Precipitating clouds, over both land and ocean, are critically important to local and global climate, energy balance, and hydrologic cycles. However, our understanding of cold-cloud precipitation, dominant in the middle and high latitudes, remains lacking in several areas.

Microwave remote sensing techniques, both active and passive, exploit relationships between ice/water and particle sizes/shapes to both separate frozen and melted precipitation and to infer geophysical properties of interest. The process is complicated by the fact that observations are often an "integrated" response within a finite field of view, which may include a range of temperatures, humidity, cloud, and a variety of precipitation particles -- all of which can significantly influence observations.

The research to be presented describes an amalgamation of methods by which one can attempt to untangle the relationship between the observation and the geophysical properties of interest, such as particle size distribution properties, precipitation rate, particle density, attenuation, etc. A case study is also presented to illustrate the retrieval capabilities, deficiencies, and sensitivities to assumptions and input parameters.

Using aircraft-based radar and radiometer data obtained during the Wakasa Bay 2003 winter field campaign over the Sea of Japan, several rain and snow events were observed. The PR-2 active radar operated at microwave frequencies of 13.4 and 35.6 GHz, and the MIR radiometer made passive observations of brightness temperature (TB) at 89, 150, 220, near 183, 220 and 340 GHz. A radar retrieval method based on the dual wavelength ratio technique described in Meneghini et. al., 1997 is used to infer particle size distribution properties, while simulated MIR TBs are compared to observed TBs to further constrain the radar retrievals.

Key model features include a 1-D microphysical model based on Petty, 2001; Mie-based 1-D radar reflectivity profiles with attenuation; and TB simulation via RT4 (Evans, 1995). New features incorporated into the models include: an explicit melting layer model; an asymmetric threecomponent dielectric-mixing formulation; an empirically calibrated ocean surface-wind speed model; and several other enhancements.

The primary result of the research is a dataset of retrieved particle properties, such as size distribution and density, that are consistent with both simulation and observation. The techniques are being developed with an eye toward the Global Precipitation Measurement Mission (GPM: http://gpm.gsfc.nasa.gov/). Parameterizations based on these retrievals will be used to identify and communicate the key characteristics of cold-cloud precipitation to the larger remote sensing and climate modeling community. This research constitutes a portion of the author's Ph.D. dissertation research from the University of Wisconsin.