Global Aerosol Climatology from ICESat-2 Lidar Observations

Shi Kuang; Matthew McGill; Joseph Gomes; Patrick Selmer; Grant Finneman; Jackson Begolka

doi:10.3390/rs17132240

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Global Aerosol Climatology from ICESat-2 Lidar Observations

Journal article

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Global Aerosol Climatology from ICESat-2 Lidar Observations

Shi Kuang, Matthew McGill, Joseph Gomes, Patrick Selmer, Grant Finneman and Jackson Begolka

Remote sensing (Basel, Switzerland), Vol.17(13), 2240

06/30/2025

DOI: 10.3390/rs17132240

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Abstract

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.

aerosol

cloud-aerosol discrimination

deep learning

ICESat-2

lidar

machine learning

remote sensing

Details

Title: Subtitle: Global Aerosol Climatology from ICESat-2 Lidar Observations
Creators: Shi Kuang - University of Iowa
Matthew McGill - University of Iowa
Joseph Gomes - University of Iowa, Chemical and Biochemical Engineering
Patrick Selmer - Science Systems and Applications
Grant Finneman - University of Iowa
Jackson Begolka - University of Iowa
Resource Type: Journal article
Publication Details: Remote sensing (Basel, Switzerland), Vol.17(13), 2240
DOI: 10.3390/rs17132240
ISSN: 2072-4292
eISSN: 2072-4292
Publisher: MDPI
Grant note: NASA: 80NSSC23K0191 ICESat-2 project through NASA
This research was funded by the ICESat-2 project through NASA grant #80NSSC23K0191.
Language: English
Date published: 06/30/2025
Academic Unit: Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
Record Identifier: 9984843592502771

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