Since our clients require frequent delivery of very short lead-time forecasts, at five minute time resolution, our methods must be computationally efficient. In this work, we demonstrate the use of three IR channels to simulate visible cloud imagery during hours before and shortly after dawn. The simulated visible imagery is then fed in to the PIM algorithm. A climatology of clear-sky brightness temperature for the past several days is first developed, supplemented by an elevation-based model of ground temperature where clear days are infrequent. Cold anomalies from this background state are correlated with visible reflectance during daytime calibration periods. We find that linear functions of brightness temperature imagery are able to simulate visible reflectance for mid- and high-level clouds, but do not reliable reproduce fog. To address this problem we are developing a method that combines mesoscale observations of dew point depression and visibility with an IR image analysis algorithm that depends on the warming evident in IR imagery of foggy scenes as the fog develops. We will present results of this work for stations in California and North Carolina, and explore how the additional time and space resolution afforded by GOES-16 impacts the accuracy of the technique.