Development of AC_FIRE version 1.0: A mesoscale model with forest canopy and fire behavior submodels

Abstract. Numerical models are essential for advancing understanding of fire–atmosphere interactions, especially where field campaigns alone cannot provide sufficient insight. Existing wildland fire models vary greatly in complexity and scale, from computational fluid dynamics models with detailed combustion submodels, to mesoscale models with empirical or semi-empirical combustion representations, to global or regional models that omit combustion byproducts altogether. However, no current framework simultaneously resolves atmospheric responses to wildland fires across scales from hundreds of meters to hundreds of kilometers, incorporates a comprehensive suite of physical parameterizations, and explicitly resolves some scales of atmospheric turbulence within and above a forest canopy. To address this gap, we introduce ARPS-CANOPY/DEVS-FIRE (hereafter, AC_FIRE), a mesoscale model that integrates a canopy resolving atmospheric model (ARPS-CANOPY) with a fire behavior model (DEVS-FIRE), and detail its development and preliminary evaluation.  Unlike the original ARPS-CANOPY, which relied on a user-imposed fire heat source, AC_FIRE employs two-way fire-atmosphere coupling to compute heat release dynamically. We demonstrate the coupled modeling system with a low-intensity prescribed fire conducted in the New Jersey Pine Barrens in March 2019,  comparing simulated fire spread rates with measurements from an array of surface thermocouples deployed during the fire. The simulated spread rates compare favorably to observed spread rates, with differences mainly attributable to the use of uniform fuels in the AC_FIRE simulation. The integration of the dynamically coupled fire heat source represents a significant advance in canopy-resolving mesoscale modeling, and beyond this case, highlights the potential of AC_FIRE to extend the application of atmosphere-fire models to a broader range of wildland fire research questions as well as future operational use.