An Interactive Data-Driven HPC System for Forecasting Weather, Wildland Fire, and Smoke

Mandel, Jan; Vejmelka, Martin; Kochanski, Adam K.; Farguell, Angel; Haley, James; Mallia, Derek V.; Hilburn, Kyle

doi:10.1109/urgenthpc49580.2019.00010

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…Coupled fire-atmosphere models such as WRF-SFIRE [11] render two-way interactions between the atmosphere and fire. In these models, heat and moisture fluxes at the fireline are fed back into the atmospheric core, altering local air temperature, humidity, and winds, which are responsible for driving fire behavior, and smoke emissions and dispersion [12,13]. Coupled fire-atmosphere models can also explicitly resolve pyroconvection and therefore do not rely on external plume rise parameterizations.…”

Section: Introductionmentioning

confidence: 99%

“…RxCADRE represents one of the most comprehensive efforts to characterize fuels and fuel consumption while simultaneously monitoring a fire, its surrounding micrometeorology, and downwind smoke dispersion [23][24][25][26]. Fire simulations and smoke dispersion for RxCADRE were generated using the Weather and Research and Forecast model coupled with a fire spread model (WRF-SFIRE) [11,13].…”

Section: Introductionmentioning

confidence: 99%

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Incorporating a Canopy Parameterization within a Coupled Fire-Atmosphere Model to Improve a Smoke Simulation for a Prescribed Burn

et al. 2020

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Forecasting fire growth, plume rise and smoke impacts on air quality remains a challenging task. Wildland fires dynamically interact with the atmosphere, which can impact fire behavior, plume rises, and smoke dispersion. For understory fires, the fire propagation is driven by winds attenuated by the forest canopy. However, most numerical weather prediction models providing meteorological forcing for fire models are unable to resolve canopy winds. In this study, an improved canopy model parameterization was implemented within a coupled fire-atmosphere model (WRF-SFIRE) to simulate a prescribed burn within a forested plot. Simulations with and without a canopy wind model were generated to determine the sensitivity of fire growth, plume rise, and smoke dispersion to canopy effects on near-surface wind flow. Results presented here found strong linkages between the simulated fire rate of spread, heat release and smoke plume evolution. The standard WRF-SFIRE configuration, which uses a logarithmic interpolation to estimate sub-canopy winds, overestimated wind speeds (by a factor 2), fire growth rates and plume rise heights. WRF-SFIRE simulations that implemented a canopy model based on a non-dimensional wind profile, saw significant improvements in sub-canopy winds, fire growth rates and smoke dispersion when evaluated with observations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporating a Canopy Parameterization within a Coupled Fire-Atmosphere Model to Improve a Smoke Simulation for a Prescribed Burn

et al. 2020

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“…WRF-SFIRE is a coupled fire-atmosphere model (Mandel et al, 2011(Mandel et al, , 2014(Mandel et al, , 2019, which simultaneously resolves atmospheric dynamics, fire progression, fuel moisture conditions, and fireatmosphere interactions. This system integrates the WRF model with a fire spread model (SFIRE), where the fire-rate-of-spread is estimated based on the Rothermel model (Rothermel, 1972).…”

Section: Wrf-sfire Descriptionmentioning

confidence: 99%

Integration of a Coupled Fire-Atmosphere Model Into a Regional Air Quality Forecasting System for Wildfire Events

Kochanski

Herron-Thorpe

Mallia

et al. 2021

Front. For. Glob. Change

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The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was <3 h for a 48 h-long forecast, suggesting that integrating coupled fire-atmosphere simulations within the daily AIRPACT cycle is feasible. While the WRF-SFIRE forecasts realistically captured fire growth 2 days in advance, the largest improvements in the air quality simulations were associated with the wildfire plume rise. WRF-SFIRE-estimated plume tops were within 300-m of satellite-estimated plume top heights for both case studies analyzed in this study. Air quality simulations produced by AIRPACT with and without WRF-SFIRE inputs were evaluated with nearby PM2.5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

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“…The WRF-SFIRE modeling system [4,5] couples Weather Research Forecasting (WRF) model with a wildfire spread model and a fuel moisture content (FMC) model. The FMC is an important factor in wildfire behavior, as it underlies the diurnal variability and different severity of wildfires.…”

Section: Dedicated To the Memory Of Professor Radim Blaheta 1 Introdu...mentioning

confidence: 99%

Building a Fuel Moisture Model for the Coupled Fire-Atmosphere Model WRF-SFIRE from Data: From Kalman Filters to Recurrent Neural Networks

Mandel¹,

Hirschi²,

Kochanski³

et al. 2023

Preprint

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An Interactive Data-Driven HPC System for Forecasting Weather, Wildland Fire, and Smoke

Cited by 8 publications

References 33 publications

Incorporating a Canopy Parameterization within a Coupled Fire-Atmosphere Model to Improve a Smoke Simulation for a Prescribed Burn

Incorporating a Canopy Parameterization within a Coupled Fire-Atmosphere Model to Improve a Smoke Simulation for a Prescribed Burn

Integration of a Coupled Fire-Atmosphere Model Into a Regional Air Quality Forecasting System for Wildfire Events

Building a Fuel Moisture Model for the Coupled Fire-Atmosphere Model WRF-SFIRE from Data: From Kalman Filters to Recurrent Neural Networks

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