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Structurally Flexible Cloud Microphysics, Observationally Constrained at All Scales Via ML-Accelerated Bayesian InferenceThe proposed research addresses all three focus areas, but primarily (2): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model /component/ parameterization selection)
Document ID
20230012198
Acquisition Source
Goddard Space Flight Center
Document Type
Conference Paper
Authors
Marcus van Lier-Walqui
(Columbia University New York, New York, United States)
Hugh Morrison
(National Center for Atmospheric Research Boulder, Colorado, United States)
Greg Elsaesser
(Columbia University New York, New York, United States)
Peter Caldwell
(Lawrence Livermore National Laboratory Livermore, California, United States)
Pierre Gentine ORCID
(Columbia University New York, New York, United States)
Derek Posselt
(Jet Propulsion Lab La Cañada Flintridge, California, United States)
Sean Santos
(Columbia University New York, New York, United States)
Po-Lun Ma
(Pacific Northwest National Laboratory)
Date Acquired
August 16, 2023
Subject Category
Meteorology and Climatology
Report/Patent Number
AI4ESP1079
Meeting Information
Meeting: AI4ESP Artificial Intelligence for Earth System Predictability
Location: Online
Country: US
Start Date: October 25, 2021
End Date: December 3, 2021
Sponsors: United States Department of Energy
Funding Number(s)
CONTRACT_GRANT: 80NSSC20M0282
CONTRACT_GRANT: 80NSSC22M0054
CONTRACT_GRANT: 80NM0018D0004
CONTRACT_GRANT: SAA-31389
Distribution Limits
Public
Copyright
Other
Technical Review
Single Expert
Keywords
cloud microphysics
machine learning
Bayesian inference
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