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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]

Wildland fire–atmosphere interaction generates complex turbulence patterns, organized across multiple scales, which inform fire-spread behaviour, firebrand transport, and smoke dispersion. Here, we utilize wavelet-based techniques to explore the characteristic temporal scales associated with coherent patterns in the measured temperature and the turbulent fluxes during a prescribed wind-driven (heading) surface fire beneath a forest canopy. We use temperature and velocity measurements from tower-mounted sonic anemometers at multiple heights. Patterns in the wavelet-based energy density of the measured temperature plotted on a time–frequency plane indicate the presence of fire-modulated ramp–cliff structures in the low-to-mid-frequency band (0.01–0.33 Hz), with mean ramp durations approximately 20% shorter and ramp slopes that are an order of magnitude higher compared to no-fire conditions. We then investigate heat- and momentum-flux events near the canopy top through a cross-wavelet coherence analysis. Briefly before the fire-front arrives at the tower base, momentum-flux events are relatively suppressed and turbulent fluxes are chiefly thermally-driven near the canopy top, owing to the tilting of the flame in the direction of the wind. Fire-induced heat-flux events comprising warm updrafts and cool downdrafts are coherent down to periods of a second, whereas ambient heat-flux events operate mainly at higher periods (above 17 s). Later, when the strongest temperature fluctuations are recorded near the surface, fire-induced heat-flux events occur intermittently at shorter scales and cool sweeps start being seen for periods ranging from 8 to 35 s near the canopy top, suggesting a diminishing influence of the flame and increasing background atmospheric variability thereat. The improved understanding of the characteristic time scales associated with fire-induced turbulence features, as the fire-front evolves, will help develop more reliable fire behaviour and scalar transport models. [ABSTRACT FROM AUTHOR]

The ventilation index (VI) is an index that describes the potential for smoke or other pollutants to disperse from a source. In this study, a Lagrangian particle dispersion model was utilized to examine smoke dispersion and the diagnostic value of VI during a September 2018 prescribed fire in southwestern Colorado. Smoke dispersion in the vicinity of the fire was simulated using the FLEXPART-WRF particle dispersion model, driven by meteorological outputs from Advanced Regional Prediction System (ARPS) simulations of the background (non-fire) conditions. Two research questions are posed: (1) Is a horizontal grid spacing of 4 km comparable to the finest grid spacing currently used in operational weather models and sufficient to capture the spatiotemporal variability in wind and planetary boundary layer (PBL) structure during the fire? (2) What is the relationship between VI and smoke dispersion during the prescribed fire event, as measured by particle residence time within a given horizontal or vertical distance from each particle's release point? The ARPS no-fire simulations are shown to generally reproduce the observed variability in weather variables, with greatest fidelity to observations found with horizontal grid spacing of approximately 1 km or less. It is noted that there are considerable differences in particle residence time (i.e., dispersion) at different elevations, with VI exhibiting greater diagnostic value in the southern half of the domain, farthest from the higher terrain across the north. VI diagnostic value is also found to vary temporally, with diagnostic value greatest during the mid-morning to mid-afternoon period, and lowest during thunderstorm outflow passage in the late afternoon. Results from this study are expected to help guide the application of VI in complex terrain, and possibly inform development of new dispersion potential metrics. [ABSTRACT FROM AUTHOR]

The impact of a forest canopy on smoke concentration is assessed by applying a numerical weather prediction model coupled with a Lagrangian particle dispersion model to two low-intensity wildland (prescribed) fires in the New Jersey Pine Barrens. A comparison with observations indicates that the coupled numerical model can reproduce some of the observed variations in surface smoke concentrations and plume heights. Model sensitivity analyses highlight the effect of the forest canopy on simulated meteorological conditions, smoke concentrations, and plume heights. The forest canopy decreases near-surface wind speed, increases buoyancy, and increases turbulent mixing. Sensitivities to the time of day, plant area density profiles, and fire heat fluxes are documented. Analyses of temporal variations in smoke concentrations indicate that the effect of the transition from a daytime to a nocturnal planetary boundary layer is weaker when sensible heat fluxes from the fires are stronger. The results illustrate the challenges in simulating meteorological conditions and smoke concentrations at scales where interactions between the fire, fuels, and atmosphere are critically important. The study demonstrates the potential for predictive tools to be developed and implemented that could help fire and air-quality managers assess local air-quality impacts during low-intensity wildland fires in forested environments. [ABSTRACT FROM AUTHOR]

Fire weather indices are commonly used by fire weather forecasters to predict when weather conditions will make a wildland fire difficult to manage. Complex interactions at multiple scales between fire, fuels, topography, and weather make these predictions extremely difficult. We define a new fire weather index called the Hot-Dry-Windy Index (HDW). HDW uses the basic science of how the atmosphere can affect a fire to define the meteorological variables that can be predicted at synoptic-and meso-alpha-scales that govern the potential for the atmosphere to affect a fire. The new index is formulated to account for meteorological conditions both at the Earth’s surface and in a 500-m layer just above the surface. HDW is defined and then compared with the Haines Index (HI) for four historical fires. The Climate Forecast System Reanalysis (CFSR) is used to provide the meteorological data for calculating the indices. Our results indicate that HDW can identify days on which synoptic-and meso-alpha-scale weather processes can contribute to especially dangerous fire behavior. HDW is shown to perform better than the HI for each of the four historical fires. Additionally, since HDW is based on the meteorological variables that govern the potential for the atmosphere to affect a fire, it is possible to speculate on why HDW would be more or less effective based on the conditions that prevail in a given fire case. The HI, in contrast, does not have a physical basis, which makes speculation on why it works or does not work difficult because the mechanisms are not clear. [ABSTRACT FROM AUTHOR]

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]

• The study offers insights into atmospheric turbulence during intense grass fires. • These findings are significant as they provide the first-ever in-situ turbulence data collected during Red Flag Warnings. • The data establishes an upper limit for turbulence in prescribed grass fires, offering valuable reference points. • These results have the potential to enhance turbulence schemes in fire behavior models, contributing to better fire predictions. • This study underscores the complexities of studying fire type effects in a real-world context. A wildland fire experiment was conducted on a 40-ha tall grass prairie in Texas to study fire-atmosphere interactions. The experiment coincided with a Red Flag Warning, signifying specific weather conditions associated with an elevated risk of wildfire ignition and rapid spread. This provided a rare opportunity to observe wildland fire behavior and its impact on atmospheric flows under conditions deliberately avoided in similar experiments. This study analyzed the high frequency meteorological data collected during the experiment using three-dimensional sonic anemometers mounted on a 43-m tower (at 5.8, 10 and 20 m) and on three 10-m towers (at 5.3 m) within the burn block. The tower placements were carefully chosen to capture the impact of both head and flank fires on the mean and turbulent flows. The hypothesis is that fire-induced perturbations in the lower atmosphere, quantified through parameters like turbulent kinetic energy (TKE) and vertical fluxes, would be substantially stronger than those observed in similar wildland fires under less fire-prone atmospheric conditions. While this hypothesis was validated for heat flux and TKE, it did not hold for momentum fluxes or friction velocity. Additionally, the observations failed to confirm the hypothesis that head fires would produce more intense perturbations than flank fires. Given the homogeneity of fuel, terrain and meteorological elements across the burn unit, this underscores the challenge of disentangling the effects of fire type from other natural factors in a real-world setting. Nevertheless, the finding holds promise for improving turbulence parameterizations in fire-behavior and smoke dispersion models. [ABSTRACT FROM AUTHOR]

Mesoscale models are a class of atmospheric numerical model designed to simulate atmospheric phenomena with horizontal scales of about 2–200 km, although they are also applied to microscale phenomena with horizontal scales of less than about 2 km. Mesoscale models are capable of simulating wildland fire impacts on atmospheric flows if combustion byproducts (e.g., heat, smoke) are properly represented in the model. One of the primary challenges encountered in applying a mesoscale model to studies of fire-perturbed flows is the representation of the fire sensible heat source in the model. Two primary methods have been implemented previously: turbulent sensible heat flux, either in the form of an exponentially-decaying vertical heat flux profile or surface heat flux; and soil temperature perturbation. In this study, the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy submodel, is utilized to simulate the turbulent atmosphere during a low-intensity operational prescribed fire in the New Jersey Pine Barrens. The study takes place in two phases: model assessment and model sensitivity. In the model assessment phase, analysis is limited to a single control simulation in which the fire sensible heat source is represented as an exponentially decaying vertical profile of turbulent sensible heat flux. In the model sensitivity phase, a series of simulations are conducted to explore the sensitivity of model–observation agreement to (i) the method used to represent the fire sensible heat source in the model and (ii) parameters controlling the magnitude and vertical distribution of the sensible heat source. In both phases, momentum and scalar fields are compared between the model simulations and data obtained from six flux towers located within and adjacent to the burn unit. The multi-dimensional model assessment confirms that the model reproduces the background and fire-perturbed atmosphere as depicted by the tower observations, although the model underestimates the turbulent kinetic energy at the top of the canopy at several towers. The model sensitivity tests reveal that the best agreement with observations occurs when the fire sensible heat source is represented as a turbulent sensible heat flux profile, with surface heat flux magnitude corresponding to the peak 1 min mean observed heat flux averaged across the flux towers, and an e -folding extinction depth corresponding to the average canopy height in the burn unit. The study findings provide useful guidance for improving the representation of the sensible heat released from low-intensity prescribed fires in mesoscale models. [ABSTRACT FROM AUTHOR]

Mesoscale models are a class of atmospheric numerical model designed to simulate atmospheric phenomena with horizontal scales of about 2-200 km, although they are also applied to microscale phenomena, with horizontal scales less than about 2 km. Mesoscale models are capable of simulating wildland fire impacts on atmospheric flows if combustion by-products (e.g., heat, smoke) are properly represented in the model. One of the primary challenges encountered in applying a mesoscale model to studies of fire-perturbed flows is the representation of the fire sensible heat source in the model. Two primary methods have been implemented previously: turbulent sensible heat flux, either in the form of an exponentially-decaying vertical heat flux profile or surface heat flux; and soil temperature perturbation. In this study, the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy submodel, is utilized to simulate the turbulent atmosphere during a low-intensity operational prescribed fire in the New Jersey Pine Barrens. The study takes place in two phases: model assessment and model sensitivity. In the model assessment phase, analysis is limited to a single control simulation in which the fire sensible heat source is represented as an exponentially-decaying vertical profile of turbulent sensible heat flux. In the model sensitivity phase, a series of simulations are conducted to explore the sensitivity of model-observation agreement to (i) the method used to represent the fire sensible heat source in the model and (ii) parameters controlling the magnitude and vertical distribution of the sensible heat source. In both phases, momentum and scalar fields are compared between the model simulations and data obtained from six flux towers located within and adjacent to the burn unit. The multi-dimensional model assessment confirms that the model reproduces the background and fire-perturbed atmosphere as depicted by the tower observations, although the model underestimates the turbulent kinetic energy at the top of the canopy at several towers. The model sensitivity tests reveal that the best agreement with observations occurs when the fire sensible heat source is represented as a turbulent sensible heat flux profile, with surface heat flux magnitude corresponding to the peak 1-min mean observed heat flux averaged across the flux towers, and an e-folding extinction depth corresponding to the average canopy height in the burn unit. The study findings provide useful guidance for improving the representation of the sensible heat released from low-intensity prescribed fires in mesoscale models. [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]

Abstract: Dispersal can play an important role in the population dynamics of forest insects, but the role of long-distance immigration and emigration remains unclear due to the difficulty of quantifying dispersal distance and direction. We designed an agent-based spruce budworm flight behavior model that, when interfaced with temperature, wind speed, and precipitation output from a high-resolution atmospheric model, produces detailed flight trajectories and deposition patterns over large landscapes. Rules and relationships describing budworm adult (moth) lift-off, ascent, horizontal flight, and descent were parameterized using a detailed empirical study of budworm dispersal behavior and corresponding meteorological conditions during a 1970s outbreak in New Brunswick, Canada. Simulated moth landings were assumed to be dependent in part on the availability of suitable host tree species. We applied the model to a 6.4million ha landscape at the border between northern Minnesota (USA) and Ontario (Canada) during an eight-day flight window in late June 2007. Specimens collected during and after this flight window indicated moths emerging from an inland source of outbreak populations dispersed over 150km to trap sites near the north shore of Lake Superior, where localized cooling was predicted to have delayed emergence of locally-produced budworm moths. Simulations suggested immigration of moths to lakeshore sites from the outbreak source was plausible on three of eight dates within the flight window, but the relatively narrow deposition footprints implied immigration occurred on different dates across lakeshore sites. Apart from wind speed and direction, precipitation and low temperatures limited dispersal to substantially shorter distances for a few dates within the simulated flight window. Key uncertainties limiting our understanding of atmospheric transport of spruce budworm include behavioral responses to vertical heterogeneity in the air temperature profile, the precipitation threshold required for the forced descent of moths from the air column, and search mechanisms affecting host and/or mate location during long-distance flight. [Copyright &y& Elsevier]

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]

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. [ABSTRACT FROM AUTHOR]

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 upstreamof the fire. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increase in wildfire risk in the United States in recent decades has been linked to rapid growth of the wildland‐urban interface and to changing climate. While there have been numerous studies on wildfires and climate change, few have separately assessed the impact of climate response to land‐use‐land‐cover change (LULCC) on wildfires. In this study, we analyse two 10‐year regional climate simulations driven by the current (2011) and future (2100) land‐use‐land‐cover patterns to assess modifications by the projected LULCC to the frequency and severity of fire‐prone atmospheric conditions described by two fire weather indices, the Canadian Forest Fire Weather Index and the Hot‐Dry‐Windy Index. The simulation corresponding to future land‐use‐land‐cover pattern yields higher surface temperature and vapour pressure deficit and lower precipitation compared to the simulation with the current pattern in areas where urbanized landscapes replace forests and grasslands, such as along the Piedmont and outside the Chicagoland region, while in areas where croplands replace forests, such as the southeast Coastal Plains, the results are reversed. These changes to local and regional atmospheric conditions lead to longer fire seasons and more extreme fire‐weather conditions in much of the eastern United States, specifically in the Southeast and Ohio River Valley where significant urban expansion is projected by the end of the century. Whereas in Southern California where some highly flammable shrublands will be replaced by urban or crop lands, fire‐prone atmospheric conditions are likely to be less frequent and less extreme in the future. However, much of California moves towards a year‐round fire season under the projected LULCC. The results suggest that by altering atmospheric conditions, LULCC may play an important role in determining fire regime, but the effects are highly heterogeneous and regionalized. [ABSTRACT FROM AUTHOR]

In this paper, we describe and analyze a climatology of the Hot-Dry-Windy Index (HDW), with the goal of providing fire-weather forecasters with information about the daily and seasonal variability of the index. The 30-year climatology (1981–2010) was produced using the Climate Forecast System Reanalysis (CFSR) for the contiguous United States, using percentiles to show seasonal and geographical variations of HDW contained within the climatology. The method for producing this climatology is documented and the application of the climatology to historical fire events is discussed. We show that the HDW climatology provides insight into near-surface climatic conditions that can be used to identify temperature and humidity trends that correspond to climate classification systems. Furthermore, when used in conjunction with daily traces of HDW values, users can follow trends in HDW and compare those trends with historical values at a given location. More usefully, this climatology adds value to HDW forecasts; by combining the CFSR climatology and a Global Ensemble Forecast System (GEFS) ensemble history and forecast, we can produce a single product that provides seasonal, climatological, and short-term context to help determine the appropriate fire-management response to a given HDW value. [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]

• Outbreaks of spruce budworm currently affect fir/spruce forests in eastern Canada. • We redeveloped an agent-based, weather-driven model of above-canopy moth dispersal. • New formulations of moth flight response to the environment improve on prior work. • Moth morphology and response differ by sex, producing different dispersal outcomes. • Sex-based roles and dispersal patterns may inform analyses of ongoing outbreaks. Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clemens). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources. [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]

Atmospheric turbulent circulations in the vicinity of wildland fire fronts play an important role in the transfer of momentum into and out of combustion zones, which in turn can potentially affect the behavior and spread of wildland fires. The vertical turbulent transfer of momentum is accomplished via individual sweep, ejection, outward interaction, and inward interaction events, collectively known as sweep-ejection dynamics. This study examined the sweep-ejection dynamics that occurred before, during, and after the passage of a surface fire front during a prescribed fire experiment conducted in an open-canopied forest in the New Jersey Pine Barrens. High-frequency (10 Hz), tower-based, sonic anemometer measurements of horizontal and vertical wind velocity components in the vicinity of the fire front were used to assess the relative frequencies of occurrence of the different types of momentum-flux events, their contributions to the overall momentum fluxes, and their periodicity patterns. The observational results suggest that the presence of surface fire fronts in open-canopied forests can substantially change the sweep-ejection dynamics that typically occur when fires are not present. In particular, sweep events resulting in the downward transport of high horizontal momentum air from above were found to be more prominent during fire-front-passage periods. [ABSTRACT FROM AUTHOR]

The article discusses the dynamics of wild grass fires in the U.S., and a field validation experiment called FireFlux to determine the causes for the same. It distinguishes between a grass fire and a forest fire, focusing on various models such as BehavePlus and Farsite, developed to describe fire behavior and learn the art of fire management. The limitations of such fire behavior models are provided, and crown experiments are discussed, which included atmospheric measurements and routine weather observations. All the relevant research works were conducted with the help of Houston Coastal Center, Texas and the Cooperative State Research, Education, and Extension Service of the U.S.

Before using a fluid dynamics physically based wildfire model to study wildfire, validation is necessary and model results need to be systematically and objectively analyzed and compared to real fires, which requires suitable data sets. Observational data from the Meteotron experiment are used to evaluate the fire-plume properties simulated by two fluid dynamics numerical wildfire models, the Fire Dynamics Simulator (FDS) and the Clark coupled atmosphere–fire model. Comparisons based on classical plume theory between numerical model and experimental Meteotron results show that plume theory, because of its simplifying assumptions, is a fair but restricted rendition of important plume-averaged properties. The study indicates that the FDS, an explicit and computationally demanding model, produces good agreement with the Meteotron results even at a relatively coarse horizontal grid size of 4 m for the FDS, while the coupled atmosphere–fire model, a less explicit and less computationally demanding model, can produce good agreement, but that the agreement is sensitive to surface vertical-grid sizes and the method by which the energy released from the fire is put into the atmosphere. [ABSTRACT FROM AUTHOR]

Lower-tropospheric wind maxima, known as low-level jets (LLJs), play a vital role in weather and climate around the world. In this study, two 10-year (2006–2015) regional climate simulations using current (2011) and future (2100) land-use/land-cover (LULC) patterns over the continental United States (CONUS) are used to assess the sensitivity of LLJ properties, including jet occurrence, maximum speed, and the elevation of the maximum, to changes in LULC. The three simulated LLJ properties exhibit greater sensitivity in summer than in winter. Summertime jets are projected to increase in frequency in the central CONUS, where cropland replaces grassland, and decrease in parts of the Ohio-River Valley and the Southeast, particularly Florida, where urban expansion occurs. Little change is projected for wintertime jet frequency. Larger modifications to jet speed and elevations are projected in parts of the Ohio River Valley, the upper Southeast, and the Intermountain West. While there is some evidence of weaker, more elevated jets with urban expansion, the connection between changes in jet speed and elevation and changes in LULC patterns at a given location is weak. This result suggests that LULC will primarily affect the large-scale atmospheric conditions that contribute to the formation of LLJs, particularly in winter. [ABSTRACT FROM AUTHOR]