Cloud and Precipitation Microphysical Retrievals from the EarthCARE Cloud Profiling Radar: The C-CLD Product

This presentation delves into the C-CLD processor and its output product, both named the same, developed for the EarthCARE mission. The C-CLD processor has been designed to extract detailed microphysical properties of clouds and precipitation from the EarthCARE Cloud Profiling Radar data. The algorithm introduces a significant advancement by incorporating Doppler velocity information for the first time in space-borne radar retrievals. Our approach integrates an optimal estimation method to deduce vertical profiles of hydrometeor water content and particle characteristic size, employing reflectivity, mean Doppler velocity measurements, and path-integrated attenuation. The algorithm's robustness is further amplified by an ensemble-based method in the ice regions, ensuring both accuracy and consistency in the forward model relations. Emphasizing the algorithm's advancements, we present a comprehensive overview of its theoretical basis and development. This includes the validation process, performance sensitivity analysis and quantification of the information content. The presentation will demonstrate the retrieval efficacy in diverse atmospheric conditions, ranging from warm to cold rain and snow. In addition to algorithmic developments, our research also emphasizes the importance of iterative testing and refinement. Our approach combines model simulations with actual campaign datasets, which include both in-situ and remote sensing measurements, to validate and refine our methods. The rigorous analysis of data from campaigns like CADDIWA or IMPACTS, provided insights that allowed us to improve the C-CLD algorithm, ensuring its robustness and improving the reliability of its retrievals.