| Summary: | Ice microphysical processes have a profound impact on the weather and climate given their ability to change radiative, thermodynamic, and precipitation properties. Their representation in models and remote sensing retrievals, however, is highly uncertain given the variable nature of ice particle properties, even in similar environmental conditions, that are typically held constant or represented as a simple function of environmental variables in models. Further, micro- physical measurements through clouds, which can provide the basis for assumptions made within models and remote sensing retrievals, can also contain uncertainties based on the statistical counting of particles and uncertainties in the measurements themselves. To improve how empirical parameters characterizing ice microphysical properties can be represented in models or retrieval schemes, a technique was developed that considers multiple coefficients from a mass-dimension (m-D) relationship as equally plausible solutions for a given environment. The technique incorporates microphysical observations from imaging probes and bulk mass probes as well as measurements from a ground-based radar to compare how quantities derived from ice particle size distributions (PSDs), such as ice water content and reflectivity, relate to the other measurements for a range of m-D coefficients. The equally plausible solutions derived using this framework are presented as a surface in an (a,b) phase space, and can be applied to a microphysics parameterization or retrieval scheme that supports random selection among a range of potential empirical parameters. Since weather and climate models are also limited by the lack of measurements made in some regions of the world, the second focus of this thesis was the result of a field campaign conducted in the Southern Ocean to collect more observations of boundary layer clouds. For one of the research flights, microphysical observations were collected at various depths within an atmospheric river and represent a unique dataset for Southern Hemisphere atmospheric rivers. The precipitation structures, microphysical processes, and vertical motions observed within the atmospheric river could offer potential areas of model improvement by comparing simulations of these systems to observations made during flight.
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