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Abstract
This contribution presents first results of the DFG-funded research project TOSCA - "Towards an Optimal estimation based Snow Characterization Algorithm". Although snow is the predominant type of precipitation sub-polar and polar latitudes, not many reliable remote-sensing methods of determining the vertical distributions of micro-physical snowfall parameters (i.e. snow mass density, snow crystal size and type) today exist. These parameters - together with temperature, humidity and turbulence - govern processes such as riming and aggregation, which in turn determine the ground-based snowfall rate. However, these parameters are highly variable in space and time and thus their measurement - and subsequent modelling - is a difficult task. The TOSCA project addresses these points in combining the unique information contained from a suite of sensors: microwave radiometers, radar, lidar, and in-situ measurement methods. During the winter of 2008/2009, such instruments have been deployed at the Environmental Research Station Schneefernerhaus (UFS at 2650 m MSL) at the Zugspitze Mountain in Germany for deriving microphysical properties of falling snow. In the high altitude region of the UFS station snow events occur much more frequently than in lower regions and the low water vapor amounts account for clearer scattering signal from ice hydrometeors. In order to analyze the sensitivities of brightness temperature and radar reflectivity to the vertical distribution of snow water content as a function of particle size and shape, we simulated data from the COSMO-DE model for snowing cases with a radiative transfer model. Here we employed single scattering properties of modelled snow crystals calculated with the Discrete Dipole Approximation. “Real” measurements from the TOSCA experiment of the winter season will be shown and compared with the expected sensitivities from the COSMO-DE simulation study. These analyses will serve as a basis for the future development of an optimal estimation retrieval scheme with goal of improving profiles of snow water content from the sensor synergy of ground-based remote sensing measurements.