Structurally Flexible Cloud Microphysics, Observationally Constrained at All Scales Via ML-Accelerated Bayesian Inference

Marcus van Lier-Walqui; Hugh Morrison; Greg Elsaesser; Peter Caldwell; Pierre Gentine; Derek Posselt; Sean Santos; Po-Lun Ma

NASA NTRS · 20230012198

Structurally Flexible Cloud Microphysics, Observationally Constrained at All Scales Via ML-Accelerated Bayesian Inference

Marcus van Lier-Walqui·Hugh Morrison·Greg Elsaesser·Peter Caldwell·Pierre Gentine·Derek Posselt·Sean Santos·Po-Lun Ma

Abstract

The 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)

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Marcus van Lier-Walqui, Hugh Morrison, Greg Elsaesser, Peter Caldwell, Pierre Gentine, Derek Posselt, Sean Santos, Po-Lun Ma. Structurally Flexible Cloud Microphysics, Observationally Constrained at All Scales Via ML-Accelerated Bayesian Inference. https://ntrs.nasa.gov/citations/20230012198

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