Please use this identifier to cite or link to this item: https://hdl.handle.net/10316.2/44621
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dc.contributor.authorKochanski, Adam K.
dc.contributor.authorMandel, Jan
dc.contributor.authorVejmelka, Martin
dc.contributor.authorBurke, Dalton
dc.contributor.authorHearn, Lauren
dc.contributor.authorHaley, James
dc.contributor.authorCaus, Angel Farguell
dc.contributor.authorSchranz, Sher
dc.date.accessioned2018-11-09T22:42:16Z
dc.date.accessioned2020-09-05T02:04:25Z-
dc.date.available2018-11-09T22:42:16Z
dc.date.available2020-09-05T02:04:25Z-
dc.date.issued2018-
dc.identifier.isbn978-989-26-16-506 (PDF)
dc.identifier.urihttps://hdl.handle.net/10316.2/44621-
dc.description.abstractIn this paper, we present an integrated wildland fire forecasting system based on combining a high resolution, multi-scale weather forecasting model, with a semi-empirical fire spread model and a prognostic dead fuel moisture model. The fire-released heat and moisture impact local meteorology which in turn drives the fire propagation and the dead fuel moisture. The prognostic dead fuel moisture model renders the diurnal and spatial fuel moisture variability. The local wind and the fuel moisture variation drive the fire propagation over the landscape. The sub-kilometer model resolution enables detailed representation of complex terrain and small-scale variability in surface properties. The fuel moisture model assimilates surface observations of the 10h fuel moisture from Remote Automated Weather Stations (RAWS) and generates spatial fuel moisture maps used for the fire spread computations. The dead fuel moisture is traced in three different fuel classes (1h, 10h and 100h fuel), which are integrated at any given location based on the local fuel description, to provide the total dead fuel moisture content at the fire-model grid, of a typical resolution of tens of meters. The fire simulations are initialized by a web-based control system allowing a user to define the fire anywhere in CONUS as well as basic simulation properties, such as simulation length, resolution, and type of meteorological forcing for any time meteorological products are available to initialize the weather model. The data is downloaded automatically, and the system monitors execution on a cluster. The simulation results are processed while the model is running and displayed as animations on a dedicated visualization portal.eng
dc.language.isoeng-
dc.publisherImprensa da Universidade de Coimbrapor
dc.relation.ispartofhttp://hdl.handle.net/10316.2/44517por
dc.rightsopen access-
dc.subjectFire forecastingeng
dc.subjectPlume riseeng
dc.subjectWRF-SFIREeng
dc.subjectWRFXeng
dc.subjectwrfxpyeng
dc.subjectCoupled fire-atmosphere modelingeng
dc.subjectWeb portaleng
dc.subjectAutomatic setupeng
dc.titleCoupled fire-atmosphere-smoke forecasting: current capabilities and plans for the futurepor
dc.typebookPartpor
uc.publication.firstPage950-
uc.publication.lastPage958-
uc.publication.locationCoimbrapor
dc.identifier.doi10.14195/978-989-26-16-506_104-
uc.publication.sectionChapter 5 - Decision Support Systems and Toolspor
uc.publication.digCollectionPBpor
uc.publication.orderno104-
uc.publication.areaCiências da Engenharia e Tecnologiaspor
uc.publication.bookTitleAdvances in forest fire research 2018-
uc.publication.manifesthttps://dl.uc.pt/json/iiif/10316.2/44621/200908/manifest?manifest=/json/iiif/10316.2/44621/200908/manifest-
uc.publication.thumbnailhttps://dl.uc.pt/retrieve/11016151-
uc.publication.parentItemId55072-
uc.itemId68184-
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Advances in forest fire research 2018
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