Dual-Polarization Radar Data Assimilation in Deep Convective Storms: Information Content in the Ice-Phase Region

In the last few decades, many studies have demonstrated the utility of dual-polarization Doppler radars for estimation of hydrometeor content and classification of hydrometeor type. Specifically, the transmission of both horizontally and vertically polarized signals increases the information on particle shape and orientation via the differences in radar backscatter cross-section. Recent studies have leveraged dual-polarization-specific information to produce improved assimilated cloud and precipitation fields from the warm rain (above-freezing) portion of deep convective storms. The success of these efforts derives largely from the systematic changes in the orientation and shape of rain drops with size. Combined use of vertical and horizontal polarized signals also enables improved detection of hail.

While the strengths of dual-polarization radar observations have been conclusively shown for rain and hail hydrometeors, it is less clear how much information is provided in mixed phase and ice-only regions. This is in large part because of the poorly determined influence of changes in particle size distribution and particle density on the information content in sub-freezing clouds. Though clouds have been observed to exhibit a range of particle shapes and size distributions, parameters that define particle shape and PSD are typically fixed, leading to a large potential source of error in estimates of liquid and ice water content. Allowing these parameters to vary (e.g., including them as control variables in an assimilation) may result in more realistic numerical simulations; however, use of cloud microphysical parameters as control variables necessarily requires the observations to contain sufficient information to constrain them.

In this presentation, we first demonstrate the impact of assimilating dual-polarization radar observations on estimates of the hydrometeor content in a deep convective storm. We then show results from a detailed Markov chain Monte Carlo (MCMC)-based study of the information content of dual-polarization specific variables in the ice-phase region of the storm. We quantify how much information is added by the specific differential phase and radar correlation coefficient, then examine how this information is degraded when we allow the assumed particle size distribution and particle density to vary. We find the following:

1. Dual-pol specific observations (Kdp and ρhv) provide significant information on the rimed ice content, and moderate information on pristine ice, especially where snow mass is more than 10% of the total volume hydrometeor mass.

2. There is a significant reduction in information content for rain and near complete loss of information for graupel/hail and snow when the particle size distribution and ice particle densities are not well known.

3. There are systematic changes in radar information gain and loss with changes in hydrometeor mass.