A Physically Consistent Particle Size Distribution Modelling of the Microphysics of Precipitation for Weather and Climate Models

The probability distribution of small drops in the boundary between cloud droplets and raindrops are difficult to model specially in moist environments. 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, where keeping all the variables within physical limits is a must. This paper discusses a modeling that provides both a better fit for measured small, supercooled and medium size drops, plus a seamless integration in the parameterizations of the cloud microphysics. The model is tested on an extensive disdrometer dataset collected in the KPOP-MS campaign in Korea. Comparison with existing models shows that the new method has substantial practical and theoretical advantages for the modeling of the microphysical process in the cloud-precipitation boundary and for supercooled drops. The research has implications in elucidating the role of clouds in the climate sensitivity of climate models.