Wednesday, 9 July 2014
We explore our ability to estimate cloud and precipitation properties of ice clouds by exploiting the complementary sensitivities of radar, sub-millimeter, and visible observations. Each of these sensors is primarily responsive to different moments of the ice particle size distribution, i.e. they often see' different size particles for a given size distribution. Likewise, each sensor sees' a different vertical profile of cloud and precipitation properties through their unique state-dependent weighting functions. Here, we seek to determine if these disparate pieces of information can be combined to produce estimates of ice cloud properties that are more accurate than those possible from the individual sensor approaches. Application of variational retrieval schemes to synthetic cloud scenes will quantify our ability to retrieve known cloud and precipitation properties given individual sensors or multiple sensor combinations. Application of these retrieval schemes to existing TC4 field campaign data will provide a means to explore the validity of our retrieval results and assumptions. For example, significant discrepancies in cloud property estimates from these retrieval schemes may indicate errors in particle size distribution assumptions employed in both airborne and satellite-based retrieval schemes.
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