Evaluations of microphysical parameterizations in retrieval algorithms for snowfall particle size distributions from dual frequency radar measurements
Dual Frequency Ku and Ka radar measurements from the Global Precipitation Measurement (GPM) satellite have the potential for novel retrievals of snowfall precipitation from space. These retrievals are dependent on assumptions that relate the maximum measured dimension of a snowflake to its mass and radar cross-section, as well as assumptions to constrain the shapes of snowflake particle size distributions. Previous algorithms that have been proposed for dual frequency retrievals assume that snowflakes have a constant density, scatter as homogeneous particles, and have an exponential size distribution. These assumptions disagree with the results of observations and simulations of snowflakes, but the impact that more realistic microphysical assumptions can have on the accuracy of simulations and retrievals of snowfall properties has not yet been determined. In this project, collocated and simultaneous measurements of ice water content, reflectivity, and particle size distributions (PSDs) are used to evaluate the effects of improved microphysical parameterizations on simulations and retrievals of microphysical properties from particle size distributions and radar measurements, respectively. It is seen that only mass-diameter relationships with varying densities are able to recreate measured reflectivity and ice water content, and that a spheroidal scattering model provides more consistent results at both Ku and Ka wavelengths. Spheroidal scattering models with varying density also retrieved PSDs that were closest to those measured.
snow, Global Precipitation Measurement (GPM), precipitation retrieval, dual frequency
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign