The FireFlux II experiment: a model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread

2019 ◽  
Vol 28 (4) ◽  
pp. 308 ◽  
Author(s):  
Craig B. Clements ◽  
Adam K. Kochanski ◽  
Daisuke Seto ◽  
Braniff Davis ◽  
Christopher Camacho ◽  
...  
Keyword(s):  
Fire Spread ◽  
Model Systems ◽  
Large Set ◽  
Tall Grass ◽  
Combustion Gases ◽  
Tall Grass Prairie ◽  
Fire Front ◽  
Air Chemistry ◽  
Atmosphere Model ◽  
Behaviour Data

The FireFlux II experiment was conducted in a tall grass prairie located in south-east Texas on 30 January 2013 under a regional burn ban and high fire danger conditions. The goal of the experiment was to better understand micrometeorological aspects of fire spread. The experimental design was guided by the use of a coupled fire–atmosphere model that predicted the fire spread in advance. Preliminary results show that after ignition, a surface pressure perturbation formed and strengthened as the fire front and plume developed, causing an increase in wind velocity at the fire front. The fire-induced winds advected hot combustion gases forward and downwind of the fire front that resulted in acceleration of air through the flame front. Overall, the experiment collected a large set of micrometeorological, air chemistry and fire behaviour data that may provide a comprehensive dataset for evaluating and testing coupled fire–atmosphere model systems.

scholarly journals Georeferencing Oblique Aerial Wildfire Photographs: An Untapped Source of Fire Behaviour Data

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 81
Author(s):  
Henry Hart ◽  
Daniel D. B. Perrakis ◽  
Stephen W. Taylor ◽  
Christopher Bone ◽  
Claudio Bozzini
Keyword(s):  
Fire Behavior ◽  
Fire Spread ◽  
Behavior Prediction ◽  
Empirical Relationships ◽  
Rate Of Spread ◽  
Front Position ◽  
Fire Front ◽  
Initial Work ◽  
The Mean ◽  
Behaviour Data

In this study, we investigate a novel application of the photogrammetric monoplotting technique for assessing wildfires. We demonstrate the use of the software program WSL Monoplotting Tool (MPT) to georeference operational oblique aerial wildfire photographs taken during airtanker response in the early stages of fire growth. We located the position of the fire front in georeferenced pairs of photos from five fires taken 31–118 min apart, and calculated the head fire spread distance and head fire rate of spread (HROS). Our example photos were taken 0.7 to 4.7 km from fire fronts, with camera angles of incidence from −19 to −50° to image centre. Using high quality images with detailed landscape features, it is possible to identify fire front positions with high precision; in our example data, the mean 3D error was 0.533 m and the maximum 3D error for individual fire runs was less than 3 m. This resulted in a maximum HROS error due to monoplotting of only ~0.5%. We then compared HROS estimates with predictions from the Canadian Fire Behavior Prediction System, with differences mainly attributed to model error or uncertainty in weather and fuel inputs. This method can be used to obtain observations to validate fire spread models or create new empirical relationships where databases of such wildfire photos exist. Our initial work suggests that monophotogrammetry can provide reproducible estimates of fire front position, spread distance and rate of spread with high accuracy, and could potentially be used to characterize other fire features such as flame and smoke plume dimensions and spotting.

scholarly journals Ignition Vulnerabilities of Combustibles around Houses to Firebrand Showers: Further Comparison of Experiments

2021 ◽  
Vol 13 (4) ◽  
pp. 2136
Author(s):  
Sayaka Suzuki ◽  
Samuel L. Manzello
Keyword(s):  
Experimental Data ◽  
Thermal Radiation ◽  
Driving Force ◽  
Fire Spread ◽  
Significant Problem ◽  
Next Generation ◽  
Similar Time ◽  
Comparison Of Experiments ◽  
Fire Front ◽  
Wui Fires

Wildland fires and wildland urban-interface (WUI) fires have become a significant problem in recent years. The mechanisms of home ignition in WUI fires are direct flame contact, thermal radiation, and firebrand attack. Out of these three fire spread factors, firebrands are considered to be a main driving force for rapid fire spread as firebrands can fly far from the fire front and ignite structures. The limited experimental data on firebrand showers limits the ability to design the next generation of communities to resist WUI fires to these types of exposures. The objective of this paper is to summarize, compare, and reconsider the results from previous experiments, to provide new data and insights to prevent home losses from firebrands in WUI fires. Comparison of different combustible materials around homes revealed that wood decking assemblies may be ignited within similar time to mulch under certain conditions.

Fine-Scale Fire Spread in Pine Straw

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Daryn Sagel ◽  
Kevin Speer ◽  
Scott Pokswinski ◽  
Bryan Quaife
Keyword(s):  
Fire Spread ◽  
Small Scale ◽  
Natural Setting ◽  
Fine Scale ◽  
Prescribed Burn ◽  
Fire Dynamics ◽  
Rate Of Spread ◽  
Scale Models ◽  
Fire Front ◽  
Visible Images

Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.

Laboratory fire spread analysis using visual and infrared images

2006 ◽  
Vol 15 (2) ◽  
pp. 179 ◽  
Author(s):  
J. Ramiro Martínez-de Dios ◽  
Jorge C. André ◽  
João C. Gonçalves ◽  
Begoña Ch. Arrue ◽  
Aníbal Ollero ◽  
...  
Keyword(s):  
Graphical Model ◽  
Fire Spread ◽  
Rate Of Spread ◽  
Virtual View ◽  
Fire Front ◽  
Computer Based ◽  
Front Shape ◽  
Processing Techniques ◽  
Spread Analysis ◽  
Base Width

This paper presents an experimental method using computer-based image processing techniques of visual and infrared movies of a propagating fire front, taken from one or more cameras, to supply the time evolutions of the fire front shape and position, flame inclination angle, height, and base width. As secondary outputs, it also provides the fire front rate of spread and a 3D graphical model of the fire front that can be rendered from any virtual view. The method is automatic and non-intrusive, has space–time resolution close to continuum and can be run in real-time or deferred modes. It is demonstrated in simple laboratory experiments in beds of pine needles set upon an inclinable burn table, with point and linear ignitions, but can be extended to open field situations.

Observing the Dynamics of Wildland Grass Fires: FireFlux—A Field Validation Experiment

2007 ◽  
Vol 88 (9) ◽  
pp. 1369-1382 ◽  
Author(s):  
Craig B. Clements ◽  
Shiyuan Zhong ◽  
Scott Goodrick ◽  
Ju Li ◽  
Brian E. Potter ◽  
...  
Keyword(s):  
Great Plains ◽  
Forest Fires ◽  
Fire Behavior ◽  
The United States ◽  
Fire Season ◽  
Turbulent Structures ◽  
Tall Grass ◽  
Tall Grass Prairie ◽  
Validation Experiment ◽  
Turbulence Data

Grass fires, although not as intense as forest fires, present a major threat to life and property during periods of drought in the Great Plains of the United States. Recently, major wildland grass fires in Texas burned nearly 1.6 million acres and destroyed over 730 homes and 1320 other buildings. The fires resulted in the death of 19 people, an estimated loss of 10,000 head of livestock, and more than $628 million in damage, making the 2005/06 fire season the worst on record for the state of Texas. As an aid to fire management, various models have been developed to describe fire behavior. However, these models strongly emphasize fuels and fail to adequately consider the role of convective dynamics within the atmosphere and its interaction with the fire due to the lack of observational data. To fill this gap, an intensive field measurement campaign called FireFlux was conducted during February 2006 near Houston, Texas. The campaign employed a variety of instrument platforms to collect turbulence data at multiple levels within and immediately downwind of a 155 acre tall-grass prairie burn unit. This paper presents some first-time observations of atmospheric turbulent structures/fluxes associated with intense grass fires and provides a basis to further our understanding of the dynamics of grass fires and their interactions with the atmosphere.

scholarly journals Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF

2018 ◽  
Vol 18 (13) ◽  
pp. 9975-10006 ◽  
Author(s):  
Leighton A. Regayre ◽  
Jill S. Johnson ◽  
Masaru Yoshioka ◽  
Kirsty J. Pringle ◽  
David M. H. Sexton ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Radiative Forcing ◽  
Computational Cost ◽  
Short Wave ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Large Set ◽  
Individual Parameter ◽  
Cloud Water Content ◽  
Atmosphere Model

Abstract. Changes in aerosols cause a change in net top-of-the-atmosphere (ToA) short-wave and long-wave radiative fluxes; rapid adjustments in clouds, water vapour and temperature; and an effective radiative forcing (ERF) of the planetary energy budget. The diverse sources of model uncertainty and the computational cost of running climate models make it difficult to isolate the main causes of aerosol ERF uncertainty and to understand how observations can be used to constrain it. We explore the aerosol ERF uncertainty by using fast model emulators to generate a very large set of aerosol–climate model variants that span the model uncertainty due to 27 parameters related to atmospheric and aerosol processes. Sensitivity analyses shows that the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties in the physical atmosphere model, particularly parameters that affect cloud reflectivity. However, uncertainty in the change in ToA flux caused by aerosol emissions over the industrial period (the aerosol ERF) is controlled by a combination of uncertainties in aerosol (around 60 %) and physical atmosphere (around 40 %) parameters. Four atmospheric and aerosol parameters account for around 80 % of the uncertainty in short-wave ToA flux (mostly parameters that directly scale cloud reflectivity, cloud water content or cloud droplet concentrations), and these parameters also account for around 60 % of the aerosol ERF uncertainty. The common causes of uncertainty mean that constraining the modelled planetary brightness to tightly match satellite observations changes the lower 95 % credible aerosol ERF value from −2.65 to −2.37 W m−2. This suggests the strongest forcings (below around −2.4 W m−2) are inconsistent with observations. These results show that, regardless of the fact that the ToA flux is 2 orders of magnitude larger than the aerosol ERF, the observed flux can constrain the uncertainty in ERF because their values are connected by constrainable process parameters. The key to reducing the aerosol ERF uncertainty further will be to identify observations that can additionally constrain individual parameter ranges and/or combined parameter effects, which can be achieved through sensitivity analysis of perturbed parameter ensembles.

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