SUBGRID-SCALE RAINFALL VARIABILITY AND ITS EFFECTS ON ATMOSPHERIC ANDSURFACE VARIABLE PREDICTIONS

A new approach, which combines the Penn State/National Center for Atmo spheric Research mesoscale model MM5 with a recently developed statist ical downscaling scheme, has been investigated for the prediction of r ainfall over scales (grid sizes) ranging from the atmospheric model sc ale (> 10 km) to subgrid scale (around 1 km). The innovation of the pr oposed dynamical/statistical hybrid approach lies on having unraveled a link between larger-scale dynamics of the atmosphere and smaller-sca le statistics of the rainfall fields [Perica and Foufoula-Georgiou, 19 96a], which then permits the coupling of a mesoscale dynamical model w ith a small-scale statistical parameterization of rainfall. This coupl ing is two-way interactive and offers the capability of investigating the feedback effects of subgrid-scale rainfall spatial variability on the further development of a rainfall system and on the surface energy balance and water partitioning over the MM5 model grids. The results of simulating rainfall in a strong convection system observed during t he Oklahoma-Kansas Preliminary Regional Experiment for STORM-Central ( PRESTORM) on June 10-11, 1985 show that (1) the dynamical/statistical hybrid approach is a useful and cost-effective scheme to predict rainf all at subgrid scales (around 1 km) based on larger-scale atmospheric model predictions, and (2) the inclusion of the subgrid-scale rainfall spatial variability can significantly affect the surface temperature distribution and the short-term (< 24 hour) prediction of rainfall int ensity.