Integrating Thermodynamic Priors and Spatiotemporal Features into a Physics-Guided Deep Learning Framework for Cloud Radar Clear-Air Echo Identification

Accurate echo classification is crucial for Millimeter-wave Cloud Radar (MMCR) data quality control. Existing approaches, however, often struggle to generalize across complex scenes or lack physical interpretability. Here we propose PhySNet, a physics-guided network that combines thermodynamic priors with spatiotemporal radar features, embedding physical information across the full pipeline from feature extraction to final outputs. Based on the coupling between the lifting condensation level (LCL) and daytime clear-air echo heights, and the lagged correlation between nocturnal clear-air echo heights and their daytime counterparts, we design a physics-constrained gating block (PCGB). The PCGB extracts thermodynamic states and evolution trends from collocated surface observations, generating a clear-air echo probability map that weights the initial radar features. Building on this, we add a parallel regression branch of effective-clutter-height (ECH). This branch fuses thermodynamic features with radar spatiotemporal features, enabling the model to learn to predict the clear-air echo boundary. Finally, we apply an adaptive height filter using the predicted ECH sequence to refine the classification results. Evaluated on a multi-region, multi-season dataset from China, PhySNet achieves a probability of detection (POD) of 98.28% for meteorological echoes and 95.87% for clear-air echoes, outperforming conventional methods. By coupling data-driven learning with physical rules, our approach provides a high-accuracy, interpretable solution for cloud radar clear-air echo identification.