Abstract: Predicting the energy costs of human travel over snow can be of significant value to the military and other agencies planning work efforts when snow is present. The ability to quantify, and predict, those costs can help planners determine if snow will be a factor in the execution of dismounted tasks and operations. To adjust predictive models for the effect of terrain, and more specifically for surface conditions, on energy costs, terrain coefficients (ƞ) have been developed. By applying knowledge gained from prior studies of the effects of terrain and snow, and by leveraging those existing dismounted locomotion models, we seek to outline the steps in developing an improved terrain coefficient (ƞ) for snow to be used in predictive modeling. Using published data, methods, and a well-informed understanding of the physical elements of terrain, e.g., characterization of snow sinkage (z), this study made adjustments to ƞ-values specific to snow. This review of published metabolic cost methods suggest that an improved ƞ-value could be developed for use with the Pandolf equation, where z=depth (h)*(1 - (snow density (ρ0)/1.186)) and ƞ=0.0005z3 + 0.0001z2 + 0.1072z + 1.2604. This paper provides data-driven improvements to models that are used to predict the energy costs of dismounted movements over snow.