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 cl

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Given the enormous uncertainties in the prediction of frozen hydrometeors in NWP and climate models, reliable monitoring of ice water content (IWC) in the presence of thick ice and snowy conditions at mid and high latitudes is an important goal of the meteorological community. Remote sensing retrievals are the only means suited for this purpose because direct observations, e.g. by airplane, can only deliver snapshots of extremely limited spatial and temporal extent. Moreover, hazards in flying in such conditions are prohibitive for the retrieval of samples of useful quantities. We propose to build a prototype of an optimal-estimation based algorithm for the characterization of snowing and thick ice clouds based on a database of single scattering (SS) computations for non-spherical ice crystals and on studies based on active and PMW observations. The prototype algorithm will be applied to the satellite A-Train constellation2 and to a ground-based suite of instruments located at Umweltforschungsstation Schneefernerhaus (UFS) with the goal of retrieving IWC profiles, and, when possible, mean size and type (habit) for given environmental conditions. Our results will gather an unprecedented test-bed for evaluating cold cloud microphysical schemes and will significantly improve the understanding of the microphysical characteristics of mid-latitude winterly precipitation systems and of related MW signatures.