Improved Prediction of Cloud Water Content from Combined CloudSat-CALIPSO Observations

A significant fraction of clouds are missing from or misrepresented in existing CloudSat radar-based products. Many liquid clouds are masked by surface clutter or have insufficient reflectivities. To account for these missing clouds, we train a random forest regression model to predict cloud optical depth and cloud top effective radius from other CloudSat and CALIPSO observables that do not include the radar reflectivity profile. By assuming a subadiabatic cloud model, we are then able to retrieve a vertical profile of cloud microphysical properties for all liquid-phase oceanic clouds that are detected by CALIPSO’s lidar but missed by CloudSat’s radar. We are also able to achieve better estimates of cloud water content for profiles that contain precipitation. Finally, the CALIPSO observations of cloud top phase allow for a more sophisticated handling of mixed phase clouds. This new algorithm offers a promising path forward for estimating cloud microphysical properties, especially for thin liquid clouds observed at night. More generally, this approach could be extended to other satellite missions such as EarthCARE and the Atmosphere Observing System. It points to the value of multi-sensor observations and the potential to combine such observations using machine learning.