Work

Using Multitask Machine Learning to Type Clouds and Aerosols from Space-Based Photon-Counting Lidar Measurements

Year: 2025

Type: article

Abstract: Space-based, photon-counting lidar instruments are effective tools for observing cloud and aerosol layers in the atmosphere. Cloud phases and several different kinds of aerosols are presently identified and typed using sophisticated, fine-tuned classification algorithms that operate using processed lidar data. We present a deep neural network semantic segmentation model that was trained using raw, uncalibrated photon count data and data products from the Cloud/Aerosol Transport System’s (CATS) 1064 nm lidar. Our approach successfully types layers in complex scenes using only raw photon counts, bin altitudes, and ground surface type at 14 to 171 times the spatial resolution of the CATS operational data product. We observe comparable cloud detection and phase determination to the CATS operational algorithm while also exhibiting a 15-point improvement in finding tenuous aerosol layers. Because the model is lightweight, does not rely upon ancillary information, and is optimized to leverage GPU computing, it has the potential to be deployed on-instrument to perform cloud and aerosol typing in real time.

Source: Remote Sensing

DOI: 10.3390/rs17162787

Authors: Chase A. Fuller, Patrick A. Selmer, Joseph Gomes, Matthew J. McGill

Institutions: University of Iowa, Science Systems and Applications (United States)

Language: English

Topic: Atmospheric aerosols and clouds

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: National Aeronautics and Space Administration, Nuclear Safety and Security Commission