Fitting Cumulus Cloud Size Distributions From Idealized Cloud Resolving Model Simulations

Whereas it is now widely accepted that cumulus cloud sizes are power-law distributed, characteristic exponents reported in the literature vary greatly, generally taking values between 1 and >3. Although these differences might be explained by variations environmental conditions or physical processes organizing ensembles, use of improper fitting methods may also introduce large biases. To address this issue, we propose to a combination maximum likelihood estimation goodness-of-fit tests provide more robust fits while systematically identifying size range over which valid. The procedure applied distributions extracted from two idealized high-resolution simulations displaying different organization characteristics. Overall, power-laws found outperformed alternative almost all situations. When clouds identified based on condensed water path threshold, using with an exponential cutoff yields best results as provides superior tail distributions. For content updraft velocity thresholds free troposphere, no substantial improvement pure can when considering complex two-parameter In context however, good as, if not better than power-laws. Finally, demonstrated emergence scale behaviors related exponentially distributed cores merging they brought closer each other underlying mechanisms.