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Turbulence and Plume Thermodynamic Structures during Low-Intensity Subcanopy Fires
Daisuke Seto and Craig B. Clements
Fire Weather Research Laboratory
San José State University
San José, CA 95192 USA
Tara Strand
Pacific Wildland Fire Sciences Laboratory
USDA Forest Service
Seattle, WA 98103
Harold Thistle
Forest Health Technology Enterprise Team
USDA Forest Service
Morgantown, WV 6505
Robert Mickler
Alion Science and Technology
Durham, NC 27713
Corresponding Author: Daisuke Seto
Email: daisuke.seto@sjsu.edu
Phone: 408-924-5189
Fax: 408-924-5191
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Abstract
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Smoke emissions and dispersion from low intensity fires are currently not well represented
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by existing models, partly because they are highly sensitive to atmospheric turbulence within
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and above forest canopies. There have been few studies linking current understanding of
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canopy turbulent flow and fire-atmosphere interaction. For this reason, in-situ turbulence
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measurements were made in the Calloway Forest in North Carolina during the winter 2010
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and 2011 to investigate subcanopy micrometeorology and smoke plume dynamics associated
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with a low intensity flaming front moving through the burn unit. The tower measurements
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showed that the largest increases in the horizontal velocities were observed near the surface.
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Observed plume vertical velocities were 5 m s-1 within the canopy and 8 m s-1 at the canopy
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top. Fine-scale in-plume temperature measurements showed intrusion of cool air with
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downdrafts and strong vertical heat transport with updrafts. Turbulence statistics suggest that
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the mean winds and friction velocity increased within and above the canopy during the fire
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front passage. Increased drag coefficient indicated increased momentum transfer in
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subcanopy fire environments occurs, and increased normalized standard deviation of vertical
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velocity σw/u* may suggest more effective mixing/dispersion in the vertical within the canopy
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during the fire front passage. Maximum 1-min averaged sensible heat fluxes of 15-65 kW m-2
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and instantaneous maximum values ranging from 326 and 856 kW m-2 were observed in the
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plume. Comparisons of our turbulence measurements with those observed during an
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experimental wind-driven grass fire suggest that in spite of low plume temperatures, low
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intensity subcanopy fires may potentially be able to generate nearly as strong fire-induced
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winds as wind-driven grass fires due to strong coupling between fire and atmosphere.
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Keywords: fire-atmosphere-canopy interactions; low-intensity fire; smoke transport;
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turbulence; heat flux; in-situ measurement
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1. Introduction
Turbulent flows in canopies have been investigated for many decades due to their distinct
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properties that do not follow other surface layer theory. Kaimal and Finnigan (1994) and
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Finnigan (2000) provide excellent reviews of flow characteristics both in and above canopies
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such as momentum absorption by canopies and resulting inflection point profile, turbulence
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dynamics controlled by large coherent eddy structure, and spectral shortcut.
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Because wildfire environments are very unstable given extremely high flame temperatures
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and large sensible heat flux, effect of instability on canopy turbulence is of particular interest. In-
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canopy dispersion studies of pheromone plumes (Thistle et al. 2004) showed that the pheromone
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plume movement and dispersion depends on both atmospheric stability and wind speed through
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the canopy. Shen and Leclerc’s (1997) modeling study suggests that ejections contribution
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increases with unstable conditions whereas the sweeps contribution to the momentum flux
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decreases. Lee and Mahrt (2005) stated that in open canopies strong diabatic heating can lead to
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increased within-canopy buoyancy generation of turbulence. Launiainen et al. (2007) explored
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how diabatic stability affects turbulence characteristics in and above a pine forest. They
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identified mean and turbulence statistics as well as spectral characteristics across a wide range of
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atmospheric stabilities. Influence of extreme local instability beyond diabatic heating has not
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been well investigated. Turbulence studies related to unstable atmosphere caused by diabatic
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heating provide indirect yet plausible information that connect current knowledge of canopy
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turbulence and plume dynamics with subcanopy and forest fires. While there is a wealth of
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canopy flow and turbulence data, there have been few studies to date directly linking canopy
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turbulence and fire-atmosphere interaction.
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Field measurements during experimental fires have been made over the past few decades. A
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major study of grassfire behavior was undertaken by CSIRO in Australia where grassfires are
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major concern (Cheney and Sullivan 2008) and for this reason, several open grassfire
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experiments were conducted (e.g., Cheney et al. 1993; Cheney and Gould 1995). However, their
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primary focus was to determine a relationship between ambient mean winds and rate of fire
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spread, the influence of turbulence on grass fire behavior was not investigated. The FireFlux
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experiment (Clements et al. 2007; 2008) provided a first comprehensive dataset to quantify
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turbulence generation during the passage of wind-driven fire fronts (FFP defined as fire front
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passage, Clements et al. 2008) and revealed detailed fire plume temperature structure and plume
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heating rates (Clements 2010). Similar in-situ measurements were made over various fuel and
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terrain to investigate spectral characteristics of turbulence generated during the passage of fire
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fronts (Seto et al. 2013). Using IR imagery and image flow analyses, Clark et al. (1999) and
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Coen et al. (2004) estimated small-scale horizontal and vertical velocities and sensible heat flux
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during crown fires.
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Recent advances in fire-atmosphere coupled models made it possible to treat small-scale
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turbulence and fire-atmosphere interactions. Sun et al. (2009) investigated the effects and relative
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importance of fire-induced turbulence in the atmospheric boundary layer on grass fire spread
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using a coupled fire-atmosphere LES model and found that a strong downdraft caused by an
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interaction between the fire-induced plume circulation and a strong eddy circulation in the ABL
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can bring down higher momentum from aloft to the surface and increase the rate of fire spread,
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especially for a large fire simulation. Fire-atmosphere coupled models have been used to
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simulate FireFlux experiment using in situ micrometeorological data (e.g., Filippi et al. 2013;
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Kochanski et al. 2013). Linn et al. (2010) used the FIRETEC model (Linn and Cunningham
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2005; Linn et al. 2005) to study coupled fire behavior over various fuels (grass, chaparral, and
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ponderosa pine forests) on flat and sloped terrain. Meanwhile, attempts in simulating fires in the
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forest have also been made recently to explore the complex relationship between the canopy
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layer and smoke dispersion (e.g., Lavrov et al. 2006; Meroney 2007; Bova and Bohrer 2010;
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Kiefer et al. 2013). On the other hand, in-situ turbulence and thermodynamic data during fires
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are still very limited to date, even without the presence of the canopy. Smoke emissions and
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dispersion from low intensity fires are currently not well represented by existing models, partly
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because they are highly sensitive to atmospheric turbulence within and above forest canopies.
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Wind and turbulence under a forest environment during fire front passage are highly
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variable (Sullivan and Knight 2001). Gould et al. (2007) examined changes in wind under a
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Eucalypt forest during surface FFP and found spatial variation in wind gusts to be high, with
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differences in gustiness recorded as little as 40 m apart. This variability in turbulence and wind
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makes understanding momentum, heat, and scalar transfer difficult. Yet understanding
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turbulence surrounding the FFP and the buoyancy produced by the fire is a necessary step
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towards improving smoke transport prediction models. Also, improved knowledge on the full
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variation of temperature with height above a surface fire is important for understanding the fire’s
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effect on the canopy, such as leaf scorch and seed/stem death, which ultimately dictates the
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ecosystem health (Mercer and Weber, 2001). With more and more lands under a managed fire
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return interval, the physical processes surrounding FFP requires further attention. Further data
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collection in the forest environment during FFP is necessary to develop relationships models can
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use to predict smoke plume transport and ecological impacts.
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The objective of this paper is to present observational results of sub-canopy turbulence
structures and micrometeorology measured during low-intensity, prescribed fires. Data were
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collected from two prescribed burns that took place in southeastern United States in a Long leaf
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pine forest (Pinus palustris Mill.) undergoing ecological restoration. The datasets collected
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afforded a unique way to compare winds and turbulence at two locations (two
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micrometeorological towers) during a similarly intense fire and to compare between two
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different fires with an intense phenomena occurring under the third tower. The two sets of
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comparisons allow for a better understanding of fire behavior similarities and differences. Plume
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buoyancy is discussed and these data are used to explore the possibility of smoke plume
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transport from sub-canopy prescribed fires.
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2. Field experiment description
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2.1 Site and fuel description
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Three low-intensity prescribed burns took place during the late winter and early spring
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(February and March) of 2010 and 2011 at The Nature Conservancy's (TNC) Calloway
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Forest/Sandhills Preserve in North Carolina, USA (Fig. 1). Three of the five burn units, with two
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burned on the same day, were used to collect turbulence data during the FFP. Table 1 lists the
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burns that were used to collect these data.
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The experimental burns were surface fires under a long leaf pine stand approximately sixty-
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five years old sitting on gentle rolling terrain of old sand dunes. The mean tree height (hc) was 20
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m. The soil was sandy with little to no organic matter beyond the surface duff layer. Majority of
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the surface fuels were in the 1-hr (defined as ¼ of an inch or less in diameter) size classification
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and consisted of long leaf pine litter, both cured and live wiregrass (Aristida stricta), American
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turkey oak (Quercus laevis), and regeneration long leaf pine. Surface fuels in the larger fuel
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classifications were present, however they were very few and did not carry the fire. Pre-burn fuel
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loadings in 2010 and 2011 were 3.14 tons/acre (7,039 kg/ha) and 5.81 tons/acre (3,024 kg/ha),
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respectively.
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2.2 Weather
Background weather observations were made using standard surface weather stations and
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upper-air rawinsondes for determining stability and local vertical wind profiles. The rawinsonde
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sounding was conducted in the large clearing area near the burn unit prior to ignition. In 2010,
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relative humidity of 20% was observed before the ignition with it ranging from 13-18% during
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the burn. Winds were very light northwesterly at the surface and < 5 m s-1 from west-west
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southwest up to 1 km AGL (above ground level) (Fig. 3a). In 2011, relative humidity was 30%
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before ignition, and it remained between 35-40% during the burn. Winds were very light from
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east-southeast at the surface and < 10 m s-1 from south to south southwest above the surface. The
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mixing height was around 1.5 km AGL in 2010 and around 1 km AGL in 2011 (Fig. 3b). The
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surface layer was generally drier in 2010 than 2011. In both years, the atmosphere below the
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inversion was neutral except near-surface where a superadiabatic layer was observed.
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2.3 Fire
Ignition was controlled by the burn manager and interior ignition teams to keep the flame
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height relatively low so that fire intensity, as defined by Alexander (1982), remained low in
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order to prevent canopy scorch and to keep the prescribed burn in control. Initial backing fires
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(fire moving into predominant wind direction) were followed by strip and spot head fires by
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interior fire crews. Rate of fire spread was recorded only in 2011 using two 1 m poles and then
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calculated using the known distance between the poles and the time it took the fire front to pass
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each pole. Maximum flame height was estimated from photos (Fig. 3) and video as it passed by
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each tower. The fire behavior characteristics are summarized in Table 1.
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In this paper, blackline refers to a burning technique used by the hand igniters during the
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experimental burns to protect the instruments. The technique surrounds the instrument with a
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small amount of consumed fuel (‘blackline’) thus reducing the heat intensity directly near the
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instrument. Fire was allowed to approach instrumented towers after the blackline was complete.
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Figure 3 shows photos taken around the time of FFP.
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3. Instrumentation and data processing
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3.1 Instrumentation
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Two 20-m guyed aluminum towers were deployed within Unit 14 in the year 2010; San José
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State University (SJSU) and U. S. Forest Service (USFS) towers. The SJSU tower was deployed
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within Unit 19 in the year 2011. Hereafter, we reference the three tower locations as SJ10, FS10,
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and SJ11, respectively. The locations of the towers are shown in Fig. 1. The towers were
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deployed in a relatively open, gap-like area in the canopy in order to place the guy cables away
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from tree branches. These gaps were randomly distributed throughout the unit and are typically
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found in southern pine regeneration forests. The SJ10 and SJ11 towers were equipped with three
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3-D ultrasonic anemometers: Applied Technologies, Inc. (ATI), Sx-probes were used at 3- and
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10-m Above Ground Level (AGL), and a R.M. Young Company, Model 81000 was mounted at
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20-m AGL. The FS10 tower was equipped with three ATI Vx-probe 3-D ultrasonic anemometers
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mounted at 3-, 12-, and 20-m AGL. An array of fine-wire thermocouples was attached to the
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SJ10 and SJ11 towers to measure plume and near-surface temperature profiles every meter from
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the ground up to 20 m AGL. The thermocouple data were sampled at 10 Hz and saved to 5 Hz.
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Total and radiative heat fluxes were measured at SJ10 and SJ11 sites using a Schmidt-
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Boelter gauge total heat flux sensor (Hukseflux, SBG01) and a Gardon gauge radiant heat flux
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sensor (Medtherm, 64P-50-24), respectively. These types of heat flux sensors are widely used for
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fire research and fire testing laboratories (Pitts et al. 2006). The heat flux sensors were mounted
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in a rectangular aluminum box and located near the towers. The sensor box was attached to a
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fence post about 1 m AGL facing horizontally. Both the tower bases and heat flux sensors were
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protected from the extreme heat of the fire using fireproof insulation material.
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3.2 Data processing
Daytime data between 9:00 to 17:00 LT on the day of the experimental fire were used in the
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analyses. Time series data from the sonic anemometer arrays were first inspected visually to
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remove unrealistic spikes. Then a despiking routine was performed to remove erroneous spikes
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that were four times the standard deviation within a 1-min moving window and the spikes were
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replaced by linearly interpolated values. The despiking was repeated 3 times following Lee et al.
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(2004). Clark et al. (1999) and Coen et al. (2004) have suggested ±5 standard deviations from the
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mean wind velocity as plausible estimate of fire-induced wind extremes, however their
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measurements were conducted during intense forest fires and thus, for our low intensity burns we
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maintained ±4 standard deviations throughout the data processing. The velocity data were further
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processed by rotating the horizontal velocities into streamwise and crosswise velocity
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components, u and v respectively, and the vertical velocity component w was tilt-corrected
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following Wilkzak et al. (2001) over 30 min blocks/periods. The signature of the fire front
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passage within the turbulence data is characterized by a sharp increase in both horizontal and
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vertical wind velocities as well as sudden shifts in the wind direction. The sonic temperature data
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also show a sharp increase during FFP. For the analyses, the period of fire influence on the
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turbulence data were kept within single 30-min windows in order to isolate the ambient
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turbulence conditions from the fire induced environment. The sonic temperatures were not
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corrected for variations in humidity. Turbulence statistics were calculated using perturbations
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from 30 min mean variables and averaged every 30 min (1 min for sensible heat flux) for the
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analyses.
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4. Results
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4.1 Wind velocities and plume temperature
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4.1.1 Horizontal velocity
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The 10 Hz horizontal wind velocities measured at SJ10, FS10, and SJ11 towers are
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presented in Figs. 4a, 4b, and 4c, respectively. Because the individual horizontal component
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velocities, u- and v- tended to be highly influenced by the location and orientation of the
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approaching fireline to each tower we used the vector (horizontal velocity, U) in our analyses. U
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was analyzed before, during, and after the FFP. Wind direction was highly variable within the
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canopy space and this made interpretation of the wind direction influenced by the FFP difficult;
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for this reason wind direction is not presented. Non-FFP flow was characterized by wind shear
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in the upper canopy layer caused by momentum absorption by canopy foliage. Data from all
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three towers demonstrated this characteristic prior and post FFP. Evidence of this was in the
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form of the horizontal winds, which were stronger above the canopy compared to those within
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the canopy except the occasional gust into the canopy understory.
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During the FFP at the SJ10 tower, increased horizontal wind speeds were evident at z =
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0.15h (Fig. 4a). Higher than ambient-level wind speeds were observed in the immediate fire
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environment with U 2-3 m s-1 stronger than the ambient gusts. The strongest winds observed
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near the surface ranged between 5-6 m s-1, which occurred for a very short duration, and
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intermittent gusts of 3-4 m s-1 occurred within the canopy outside the FFP window.
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Increased U at the top of the canopy due to the fire was small compared to the ambient
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winds, which were stronger at the canopy top compared to the forest floor. The maximum sonic
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temperature measured during FFP at the SJ10 tower was 134°C at z = 0.15h, (Fig. 4g), and this
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temperature peak corresponded to the blacklining around the tower base, which was at 13:57
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EST. The sonic temperatures at z = 0.5h and 1.0h did not exceed 60°C.
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Horizontal winds at the FS10 tower, located upwind of the SJ10 tower, followed a similar
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pattern with minimal increased horizontal velocity at the top of the canopy during FFP. The in-
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canopy FFP winds were less, ranging from 1-2 m s-1 above ambient wind gusts near the surface
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(0.15h). The sonic temperature values at all measurement heights were similar to those observed
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at the SJ10 tower.
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The SJ11 tower data showed that the horizontal wind speeds increased to a maximum of 7.9
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m s-1 at z = 0.15h and 9.1 m s-1 at z = 0.5h during the FFP. The wind velocity above the canopy
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also increased in the form of a strong downdraft with a peak of 7.8 m s-1; however, the ambient
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winds above the canopy prior to the FFP showed gusts ranging from 5 and 6.8 m s-1, indicating
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limited influence of the fire on the above-canopy flow.
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At the SJ11 tower two large peaks in sonic temperature were observed at z = 0.15h. The first
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occurred at 16:00 EST and was caused by the initial blacklining around the tower. During this
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ignition, the sonic anemometer at z = 0.15h recorded a temperature of 158°C (Fig. 4h). Similar
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to FS10 and SJ10, the sonic temperatures measured at mid-canopy and at the top of the canopy
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indicated a smaller temperature perturbation than the sonic temperature near the surface, with
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temperature values of 85°C and 43°C at z = 0.5h and 1.0h, respectively. At this tower there was
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a second sonic temperature peak, which was the maximum for z = 0.5h and 1.0h. This was
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caused by a head fire moving toward the tower from southern edge of the burn perimeter, and
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this was when the strongest influence of the fire was observed in the time series. The SJ10 and
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FS10 towers also have a head fire signature in their dataset, but it is small relative to the initial
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blackline FFP. The head fire during 2011 was more intense compared to what was allowed to
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pass by the towers in 2010.
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4.1.2 Vertical velocity
The vertical velocity fields can be used as an indication of the plume’s updraft intensity and
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as a general indication of the fire’s intensity (Coen et al. 2004). Hereafter, we use the terms
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‘updraft’ and ‘downdraft’ to refer to fire-induced buoyant upward motion and resulting
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downward motion typically mentioned as fire-induced circulation in order to distinguish from the
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normal process of ‘ejections’ and ‘sweeps’. Pre- and post-FFP maximum vertical velocities (i.e.,
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sweeps-ejections) were generally between 1-2.5 m s-1 below the canopy and 1.5-3.5 m s-1 at the
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canopy top. There was a clear increase in the vertical velocity during the FFP due to the fire-
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driven buoyancy of the plume (Figs. 4d, 4e, and 4f). A majority of the observed maximum
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updrafts were on the order of 5 m s-1 at all towers. The strongest updraft occurred at the top of
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the canopy during at SJ11 (Fig. 4f) and was 8.7 m s-1. This occurred when the head fire
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approached the tower, with a sonic temperature of 135°C at z = 1.0h (Fig. 4i). At all towers, fire-
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induced downdrafts were evident during FFP but mainly within the canopy (z = 0.15h and z =
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0.5h). The magnitude of the downdrafts associated with FFP was weaker during the 2010 burns
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(SJ10 and FS10) compared to those observed in 2011 (SJ11). The difference between fire-
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induced vertical velocities and sweep-injection velocities was 2-4 m s-1 within the canopy and 2-
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4.5 m s-1at the canopy top. The updrafts were largest in magnitude at the canopy top
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measurement height (1.0h) while the downdrafts were largest in magnitude at the lowest
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measurement height (0.15h).
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4.1.3 Plume thermodynamics
Data from the thermocouple arrays mounted on the SJ10 and SJ11 towers combined with 10
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Hz in-plume wind velocity data allowed for assessment of the thermal structure of the plume
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above the fire. It is noted first that 1 s averaged thermocouple temperatures presented in this
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section appear lower than the 10Hz sonic temperatures presented in the previous section as a
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consequence of the averaging. The plume temperature profile, observed at the SJ10 tower (Fig.
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5a), suggests a vertically oriented plume around 12:58: 00 EST and tilted plume structure around
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13:00:00 EST. The tilted plume structure is noted by high temperatures near the ground rather
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than uniform temperatures through the vertical column. The thermocouple temperature reached
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30-60°C near the surface while the temperatures above mid canopy remained below 30°C. Both
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the sonic temperature and thermocouple measurements suggest that a majority of the high in-
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plume temperatures were confined to near the surface, suggesting either rapid entrainment and
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cooling of the plume aloft or a horizontally tilted plume.
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Vertically oriented plume structure was observed at the SJ11 tower (Fig. 5b). In-canopy
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plume temperatures were high even above the mid-canopy height, with a maximum temperature
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of 105°C near the top of the canopy. This occurred at 16:17:00 EST. The timing of this maxima
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coincides with a strong updraft with peak instantaneous vertical velocity of 8.7 m s-1 at z = 1.0h
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(indicated as arrow A in Fig. 5). Downdrafts exceeded the sweeps in magnitude at z = 0.15h and
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0.5h (indicated by arrows B and C in Fig. 5d) and pockets of lower temperature air were evident
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between high temperature plume features, signifying an intrusion of cool air near the ground. It
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is possible that multiple individual thermal plumes merged and accelerated near the top of the
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canopy resulting in the temperature maxima and strong updrafts. Another possible explanation of
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the warm canopy-top temperature is the absorption of the radiative and convective heating from
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the surface fire by the canopy and the horizontal transport of the heat near the canopy top.
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Although we cannot be conclusive about this plume behavior near the canopy top due to
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inadequate spatial velocity and pressure measurements, the warming near the canopy top is a
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result of the interaction between the fire, atmosphere, and forest canopies. The extinction depth
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of heat flux below and above the canopy and the heat flux absorption rate by the canopy
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determines when a surface fire causes severe desiccation of foliage during FFP.
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4.2 Turbulence statistics
To predict local smoke impacts from low-intensity, sub-canopy fires, quantitative
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knowledge of the dynamic buoyant phase of the smoke plume, which determines the final plume
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rise height and therefore vertical distribution of pollutants, is required (Goodrick et al. 2012).
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Previous studies have shown that current models describe poorly the evolution of strongly
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buoyant plumes (Kiefer et al. 2011), and this is partially due to the lack of in-situ observations of
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the turbulence field. The observed in-plume turbulence characteristics are discussed in this
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section to quantify their role on fire-atmosphere interactions and plume dynamics during sub-
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canopy fires.
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4.2.1 Friction velocity and drag coefficient
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Friction velocity, u*, is an important atmospheric scaling variable because it is a measure of
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the rate of momentum transport, and it varies with the surface property and the magnitude of the
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wind (Kaimal and Finnigan 1994). The friction velocity was calculated following Stull (1988)
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as 𝑢∗ = (𝑢′𝑤′ + 𝑣′𝑤′ )
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velocity increased from near-surface to the canopy top (Fig. 6) and these trends are similar to
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those shown by Shaw et al. (1988), Amiro (1990) and others. The friction velocity increased at
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all heights during FFP as compared to pre- and post-FFP. The u* values ranged from 0.4 and 0.9
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m s-1 during the FFP. Additionally, the vertical linear trends remained the same during FFP with
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higher friction velocity at the top of the canopy compared to mid-canopy and near the surface.
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2 1/4
. Without the presence of a fire (i.e., pre- and post-FFP), friction
Drag coefficient, CD, is required for the parameterization of the surface stress or relating the
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momentum flux to the mean wind profile. The subcanopy drag coefficient is calculated as CD =
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u*2/U2 following Mahrt et al. (2000). Table 2 shows the drag coefficients measured at 0.5h (10
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m AGL). The averaged values of CD before and after the FFP were lower than the averaged CD
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during the FFP. Increased values of the drag coefficient were evident during the FFP at all three
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towers suggesting increased momentum transfer during FFP. The CD values during the FFP are
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in the range 0.11‒0.23. Interestingly, at the SJ10 tower, the CD after FFP was higher compared to
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before FFP and the opposite occurred at the FS10 tower with the CD before FFP higher compared
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to after FFP. The prescribed burn started near the SJ10 tower and progressed past the SJ10 tower
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towards the FS10 tower, ending shortly after it passed the FS10 tower. Most of the burning took
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place between the two towers and the elevated CD values demonstrate the increase in turbulence
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and exchange of momentum, heat and mass during the burn.
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Typically, increases in wind speed and friction velocity are non-linear and consequently, the
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drag coefficient may systematically decrease while the mean wind increases due to streamlining
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effects (Finnigan 1979; Brunet et al. 1994; Rudnicki et al. 2004). This may not always be the
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case within the fire environment as friction velocity was found to substantially increase (Figs.
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6a-c). The 𝑢′, 𝑣′, and 𝑤′ components all increase during FFP, hence, it is not surprising to see an
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increase in momentum transfer, represented by the increased drag coefficients, despite the
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corresponding increase in wind speeds 𝑈 (Figs. 6d-f). These results demand further work such
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as model sensitivity analysis because temporal variability of the 𝑢′ , 𝑣′, and 𝑤′ is hypothesized to
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lead to uncertainty.
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4.2.2 Normalized standard deviations of velocity
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Normalized standard deviations were used to compare the variation in the velocity
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components by height and between towers. They are defined as σi/u* where σ is the standard
401
deviation and i represents the u, v, or w wind component. The standard deviations were
402
normalized by u*, which was calculated from the velocity components measured at the top of the
403
canopy. Normalized standard deviations of u velocity at the canopy top before and after the FFP
404
(Figs. 7a-7c) were similar in magnitude for all towers and to those presented by Finnigan (2000),
405
although the characteristic ‘s’ shape was not observed in this study. For pre, post, and during
406
FFP variation in the u component was greater near the surface, indicating eddy motion near the
407
surface was dominant compared to the shear stress at the canopy top. Strangely at FS10, the
408
overall σu/u* profile decreased during FFP and this was due to the fact that u* increased more
409
than σu did. For SJ11, σu/u* was greater at the surface during FFP compared to pre and post FFP
410
values. It is probable that this was due to conflicting fire fronts, the black line and the head fire,
411
which caused directional shifts in the u component. Normalized standard deviations of v velocity
412
in Figs 7d-7f closely resemble those of u velocity. The overall limited shift in σu/u* values during
17
413
FFP can be related to the mean wind profiles. The shape of the mean wind profile during FFP did
414
not differ from pre- or post-FFP (Fig. 6), which suggests limited influence of the FFP on the
415
relative contribution of wind shear at each height.
416
During the FFP, normalized standard deviations of w shown in Figs. 7g-7i increased near the
417
surface and at mid-canopy (SJ10 and SJ11 towers), but they remained relatively unchanged at
418
the FS10 tower. For SJ11, σw/u* near the surface more than doubled during FFP, however it
419
remained the same at the canopy top. The more intense head fire, at FS11, generated strong
420
updrafts at the surface but this did not extend beyond z=0.5h. The high plume temperatures
421
observed at the SJ11 tower (Fig. 5b) are associated with the increased mixing as indicated by the
422
increased σw/u* at the surface and mid-canopy during the FFP. At this tower, like the other
423
towers, the normalized w velocity was similar to the pre and post FFP values so the updrafts
424
generated by the fire encountered the strong horizontal mean winds at the canopy top. These
425
results suggest that subcanopy fires increase turbulence near the surface but their influence on
426
mixing at the canopy top is not stronger than the background shear stress produced by the
427
foliage.
428
429
430
4.2.3 Sensible heat flux
Heat flux is one of the most important feedbacks from fire to the atmosphere (Jenkins et al.
431
2001). Observations of heat flux allow us to quantify the amount of heat transported from the
432
fire front into the near-surface environment and can be used to evaluate coupled fire-atmospheric
433
model physics (e.g. Filippi et al. 2013; Kochanski et al. 2013). The sensible heat flux, Hs, was
434
calculated as Hs = 𝜌𝑐𝑃 𝑤′𝑇′, where ρ is density of air and cp is the specific heat of dry air. We
435
neglect the density variations associated with different plume gases (e.g., Clements et al. 2008).
18
436
The time series of 1-min averaged sensible heat flux observed at SJ10, FS10, and SJ11 towers is
437
presented in Fig. 8. The maximum sensible heat flux at SJ10 and FS10 towers was observed at
438
0.15h with smaller magnitudes observed at 0.5h and 1.0h, and these peaks coincide with
439
blacklining operations around each tower base. Figure 5a also shows that between 13:00 and
440
13:01 EST at SJ10 tower the hot plume remained below mid-canopy height, resulting in the
441
lower sensible heat fluxes above the mid-canopy height. The maximum sensible heat flux value
442
was 30 kW m-2 at SJ10 tower and 15 kW m-2 at the FS10 tower. The blacklining operation at
443
SJ11 resulted in a sensible heat flux peak of 64 kW m-2. When both the backing and head fire
444
approached the SJ11 towers, the sensible heat flux increased to 31 kW m-2 and 67 kW m-2 at the
445
mid-canopy and top-canopy levels, respectively, while the sensible heat flux at z = 0.15h
446
remained lower at 22 kW m-2.
447
Total and radiative heat fluxes emitted from the fire front in the horizontal direction are used
448
to estimate fire front residence time and fraction of radiative heat flux to total heat flux.
449
Observed total and convective heat fluxes at SJ10 and SJ11 towers are presented in Fig. 9.
450
Ignition was made to blackline around the instrument box so strictly speaking, the measured heat
451
fluxes are more representative of the intensity of the fire generated by nearby fuel. The total and
452
radiative heat fluxes measured at SJ10 tower were 50 kW m-2 and 18 kW m-2, respectively, and
453
they were nearly twice as large compared to those observed at SJ11 tower around 15:58 EST
454
when the fuel around the sensor was ignited. It is worth mentioning that the secondary peak of
455
total and radiative heat fluxes observed at 16:17 EST in Fig. 6b was caused by the head fire
456
approaching the tower from the south and the heat flux sensors captured the approaching head
457
fire some distance away. However, because the heat flux sensors were originally pointed to the
458
direction of the backing fire, the measurements likely underestimated the actual heat flux
19
459
intensities of the head fire. Peak radiative heat flux was 36% and 48% of the total heat flux
460
measured at SJ10 and SJ11 site, respectively.
461
462
5. Discussion
463
5.1 Comparison to FireFlux experiment and other field observations
464
In order to understand the relative strength of fire-induced winds and heat flux during low
465
intensity subcanopy fires, the observed winds and plume thermodynamic structures in our study
466
are compared to winds and buoyant plume generated by a higher intensity grass fire during the
467
FireFlux experiment. Observed fire-induced wind velocity at 2 m AGL during the FireFlux was
468
7 m s-1 which is three times greater than the ambient winds. Observed peak fire-induced winds in
469
our study showed 4-6 m s-1 increase from ambient winds at z = 0.15h and the mean horizontal
470
winds within and at the top of the canopy doubled from the ambient mean values (Fig. 4c) during
471
the FFP, despite the fact that in-plume maximum temperature of 150°C observed during our
472
study was about half of what was observed during the FireFlux (295°C at 4.5 m AGL).
473
Subcanopy fires are relatively slow-spreading and low in intensity compared to fast-spreading,
474
moderate intensity grass fires or high intensity crown fires. Even so, a head fire in the
475
subcanopy environment generated fire-induced winds at z = 0.15h that quadrupled from the
476
ambient sub-canopy wind velocity. This result suggests that subcanopy fires may potentially be
477
able to generate nearly as strong fire-induced flow as wind-driven grass fires, depending on
478
spread patterns (i.e., head fires vs. backing fires) and fuel conditions (dryness, thickness, etc.). It
479
should be noted that ambient flow may not be dominated by the heating supplied by the fire in
480
wind-driven fires. In contrast, the effect of buoyancy forcing on the acceleration of the flow
481
could be significant even for small fires in weak winds, and the flow and fire are dynamically
20
482
coupled so that the flow responds strongly to the heating supplied by the fire (Jenkins et al.
483
2001). Fire-induced buoyancy and the effect of ambient wind shear can be quantified by the flux
484
Richardson number Rf, which is the ratio of production of turbulence by buoyancy to that by
485
ambient wind shear. The flux Richardson number is defined in Stull (1988) as
486
487
𝑅𝑓 =
(𝑔/𝜃𝑣 )(𝑤′𝜃𝑣 ′)
(𝑢′ 𝑤 ′ )𝜕𝑢/𝜕𝑧 + (𝑣 ′ 𝑤 ′ )𝜕𝑣/𝜕𝑧
488
489
Mean vertical gradient of wind velocities u and v was calculated between 3 m and 10 m (12 m
490
for FS10) and 10 m and 20 m and flux values were calculated as layer-average. Calculated
491
stability parameter h/L and Rf during the FFP in Table 4 show at the surface an unstable stability
492
(h/L << 0) and buoyancy-driven environment as indicated by larger numerator. The values of Rf
493
decrease with height because there is strong wind shear near the upper canopy even during the
494
FFP. The measured peak updraft of 8.7 m s-1 and peak downdraft of 5 m s-1 in our study showed
495
similar strength to those observed during the FireFlux experiment. It is hypothesized that
496
downdrafts may have influence on surface fire spread in subcanopy fires even with the presence
497
of forest canopy and stronger updrafts above the convection column.
498
Large values of σu/u and σw/u found at z = 0.15h at SJ11 tower can be explained by intense
499
turbulent motion as discussed in Launiainen et al. (2007), although the SJ10 and FS10 towers did
500
not show identical structures. Similar to Lee and Mahrt’s (2005) discussion for daytime
501
subcanopy turbulence generation by local buoyancy rather than downward transport of
502
turbulence from above the canopy, subcanopy fire environments likely produce and enhance
503
turbulence within the canopy generated by local fire-induced buoyancy. The within-canopy
504
turbulence generated by diabatic heating and resulting local buoyancy is known to be inactive
21
505
and does not contribute to momentum fluxes. In contrast, elevated CD values may suggest an
506
increase in active turbulence that transports heat during the burn as upward momentum.
507
Differences in plume temperature variations were observed between the 2010 and 2011
508
subcanopy burns (Fig. 5). Morvan et al. (2011) showed in their numerical simulations of grass
509
and shrub fires that the merging of a head and back fires propagating in opposite directions can
510
result in a quick increase in total heat release rate, fireline intensity, and flame height. The
511
difference in the observed maximum plume temperatures between SJ10 and SJ11 towers (Fig. 5)
512
indicate that this phenomenon likely occurred under the SJ11 tower. The head fire produced
513
higher fire intensity and therefore released more heat into the atmosphere. The results of in-
514
canopy vertical plume temperature profiles have an implication on prescribed fire management
515
in terms of impacts on ecosystem and vegetation survival, especially during low-intensity
516
prescribed fires in forests because high plume temperatures can be produced locally, depending
517
on ignition patterns and possibly sub-canopy atmospheric conditions.
518
The maximum sensible heat flux values from the SJ10 tower and FS10 tower are
519
comparable to the FireFlux grass fire (Clements et al. 2007), despite the fact that the plume
520
temperature was much higher during the grass fire. This is because the fast-moving nature of
521
wind-driven fire over the grass field results in short duration of intense plume heating, whereas
522
slow-moving backing fire within the canopy generates a less intense, yet increased duration of
523
atmospheric heating due to light winds and weaker entrainment. Peak instantaneous sensible heat
524
flux of 856 kW m-2 observed at SJ11 (Table 3) were rather similar to the estimated instantaneous
525
sensible heat flux of 1 MW m-2 during FireFlux. There were periods when negative sensible heat
526
fluxes were observed at z = 0.15h and 0.5h (not shown), indicating recirculating downdrafts of
527
warm air into the base of convective column.
22
528
Our measurements of total and radiative heat fluxes during the sub-canopy fire experiments
529
are similar in magnitude to the peak total heat flux of 40 kW m-2 and radiative heat flux of 25
530
kW m-2 observed by Silvani and Morandini (2009), who conducted a fire spread experiment in
531
the field over a pine needle fuel bed, despite the fact that fuel characteristics (moisture content,
532
fuel load and geometry etc.) are most likely different and can have a significant role in heat flux
533
values. The heat flux radiometers during the 2010 and 2011 experiments were sampled at 10 Hz
534
which could lead to lower heat flux values according to Frankman et al. (2013) who showed that
535
degradation in convective heating measurements can occur for sampling rates less than 100 Hz.
536
Nonetheless, our heat flux measurements should provide values representative of low intensity
537
subcanopy fires and comparable to future model comparisons.
538
539
5.2 Comparison to existing model simulations
540
Fire-induced flow dynamics during low intensity subcanopy fires can be better understood
541
by comparing the relative intensity of the observed fire induced flow during subcanopy fires in
542
our study with surface fire portion of numerical simulations of a crown fire. Simulations of wind
543
velocities and fuel temperatures in a ponderosa pine forest with ground fuel bed over flat terrain
544
were presented previously by Linn et al. (2010) using FIRETEC, a coupled fire-atmospheric
545
model (Linn and Cunningham 2005; Linn et al. 2005). Based on their simulated u- and v-
546
velocities near the surface (z = 0.7 m AGL), we estimate their peak fire-induced horizontal wind
547
velocity to be 7-10 m s-1, and their peak updrafts of 6-8 m s-1. The fire-induced horizontal wind
548
velocities of 5-8 m s-1 and updrafts on the order of 5 m s-1 in our study compare fairly well in
549
magnitude with their simulated values near the ground, despite the fact that the simulation
550
included the canopy fire component. It is possible canopy fire has less influence on fire-induced
23
551
winds near the surface. Through a model canopy experiment, Beer (1991) argued the importance
552
of sweeps of air and downward recirculation of heat to unburnt fuel ahead of the fire during
553
subcanopy fires, whereas Taylor et al. (2004) suggest that downdrafts may have less influence on
554
head fire shape in crown fires because of the presence of the forest canopy and strong updraft.
555
These issues invite more field measurements and modeling studies.
556
557
558
6. Summary and Conclusions
Smoke emissions and dispersion from low-intensity, subcanopy prescribed fires are
559
currently not well characterized by existing models, partly because emissions are highly sensitive
560
to atmospheric turbulence within and above forest canopies. This study focused on analyses of
561
in-situ turbulence and plume thermodynamic structures in canopy space when low-intensity
562
surface fires burn through a surface fuel bed dominated by pine litter fuel. Even though the two
563
burns took place in the same forest with similar fuel beds, measured wind velocity and
564
temperature data during FFP produced different results, and this was likely due to a head fire
565
ignition in the 2011 burn. Some of the key findings from this study are;
566
567
1) In-situ tower data showed that low intensity subcanopy fire is capable of modified
568
horizontal velocity fields. The magnitudes of the fire-induced horizontal flow observed
569
during lower intensity fires were smaller than horizontal winds observed during a higher
570
intensity head fire converging with a backing fire.
571
2) Both tilted and vertically oriented plume structures were observed. The vertically
572
oriented plume structure was accompanied with highest plume temperature and strongest
573
vertical wind velocities near the top of the canopy height when two firelines merged near
24
574
the tower. The strongest updraft of 8.7 m s-1 observed near the canopy top at SJ11 tower
575
may be caused by the plume convergence at the canopy top or heat absorption by
576
canopies. In-plume downdrafts within the canopy and entrainment of cool air were also
577
identified from in-plume vertical velocity combined with the time-height contour plot of
578
temperature.
579
3) The presence of subcanopy fire resulted in the increased mean horizontal wind and
580
friction velocity within and at the top of the canopy. Fire-induced flow observed within
581
and above the canopy at all towers suggests locally increased horizontal transport of heat,
582
momentum, and pollutants. Despite the increased mean winds, drag coefficient increased
583
during the FFP, indicating increased momentum transfer in subcanopy fire environments.
584
Normalized standard deviation of vertical velocity σw/u* increased under the influence of
585
the fire, whereas increases in the normalized standard deviation of streamwise and
586
crosswise velocities σu/u* and σv/u* were not as obvious as increases in σw/u*. This may
587
suggest more effective mixing/dispersion in the vertical than in the horizontal within the
588
canopy during the FFP.
589
4) Maximum measured sensible heat fluxes from all towers ranged from 15 to 65 kW m-2
590
when averaged over 1 min, with instantaneous maximum values ranging from 326 to 856
591
kW m-2 (Table 3). Peak radiative heat flux was 36% and 48% of the total heat flux
592
measured at SJ10 and SJ11 site, respectively.
593
5) Comparisons of our turbulence measurements with those observed during an
594
experimental head fire over grass fuel suggest that in spite of low plume temperatures,
595
low intensity subcanopy fires may potentially be able to generate nearly as strong fire-
596
induced winds as wind-driven grass fires under favorable fuel conditions due to strong
25
597
coupling between fire and atmosphere. The fire-induced horizontal wind velocities of 5-8
598
m s-1 and vertical velocities of 5 m s-1 with peak updraft velocity of 8.7 m s-1 in our study
599
compare well in magnitude with near-surface horizontal and vertical wind velocities for a
600
pine forest fire simulation by Linn et al. (2010), suggesting a possibility that as long as
601
fire-induced winds near the surface are concerned, canopy fire has less influence on
602
surface fire.
603
604
While it is unknown in what degree our measurements represent the turbulence and plume
605
structures in other types of forests as fuel heterogeneity and fire-induced flow drive different fire
606
behavior, this data can be used for both qualitative and quantitative comparisons with results of
607
numerical model simulations of low-intensity fires in canopies. Also, the results of this analysis
608
will help fire researchers conducting field experiments of subcanopy fire behavior in the future
609
and land managers concerning safe fire operations and smoke managements during prescribed
610
fires.
611
612
Acknowledgement
613
This project was funded by the Joint Fire Science Program (JFSP 09-1-4-2). We thank The
614
Nature Conservancy (TNC) North Carolina Chapter who put time and effort into assisting us
615
with this project. In particular, we thank burn boss Mike Norris, TNC Fire Manger Margit
616
Bucher, and the TNC fire crew and volunteers. Their support was essential to success of the
617
project. We also give a special thanks to Andy Trend, Scott Gilmour, and Gary Kees for help
618
with the installation and operation of our towers and instrumentations.
619
26
620
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Shen, S., Leclerc, M.Y., 1997: Modeling the turbulence structure in the canopy layer. Agric. For.
Meteorol. 87, 3-27.
767
768
769
Silvani, X., Morandini, F., 2009: Fire spread experiments in the field: Temperature and heat
fluxes measurements. Fire Safety Journal, 44, 279-285.
770
771
772
Stull, R.B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic Publisher,
Dordrecht, 666 pp.
773
774
Sun, R., Krueger, S.K., Jenkins, M.A., Zulauf, M.A., Charney, J.J., 2009: The importance of fire31
775
atmosphere coupling and boundary-layer turbulence to wildfire spread. Int. J. Wildland Fire,
776
18, 50-60.
777
778
Taylor, S.W., Wotton, B.M., Alexander, M.E., Dalrymple, G.N., 2004: Variation in wind and
779
crown fire behavior in a northern jack pine – black spruce forest. Can. J. For. Res., 34, 1561-
780
1576.
781
782
Thistle, H.W., Peterson, H., Allwine, G., Lamb, B., Strand, T., Holsten, E.H., Shea, P.J., 2004:
783
Surrogate pheromone plumes in three forest trunk spaces: composite statistics and case
784
studies. Forest Science, 50(5), 610-625.
785
786
787
Wilczak, J.M., Oncley, S.P., Stage, S.A., 2001. Sonic anemometer tilt correction algorithms.
Boundary-Layer Meteorol. 99, 127-150.
788
789
32
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
Figures
Figure 1. A map of experimental site showing the burn units 14 and 18/19 and instrument tower
locations. Initial ignition locations and directions of backing fire progression are indicated by
flame symbols and orange arrows, respectively.
33
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
Figure 2. Sounding profiles of temperature (red), dew point (green), and wind speeds and
directions plotted with height observed prior to the ignition; (a) at 11:20 EST on 7 Mar 2010, (b)
11:18 EST on 16 Feb 2011. Lines of constant temperature, dry adiabat, and saturation mixing
ratio are also shown in the background.
34
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
Figure 3. Photos of fire front passage at (a) SJ10 tower at 13:00 EST, (b) FS10 tower at 14:05
EST, (c) SJ11 tower at16:01 EST and (d) a photo of tilted plume above the canopy at 16:16 EST
in 2011.
35
873
874
875
876
877
878
879
880
881
882
883
884
885
886
Figure 4. Time series of 10 Hz horizontal wind speed U (a-c), vertical velocity w (d-f), and sonic
temperature Ts (g-i) observed at SJSU tower (year 2010; left column), FS tower (year 2010;
center column), and SJSU tower (year 2011; right column). The periods between the two vertical
dashed lines indicate the FFP.
36
887
888
889
890
891
892
893
894
895
896
897
898
899
900
Figure 5. Time-height plot of 1 Hz averaged plume temperature (top), and time series of 10 Hz
in-plume vertical wind velocity (middle) and horizontal wind velocity (bottom) observed at
SJSU tower in 2010 (left) and in 2011 (right).
37
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
Figure 6. Vertical profiles of friction velocity (top, a-c) and horizontal mean wind velocity
(bottom, d-f) measured at each tower. Error bars indicate one standard deviation.
38
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
Figure 7. Vertical profiles of standard deviations of streamwise (top), crosswise (middle), and
vertical (bottom) velocities normalized by friction velocities measured at the canopy top at each
tower. Error bars indicate ±1 standard deviation.
39
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
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950
951
952
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954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
Figure 8. Time series of 1-min averaged Sensible heat flux measured at (a) SJ10, (b) FS10, and
(c) SJ11 towers.
40
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
Figure 9. Time series of 1 Hz total (red) and radiative (blue) heat fluxes measured at SJ10 and
SJ11 tower sites.
41
7-Mar-10
Burn
date
FS10/
SJ10
12-Feb-11
Tower
SJ11
Latitude,
Longitude
35.2333,
79.369
35.6360,
79.5038
Post-fire
1.5/1.8
42
Pre-burn
35
2
Size
(hectares)
0.86
38
Maximu
Fuel
m flame
moisture
height
content
(m AGL)
(%)
3.14
0.86
Fuel load Fuel load
(tons/acre) (tons/acre)
11:20 AM 3:20 PM
5.81
End of
hand
ignition
(EST)
25
11:00 AM 5:00 PM
Start of
hand
ignition
(EST)
71
Table 1. Tower and its associated burn information including the date the prescribed burn took place, the
latitude and longitude of the starting location of the prescribed burn, size of the burn, and time of start and
end of hand ignition.
1006
1007
1008
1009
1010
1011
1012
Tables
1003
1004
1005
pre-FFP
FFP
post-FFP
1013
1014
1015
1016
1017
SJ10
0.14 ± 0.01(5)
0.19 (1)
0.16 ± 0.01(6)
FS10
0.21 ± 0.02 (5)
0.23 (1)
0.14 ± 0.01 (3)
SJ11
0.08 ± 0.01 (13)
0.11(1)
0.05 (2)
Table 2. Values of drag coefficient observed at z = 0.5h (0.6h for FS10) before, during, and after
the fire passage at each tower. Numbers in parentheses are the numbers of 30 min runs analyzed.
Values after the ± sign represent the standard errors.
43
1018
1.0h
0.5 (0.6)h
0.15h
1019
1020
1021
1022
1023
SJ10
12/19
19/188
29/477
FS10
10/143
11/226
15/326
SJ11
67/856
32/800
63/738
Table 3. Summary of observed 1 min average/10Hz peak sensible heat fluxes (kW m-2) at each
tower.
44
1024
1025
1026
1027
1028
1029
1030
1031
1032
SJ10
FS10
SJ11
h/L
Rf (upper)
-2.06
-2.5
-1.63
-10.01
-5
-5.77
Rf (lower)
-4.73
-10.16
9.67
Table 4. Values of 30-min averaged, during-FFP stability parameter h/L (ratio of canopy height h
to Obukhov length L evaluated at canopy top height) and flux Richardson number Rf computed
between adjacent levels.
45