{'fraction_non_alphanumeric': 0.027192386131883073, 'fraction_numerical': 0.01291638341264446, 'mean_word_length': 5.082644628099174, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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.