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\begin{document}

\title{Bin, bulk, or BOSS? Producing bulk microphysics schemes that emulate bin
microphysics using Bayesian inference}



\author[1]{Sean Patrick Santos}%
\author[1]{Marcus van Lier-Walqui}%
\author[2]{Hugh Morrison}%
\affil[1]{Columbia University/NASA GISS}%
\affil[2]{National Center for Atmospheric Research}%


\vspace{-1em}



  
  \date{June 15, 2024}


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\begin{abstract}
The Bayesian Observationally Constrained Statistical--Physical Scheme
(BOSS) is a bulk microphysics framework that calculates process rates
using generalized power laws of the moments of the droplet size
distribution. Formula parameters are inferred with Monte Carlo methods,
evaluating a proposed set of parameter values by comparing model outputs
to observations. In this study, we produce a set of 2- and 3-moment BOSS
schemes that effectively emulate the behavior of a bin microphysics
model, using an idealized 1D driver for various forcing conditions that
produce either non-precipitating or drizzling cloud. In this driver,
BOSS is fully responsible for collision-coalescence,
condensation/evaporation, and calculation of hydrometeor fall speeds,
with only dynamical forcing and droplet activation handled externally.
Comparing BOSS schemes with different terms and different numbers of
prognostic cloud moments allows us to evaluate whether the model
benefits from the added complexity of these changes. BOSS also has an
advantage over black-box machine-learning methods in that the process
rate formulas are ``human-readable'', allowing us to analytically
identify potential sources of instability.%
\end{abstract}%



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