Multiscale Statistical Properties of a High-Resolution Precipitation Forecast

Abstract

Small-scale (less than ϳ15 km) precipitation variability significantly affects 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 rainfall estimation from microwave sensors. Because both land-atmosphere and cloud-radiation interactions are nonlinear and occur over a broad range of scales (from a few centimeters to several kilometers), it is important that, over these scales, cloudresolving numerical models realistically reproduce the observed precipitation variability. This issue is examined herein by using a suite of multiscale statistical methods to compare the scale dependence of precipitation variability of a numerically simulated convective storm with that observed by radar. In particular, Fourier spectrum, structure function, and moment-scale analyses are used to show that, although the variability 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 application, one has to resort either to very high resolution model runs (resolutions 5 times higher than the scale of interest) or to stochastic methods that can introduce the missing small-scale variability. The latter involve upscaling the model output to a scale approximately 5 times the model resolution and then stochastically downscaling it to smaller scales. The results of multiscale analyses, such as those presented herein, are key to the implementation of such stochastic downscaling methodologies.

Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations

“…The MR method (see Mallat 1989 for details) has been used in several studies to analyze the temporal (Howell and Mahrt 1997;Vickers and Mahrt 2002) and spatial (Zepeda-Arce et al 2000;Harris et al 2001) variability of atmospheric variables. The MR method consists in the application of numerical filters in order to aggregate the original highresolution time-varying precipitation fields into lowerresolution temporal and spatial scales.…”

On ALADIN precipitation modeling and validation in an Alpine watershed

“…For stability reasons, the numerical schemes of meteorological models suppress smallest waves (e.g. Grasso, 2000;Harris et al, 2001). On the other hand Ahrens et al (2001a) show that ALADIN-nh simulations overestimate spatial variability of precipitation if compared to radar data in the area of interest.…”

The Setup of the MesoVICT Project

“…Double penalties arising from spatial displacement (or perhaps timing) errors and more rapid growth of small-scale errors often result in poorer performance scores for higher-resolution forecasts than their coarser counterparts, even when subjective evaluation would support the higher-resolution models as being better. Subsequently, a host of new verification methods were developed very rapidly (Ebert and McBride 2000;Harris et al 2001;Casati et al 2004;Nachamkin 2004;Davis et al 2006;Keil and Craig 2007;Roberts and Lean 2008;Marzban et al 2009;Gilleland et al 2010b), which we will refer to as spatial methods. There still are gaps in our understanding when it comes to interpreting what all the new spatial methods tell us; gaining an in-depth understanding of forecast performance depends on grasping the full meaning of the verification results.…”