Retrieval of microphysical properties of snow using dual polarization spectral analysis: model and data analysis

Snow crystals consist of many different types of ice particles. Typical radar measurements observe only bulk properties of all types of ice particles present in a radar volume. Due to their difference in radar cross-section, larger particles will reflect more power of the transmitted radar signal, than small particles. If small and large particles are present in the same radar volume with comparable volume concentration, the radar measurements will be dominated by larger particles. Because of this, it is difficult to obtain microphysical properties of both large and small particle types, based on reflectivity alone. In this work, application of dual polarization spectral analysis for retrieval of microphysical properties of ice particles in stratiform precipitation is presented. Based on literature research, a selection on the particle types that are predominantly present in radar measurements is done. The selection is based on both radar cross sections of the different particles and meteorological conditions. The radar cross-sections are calculated with a model derived from literature, on the microstructure of ice crystals. With the obtained knowledge, it is shown that only aggregates and plates dominate spectral radar retrievals above the melting layer of stratiform precipitation. A model for the spectral horizontal reflectivity and spectral differential reflectivity of plates and aggregates, is created. This model is dependent on the parameters of the drop size distribution of plates and aggregates, the ambient wind velocity and spectral broadening. Using a sensitivity study of the spectral radar observables on the drop size distribution parameters, an algorithm is developed that is able to retrieve drop size distribution parameters of plates and aggregates from spectral radar measurements collected above the melting layer in stratiform precipitation. The drop size distribution parameters are retrieved by fitting the modelled spectral radar observables to the radar measurements using a non-linear least squares optimization technique. This kind of optimization algorithms, change the dependent variables of the model to obtain the best fit of the model to the measurement. The retrieval of drop size distribution parameters of plates and aggregates, is illustrated on data of stratiform precipitation collected by TARA. TARA is an S-band FM-CW dual polarization Doppler radar, situated at the measurement site Cabauw, The Netherlands. Using the outputs of the algorithm, it is shown that the obtained drop size distribution parameters are consistent. Verification of the outputs of the algorithm is performed by comparison of the estimated ice water content with the liquid water content, obtained below the melting layer. The estimated ice water content is also compared to the measured reflectivity. It is shown that the obtained results for plates are in good agreement with relations between ice water content and reflectivity obtained from literature. A new relation between the ice water content and the reflectivity is derived for aggregates.