As fire-related air pollution becomes more important, due to both climate change and growth of the wildland-urban interface, the effects of wildfires and prescribed burns on air quality becomes a growing concern. Assessing fire effects on air quality generally requires fire area in order to compute the fire emissions. Fire detections are available from VIIRS, MODIS, AVHRR, and GOES, but they are for the past, limited by clouds, overflight timing, and resolution, and do not provide directly the diurnal fire activity.
In this presentation, we describe an integrated approach to the fire and smoke prediction utilizing a coupled fire-atmosphere model WRF-Sfire-Chem, driven by near-real-time meteorology and satellite fire detection data. In this framework, the fire progression though the landscape is modeled taking into account two-way interactions between fire and the atmosphere is used. The surface heat flux from the fire causes strong updrafts, which in turn change the winds and thus fire spread. Fire emissions, estimated from the fuel consumption rate (affected by the local weather conditions and fuel properties), are inserted in every time step into at the lowest atmospheric layer in a form of a passive tracer or a mixture of chemically active species. The buoyancy caused by the fire directly impacts plume dynamics, and the chemical transport algorithm in WRF-Chem predicts smoke dispersion as well as its chemical transformations. The modeling system also contains a fuel moisture model with assimilation of near-real-time RAWS data, which forecasts the evolution of fuel moisture based on the atmospheric state over time. Assimilation of satellite fire detection data is in progress. We present test cases as well as a practical implementation of a coupled fire and smoke forecasting system based on WRF-SFIRE. Map-based visualization of fire behavior and smoke is available online.
This research was partially supported by NASA grant NNX13AH59G and NSF grant DMS-1216481.
Jan Mandel is a Professor in the Department of Mathematical and Statistical Sciences at the University of Colorado, Denver.
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