The precipitation output of a mesoscale atmospheric numerical model is usually interpreted as the average rainfall intensity over the grid cell of the model (typically 30 × 30 km to 60×60 km). However, rainfall exhibits considerable heterogeneity over subgrid scales (i.e., scales smaller than the grid cell), so it is necessary for hydrologic applications to recreate or simulate the small‐scale rainfall variability given its large‐scale average. Rainfall disaggregation is usually done statistically. In this paper, a new subgrid scale rainfall disaggregation model is developed. It has the ability to statistically reproduce the rainfall variability at scales unresolved by mesoscale models while being conditioned on large‐scale rainfall averages and physical properties of the prestorm environment. The model is based on two extensively tested hypotheses for midlatitude mesoscale convective systems [Perica and Foufoula‐Georgiou, 1996]: (1) standardized rainfall fluctuations (defined via a wavelet transform) exhibit simple scaling over the mesoscale, and (2) statistical scaling parameters of rainfall fluctuations relate to the convective available potential energy (CAPE), a measure of the convective instability of the prestorm environment. Preliminary evaluation of the model showed that the model is capable of reconstructing the small‐scale statistical variability of rainfall as well as the fraction of area covered with rain at all analyzed subgrid scales. The performance evaluation was based on comparison of summary statistics and spatial pattern measures of simulated fields with those of known fields observed during the Oklahoma‐Kansas Preliminary Regional Experiment for Storm‐Central (PRE‐STORM).

Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density.The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m Ϫ3 day Ϫ1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

In this paper we explore the possibility of establishing predictive relationships between statistical characteristics of rainfall at the mesoscale (approximately 102 to 104 km2) and representative meteorological parameters of the storm environment. To increase the usefulness of these relationships and, in particular, to explore their use in subgrid‐scale rainfall parameterization, special attention is given to statistical characteristics of rainfall that are scale invariant, i.e., are constant at least within a significant range of scales. The main contributions of this paper are the following: (1) we establish the presence of statistical (simple) scaling in “standardized rainfall fluctuations” (derived from rainfall intensities via an orthogonal wavelet transform and normalization by local means) and (2) we establish empirical connections between statistical and physical storm characteristics by quantifying relations between the scaling parameters and kinematic and thermodynamic indices of the prestorm environment. The data used for this analysis are rainfall events and corresponding soundings observed during the PRE‐STORM experiment (May and June 1985) over Oklahoma and Kansas. The developed relationships are applicable to midlatitude mesoscale convective systems, which are the major rainfall producers over most of the Global Energy and Water Cycle Experiment (GEWEX) Continental International Project (GCIP) region, and are envisioned to play a key role in disaggregating rainfall (predicted by mesoscale numerical models) to subgrid scales for runoff prediction and other hydrologic applications.