Applicability of an integrated plume rise model for the dispersion from wild-land fires

Abstract

Abstract.We have presented an overview of a mathematical model, BUOYANT, that was originally designed for the evaluation of the dispersion of buoyant plumes originated from major warehouse fires. The model addresses the variations of the cross-plume integrated properties of a buoyant plume in the presence of a vertically varying atmosphere. The model also includes a treatment for a rising buoyant plume interacting with an inversion layer. We have compared the model predictions with the data of two prescribed wild-land fire experiments. For the SCAR-C experiment in Quinault (US) in 1994, the predicted vertical extents of the plume at maximum plume rise were between 500 and 800 m and between 200 and 700 m, using two alternative meteorological data sets. The corresponding observed injection heights of the aerosol particles measured using an airborne lidar (light detection and ranging) ranged from 250 to 600 m. For the prescribed burning experiment in Hyytiälä (Finland) in 2009, the model predictions were compared with plume elevations and diameters, determined based on particulate matter number concentration measurements onboard an aeroplane. However, the agreement between modelled and measured results substantially depends on how the properties of the source term are evaluated, especially regarding the convective heat fluxes from the fire. The results demonstrate that in field experiments on wild-land fires, there are substantial uncertainties in estimating both (i) the source terms for the atmospheric dispersion computations and (ii) the relevant vertical meteorological profiles.

A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models

“…They found the best comparison PRMv0-MISR for the FLAMBE-based initialization with a one-to-one correlation of 0.45. They infer the bad response of PRMv0 partly to the quality of their atmospheric profile, emphasize the importance of correct atmospheric profile as already mentioned by Kahn et al (2007) or Kukkonen et al (2014).…”

Two global data sets of daily fire emission injection heights since 2003

“…In this work the two-step version of the IS4FIRES algorithm is used. IS4FIRES was further refined by Kukkonen et al (2014) and Veira et al (2015a). Kukkonen et al (2014) improved the results of the algorithm when replacing the Brunt-Väisälä frequency of the free troposphere with the inversion layer Brunt-Väisälä frequency in case of a stable nocturnal boundary layer.…”

“…IS4FIRES was further refined by Kukkonen et al (2014) and Veira et al (2015a). Kukkonen et al (2014) improved the results of the algorithm when replacing the Brunt-Väisälä frequency of the free troposphere with the inversion layer Brunt-Väisälä frequency in case of a stable nocturnal boundary layer. Since our proposed implementation will use a daily FRP product that is based on daytime satellite observations from MODIS, this improvement was not tested and its impact would probably not be significant.…”

Dispersion modeling of thermal power plant emissions on stochastic space

“…Four uncertain parameters-two for stack gas exit conditions and two for meteorological conditions-are considered which form the quadrature nodes for the calculations. Inherent variability in the stack gas thermodynamic properties is due to a wide range of factors as explained by Paine et al (2014) while Kukkonen et al (2014) and Pagnon et al (2011) noted the randomness in ambient conditions. Starting with the parametric analysis of plant's SO 2 emission in terms of pollutant's concentration as a function of atmospheric stability, ambient conditions, stack exit properties, and dispersion coefficients, the objective of this study is achieved by revealing a robust methodology for quantifying the multiple uncertainties in atmospheric dispersion through nonintrusive method.…”

“…These models however suffer from predictability and applicability limitations in chaotic and nonlinear model equations (Errico and Raeder 1999). While applying the plume rise model for emission dispersion in wild-land forms, Kukkonen et al (2014) considered the uncertainties in source and meteorological properties. But, this study missed the probabilistic estimation.…”