Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models

Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on semi-empirical fire spread by the level let method. The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.Comment: Minor revision, except description of the model expanded. 29 pages, 9 figures, 53 reference

Towards a Real-Time Data Driven Wildland Fire Model

A wildland fire model based on semi-empirical relations for the spread rate of a surface fire and post-frontal heat release is coupled with the Weather Research and Forecasting atmospheric model (WRF). The propagation of the fire front is implemented by a level set method. Data is assimilated by a morphing ensemble Kalman filter, which provides amplitude as well as position corrections. Thermal images of a fire will provide the observations and will be compared to a synthetic image from the model state.Comment: 5 pages, 4 figure

A wildland fire model with data assimilation

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.Comment: 35 pages, 12 figures; minor revision January 2008. Original version available from http://www-math.cudenver.edu/ccm/report

Fire as a fundamental ecological process: Research advances and frontiers

© 2020 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society 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

Fire as a fundamental ecological process: Research advances and frontiers

© 2020 The Authors.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.Support was provided by NSF‐DEB‐1743681 to K.K.M. and A.J.T. We thank Shalin Hai‐Jew for helpful discussion of the survey and qualitative methods.Peer reviewe

Computational Modeling of Large Wildfires: A Roadmap

Abstract—Wildland fire behavior, particularly that of large, uncontrolled wildfires, has not been well understood or predicted. Our methodology to simulate this phenomenon uses high-resolution dynamic models made of numerical weather prediction (NWP) models coupled to fire behavior models to simulate fire behavior. NWP models are capable of modeling very high resolution (< 100 m) atmospheric flows. The wildland fire component is based upon semi-empirical formulas for fireline rate of spread, post-frontal heat release, and a canopy fire. The fire behavior is coupled to the atmospheric model such that low level winds drive the spread of the surface fire, which in turn releases sensible heat, latent heat, and smoke fluxes into the lower atmosphere, feeding back to affect the winds directing the fire. These coupled dynamic models capture the rapid spread downwind, flank runs up canyons, bifurcations of the fire into two heads, and rough agreement in area, shape, and direction of spread at periods for which fire location data is available. Yet, intriguing computational science questions arise in applying such models in a predictive manner, including physical processes that span a vast range of scales, processes such as spotting that cannot be modeled deterministically, estimating the consequences of uncertainty, the efforts to steer simulations with field data ("data assimilation"), lingering issues with short term forecasting of weather that may show skill only on the order of a few hours, and the difficulty of gathering pertinent data for verification and initialization in a dangerous environment. Keywords-Numerical model; weather prediction, wildland fire model, wildfire, forest fire, fire behavior I

The Generation and Forecast of Extreme Winds during the Origin and Progression of the 2017 Tubbs Fire

On 8–9 October 2017, fourteen wildfires developed rapidly during a strong Diablo wind event in northern California including the Tubbs Fire, which travelled over 19 km in 3.25 h. Here, we applied the CAWFE® coupled numerical weather prediction-fire modeling system to investigate the airflow regime and extreme wind peaks underlying the extreme fire behavior using simulations that refine from a 10 km to a 185 m horizontal grid spacing. We found that as Diablo winds travelled south down the Sacramento Valley and fanned out southwestward over the Wine Country, their strength waxed and waned and their direction wavered, creating varying locations near fire origins where wind overrunning topography reached 30–40 m/s, along with streaks and bursts of strong winds in the lee of some topographic features and stagnation downstream of others. Despite a statically stable layer in the lowest 1.5 km, the high Froude number flow sometimes resembled a hydraulic jump. Elsewhere, the flow behaved similarly to neutrally-stratified flow over small hills, creating wind extrema that exceeded 40 m/s at the crest of some lesser hills including near the Tubbs fire ignition, but which shed bursts of high speed winds that travel downstream at approximately 5–7-min intervals. Nonetheless, simulated fire growth lagged satellite detection of fire arrival in Santa Rosa by up to 1 h, although whether the data detect fire line or spotting is ambiguous. A forecast simulation with a 370 m horizontal grid spacing produced an on-time fire line arrival in Santa Rosa, with calculations executed 4 times faster than real time on a single computer processor