 52242, USAScience System and Applications, Inc., Lanham, MD 20706, USADepartment of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA 52242, USADepartment of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA 52242, USAThis study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models.https://www.mdpi.com/2072-4292/17/13/2240aerosolcloud–aerosol discriminationdeep learningICESat-2lidarmachine learning |
| spellingShingle | Shi Kuang Matthew McGill Joseph Gomes Patrick Selmer Grant Finneman Jackson Begolka Global Aerosol Climatology from ICESat-2 Lidar Observations Remote Sensing aerosol cloud–aerosol discrimination deep learning ICESat-2 lidar machine learning |
| title | Global Aerosol Climatology from ICESat-2 Lidar Observations |
| title_full | Global Aerosol Climatology from ICESat-2 Lidar Observations |
| title_fullStr | Global Aerosol Climatology from ICESat-2 Lidar Observations |
| title_full_unstemmed | Global Aerosol Climatology from ICESat-2 Lidar Observations |
| title_short | Global Aerosol Climatology from ICESat-2 Lidar Observations |
| title_sort | global aerosol climatology from icesat 2 lidar observations |
| topic | aerosol cloud–aerosol discrimination deep learning ICESat-2 lidar machine learning |
| url | https://www.mdpi.com/2072-4292/17/13/2240 |
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