A new statistical model for predicting daily ground level fine scale particulate matter (PM2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day's PM2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration's Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance.