A probabilistic backscatter coefficient generating function (CGF) is introduced which produces realistic backscatter coefficient values for various terrain types over all incidence angles. The CGF was developed in direct support of a multi-layer 3-D clutter modeling effort which successfully incorporated probabilistic clutter reflectivity characteristics and measured terrain elevation data to enhance clutter suppression and improve Signal-to-Clutter Ratio performance in radar applications. This probabilistic clutter modeling approach is in sharp contrast to traditional 2-D modeling techniques which typically include deterministic backscatter characteristics and assume constant terrain features within regions of interest. The functional form and parametric representation of the CGF were empirically determined by comparison with published backscatter data for nine different terrain 'types,' including, soil and rock, shrubs, trees, short vegetation, grasses, dry snow, wet snow, road surfaces, and urban areas. The statistical properties of the output, i.e., the mean and standard deviation, match published measured values to the number of significant figures reported. Likewise, the CGF output frequency of occurrence closely matches measured terrain data frequency of occurrence; a Chi-Square test fails to reject the method at a 0.05 level of significance, indicating a high level of confidence in the results. As developed, the CGF provides a computationally efficient means for incorporating probabilistic clutter characteristics into both simple and complex radar models by accurately reflecting the probabilistic scattering behavior associated with real terrain.