Improve the Parameterizations of Clouds and Precipitation in Regional Models

The scale mismatch inherent in the representation of cumulus convection in GCMs and large-scale forecast models requires that these processes be parameterized, rather than estimated explicitly. The resulting condensation and heating profiles have profound consequences on upper-level moisture and atmospheric diabatic heating processes. How should this convection be parameterized from cloud-scale models that explicitly model convection to large-scale hydrostatic models that handle convection mainly through parameterization? The GEWEX Cloud Systems Study (GCSS) (see Moncrieff, 1997) have begun to compare cloud systems over the tropical western Pacific; it would be useful to promote a companion study for the GCIP region. Scales and intensity of precipitation at the surface are also an issue. How should model precipitation be scaled to better match the characteristics of observed precipitation? How could orographic precipitation in large-scale models be adequately described?

Cloud formation and dissipation are important processes because of their effects on the atmospheric and surface energy balance. The patterns of cloud, precipitation, and surface temperatures, among properties, are generally quite inhomogeneous. How the distribution of such quantities affects radiative heating and whether GCIP models can adequately predict the distributions are unknown. The cloud modeling community has developed microphysical methods for describing clouds and precipitation. These microphysical methods are too computer intensive for large-scale models, but they have led to simpler ways of modeling clouds and precipitation that are just beginning to be incorporated into atmospheric models.

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