Profiling cloud liquid water by combining active and passive microwave measurements with cloud model statistics

A method for combining ground-based passive microwave radiometer retrievals of integrated liquid water (LWP), radar reflectivity profiles (Z), and statistics of a cloud model is proposed for deriving cloud liquid water profiles (LWC). A dynamic cloud model is used to determine Z-LWC relations and their errors as functions of height above cloud base. The cloud model is also used to develop an LWP algorithm based on simulations of brightness temperatures of a 20-30-GHz radiometer. For the retrieval of LWC, the radar determined Z profile, the passive microwave retrieved LWP, and a model climatology are combined by an inverse error covariance weighting method. Model studies indicate that LWC retrievals with this method result in rms errors that are about 10%-20% smaller in comparison to a conventional LWC algorithm, which constrains the LWC profile exactly to the measured LWP. According to the new algorithm, errors in the range of 30%-60% are to be anticipated when profiling LWC. The algorithm is applied to a time series measurement of a stratocumulus layer at GKSS in Geesthacht, Germany. The GKSS 95-GHz cloud radar, a 20-30-GHz microwave radiometer, and a laser ceilometer were collocated within a 5-m radius and operated continuously during the measurement period. The laser ceilometer was used to confirm the presence of drizzle-sized drops.