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Fire lines are complex phenomena with a broad range of scales of cross-line dimension, undulations, and along-line variation in heating rates. While some earlier studies have examined parcel processes in two-dimensional simulations, the complexity of fire lines in nature motivates a study in which the impact of three-dimensional fire line details on parcel processes is examined systematically. This numerical modeling study aims to understand how fundamental processes identified in 2D simulations operate in 3D simulations where the fire line is neither straight nor uniform in intensity. The first step is to perform simulations in a 3D model, with no fire line undulations or inhomogeneity. In general, convective modes simulated in the 2D model are reproduced in the 3D model. In one particular case with strong vertical wind shear, new convection develops separate from the main line of convection as a result of local changes to parcel speed and heating. However, in general the processes in the 2D and 3D simulations are identical. The second step is to examine 3D experiments wherein fire line shape and along-line inhomogeneity are varied. Parcel heating, as well as convective mode, is shown to exhibit sensitivity to fire line shape and along-line inhomogeneity. DOI: 10.1175/2009JAS3226.1

Wildfires are capable of inducing atmospheric circulations that result predominantly from large temperature anomalies produced by the fire. The fundamental dynamics through which a forest fire and the atmosphere interact to yield different convective regimes is still not well understood. This study uses the Advanced Regional Prediction System (ARPS) model to investigate the impact of the environmental (i.e., far upstream, undisturbed by fire) wind profile on dry convection above a prescribed heat source of an intensity and spatial scale comparable to a wildfire. Dimensional analysis of the fire-atmosphere problem provides two relevant parameters: a surface buoyancy parameter that addresses the amount of heat a parcel of air receives in transiting above the fire and an advection parameter that addresses the degree to which the environmental wind advects updrafts away from the fire. Two-dimensional simulations are performed in which the upstream surface wind speed and mixed layer mean wind speed are varied independently to better understand the fundamental processes governing the organizational mode and updraft strength. The result of these experiments is the identification of two primary classes of dry convection: plume and multicell. Simulated plume cases exhibit weak advection by the mean wind and are subdivided into intense plume and hybrid classes based on the degree of steadiness within the convection column. Hybrid cases contain columns of largely discrete updrafts versus the more continuous updraft column associated with the intense plume mode. Multicell cases develop with strong mixed layer advection and are subdivided into strong and weak classes based on the depth of convection. Intense plume and strong multicell (hybrid and weak multicell) cases occur when the surface buoyancy is large (small). Parcel analyses are performed to more closely examine the forcing of convection within different areas of the parameter space. The multicell (strong and weak) and intense plume modes are forced by a combination of buoyancy and dynamic pressure gradient forcing associated with the perturbation wind field, whereas the hybrid mode is forced by a combination of buoyancy and dynamic pressure gradient forcing associated with the strong background shear. The paper concludes with a discussion of the degree of nonlinearity that is likely to exist at the fire front for each of the convective modes; nonlinear fire behavior is most likely for the hybrid mode and least likely for the weak multicell mode. Knowledge of the sensitivity of the convective mode to upstream conditions can provide information about the degree of nonlinear or erratic fire behavior expected for a given wind profile upstream of the fire.

This study investigates the impact of wind speed and critical level height on dry convection above a prescribed heat source. This is done using the Advanced Regional Prediction System (ARPS) model in its two-dimensional form with an imposed 400-K soil potential temperature perturbation. The result of these experiments is the identification of three modes of convective plumes. The first, termed multicell convective plumes, is analogous to multicell convection generated from squall-line cold pools in the moist atmosphere. The second mode, a deep wave mode, consists of disturbances with wavelengths of 7-10 km and results from the multicell plumes perturbing the dynamically unstable shear flow centered at the critical level. The third mode, termed the intense fire plume, has stronger updrafts than the multicell mode and is marked by quasi-stationary movement and substantial low-level inflow and upper-level outflow. The presence of a critical level is shown to be crucial to the development of both the deep wave and intense plume modes. The intense fire plume mode is most consistent with the so-called fire storm, or conflagration phenomenon, in which strong updrafts and low-level indrafts can produce mesocyclones and tornadic fire whirls capable of significant damage. This study marks an important step in understanding the dynamics behind the fire storm phenomenon, as well as other types of convection (multicell and deep wave) that may be generated by a fire.

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.

A state-of-the-science meso-(beta)-scale numerical weather prediction model is being employed in a prototype forecast system for potential operational use at the Dallas-Fort Worth International Airport (DFW). The numerical model is part of a unique operational forecasting system being developed to support the National Aeronautics and Space Administration's (NASA) Terminal Area Productivity Program. This operational forecasting system will focus on meso-(beta)-scale aviation weather problems involving planetary boundary layer (PBL) turbulence, and is named the Terminal Area PBL Prediction System (TAPPS). TAPPS (version 1) is being tested and developed for NASA in an effort to improve 1-6-h terminal area forecasts of wind, vertical wind shear, temperature, and turbulence within both stable and convective PBLs at major airport terminal areas. This is being done to enhance terminal area productivity, that is, aircraft arrival and departure throughput, by using the weather forecasts as part of the Aircraft Vortex Spacing System (AVOSS). AVOSS is dependent upon nowcasts or short-period forecasts of wind, temperature, and eddy dissipation rate so that the drift and dissipation of wake vortices can be anticipated for safe airport operation. This AVOSS system will be demonstrated during calendar year 2000 at DFW. This paper describes the numerical modeling system, which has three basic components: the numerical model, the initial data stream, and the postprocessing system. Also included are the results of several case study simulations with the numerical model from a field program that occurred in September 1997 at DFW. During this field program, detailed local measurements throughout the troposphere, with special emphasis on the PBL, were taken at and surrounding DFW in an effort to verify the numerical model simulations. Comparisons indicate that the numerical model is capable of an accurate simulation of the vertical wind shear structure during the diurnal evolution of the PBL when compared directly to specific local observations. The case studies represent unambiguous examples of the dynamics of the Great Plains diurnal low-level jet stream. This diurnal jet stream represents the dominant low-level wind shear-production mechanism during quiescent synoptic-scale flow regimes. Five consecutive daily case studies, during which this phenomenon was observed over and in proximity to DFW, are compared to the products derived from TAPPS.

In this study, the Flexible Particle (FLEXPART)-WRF, a Lagrangian particle dispersion model, is employed to simulate pollutant dispersion in and near the Lehigh Gap, a gap in a prominent ridgeline in eastern Pennsylvania. FLEXPART-WRF is used to evaluate the diagnostic value of the ventilation index (VI), an index that describes the potential for smoke or other pollutants to ventilate away from a source, for indicating dispersion potential in complex terrain. Little is known about the effectiveness of the ventilation index in diagnosing dispersion potential in complex terrain. The modeling approach used in this study is to release a dense cloud of particles across a portion of the model domain and evaluate particle behavior and VI diagnostic value in areas of the domain with differing terrain characteristics. Although both horizontal and vertical dispersion are examined, the study focuses primarily on horizontal dispersion, assessed quantitatively by calculating horizontal residence time (HRT) within a 1-km-radius circle surrounding the particle release point. Analysis of HRT across the domain reveals horizontal dispersion patterns that are influenced by the ridgeline and the Lehigh Gap. Comparison of VI and HRT in different areas of the domain reveals a robust relationship windward of the ridgeline and a weak relationship leeward of the ridgeline and in the vicinity of the Lehigh Gap. The results of this study suggest that VI users should consider whether they are windward or leeward of topographic features, and highlight the need for an alternative metric that better takes into account the influence of the terrain on dispersion. [ABSTRACT FROM AUTHOR]

The Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of -1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg-1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CAreveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes. [ABSTRACT FROM AUTHOR]

A fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average ('climatology') using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS. [ABSTRACT FROM AUTHOR]

This study examines the sensitivity of mean and turbulent flow in the planetary boundary layer and roughness sublayer to a low-intensity fire and evaluates whether the sensitivity is dependent on canopy and background atmospheric properties. The ARPS-CANOPY model, a modified version of the Advanced Regional Prediction System (ARPS) model with a canopy parameterization, is utilized for this purpose. A series of numerical experiments are conducted to evaluate whether the ability of the fire to alter downstream wind, temperature, turbulent kinetic energy (TKE), and vertical heat flux differs between forested and open areas, sparse and dense forests, weak and strong background flow, and neutral and convective background stability. Analysis of all experiments shows that, in general, mean and turbulent flow both prior to and during a low-intensity fire is damped in the presence of a canopy. Greater sensitivity to the fire is found in cases with strong ambient wind speed than in cases with quiescent or weak wind speed. Furthermore, sensitivity of downstream atmospheric conditions to the fire is shown to be strongest with a neutrally stratified background. An analysis of the TKE budget reveals that both buoyancy and wind shear contribute to TKE production during the period of time in which the fire conditions are applied to the model. On the basis of the results of the ARPS simulations, caution is advised when applying ARPS-simulation results to predictions of smoke transport and dispersion: smoke-model users should consider whether canopy impacts on the atmosphere are accounted for and whether neutral stratification is assumed. [ABSTRACT FROM AUTHOR]

Smoke prediction products are one of the tools used by land management personnel for decision making regarding prescribed fires. This study documents the application to a prescribed fire of a smoke prediction system that employs ARPS-CANOPY, a modified version of the Advanced Regional Prediction System (ARPS) model containing a canopy submodel, as the meteorological driver. In this paper, the performance of ARPS-CANOPY in simulating meteorological fields in the vicinity of a low-intensity fire is assessed using flux-tower data collected prior to and during a low-intensity prescribed fire in the New Jersey Pine Barrens in March 2011. A three-dimensional high-resolution plant area density dataset is utilized to define the characteristics of the canopy, and the fire is represented in ARPS-CANOPY as a heat flux to the atmosphere. The standard ARPS model is compared with reanalysis and upper-air data to establish that the model can simulate the observed synoptic-mesoscale and planetary boundary layer features that are salient to this study. ARPS-CANOPY profiles of mean turbulent kinetic energy, wind speed/direction, and temperature exhibit patterns that appear in the flux-tower observations during both the preburn phase of the experiment and the period of time the flux tower experienced perturbed atmospheric conditions due to the impinging fire. Last, the character and source of turbulence in and around the fire line are examined. These results are encouraging for smoke prediction efforts since transport of smoke from low-intensity fires is highly sensitive to the near-surface meteorological conditions and, in particular, turbulent flows. [ABSTRACT FROM AUTHOR]

The Haines index (HI) is a fire-weather index that is widely used as an indicator of the potential for dry, low-static-stability air in the lower atmosphere to contribute to erratic fire behavior or large fire growth. This study examines the interannual variability of HI over North America and its relationship to indicators of large-scale circulation anomalies. The results show that the first three HI empirical orthogonal function modes are related respectively to El Niño-Southern Oscillation (ENSO), the Arctic Oscillation (AO), and the interdecadal sea surface temperature variation over the tropical Pacific Ocean. During the negative ENSO phase, an anomalous ridge (trough) is evident over the western (eastern) United States, with warm/dry weather and more days with high HI values in the western and southeastern United States. During the negative phase of the AO, an anomalous trough is found over the western United States, with wet/cool weather and fewer days with high HI, while an anomalous ridge occurs over the southern United States-northern Mexico, with an increase in the number of days with high HI. After the early 1990s, the subtropical high over the eastern Pacific Ocean and the Bermuda high were strengthened by a wave train that was excited over the tropical western Pacific Ocean and resulted in warm/dry conditions over the southwestern United States and western Mexico and wet weather in the southeastern United States. The above conditions are reversed during the positive phase of ENSO and AO and before the early 1990s. [ABSTRACT FROM AUTHOR]

This study presents a spatial and temporal climatology of major wildfire events, defined as >100 acres burned (>40.47 ha, where 1 ha = 2.47 acre), in the northeast United States from 1999 to 2009 and the meteorological conditions associated with these events. The northeast United States is divided into two regions: region 1 is centered over the higher terrain of the northeast United States and region 2 is primarily over the coastal plain. About 59% of all wildfire events in these two regions occur in April and May, with ~76% in region 1 and ~53% in region 2. There is large interannual variability in wildfire frequency, with some years having 4-5 times more fire events than other years. The synoptic flow patterns associated with northeast United States wildfires are classified using the North American Regional Reanalysis. The most common synoptic pattern for region 1 is a surface high pressure system centered over the northern Appalachians, which occurred in approximately 46% of all events. For region 2, the prehigh anticyclone type extending from southeast Canada and the Great Lakes to the northeast United States is the most common pattern, occurring in about 46% of all events. A trajectory analysis highlights the influence of large-scale subsidence and decreasing relative humidity during the events, with the prehigh pattern showing the strongest subsidence and downslope drying in the lee of the Appalachians. [ABSTRACT FROM AUTHOR]

The performance of a multimodel ensemble over the northeast United States is evaluated before and after applying bias correction and Bayesian model averaging (BMA). The 13-member Stony Brook University (SBU) ensemble at 0000 UTC is combined with the 21-member National Centers for Environmental Pre-diction (NCEP) Short-Range Ensemble Forecast (SREF) system at 2100 UTC. The ensemble is verified using 2-m temperature and 10-m wind speed for the 2007-09 warm seasons, and for subsets of days with high ozone and high fire threat. The impacts of training period, bias-correction method, and BMA are explored for these potentially hazardous weather events using the most recent consecutive (sequential training) and most recent similar days (conditional training). BMA sensitivity to the selection of ensemble members is explored. A running mean difference between forecasts and observations using the last 14 days is better at removing temperature bias than is a cumulative distribution function (CDF) or linear regression approach. Wind speed bias is better removed by adjusting the modeled CDF to the observation. High fire threat and ozone days exhibit a larger cool bias and a greater negative wind speed bias than the warm-season average. Conditional bias correction is generally better at removing temperature and wind speed biases than sequential training. Greater probabilistic skill is found for temperature using both conditional bias correction and BMA com-pared to sequential bias correction with or without BMA. Conditional and sequential BMA results are similar for 10-m wind speed, although BMA typically improves probabilistic skill regardless of training. [ABSTRACT FROM AUTHOR]