A Regression Model for Smoke Plume Rise of Prescribed Fires Using Meteorological Conditions

Abstract

moke plume rise is an important factor for smoke transport and air quality impact modeling. This study provides a practical tool for estimating plume rise of prescribed fires. A regression model was developed on the basis of observed smoke plume rise for 20 prescribed fires in the southeastern United States. The independent variables include surface wind, air temperature, fuel moisture, and atmospheric planetary boundary layer (PBL) height. The first three variables were obtained from the Remote Automatic Weather Stations, most of which are installed in locations where they can monitor local fire danger and are easily accessed by fire managers. The PBL height was simulated with the Weather Research and Forecasting Model. The confidence and validation analyses indicate that the regression model is significant at the 95% confidence level and able to predict hourly values and the average smoke plume rise during a burn, respectively. The prediction of the average smoke plume rise shows larger skills. The model also shows improved skills over two extensively used empirical models for the prescribed burn cases examined in this study, suggesting that it may have the potential to improve smoke plume rise and air quality modeling for prescribed burns. The regression model, however, tends to underestimate large plume rise values and overestimate small ones. A suite of alternative regression models was also provided, one of which can be used when no PBL information is available.