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Keywords:

  • cloud water content;
  • CloudSat;
  • probability density function

[1] The development of realistic cloud parameterizations requires accurate characterizations of subgrid distributions of thermodynamic variables. To this end, cloud liquid water content (CLWC) distributions are characterized with respect to cloud phase, cloud type, precipitation occurrence, and geolocation using CloudSat radar measurements. The probability density function (PDF) of CLWC is estimated using maximum likelihood estimation. The best-estimated PDF of CLWC is found to follow either a gamma or a lognormal distribution depending on temperature (cloud phase), cloud type, the occurrence of precipitation, and geolocation. The data sampling with respect to cloud phase and precipitation significantly affects the distributional characteristics of CLWC in some regions. In the lower to midtroposphere (altitudes of 1–6 km) in the tropics and subtropics, where nonprecipitating and pure liquid phase clouds are dominant, the PDFs of CLWC are best described by lognormal distributions. In contrast, at altitudes above 6 km and in regions poleward of the midlatitudes, the CLWC more closely resembles a gamma distribution that coincides with a high frequency of occurrence of supercooled liquid clouds containing low CLWC values. When the contributions of supercooled water and precipitation are removed, the CLWC PDFs transition from gamma to lognormal distributions in two areas: (1) the high altitude and middle-to-polar latitude regions where the contribution of supercooled cloud is significant and (2) in the lower troposphere where precipitation is frequently detected. Although the CloudSat radar does not sample all cloud hydrometeors, coherent regional and cloud type dependence of CLWC distributional characteristics are observed that may provide useful constraints for cloud parameterizations in climate models.