Ice particle type identification for shallow Arctic mixed-phase clouds using X-band polarimetric radar

Ice particle type identification for shallow Arctic mixed-phase clouds is studied using X-band polarimetric radar variables: horizontal reflectivity factor Zh, differential reflectivity Zdr, specific differential phase Kdp, and cross-correlation coefficient ρhv The problem is formulated in a Bayesian classification framework, which consists of a probability density function (PDF) and a prior probability. The PDF is approximated using a Gaussian mixture model with parameters obtained by a clustering technique. The prior probability is constructed with the spatial contextual information based on a Markov random field. The PDF and prior probability are incorporated to produce the posterior probability, the maximum of which indicates the most likely particle type. The proposed algorithm is used to first derive the PDFs for the X-band polarimetric radar observations, and then identify the particle types within Arctic precipitating cloud cases sampled in Barrow, Alaska. The results are consistent with ground-based observations and the technique is capable of detecting and characterizing the variability of cloud microphysics in Arctic clouds. •The PDFs associated with the ice particle habits most commonly found in Arctic clouds are characterized.•The particle identification is formulated in a Bayesian classification framework.•The particle identification algorithm is improved by considering a smoothness prior modeled as Markov random field.