Evaluation of Geo-Stationary Ice Cloud Optical Depth Retrievals using High Spectral Resolution Lidar

Abstract:

Despite playing a key role in modulating earth’s upper tropospheric radiation budget, ice cloud optical thickness (IOT) remains a challenging parameter to retrieve from satellite observations. A significant limitation due to a previous lack of direct measurements motivates an evaluation of satellite observed IOT products. Recent advancements in geo-stationary capabilities with the launch of next generation imagers such as the Advanced Baseline Imager (ABI) and the Japanese Advanced Himawari Imager (AHI) provide new opportunities to leverage ground-based measurements due to the greatly increased sampling over individual sites compared to LEO. In this study, we utilize data collected by the University of Wisconsin High Spectral Resolution Lidar (HSRL) during the NASA KORUS-AQ in Seoul South Korea, the CAMP2Ex mission based in Manila Philippines, the ONR ship based PISTON campaign in the South West Pacific, and data collected in Madison WI to evaluate IOT from a beta version of the MODIS/CLDPROP and the NOAA CLAVR-x IR/optical retrievals processed on AHI/ABI data. An ice cloud detection algorithm is developed for the HSRL instrument capable of accurate detection and measurements of ice properties including direct measurements of IOT. Direct comparisons of the IOT retrievals with the HSRL over multiple months of observation will be presented with a focus on investigating systematic biases in the satellite retrievals and the impact of regional ice crystal morphology.