Multiscale statistical properties of a high-resolution precipitation forecast

Small-scale (less than;15 km) precipitation variability significantly affec ts the hydrologic response of a basin and the accurate estimation of water and energy fluxes through coupled land-atmosphere modeling schemes. It also affects the radiative transfer through precipitating clouds and thus rainf all estimation from microwave sensors. Because both land-atmosphere and clo ud-radiation interactions are nonlinear and occur over a broad range of sca les (from a few centimeters to several kilometers), it is important that, o ver these scales, cloud-resolving numerical models realistically reproduce the observed precipitation variability. This issue is examined herein by us ing a suite of multiscale statistical methods to compare the scale dependen ce of precipitation variability of a numerically simulated convective storm with that observed by radar. In particular, Fourier spectrum, structure fu nction, and moment-scale analyses are used to show that, although the varia bility of modeled precipitation agrees with that observed for scales larger than approximately 5 times the model resolution, the model shows a falloff in variability at smaller scales. Thus, depending upon the smallest scale at which variability is considered to be important for a specific applicati on, one has to resort either to very high resolution model runs (resolution s 5 times higher than the scale of interest) or to stochastic methods that can introduce the missing small-scale variability. The latter involve upsca ling the model output to a scale approximately 5 times the model resolution and then stochastically downscaling it to smaller scales. The results of m ultiscale analyses, such as those presented herein, are key to the implemen tation of such stochastic downscaling methodologies.