Subgrid-scale rainfall variability and its effects on atmospheric and surface variable predictions

Abstract

A new approach, which combines the Penn State/National Center for Atmospheric Research mesoscale model MM5 with a recently developed statistical downscaling scheme, has been investigated for the prediction of rainfall over scales (grid sizes) ranging from the atmospheric model scale (> 10 km) to subgrid scale (around 1 km). The innovation of the proposed dynamical/statistical hybrid approach lies on having unraveled a link between larger-scale dynamics of the atmosphere and smaller-scale statistics of the rainfall fields [Perica and Foufoula-Georgiou, 1996a], which then permits the coupling of a mesoscale dynamical model with a small-scale statistical parameterization of rainfall. This coupling is two-way interactive and offers the capability of investigating the feedback effects of subgridscale 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 the 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 rainfall 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 intensity.