| description abstract | ds an Optimal estimation based Snow Characterization Algorithm (TOSCA) project addresses possible novel measurement synergies for deriving snowfall microphysical parameters from the ground by combining the unique information obtained from a suite of ground-based sensors: microwave radiometers (22?150 GHz), 24- and 36-GHz radar, lidar, and in situ optical disdrometer methods. During the winter of 2008/09, such instruments were deployed at the Environmental Research Station Schneefernerhaus (UFS; at 2650 m MSL) at the Zugspitze Mountain in Germany for deriving microphysical properties of snowfall. This contribution gives an overview of the measurements carried out and discusses the potential for the developments of synergetic retrieval algorithms for deriving snow water content within the vertical column. The identification of potentially valuable ground-based instrument synergy for the retrieval of snowfall parameters from the surface will also be of importance for the development of new space-borne observational techniques. Microwave radiometer measurements show that brightness temperature enhancements at 90 and 150 GHz are correlated with the radar-derived snow water path, which is supported by radiative transfer simulations. The synergy of these measurements toward an improved snow mass content, however, needs to be augmented by knowledge on water vapor, supercooled liquid water, particle size distribution, and shape, thus making clear the necessity of synergetic remote sensing and in situ measurements. The radiometric measurements also reveal the very frequent presence of supercooled water within snow clouds and its importance to microphysical diffusion and aggregation growth of snow crystals. Analysis of the disdrometer measurements shows a ?secondary aggregation peak? around ?12° to ?15°C, a temperature range where the Wegener?Bergeron?Findeisen process is most effective and typically dendrite snow crystals forms dominate. | |
