Advanced Liquid-Phase Cloud Microphysical Retrievals and Automated Tracking of High Drop Concentration Features (OVERCAST TechCan)
Abstract
Forecasts of visibility in cloudy conditions depend sensitively on the concentration of cloud drops. Techniques exist to retrieve cloud droplet number concentration, but they have not been applied to widely available geostationary satellite datasets which limits their application to improve visibility forecasts. This project proposes to implement a routine cloud drop number concentration retrieval for low-level liquid clouds using geostationary satellite imagery (GOES). A machine learning algorithm will then be applied tothe retrievals to automatically diagnose and track patches of highly polluted clouds over the ocean to monitor their development and better understand the lifecycle of pollution in clouds over remote marine areas. Finally, the geostationary retrievals will be validated against polar-orbiting satellites and aircraft in situ measurements. Approved for Public Release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N000142412767
Entities
People
- Matthew Lebsock
Organizations
- Office of Naval Research
- United States Navy
- University of California, Los Angeles