Relationship between the hydrometeor size distribution and cloud microphysical properties using disdrometric data.

One of the most complete descriptions of rain is given by its drop size distribution (DSD). Several studies on the DSD, over different climatic regions and in different parts of the world, have been encouraged in the past, since the early work of Marshall and Palmer (1948), by the different applications of the DSD and variety of instruments related to the DSD applications. The problem of accurately describing the DSD temporal evolution and finding analytical functions which fit the observed DSD remains, however, a major obstacle in cloud physics. Only a few works focus the attention on mid-latitude continental rain. Most of them concern the study of tropical rains. Moreover there is an almost total lack of studies performed in Italy. This work aims at testing the significance of the existent DSD models and parameterizations in the different meteorological conditions in Ferrara (northern Italy), by using a Joss and Waldvogel disdrometer and, after a critical review of previous works and approaches, to propose modifications and improvements. In particular, two recent normalization approaches (one and two-parameter) to the DSD are tested. Case studies of the DSDs and rainfall integral parameters time variability are also analysed in order to characterize microphysically the Ferrara events, given the total lack of studies in this area. This aspect also allows a comparison of the results with the ones that have recently appeared in the literature. The identification between convective (C) and stratiform (S) precipitation involves a variety of subtleties. In the past considerable effort has gone into the development of methods for separating these two types of rainfall, especially ones based on radar images and wind-profilers data. A disdrometer approach involves the distinction between the properties of the DSDs. In this work, to face the specific characteristics of continental mid-latitude rain, a new C/S discrimination algorithm is developed