TOSCA: Development of an optimal estimation technique to profile snowing and thick ice clouds by exploiting a suite of active and passive microwave (PHW) sensors in two configurations by coupling detailed microphysical and electromagnetic models of ice clouds with state-of-the-art radiative transfer (RT) tools.
Zusammenfassung der Projektergebnisse
The “Towards an Optimal estimation based Snow Characterization Algorithm” (TOSCA) project addresses possible novel measurement synergies for deriving snowfall microphysical parameters from space and 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 2008/2009, 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. TOSCA has analysed 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. Additionally polarization observations at 150 GHz have shown how information on snow particle orientation, aspect ration and the discrimination between integrated liquid and ice water path may be obtained. Multiple frequency cloud radar measurements also show potential for discriminating particle shape and particle size distribution. A concept for developing low-power, affordable multi-frequency FMCW-system seem sensible and is currently being planned by different institutions. For space-based application it was shown, that a correct treatment of the multiple scattering effects is essential for future snowfall retrieval applications. The synergy of all these measurements towards a retrieval of i.e. snow mass content profile, however, needs to be augmented by knowledge on water vapor, super-cooled 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 super-cooled 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.
Projektbezogene Publikationen (Auswahl)
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2008. Identifying multiple-scattering-affected profiles in CloudSat observations over the Oceans. J. Geoph. Res., 113, D00A17
Battaglia, A., J. Haynes, T. L'Ecuyer, and C. Simmer
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2010. A Rain Rate retrieval algorithm for Attenuating Radar Measurements. J. Appl. Met. Clim., 49(3), 381-393
Koner, P., Battaglia, A., and C. Simmer
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2010. PARSIVEL Snow Observations: A Critical Assessment. J. Atmos. Ocean. Tech., 27(2)
Battaglia, A., E. Rustemeier, A. Tokay, U. Blahak, C. Simmer
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2010. Snow scattering signals in ground-based Passive Microwave Radiometer measurements. J. Geophys. Res., 115, D16214
Kneifel, S., U. Löhnert, A. Battaglia, S. Crewell and D. Siebler
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2011. A Multisensor Approach Toward a Better Understanding of Snowfall Microphysics: The TOSCA Project. Bull. Amer. Meteor. Soc., 92, 613–628
Löhnert, U., S. Kneifel, A. Battaglia, M. Hagen, L. Hirsch, S. Crewell
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2011. A triple-frequency approach to retrieve microphysical snowfall parameters. J. Geophys. Res., 116, D11203
Kneifel, S., M. S. Kulie, and R. Bennartz
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2011. Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar). Meteor. Atmos. Phys.
Kneifel, S., M. Maahn, G. Peters and C. Simmer
