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Terry L. Clark, Larry Radke, Janice Coen, and Don Middleton


A good physical understanding of the initiation, propagation, and spread of crown fires remains an elusive goal for fire researchers. Although some data exist that describe the fire spread rate and some qualitative aspects of wildfire behavior, none have revealed the very small timescales and spatial scales in the convective processes that may play a key role in determining both the details and the rate of fire spread. Here such a dataset is derived using data from a prescribed burn during the International Crown Fire Modelling Experiment. A gradient-based image flow analysis scheme is presented and applied to a sequence of high-frequency (0.03 s), high-resolution (0.05–0.16 m) radiant temperature images obtained by an Inframetrics ThermaCAM instrument during an intense crown fire to derive wind fields and sensible heat flux. It was found that the motions during the crown fire had energy-containing scales on the order of meters with timescales of fractions of a second. Estimates of maximum vertical heat fluxes ranged between 0.6 and 3 MW m−2 over the 4.5-min burn, with early time periods showing surprisingly large fluxes of 3 MW m−2. Statistically determined velocity extremes, using five standard deviations from the mean, suggest that updrafts between 10 and 30 m s−1, downdrafts between −10 and −20 m s−1, and horizontal motions between 5 and 15 m s−1 frequently occurred throughout the fire.

The image flow analyses indicated a number of physical mechanisms that contribute to the fire spread rate, such as the enhanced tilting of horizontal vortices leading to counterrotating convective towers with estimated vertical vorticities of 4 to 10 s−1 rotating such that air between the towers blew in the direction of fire spread at canopy height and below. The IR imagery and flow analysis also repeatedly showed regions of thermal saturation (infrared temperature > 750°C), rising through the convection. These regions represent turbulent bursts or hairpin vortices resulting again from vortex tilting but in the sense that the tilted vortices come together to form the hairpin shape. As the vortices rise and come closer together their combined motion results in the vortex tilting forward at a relatively sharp angle, giving a hairpin shape. The development of these hairpin vortices over a range of scales may represent an important mechanism through which convection contributes to the fire spread.

A major problem with the IR data analysis is understanding fully what it is that the camera is sampling, in order physically to interpret the data. The results indicate that because of the large amount of after-burning incandescent soot associated with the crown fire, the camera was viewing only a shallow depth into the flame front, and variabilities in the distribution of hot soot particles provide the structures necessary to derive image flow fields. The coherency of the derived horizontal velocities support this view because if the IR camera were seeing deep into or through the flame front, then the effect of the ubiquitous vertical rotations almost certainly would result in random and incoherent estimates for the horizontal flow fields. Animations of the analyzed imagery showed a remarkable level of consistency in both horizontal and vertical velocity flow structures from frame to frame in support of this interpretation. The fact that the 2D image represents a distorted surface also must be taken into account when interpreting the data.

Suggestions for further field experimentation, software development, and testing are discussed in the conclusions. These suggestions may further understanding on this topic and increase the utility of this type of analysis to wildfire research.

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Terry L. Clark, Mary Ann Jenkins, Janice Coen, and David Packham


The object of this paper is to describe and demonstrate the necessity and utility of a coupled atmosphere-fire model: a three-dimensional, time-dependent wildfire simulation model, based on the primitive equations of motion and thermodynamics, that can represent the finescale dynamics of convective processes and capture ambient meteorological conditions.

In constructing this coupled model, model resolution for both the atmosphere and the fuel was found to be important in avoiding solutions that are physically unrealistic, and this aspect is discussed. The anelastic approximation is made in the equations of motion, and whether this dynamical framework is appropriate in its usual form for simulating wildfire behavior is also considered.

Two simple experiments-the first two in a series of numerical simulations using the coupled atmosphere- fire model-are presented here, showing the effect of wind speed on fire-line evolution in idealized and controlled conditions. The first experiment considers a 420-m-long fire line, and the second considers a 1500-m-long fire fine, where wind speeds normal to the initial fire lines vary from 1 to 5 m s−1. In agreement with some general observations, the short fire line remains stable and eventually develops a single conical shape, providing the wind speed is greater than about 1–2 m s−1, while under similar conditions, the longer fire line breaks up into multiple conical shapes. In both cases, the conical shapes are attributed to a feedback between the hot convective plumes and the near-surface convergence at the fire front. The experimental results reveal a dynamical explanation for fire-line breakup and geometry, demonstrating that the model is a valuable tool with which to investigate fire dynamics, and eventually it may be able to provide a credible scientific basis for policy decisions made by the meteorological and fire-management communities.

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Roger F. Reinking, Jack B. Snider, and Janice L. Coen


This study illustrates opportunities for much improved orographic quantitative precipitation forecasting, determination of orographic cloud seedability, and flash flood prediction through state-of-the-art remote sensing and numerical modeling of gravity wave clouds. Wintertime field observations with multiple remote sensors, corroborated in this and related papers with a mesoscale–cloud scale numerical simulation, confirm that storm-embedded gravity waves can have a strong and persistent influence on orographic cloud liquid water (CLW) and precipitation. Where parallel mountain ridges dominate the landscape, an upwind ridge can force the wave action, and a downwind ridge can receive the precipitation. The 1995 Arizona Program was conducted in such terrain. In the scenario examined, traveling waves cyclically caused prefrontal cross-barrier winds that produced gravity waves. Significant cloud bands associated with the waves carried substantial moisture to the area. With the passage and waning of the cloud bands, vapor influxes (precipitable water P w) cycled through large changes in magnitude, and prefrontal peaks in P w coincided with the gravity waves in a succession of episodes during a five-day period. Thus, the cyclic trend in P w and the magnitudes of peak P w were simple indicators of wave cloud development. The first two cycles, with minor peak P w, were precursors. Significant wave clouds first appeared during the second episode. During the final two episodes with large vapor influxes, very deep, precipitating wave clouds were coupled with underlying clouds formed in flow up the mountain slopes to create the prefrontal storms. Rain fell on an existing snowpack on the main recipient ridge and, in the end, produced rapid runoff and flash flooding.

The gravity waves persistently condensed CLW that averaged 0.5 mm and reached 1.0 mm in the first of the main storm episodes, and averaged 1.0 mm and reached 2.0 mm and more in the second (column-integrated values). These values equaled or exceeded the larger of those represented in liquid water climate datasets for orographic cloud systems in other locations in the West, where only the upslope and not the wave component had been examined. The effect of shifts between cross-barrier and barrier-parallel flows was reflected in abrupt buildups and declines in wave CLW, but the gravity wave clouds persisted for a total of 22 h during the two storm periods. In the wave updrafts, the condensation rate regularly exceeded the consumption rate by ice, even though ice was usually present. Conversion to ice consumed and precipitated wave CLW. Pulses of available P w and wave CLW on a 2- to 4-h timescale, cyclically followed by partial glaciation, produced the precipitation from the wave clouds. Their seeder effect on the upslope feeder clouds was to enhance the total precipitation from the coupled system. Estimates of the liquid water fluxes in comparison with the precipitation rates suggest precipitation efficiencies in the 11%–33% range from the seeder–feeder couplets. The periods of gravity wave forcing contributed some 80% or more of the total precipitation, and trailing fronts produced the remainder.

Several factors derived from the observed availability of CLW determine the potential for precipitation enhancement by seeding wave clouds; these are enumerated. Given demands for improved water supply, the challenge often presented in mountain watersheds of separating seeding opportunities from potential flash flood situations is examined. The results here show that storms that could threaten flash floods can be readily identified by continuous monitoring with polarization radar and in real-time simulations as those with the altitude of the melting level above the elevation of the highest terrain with existing snowpack.

In the sense that orographically generated gravity waves will significantly influence cloud water and precipitation, geographic transferability of the results is indicated by the existence of wave-generating and precipitation-generating parallel ridges in many places throughout the world. The quantitative effects will, of course, depend on particulars of the locale such as nature of the prevalent forcing, available moisture, and physical stature of the ridges.

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Terry L. Clark, Teddie Keller, Janice Coen, Peter Neilley, Hsiao-ming Hsu, and William D. Hall


Numerical simulations of terrain-induced turbulence associated with airflow over Lantau Island of Hong Kong are presented. Lantau is a relatively small island with three narrow peaks rising to between 700 and 950 m above mean sea level. This research was undertaken as part of a project to better understand and predict the nature of turbulence and shear at the new airport site on the island of Chek Lap Kok, which is located to the lee of Lantau. Intensive ground and aerial observations were taken from May through June 1994, during the Lantau Experiment (LANTEX). This paper focuses on flow associated with the passage of Tropical Storm Russ on 7 June 1994, during which severe turbulence was observed.

The nature of the environmental and topographic forcing on 7 June 1994 resulted in the turbulence and shear being dominated by the combination of topographic effects and surface friction. High-resolution numerical simulations, initialized using local sounding data, were performed using the Clark model. The simulation results indicate that gravity-wave dynamics played a very minor role in the flow distortion and generation of turbulence. As a result of this flow regime, relatively high vertical and horizontal resolution was required to simulate the mechanically generated turbulence associated with Tropical Storm Russ.

Results are presented using a vertical resolution of 10 m near the surface and with horizontal resolutions of both 125 and 62.5 m over local, nested domains of about 13–24 km on a side. The 125-m model resolution simulated highly distorted flow in the lee of Lantau, with streaks emanating downstream from regions of sharp orographic gradients. At this resolution the streaks were nearly steady in time. At the higher horizontal resolution of 62.5 m the streaks became unstable, resulting in eddies advecting downstream within a distorted streaky mean flow similar to the 125-m resolution simulation. The temporally averaged fields changed little with the increase in resolution; however, there was a three- to fourfold increase in the temporal variability of the flow, as indicated by the standard deviation of the wind from a 10-min temporal average. Overall, the higher resolution simulations compared quite well with the observations, whereas the lower resolution cases did not. The high-resolution experiments also showed a much broader horizontal and vertical extent for the transient eddies. The depth of orographic influence increased from about 200 m to over 600 m with the increase in resolution. A physical explanation, using simple linear arguments based on the blocking effects of the eddies, is presented. The nature of the flow separation is analyzed using Bernoulli’s energy form to display the geometry of the separation bubbles. The height of the 80 m2 s−2 energy surface shows eddies forming in regions of large orographic gradients and advecting downstream.

Tests using both buoyancy excitation and stochastic backscatter to parameterize the underresolved dynamics at the 125-m resolution are presented, as well as one experiment testing the influence of static stability suppressing turbulence development. All these tests showed no significant effect. Implications of these results to the parameterization of mechanically induced turbulence in complex terrain are discussed.

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Janice L. Coen, Marques Cameron, John Michalakes, Edward G. Patton, Philip J. Riggan, and Kara M. Yedinak


A wildland fire-behavior module, named WRF-Fire, was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire-behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and are used, with fuel properties and local terrain gradients, to determine the fire’s spread rate and direction. Fuel consumption releases sensible and latent heat fluxes into the atmospheric model’s lowest layers, driving boundary layer circulations. The atmospheric model, configured in turbulence-resolving large-eddy-simulation mode, was used to explore the sensitivity of simulated fire characteristics such as perimeter shape, fire intensity, and spread rate to external factors known to influence fires, such as fuel characteristics and wind speed, and to explain how these external parameters affect the overall fire properties. Through the use of theoretical environmental vertical profiles, a suite of experiments using conditions typical of the daytime convective boundary layer was conducted in which these external parameters were varied around a control experiment. Results showed that simulated fires evolved into the expected bowed shape because of fire–atmosphere feedbacks that control airflow in and near fires. The coupled model reproduced expected differences in fire shapes and heading-region fire intensity among grass, shrub, and forest-litter fuel types; reproduced the expected narrow, rapid spread in higher wind speeds; and reproduced the moderate inhibition of fire spread in higher fuel moistures. The effects of fuel load were more complex: higher fuel loads increased the heat flux and fire-plume strength and thus the inferred fire effects but had limited impact on spread rate.

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Jordan G. Powers, Joseph B. Klemp, William C. Skamarock, Christopher A. Davis, Jimy Dudhia, David O. Gill, Janice L. Coen, David J. Gochis, Ravan Ahmadov, Steven E. Peckham, Georg A. Grell, John Michalakes, Samuel Trahan, Stanley G. Benjamin, Curtis R. Alexander, Geoffrey J. Dimego, Wei Wang, Craig S. Schwartz, Glen S. Romine, Zhiquan Liu, Chris Snyder, Fei Chen, Michael J. Barlage, Wei Yu, and Michael G. Duda


Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

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