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OMB Individually Reported

Application of High-Dimensional Fuzzy K-mean Cluster Analysis to CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) / CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) Version 4.1 Feature Classifications

Low riskExact public inventory row

Description

This project uses Fuzzy K-means clustering (unsupervised learning) to validate the cloud-aerosol discrimination algorithm used in the publibly-distributed CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) / CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) data products.

Detailed example

Applies convolutional neural networks (supervised learning) to automatically identify the presence of different aerosol species (e.g., dust and smoke) in CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) lidar backscatter measurements.

AI / analytics pattern

Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.

Automation level / stage

a) Pre-deployment – The use case is in a development or acquisition status.

Expected benefit

This project uses Fuzzy K-means clustering (unsupervised learning) to validate the cloud-aerosol discrimination algorithm used in the publibly-distributed CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) / CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) data products.

Controls / human review

ATO: Not reported; PIA: Not published

Source

OMB 2025 Federal Agency AI Use Case Inventory

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