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.

Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire‐dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study. Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above‐ground ecology, (d) fire effects on below‐ground ecology, (e) fire behaviour and (f) fire ecology modelling. We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts. Synthesis: As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives.

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 firebehavior 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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This paper describes a coupled fire–atmosphere model that uses a sophisticated high-resolution non-hydrostatic numerical mesoscale model to predict the local winds which are then used as input to the prediction of fire spread. The heat and moisture fluxes from the fire are then fed back to the dynamics, allowing the fire to influence its own mesoscale winds that in turn affect the fire behavior. This model is viewed as a research model and as such requires a fireline propagation scheme that systematically converges with increasing spatial and temporal resolution. To achieve this, a local contour advection scheme was developed to track the fireline using four tracer particles per fuel cell, which define the area of burning fuel. Using the dynamically predicted winds along with the terrain slope and fuel characteristics, algorithms from the BEHAVE system are used to predict the spread rates. A mass loss rate calculation, based on results of the BURNUP fuel burnout model, is used to treat heat exchange between the fire and atmosphere. Tests were conducted with the uncoupled model to test the fire-spread algorithm under specified wind conditions for both spot and line fires. Using tall grass and chaparral, line fires were simulated employing the full fire–atmosphere coupling. Results from two of these experiments show the effects of fire propagation over a small hill. As with previous coupled experiments, the present results show a number of features common to real fires. For example, we show how the well-recognized elliptical fireline shape is a direct result of fire–atmosphere interactions that produce the ‘heading’, ‘flanking’, and ‘backing’ regions of a wind-driven fire with their expected behavior. And, we see how perturbations upon this shape sometimes amplify to become fire whirls along the flanks, which are transported to the head of the fire where they may interact to produce erratic fire behavior.

A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.

Models that simulate wildland fires span a vast range of complexity; the most physically complex present a difficult supercomputing challenge that cannot be solved fast enough to become a forecasting tool. Coupled atmosphere–fire model simulations of the Big Elk Fire, a wildfire that occurred in the Colorado Front Range during 2002, are used to explore whether some factors that make simulations more computationally demanding (such as coupling between the fire and the atmosphere and fine atmospheric model resolution) are needed to capture wildland fire parameters of interest such as fire perimeter growth. In addition to a Control simulation, other simulations remove the feedback to the atmospheric dynamics and use increasingly coarse atmospheric resolution, including some that can be computed in faster than real time on a single processor. These simulations show that, although the feedback between the fire and atmosphere must be included to capture accurately the shape of the fire, the simulations with relatively coarse atmospheric resolution (grid spacing 100–500 m) can qualitatively capture fire growth and behavior such as surface and crown fire spread and smoke transport. A comparison of the computational performance of the model configured at these different spatial resolutions shows that these can be performed faster than real time on a single computer processor. Thus, although this model still requires rigorous testing over a wide range of fire incidents, it is computationally possible to use models that can capture more complex fire behavior (such as rapid changes in intensity, large fire whirls, and interactions between fire, weather, and topography) than those used currently in the field and meet a faster-than-real-time operational constraint.

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 750C), 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 rot...