Toward an integrated system for fire, smoke and air quality simulations
Adam K. Kochanski A G , Mary Ann Jenkins A B , Kara Yedinak C , Jan Mandel D , Jonathan Beezley E and Brian Lamb FA Department of Atmospheric Sciences, University of Utah, 135 S 1460 E, 84112 Salt Lake City, UT, USA.
B Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, Canada.
C College of Natural Resources, Forest, Rangeland, and Fire Sciences Department, University of Idaho, ID 83844, USA.
D Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA.
E Kitware, Inc., 28 Corporate Drive, Clifton Park, New York, NY 12065, USA.
F Department of Civil and Environmental Engineering, Washington State University, Pullman, WA 99164, USA.
G Corresponding author. Email: adam.kochanski@utah.edu
International Journal of Wildland Fire 25(5) 534-546 https://doi.org/10.1071/WF14074
Submitted: 3 May 2014 Accepted: 20 January 2015 Published: 11 May 2015
Abstract
In this study, WRF-Sfire is coupled with WRF-Chem to construct WRFSC, an integrated forecast system for wildfire behaviour and smoke prediction. WRF-Sfire directly predicts wildfire spread, plume and plume-top heights, providing comprehensive meteorology and fire emissions to chemical transport model WRF-Chem, eliminating the need for an external plume-rise model. Evaluation of WRFSC was based on comparisons between available observations of fire perimeter and fire intensity, smoke spread, PM2.5 (particulate matter less than 2.5 μm in diameter), NO and ozone concentrations, and plume-top heights with the results of two WRFSC simulations, a 48-h simulation of the 2007 Witch–Guejito Santa Ana fires and a 96-h WRF-Sfire simulation with passive tracers of the 2012 Barker Canyon fire. The study found overall good agreement between forecast and observed local- and long-range fire spread and smoke transport for the Witch–Guejito fire. However, ozone, PM2.5 and NO concentrations were generally underestimated and peaks mistimed in the simulations. This study found overall good agreement between simulated and observed plume-top heights, with slight underestimation by the simulations. Two promising results were the agreement between plume-top heights for the Barker Canyon fire and faster than real-time execution, making WRFSC a possible operational tool.
References
Albini F (1994) PROGRAM BURNUP: a simulation model of the burning of large woody natural fuels. USDA Forest Service, Missoula Fire Science Laboratory, Technical Report Research Grant INT-92754-GR. (Missoula, MT)Anderson H (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, Technical Report INT-122. (Ogden, UT)
Bytnerowicz A, Schilling S, Alexander D, Fraczek W, Hansen M (2010) Passive monitoring to estimate N (NO2, HNO3, NH3) exposure in remote areas and geospatial analysis to optimize monitoring networks in the Athabasca Oil Sands Region. In ‘103 Annual Conference and Exhibition of the Air & Waste Management Association, Calgary Canada’, 22–25 June 2010, Calgary, AB. Extended Abstract 2010-A-563-AWMA. (Curran Associates: Red Hook, NY)
Eidsmoe G (2007) Investigation report: Guejito fire. California Department of Forestry and Fire Protection, Technical Report CA-MVU-010484. (Sacramento, CA)
Emmons L, Walters S, Hess P, Lamarque J, Pfister G, Fillmore D, Granier C, Guenther A, Kinnison D, Laepple T, Orlando J, Tie X, Tyndall G, Wiedinmyer C, Baughcum S, Kloster S (2010) Description and evaluation of the model for ozone and related chemical tracers, version 4 (MOZART-4). Geoscientific Model Development 3, 43–67.
| Description and evaluation of the model for ozone and related chemical tracers, version 4 (MOZART-4).Crossref | GoogleScholarGoogle Scholar |
Freitas S, Longo K, Chatfield R, Latham D, Silva Dias M, Andreae M, Prins E, Santos J, Gielow R, Carvalho J (2007) Including the sub-grid-scale plume rise of vegetation fires in low-resolution atmospheric transport models. Atmospheric Chemistry and Physics 7, 3385–3398.
| Including the sub-grid-scale plume rise of vegetation fires in low-resolution atmospheric transport models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtVWntbfO&md5=f07a933e43771c253e41c31a1a29e842CAS |
Freitas S, Longo K, Trentmann J, Latham D (2010) Technical note: Sensitivity of 1-D smoke plume-rise models to the inclusion of environmental wind drag Atmospheric Chemistry and Physics 10, 585–594.
| Technical note: Sensitivity of 1-D smoke plume-rise models to the inclusion of environmental wind dragCrossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXjsFGjsb4%3D&md5=0456ac2ed4ad7a537fe7770bb496339bCAS |
Goodrick S, Achtemeier G, Larkin N, Liu Y, Strand T (2013) Modelling smoke transport from wildland fires: a review. International Journal of Wildland Fire 22, 83–94.
| Modelling smoke transport from wildland fires: a review.Crossref | GoogleScholarGoogle Scholar |
Grell G, Peckham S, Schmitz R, McKeen S, Frost G, Skamarock W, Eder B (2005) Fully coupled ‘online’ chemistry within the WRF model. Atmospheric Environment 39, 6957–6975.
| Fully coupled ‘online’ chemistry within the WRF model.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXhtFGht7vK&md5=ddd45d6ec9e776ace2df33795a6447beCAS |
Jaffe D, Wigder N (2012) Ozone production from wildfires: a critical review. Atmospheric Environment 51, 1–10.
| Ozone production from wildfires: a critical review.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XjtFGqtbo%3D&md5=8b2ad30e648a5bb501c4520c8eeadc59CAS |
Kochanski A, Jenkins MA, Krueger S, Mandel J, Beezley J (2013) Real-time simulation of 2007 Santa Ana fires Forest Ecology and Management 15, 136–149.
| Real-time simulation of 2007 Santa Ana firesCrossref | GoogleScholarGoogle Scholar |
Larkin N, O’Neill S, Solomon R, Raffuse S, Strand T, Sullivan D, Krull C, Rorig M, Peterson J, Ferguson S (2009) The BlueSky smoke modeling framework. International Journal of Wildland Fire 18, 906–920.
| The BlueSky smoke modeling framework.Crossref | GoogleScholarGoogle Scholar |
Lavdas L (1996) Program VSMOKE user’s manual. USDA Forest Service, Southeastern Forest Experiment Station General, Technical Report SRS-6. (Macon, GA)
Mandel J, Beezley J, Kochanski A (2011) Coupled atmosphere–wildland fire modeling with WRF 3.3 and Sfire. Geoscientific Model Development 4, 591–610.
| Coupled atmosphere–wildland fire modeling with WRF 3.3 and Sfire.Crossref | GoogleScholarGoogle Scholar |
Mandel J, Amram S, Beezley J, Kelman G, Kochanski A, Kondratenko V, Lynn B, Regev B, Vejmelka M (2014) Recent advances and applications of WRF-SFIRE. Natural Hazards and Earth System Sciences 14, 2829–2845.
| Recent advances and applications of WRF-SFIRE.Crossref | GoogleScholarGoogle Scholar |
Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran P, Ebisuzaki W, Jovi’c D, Woollen J, Rogers E, Berbery E, Ek M, Fan Y, Grumbine R, Higgins W, Li H, Lin J, Manikin G, Parrosh D, Shi W (2006) North american regional reanalysis. Bulletin of the American Meteorological Society 87, 343–360.
| North american regional reanalysis.Crossref | GoogleScholarGoogle Scholar |
Pfister G, Avise J, Wiedinmyer C, Edwards D, Emmons L, Diskin G, Podolske J, Wisthaler A (2011) CO source contribution analysis for California during ARCTAS-CARB. Atmospheric Chemistry and Physics 11, 7515–7532.
| CO source contribution analysis for California during ARCTAS-CARB.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsVOrt7bI&md5=3a90a381cdefc8e113cb3ac705945e0cCAS |
Rothermel R (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-115. (Ogden, UT)
Scire J (2000) CALPUFF: overview of capabilities. In ‘Technical highlights of EPA’s 7th Conference on air pollution modeling’. Available at http://www.epa.gov/scram001/7thconf/information/t029day1.pdf [Verified 25 March 2013]
Sestak M, Riebau A (1988) SASEM, Simple Approach Smoke Estimation Model. US Bureau of Land Management, Technical Report Note 382. (US Bureau of Land Management: Wyoming State Office, Cheyenne, WY)
Skamarock W, Klemp J, Dudhia J, Gill D, Barker D, Duda M, Wang W, Powers J (2008) A description of the Advanced Research WRF version 3. National Center for Atmospheric Research, Technical Report NCAR Technical Note 475. (Boulder, CO). Available at http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf [Verified 25 March 2015]
Trentmann J, Luderer G, Winterrath T, Fromm M, Servranckx R, Textor C, Herzog M, Graf H-F, Andreae M (2006) Modeling of biomass smoke injection into the lower stratosphere by a large forest fire (part I): reference simulation. Atmospheric Chemistry and Physics 6, 5247–5260.
| Modeling of biomass smoke injection into the lower stratosphere by a large forest fire (part I): reference simulation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXht1eit78%3D&md5=8c3f26cd1de9ee6e3dcd357bbcfed33bCAS | www.atmos-chem-phys.net/6/5247/2006
Val Martin M, Khan R, Logan J, Paugam R, Wooster M, Inhoku C (2012) Space-based observational constraints for 1-D fire smoke plume-rise models. Journal of Geophysical Research 117, D22204
| Space-based observational constraints for 1-D fire smoke plume-rise models.Crossref | GoogleScholarGoogle Scholar |
Van Wagner C, Pickett T (1985) Equations and FORTRAN program for the Canadian Forest Fire Weather Index System. Canadian Forestry Service, Technical Report 33. (Petawawa National Forestry Institute: Chalk River, ON)
Wiedinmyer C, Akagi S, Yokelson R, Emmons L, Al-Saadi J, Orlando J, Soja A (2011) The Fire INventory from NCAR (FINN): a high-resolution global model to estimate the emissions from open burning. Geoscientific Model Development 4, 625–641.
| The Fire INventory from NCAR (FINN): a high-resolution global model to estimate the emissions from open burning.Crossref | GoogleScholarGoogle Scholar |
