The National Severe Storms Laboratory (NSSL) has developed a technique that intelligently mosaics data from multiple radars into a rapidly-updating three-dimensional grid. This application uses terrain information to determine adequate merging techniques where 3D voxels are obscured by radar “terrain shadows”. While developing the application, it was necessary to determine relationships between the terrain obscuration for each single-radar location and for every available elevation angle in the various volume coverage patterns used by the radars. For this paper, these relationships are used to create national composites of severe storm “sampling efficiency”. We employ a number of objective criteria that take into account a hypothetical storm depth column (for a variety of storm depths), the percentage of the storm column sampled, the preciseness of the sampling (i.e., the beam width is not too wide), and an even sampling (no cone-of-silence or radar horizon issues). The sampling efficiency is measured for each single radar location, as well as a CONUS mosaic of multiple-radars. Using a GIS, these data are then compared to population density coverage data and aviation terminal and en-route coverage data. We will compare the results of the single-radar efficiencies versus the multiple radar efficiencies to demonstrate that a national multi-radar mosaic can provide more robust storm diagnoses to support warning decision making for public and aviation interests.