Novel Approaches for Studying Ice Microphysics in Stratiform Clouds with Dual-Polarization Radars

It is widely recognized that existing numerical weather prediction models need improvement in their treatment of microphysical processes involving ice and snow. The processes of ice nucleation, growth, and melting are poorly parameterized in the models with bulk microphysics. Emerging networks of polarimetric weather radars provide unique opportunities to fill the gap in our understanding of ice microphysics and to advance radar techniques for microphysical and thermodynamic retrievals in cold parts of the clouds.

A systematic use of the quasi-vertical profiles (QVP) methodology on a whole fleet of the WSR-88D radar allows revealing repetitive high-resolution polarimetric radar signatures in stratiform clouds and their correlations with the parameters of the melting layer and vertical structure of clouds. In addition to a radar-centric QVP product, the columnar vertical radar (CVP) products have been introduced to represent temporal evolution of the vertical profiles of radar variables anywhere within a radar coverage area.

Most notable polarimetric radar signatures are routinely identified within the dendritic growth layer (DGL). Extensive statistical analysis of these features using a number of WSR-88D radars shows strong anti-correlation between the DGL Z_DR and K_DP values and their dependence on the cloud top temperature. The melting layer mirrors all important microphysical processes in ice aloft and its polarimetric properties can be used to infer information about ice deposition growth, riming, and aggregation. New techniques for radar data analysis ensure reliable measurements of the backscatter differential phase δ and K_DP in the melting layer which have never been explored before.

A sole radar reflectivity factor Z has been traditionally used to estimate ice water content IWC and precipitation flux S in ice and snow. It is demonstrated that the accuracy of the IWC and S estimates can be dramatically improved if a combination of Z and K_DP is utilized. Testing new IWC(Z,K_DP) and S(Z,K_DP) relations requires comparison with in situ data collected by surface 2D video disdrometer and in situ aircraft measurements which will be briefly discussed.

It turns out that a reliable estimation of K_DP in ice and snow is a key for success of their classification and quantification. Therefore, possible ways to improve the quality of K_DP measurements in ice and snow by utilizing spatial averaging and range oversampling will be also addressed in the presentation.