科研之友
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摘要
Analysis of Next Generation Weather Radar rainfall data indicates that for the central United States, rainfall exhibits a composite behavior with respect to its spatial and temporal scaling characteristics. Our data analysis shows that rainfall fluctuations at spatial scales smaller than a reference scale exhibit self-similarity and that at scales larger than the reference scale, rainfall fluctuations are scale dependent. Accordingly, we present a new methodology for downscaling large-scale rainfall consistent with this composite character of rainfall variability. The new downscaling model is a composite of a stochastic space-time submodel that preserves the spatial and temporal dependency characteristics at scales larger than the reference scale and an intermittent random cascade submodel that preserves the statistical self-similarity and spatial intermittency at scales smaller than the reference scale. The new model is applied to downscale summer daily rainfall for the central United States from a scale of 256 km to a scale of 2 km. We show that the new model reproduces quite well the intermittency and self-similarity features and the interscale and across-scale correlation structures of observed rainfall with a relatively low computational burden.
- 出版日期2010-10-21
