Abstract The probability density function of drops is difficult to model. Current approaches make assumptions that are often problematic, as they allow negative values for the mean of the distribution. While the statistical goodness of fit of those models might be reasonable for precipitation radar estimation, the situation is unsatisfactory if a fully consistent physical modeling of precipitation across scales is desired. This is the case of weather and climate models. This paper discusses a model that satisfies mathematical and physical consistency. The model can be seamlessly integrated into the parameterizations of the microphysics of precipitation and is tested on an extensive disdrometer data set. Comparison with existing models shows that the new method has substantial practical and theoretical advantages. The research has implications in elucidating the role of clouds in the climate sensitivity of climate models.
Tapiador et al. (Wed,) studied this question.