Development of a Dynamically Based Stochastic Precipitation Model at the Sub-Synoptic Scale

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Convection produces significant amounts of precipitation in a wide variety of atmospheric disturbances. In some places and times convection produces the entire annual rainfall. Hydrologists, largely concerned with the structure and evolution of observed rainfall patterns, have been successful in devising statistical description of these events though unable to relate the latter to the relevant physical processes in the atmosphere. Meteorologists on the other hand, have devised various formulations of convection but often are unable to compute or infer rainfall amounts at the ground in sufficient detail for hydrological purposes. This research will combine these disparate approaches to produce a stochastic model capable of relating the statistical results to several known meteorological processes using an existing model of convection. The results should not only illuminate the physics of mesoscale precipitation but provide some measure of insight into the illusive character of how convection modifies the near atmospheric environment. Such a model could account for both the spatial structure and temporal evolution of convective rainfall. The work will be jointly supported by the Meteorology Program and NASA.