Revisiting Clear-Air Echo Classification in Cloudnet: A Deep Learning Perspective

Accurate identification of clear-air echoes is crucial for reliable cloud boundary detection using ground-based radar. The clear-air echo classification method in the Cloudnet processing chain, which depends on temperature and the depolarization ratio (LDR), faces issues with height-based false alarms and misclassifications during precipitation, especially when LDR data are missing. This study introduces and assesses a deep learning (DL) algorithm for identifying clear-air echoes across multiple sites and climatic conditions. Compared to the Cloudnet algorithm, the DL model provides more continuous classifications and notably reduces errors—reducing cloud-base height underestimation by 19.5% and false detection of meteorological echoes by 1.7%. Furthermore, seasonal analyses of the 1-year dataset at Cloudnet sites with different geophysical features (Jülich, Germany and Lampedusa, Italy) reveal that the influence of temperature on clear-air echoes varies significantly across environments. As a result, a single temperature-based probability function is insufficient to robustly distinguish non-meteorological echoes under diverse climatic conditions. These findings highlight the robustness of DL methods and their potential to enhance cloud radar data quality in complex observational environments.