The development of realistic cloud parameterizations for climate models requires accurate characterizations of sub-grid distributions of thermodynamic variables. To this end, a software tool was developed to characterize cloud water-content distributions in climate-model sub-grid scales.

This software characterizes distributions of cloud water content with respect to cloud phase, cloud type, precipitation occurrence, and geo-location using CloudSat radar measurements. It uses a statistical method called maximum likelihood estimation to estimate the probability density function of the cloud water content.

A crude treatment of sub-grid scale cloud processes in current climate models is widely recognized as a major limitation in predictions of global climate change. At present, typical climate models have a horizontal resolution on the order of 100 km and a variable vertical resolution between 100 m and 1 km. Since climate models cannot explicitly resolve what happens at the sub-grid scales, the physics must be parameterized as a function of the resolved motions. The fundamental problem of cloud parameterization is to characterize the distributions of cloud variables at sub-grid scales and to relate the sub-grid variations to the resolved flow. This software solves the problem by estimating the probability density function of cloud water content at the sub-grid scale using CloudSat measurements.

This work was done by Seungwon Lee of Caltech for NASA’s Jet Propulsion Laboratory. For more information, contact iaoffice@jpl. nasa.gov.

This software is available for commercial licensing. Please contact Daniel Broderick of the California Institute of Technology at This email address is being protected from spambots. You need JavaScript enabled to view it.. Refer to NPO-47248.

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