Tuesday, 18 November 2003: 3:45 PM
Assessing accuracy of the BlueSky smoke modeling framework during wildfire eventsPoster PDF (593.1 kB)
Case study analyses of the BlueSky smoke modeling framework help identify the input values or modeling components that require improvement. BlueSky is a smoke forecasting system that combines burn information with models of consumption, emissions, meteorology, and dispersion to yield a prediction of PM2.5 and PM10 dispersion and resulting surface concentrations from wildland fire. In this work BlueSky has been applied to several wildfires to provide a thorough analysis of system performance. Case studies include the Hayman Fire of 2002 in Colorado and the Bitterroot fires of 2000 in Montana and Idaho. Fuels data are extracted from a 1km-gridded analysis of North America and used as input for the Emissions Production Model (EPM version 1.02) which is coupled with the CONSUME fuel consumption model (version 1.1). Meteorology from the MM5 meteorological model are used to drive the CALMET diagnostic wind model. The smoke dispersion model CALPUFF employs emissions from EPM and diagnosed wind fields from CALMET to simulate smoke plumes over a 280 by 280 km domain with a terrain and land-use resolution of 2 km. The Hayman Fire dramatically impacted the Denver metropolitan area with measured hourly PM2.5 concentrations of approximately 200 µg/m3 during the afternoon of June 9, 2002. Initial BlueSky results indicate that while the model reasonably captured the timing of impact, it predicted significantly lower concentrations than observed. The Montana / Idaho Bitterroot fires in 2000 were a large complex of fires ignited by “dry” lighting. The fires burned across a vast region of mountainous terrain. Ground-level observations of particle concentrations from nephelometers and TEOMS are compared with model estimates of surface concentrations. Time series of satellite images, digital photographs, and surface weather observations supplement verification for both case studies.