Cloud Phase Classification Based on the Submillimeter and Microwave Radiometer Synergistic Observations

The submillimeter and microwave radiometers exhibit distinct sensitivities toward hydrometeor in different cloud phases. This work aimed to investigate the application of the synergistic observations of spaceborne submillimeter and microwave radiometers in cloud detection and thermodynamic phase classification. The spectral variances between the submillimeter and microwave bands under the scenarios of clear sky, ice-phase cloud, liquid-phase cloud, and mixed-phase cloud were highlighted by quantifying the disparities in the observed spectra. Cloud detection and phase classification models based on random forests were trained and tested by the simulated synergistic observation datasets. Results from assessment metrics revealed that the overall accuracy of the classification model incorporating all features was 88.75%. Feature importance analysis provided the key metrics for cloud phase classifications, classification models trained with these simplified combinations of channels, and metrics achieved an accuracy of 86.25%. The results demonstrated the feasibility of combining submillimeter and microwave radiometer observations for detection and phase classification.