Statistical analysis of CloudSat data for climate model parameterization

The development of realistic cloud parameterizations in climate models requires accurate characterizations of sub-grid 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 geo-location 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 closely follow either a gamma or a lognormal distribution depending on temperature (cloud phase), cloud type, precipitation, and geo-location. In the regions from the lower to mid-troposphere (altitudes of 1-6 km) and between the tropical and the subtropical latitudes where non-precipitating and pure-water 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 exhibits a gamma distribution due to a high frequency of occurrence of supercooled liquid clouds, which results in the increased occurrence of low CLWC values. The data sub-sampling with respect to cloud phase and precipitation significantly affects the distribution characteristics of CLWC in some regions. After removing the contributions of supercooled water and precipitation the CLWC PDFs transition from gamma to lognormal distributions in two types of regions: (1) the high altitude and middle-to-polar latitude regions where the contribution of mixed-phase cloud is significant; (2) the region near the surface where the contribution of precipitating cloud is considerable.