Synergistic Retrievals of Ice Cloud Microphysics by Spaceborne Submillimeter and Infrared Observations

Improving the accuracy of measuring ice cloud properties is crucial for the study of atmospheric circulation and climate models, and for understanding the radiative forcing effects of ice clouds. In this study, a novel approach to retrieve ice cloud microphysics involving synergistically analyzing the spectra of submillimeter (sub-mm) and infrared (IR) is proposed, combining the complementary information regarding ice cloud properties from each spectrum. The sensitivity of the synergistic channel pairs to ice water paths (IWPs) and mean mass diameters is thoroughly investigated by the synthetic lookup tables and sensitivity parameter analysis. A synergistic retrieval algorithm based on Quantile Regression Neural Networks is constructed toward a better evaluation of the retrieval biases and uncertainties quantitatively. The simulated retrieval results reveal that the synergistic retrievals outperform the results from individual spectra across the full range of IWP from 1 to 1000 g/m2 and mean mass diameter from 1 to $500~\mu $ m. Specifically, the mean root-mean-square-error of the synergistic retrievals for IWP is 68% (95%) lower than that of the sub-mm-only (IR-only) retrievals, and a 10% (24%) lower root-mean-square-error for mean mass diameter, respectively. In addition, the synergy can improve the correlation for IWP by 3.7% (5.6%) and yields a 12.5% (17.6%) higher correlation for mean mass diameter compared to the sub-mm-only (IR-only) retrievals. With the upcoming spaceborne sub-mm and IR passive sensors, the results demonstrate a pressing need and the potential of combining observations to better understand ice cloud properties in the future.