Most bulk cloud microphysics schemes predict up to three standard properties of hydrometeor size distributions, namely, the mass mixing ratio, number concentration, and reflectivity factor in order of increasing scheme complexity. However, it is unclear whether this combination of properties is optimal for obtaining the best simulation of clouds and precipitation in models. In this study, a bin microphysics scheme has been modified to act like a bulk microphysics scheme. The new scheme can predict an arbitrary combination of two or three moments of the hydrometeor size distributions. As a first test of the arbitrary moment predictor (AMP), box model simulations of condensation, evaporation, and collision‐coalescence are conducted for a variety of initial cloud droplet distributions and for a variety of configurations of AMP. The performance of AMP is assessed relative to the bin scheme from which it was built. The results show that no double‐ or triple‐moment configuration of AMP can simultaneously minimize the error of all cloud droplet distribution moments. In general, predicting low‐order moments helps to minimize errors in the cloud droplet number concentration, but predicting high‐order moments tends to minimize errors in the cloud mass mixing ratio. The results have implications for which moments should be predicted by bulk microphysics schemes for the cloud droplet category.

Simulation output. See the manuscript for details.

Igel 2019 AMP Data

Notes on the directory naming conventions:
Folders are named as follows: nu#_N#_M# where
the number after nu indicates the value of the shape parameter,
the number after N indicates the initial droplet concentration (#/cm3),
and the number after M indicates the initial droplet mass mixing ratio (g/kg).

Notes on the file naming convention:
There are two files per simulation. File names indicate the two moments 
that were predicted in addition to the 3rd moment. Double-moment simulations
are indicated by a repetation of the moment value. E.g. M0M0 is a double-moment
simulation with the 0th and 3rd moments predicted. M0M2 is a triple-moment 
simulation with the 0th, 2nd, and 3rd moments predicted. 

Notes on the two file types:
cM#M#.txt files contain the 0th-9th moments of the cloud and rain distributions
plus the shape parameter obtained by the distribution initialization.

cpdf*.txt files contain the binned water distributions at the end of each time step.

Also see the README file.