AI-Assisted Parameter Tuning Will Speed Development and Clarify Uncertainty in E3SM
Focal Area: This idea is best aligned with predictive modeling. It targets Earth System Model improvement and uncertainty quantification
54 ENVIRONMENTAL SCIENCES↗
Engineering topics
Lier-Walqui, Marcus
Publications and source records attributed to Lier-Walqui, Marcus.
Structurally flexible cloud microphysics, observationally constrained at all scales via ML-accelerated Bayesian inference
We discuss the challenge of developing observationally informed parameterizations of microphysics for use at a hierarchy of modeling scales. Our proposed approach is applicable to any domain that suffers from a two-fold parameterization problem, where physical processes are not resolved at the model scale (the first problem), and those processes are uncertain at any scale (the second problem). For such problems, a physical approach facilitates modeling across scales, as well as systematic observational inference accelerated by machine learning (ML) surrogate models, and quantification of physical uncertainties.
54 ENVIRONMENTAL SCIENCES↗