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Predicting OH Measurements from In-Situ Aircraft Campaigns Using Machine Learning
Document ID
20220015573
Acquisition Source
Goddard Space Flight Center
Document Type
Poster
Authors
Adiel Felsen
(GSFC INTERNS)
Julie Nicely
(University System of Maryland Adelphi, Maryland, United States)
Bryan Duncan
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Date Acquired
October 17, 2022
Subject Category
Geosciences (General)
Meeting Information
Meeting: OSTEM Poster Session
Location: Greenbelt, MD
Country: US
Start Date: August 10, 2022
Sponsors: Goddard Space Flight Center
Funding Number(s)
CONTRACT_GRANT: NNX17AE79A
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
NASA Peer Committee
Keywords
Hydroxyl radical
machine learning
atmospheric chemistry
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