Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms

Abstract

Abstract. The 2017 National Academy of Sciences Decadal Survey highlighted several high priority objectives to be pursued during the next decadal timeframe, and the next-generation Cloud Convection Precipitation (CCP) observing system is thereby contemplated. In this study, we investigate the capability of two CCP candidates, i.e. a W-band cloud radar and a submillimeter-wave radiometer, in ice cloud remote sensing by developing hybrid Bayesian algorithms for the active-only, passive-only, and synergistic retrievals. The hybrid Bayesian algorithms combine the Bayesian MCI and optimization process to retrieve quantities and uncertainty estimates. The radar-only retrievals employ the optimal estimation methodology, while the radiometer-involved retrievals employ ensemble approaches to maximize the posterior probability density function. The a priori information is obtained from the Tropical Composition, Cloud and Climate Coupling (TC4) in situ data and CloudSat radar observations. Simulation experiments are conducted to evaluate the retrieval accuracies by comparing the retrieved parameters with known values. The experiment results suggest that the radiometer measurements possess high sensitivity for large ice cloud particles, even though the brightness temperature measurements do not contain direct information on the vertical distributions of ice cloud microphysics. The radar-only retrieval demonstrates skill in retrieving ice water content profiles, but not in retrieving number concentration profiles. The synergistic information is demonstrated to be helpful in improving retrieval accuracies, especially in terms of ice water path. The end-to-end simulation experiments also provide a framework that could be extended to the inclusion of other remote sensors to further assess the CCP observing system in future studies.