Rainfall droplet size distributions (DSD) parameterization: physics and sensibility
Tuesday, 16 December 2014
The CHUVA project (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)) is a Brazillian experiment that aims to understand the several cloud processes that occur in different precipitating regimes. At present, the CHUVA project has conducted 6 field campaigns, the last one being in Manaus jointly with GoAmazon, IARA and ACRIDICON. The main focus of the present study is to bring into perspective the different characteristics of precipitation that reaches the surface in Brazil over several locations. To do so, disdrometer data is analyzed in detail, employing a Gamma fit for each DSD measurement which provides the respective parameters to be studied. Those are disposed in a 3D space, each axis corresponding to one parameter, and the patterns are analyzed. A correlation between the Gamma parameters is defined as a parametric surface that fits the observations with errors smaller than 10% and R2 greater than 0.95. In this way, one parameter can be estimated with respect to the other two, reducing the degrees of freedom of the problem from 3 to 2. As the 3 parameters are defined over this surface, it’s possible to obtain a surface representing integral DSD properties such as rainfall intensity (RI). Sensibilities tests are conducted on this estimation and also on other DSD characteristics such as total droplet concentrations and mean mass-weighted diameter. It’s shown that the DSD integral properties are generally very sensitive to the Gamma parameters. Nonetheless, the sensibility varies over the surface, being higher in a region where the parameters are not balanced (i.e. a relatively high value in one parameter and low values on the other two). It’s suggested that any study proposing parameterization/estimation of DSD properties should be aware of this region of high sensitivity. To further the collaboration with GoAmazon and ACRIDICON, the disdrometer results regarding rainfall droplets are compared against in-situ cloud DSDs. The same 3D pattern recognition on the parameters is conducted and discussed, highlighting the effects of aerosol particles on the DSD characteristics.