This talk will discuss experiments examining the practicality and impact of assimilating dense surface pressure (or altimeter setting) observations using an ensemble Kalman filter. Fewer representativeness problems and strong covariances between surface pressure and the deep atmosphere make surface pressure attractive for trying to capture mesoscale weather phenomena given a dense observing network. Numerous surface pressure or altimeter setting observations are readily available on a regular basis from supplementary observing networks--such as the Cooperative Weather Observer Program and the Weather Underground network--at a much higher density than the current ASOS/METAR network. The quantity and quality of these observations will be discussed, with a description of innovative bias correction procedures applied to these observations including the use of pressure tendency assimilation. The assimilation of these additional bias-corrected.pressure observations is shown to reduce the errors in the analyzed model state and makes adjustments that strongly project onto the prevailing surface pressure pattern. A brief look at the changes in the subsequent forecasts from these new analyzed states will also be given.