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"
A new microphysics method using collocation of basis functions is presented.• The method improves spectral accuracy and precipitation predictions over bulk and bin methods.• The method applies to a flexible range of computational complexity, providing a way to unify microphysics models.
A new microphysics method using collocation of basis functions is presented.• The method improves spectral accuracy and precipitation predictions over bulk and bin methods.• The method applies to a flexible range of computational complexity, providing a way to unify microphysics models.
Abstract. A key constraint of particle-based methods for modeling cloud microphysics is the conservation of total particle number, which is required for computational tractability. The process of collisional breakup poses a particular challenge to this framework, as breakup events often produce many droplet fragments of varying sizes, which would require creating new particles in the system. This work introduces a representation of collisional breakup in the so-called “superdroplet” method which conserves the total number of superdroplets in the system. This representation extends an existing stochastic collisional-coalescence scheme and samples from a fragment size distribution in an additional Monte Carlo step. This method is demonstrated in a set of idealized box model and single-column warm-rain simulations. We further discuss the effects of the breakup dynamic and fragment size distribution on the particle size distribution, hydrometeor population, and microphysical process rates. Box model experiments serve to characterize the impacts of properties such as coalescence efficiency and fragmentation function on the relative roles of collisional breakup and coalescence. The results demonstrate that this representation of collisional breakup can produce a stationary particle size distribution, in which breakup and coalescence rates are approximately equal, and that it recovers expected behavior such as a reduction in precipitate-sized particles in the column model. The breakup algorithm presented here contributes to an open-source pythonic implementation of the superdroplet method, PySDM, which will facilitate future research using particle-based microphysics.
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