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Tire traction and braking on snow differ substantially from on-road performance, which makes accurate snow characterization essential for predictive tire simulation. This study presents a methodology for rapidly identifying the material parameters of compacted snow from in-situ test data. The focus is on two parameters central to widely used elastic-plastic snow models: the internal friction angle (ϕ) and material cohesion (d). These were identified from in-situ Compliance Testing Incorporated (CTI) Penetrometer measurements. The workflow had four stages. First, several candidate snow modeling approaches were compared on computational cost, robustness, and their ability to represent large snow deformation. Second, a full factorial design of experiments was used to build response surfaces for the defined objective functions. Third, optimization was applied to these response surfaces to find the best-fit parameter values. Fourth, the identified parameters were validated against in-situ tire traction tests. Results show that the Hybrid Lagrangian–Smoothed Particle Hydrodynamics (HSPH) approach supports high-fidelity, physics-based simulation while cutting normalized computational cost per node by 84% compared with a pure SPH approach.