Handle URI:
http://hdl.handle.net/10754/597823
Title:
Computational Modeling of Large Wildfires: A Roadmap
Authors:
Coen, Janice L.; Douglas, Craig C.
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. © 2010 IEEE.
Citation:
Coen JL, Douglas CC (2010) Computational Modeling of Large Wildfires: A Roadmap. 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science. Available: http://dx.doi.org/10.1109/DCABES.2010.29.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Aug-2010
DOI:
10.1109/DCABES.2010.29
Type:
Conference Paper
Sponsors:
This research was supported in part by NSF grants1018072, 1018079, 0324910 and 0835598 and Award No.KUS-C1-016-04, made by King Abdullah University ofScience and Technology (KAUST). The National Centerfor Atmospheric Research is sponsored by the NationalScience Foundation. Any opinions, findings, andconclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect theviews of the National Science Foundation. Phillip J.Riggan, U.S.D.A. Forest Service, provided image 4.
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Full metadata record

DC FieldValue Language
dc.contributor.authorCoen, Janice L.en
dc.contributor.authorDouglas, Craig C.en
dc.date.accessioned2016-02-25T12:57:20Zen
dc.date.available2016-02-25T12:57:20Zen
dc.date.issued2010-08en
dc.identifier.citationCoen JL, Douglas CC (2010) Computational Modeling of Large Wildfires: A Roadmap. 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science. Available: http://dx.doi.org/10.1109/DCABES.2010.29.en
dc.identifier.doi10.1109/DCABES.2010.29en
dc.identifier.urihttp://hdl.handle.net/10754/597823en
dc.description.abstractWildland 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. © 2010 IEEE.en
dc.description.sponsorshipThis research was supported in part by NSF grants1018072, 1018079, 0324910 and 0835598 and Award No.KUS-C1-016-04, made by King Abdullah University ofScience and Technology (KAUST). The National Centerfor Atmospheric Research is sponsored by the NationalScience Foundation. Any opinions, findings, andconclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect theviews of the National Science Foundation. Phillip J.Riggan, U.S.D.A. Forest Service, provided image 4.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectFire behavioren
dc.subjectForest fireen
dc.subjectNumerical modelen
dc.subjectWeather predictionen
dc.subjectWildfireen
dc.subjectWildland fire modelen
dc.titleComputational Modeling of Large Wildfires: A Roadmapen
dc.typeConference Paperen
dc.identifier.journal2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Scienceen
dc.contributor.institutionNational Center for Atmospheric Research, Boulder, United Statesen
dc.contributor.institutionUniversity of Wyoming, Laramie, United Statesen
kaust.grant.numberKUS-C1-016-04en
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