Understanding fluid flow is a difficult problemand of increasing importance as computational fluid dynamics produces an abundance of simulation data. Experimental flow analysis has employed techniques such as shadowgraph and schlieren imaging for centuries which allow empirical observation of inhomogeneous flows. Shadowgraphs provide an intuitive way of looking at small changes in flow dynamics through caustic effects while schlieren cutoffs introduce an intensity gradation for observing large scale directional changes in the flow. The combination of these shading effects provides an informative global analysis of overall fluid flow. Computational solutions for these methods have proven too complex until recently due to the fundamental physical interaction of light refracting through the flow field. In this paper, we introduce a novel method to simulate the refraction of light to generate synthetic shadowgraphs and schlieren images of time-varying scalar fields derived from computational fluid dynamics (CFD) data. Our method computes physically accurate schlieren and shadowgraph images at interactive rates by utilizing a combination of GPGPU programming, acceleration methods, and data-dependent probabilistic schlieren cutoffs. Results comparing this method to previous schlieren approximations are presented.