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A numerical perspective on wildfire plume-rise dynamics Moisseeva, Nadejda

Abstract

The buoyant rise of wildfire smoke and the resultant vertical distribution of emission products in the atmosphere have a strong influence on downwind pollutant concentrations at the surface, and provide key input into regional and global chemical transport models. Due to inherent complexity of wildfire plume dynamics, smoke injection height predictions are subject to large uncertainties. One of the obstacles to the development of new plume rise parameterizations has been the scarcity of detailed simultaneous observations of fire-generated turbulence, entrainment, smoke concentrations and fire behavior. This thesis makes contributions on two fronts: (i) it demonstrates the feasibility of using coupled fire-atmosphere large-eddy simulations to model wildfire smoke dynamics to produce "synthetic" plume data, and (ii) develops a new energy balance plume rise parameterization to predict the vertical distribution of smoke in the atmosphere. The first part of the thesis focuses on evaluating the large-eddy simulation model used in this work with a detailed observational dataset from a real prescribed burn. The next portion explores the effect of various fire parameters and ambient atmospheric conditions on smoke plume behavior using a range of sensitivity studies. Analysis of flow dynamics shows that the updraft is shaped by complex interactions of fire-induced winds and vorticity generated in response to a near-surface convergence, and does not conform to commonly used mixing and entrainment assumptions. With the knowledge gained through the above numerical experiments, the second half of the thesis introduces a simple parameterization for predicting the mean centerline height for penetrative plumes from fires of arbitrary shape and intensity. Lastly, the proposed parameterization is extended to capture the full vertical distribution of smoke in the atmosphere. The broad goal of this work is to better our understanding of plume rise dynamics and improve smoke dispersion predictions within air quality applications.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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