Cloud Identification and Phase Classification by Submillimeter and Infrared Synergistic Observations in the Arctic

Accurate identification of cloud phase in the Arctic is critical for evaluating surface energy budgets and reducing uncertainties in climate models, as clouds exert a complex, warming influence highly sensitive to phase partitioning amidst amplified warming. The submillimeter and infrared radiation exhibit distinct sensitivities toward hydrometeors in different cloud phases. In this study, the performance of synergistic observations of submillimeter and infrared spectrum for cloud identification and phase classification in the Arctic is explored, through sensitivity analysis and classification accuracy analysis. The spectral variances between the submillimeter and infrared bands under the scenarios of clear sky, ice cloud, liquid water cloud, and mixed-phase cloud are analyzed by quantifying the disparities in the observed spectra. The sensitivity analysis reveals that the synergistic metrics constructed by synergistic channels can distinguish clouds from clear skies or identify cloud thermodynamic phases. To quantitatively estimate the classification performance of the combined spectrum, a synergistic classification algorithm is constructed based on the Random Forest framework, and then trained and tested by the simulated synergistic observation datasets in the Arctic. Results from assessment metrics revealed that the overall accuracy of the classification model reaches 91.35%. Especially for clear skies and ice clouds, the classification accuracy is 99.43% and 92.23%, respectively. While for mixed-phase clouds, the overall classification accuracy reaches 87.34%. Specifically, ice-over-water clouds demonstrate 89.40% classification accuracy, while water-over-ice clouds achieve lower accuracy of 85.52%, reflecting fundamental differences in their thermodynamic stability and radiometric signatures. Results provide a robust statistical foundation for advanced cloud detection and phase classification algorithms, demonstrating notable improvements in classification accuracy by synergistic channels. With the upcoming spaceborne submillimeter and infrared passive sensors, the results demonstrate a pressing need and the potential of combining observations to better understand cloud phase and evolution in the future.