Rainfall prediction by weather forecasting models is strongly dependent on the microphysical
parametrization being utilized within the model. As forecasting models have become
more advanced, they are more commonly using double-moment bulk microphysical parametrizations.
While these double-moment schemes are more sophisticated and require fewer a priori
parameters than single-moment parametrizations, a number of parameter values must
still be fixed for quantities that are not Prognosed or diagnosed. Two such parameters,
the width of the rain drop size distribution and the choice of collection efficiencies
between liquid hydrometeors, are examined here. Simulations of deep convective storms
were performed in which the collection efficiency dataset and the a priori width of
the rain drop size distribution (RSD) were individually and simultaneously modified.
Analysis of the results show that the a priori width of the RSD was a larger control
on the total accumulated precipitation (a change of up to 75% over the typical values
tested in this article) than the choice of collection efficiency dataset used (a change
of up to 10%). Changing the collection efficiency dataset produces most of the impacts
on precipitation rates through changes in the warm rain process rates. On the other
hand, the decrease in precipitation with narrowing RSDs occurs in association with
the following processes: (a) decreased rain production due to increased evaporation,
(b) decreased rain production due to decreased ice melting, and (c) slower raindrop
fall speed which leads to longer residency times and changes in rain self-collection.
These results add to the growing body of work showing that the representation of hydrometeor
size distributions is critically important, and suggests that more work should be
done to better represent the width of the RSD in models, including further development
of triple-moment and bin schemes.