Melting the Inconsistencies: Enhancing Coherence in Microphysical Properties of Precipitation across the Melting Layer in the Dual-Frequency Precipitation Radar Data

The Dual-frequency Precipitation Radar (DPR) onboard of the Global Precipitation Measurement (GPM) mission core satellite provides a unique opportunity to retrieve two key microphysical parameters, size and mass concentration of hydrometeors, from space. Our research focuses on improving the consistency of these parameters in stratiform rain conditions. The official algorithm shows a sharp increase in mass flux from ice to rain phase across the bright band, which is inconsistent with the expected little variation of the precipitation rate. To address this, we propose a new algorithm that enforces continuity of the precipitation rate across the bright band and derives bulk ice density. The algorithm utilizes Bayes' rule with riming parameterized by the "fill-in" model and realistic snowflake shapes to simulate radar reflectivity. Validation is conducted using co-located polarimetric radar data from the GPM ground validation program. Our results will be valuable for developing high-quality training datasets for dual-frequency artificial intelligence algorithms needed for future space missions such as Tomorrow.io.