HR: 16:35h
AN: NG54A-02 INVITED     [Abstracts]
TI: Multi-Scale Variability of Orographic Precipitation and Topographic Attributes: A Wavelet Deconvolution Approach for Rainfall Downscaling
AU: * Foufoula-Georgiou, E
EM: efi@umn.edu
AF: St. Anthony Falls Laboratory, University of Minnesota, 2 Third Avenue SE, Minneapolis, MN 55414, United States
AU: Fienberg, K S
EM: fienb004@umn.edu
AF: St. Anthony Falls Laboratory, University of Minnesota, 2 Third Avenue SE, Minneapolis, MN 55414, United States
AB: Stochastic downscaling requires a priori knowledge of the way in which rainfall statistics vary across scales. For orographic rainfall, these statistics are influenced by the interaction of the larger scale meteorological forcing with the underlying terrain. In this study we use wavelet-based multiresolution analysis to extract the signature that topography leaves on the multiscale structure of rainfall fields and to construct a scale and time- dependent transfer function (kernel) relating topographic attributes to precipitation. The application of this transfer function to the readily available topography data produces that fraction of the sub-grid scale precipitation variability that is mostly explainable by the terrain, leaving the remaining variability to be explained by scale-invariant parameterizations and reconstructed by conventional stochastic downscaling techniques.
DE: 1631 Land/atmosphere interactions (1218, 1843, 3322)
DE: 1854 Precipitation (3354)
DE: 4440 Fractals and multifractals
DE: 4475 Scaling: spatial and temporal (1872, 3270, 4277)
SC: Nonlinear Geophysics [NG]
MN: 2007 Joint Assembly