Historically, microphysics schemes were tuned to data in an ad-hoc way, resulting in parameter values that are not repeatable or explainable • Bayesian inference puts uncertainty quantification and parameter learning on solid mathematical grounds, but is computationally expensive • We present a proof-of-concept of computationally efficient Bayesian learning applied to a new bulk microphysics scheme called "Cloudy"
Historically, microphysics schemes were tuned to data in an ad-hoc way, resulting in parameter values that are not repeatable or explainable• Bayesian inference puts uncertainty quantification and parameter learning on solid mathematical grounds, but is computationally expensive• We present a proof-of-concept of computationally efficient Bayesian learning applied to a new bulk microphysics scheme called "Cloudy"
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