Coupled fire-atmosphere simulation of the 2018 Camp Fire using WRF-Fire
Kasra Shamsaei
A , Timothy W. Juliano B , Matthew Roberts C , Hamed Ebrahimian A * , Branko Kosovic B , Neil P. Lareau C and Ertugrul Taciroglu D
+ Author Affiliations
- Author Affiliations
A Department of Civil and Environmental Engineering, University of Nevada-Reno, Reno, NV, USA.
B National Center for Atmospheric Research, Boulder, CO, USA.
C Department of Physics, University of Nevada-Reno, Reno, NV, USA.
D Department of Civil and Environmental Engineering, University of California-Los Angeles (UCLA), Los Angeles, CA, USA.
* Correspondence to: hebrahimian@unr.edu
International Journal of Wildland Fire 32(2) 195-221 https://doi.org/10.1071/WF22013
Submitted: 17 February 2022 Accepted: 30 November 2022 Published: 5 January 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
Background: Accurate simulation of wildfires can benefit pre-ignition mitigation and preparedness, and post-ignition emergency response management.
Aims: We evaluated the performance of Weather Research and Forecast-Fire (WRF-Fire), a coupled fire-atmosphere wildland fire simulation platform, in simulating a large historic fire (2018 Camp Fire).
Methods: A baseline model based on a setup typically used for WRF-Fire operational applications is utilised to simulate Camp Fire. Simulation results are compared to high-temporal-resolution fire perimeters derived from NEXRAD observations. The sensitivity of the model to a series of modelling parameters and assumptions governing the simulated wind field are then investigated. Results of WRF-Fire for Camp Fire are compared to FARSITE.
Key results: Baseline case shows non-negligible discrepancies between the simulated fire and the observations on rate of spread (ROS) and spread direction. Sensitivity analysis results show that refining the atmospheric grid of Camp Fire’s complex terrain improves fire prediction capabilities.
Conclusions: Sensitivity studies show the importance of refined atmosphere modelling for wildland fire simulation using WRF-Fire in complex terrains. Compared to FARSITE, WRF-Fire agrees better with the observations in terms of fire propagation rate and direction.
Implications: The findings suggest the need for further investigation of other possible sources of wildfire modelling uncertainties and errors.
Keywords: Camp Fire, coupled fire-atmosphere simulation, FARSITE, NEXRAD, sensitivity, wildfire simulation, wind, WRF-Fire.
References
Abatzoglou JT, Williams AP (2016) Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences 113, 11770–11775.| Impact of anthropogenic climate change on wildfire across western US forests.Crossref | GoogleScholarGoogle Scholar |
Bauer HS, Muppa SK, Wulfmeyer V, Behrendt A, Warrach-Sagi K, Späth F (2020) Multi-nested WRF simulations for studying planetary boundary layer processes on the turbulence-permitting scale in a realistic mesoscale environment. Tellus, Series A: Dynamic Meteorology and Oceanography 72, 1761740
| Multi-nested WRF simulations for studying planetary boundary layer processes on the turbulence-permitting scale in a realistic mesoscale environment.Crossref | GoogleScholarGoogle Scholar |
Benjamin SG, Weygandt SS, Brown JM, Hu M, Alexander CR, Smirnova TG, Olson JB, James EP, Dowell DC, Grell GA, Lin H, Peckham SE, Smith TL, Moninger WR, Kenyon JS, Manikin GS (2016) A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh. Monthly Weather Review 144, 1669–1694.
| A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh.Crossref | GoogleScholarGoogle Scholar |
Brewer MJ, Clements CB (2020) The 2018 Camp Fire: Meteorological Analysis Using In Situ Observations and Numerical Simulations. Atmosphere 11, 47
| The 2018 Camp Fire: Meteorological Analysis Using In Situ Observations and Numerical Simulations.Crossref | GoogleScholarGoogle Scholar |
Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review 129, 569–585.
| Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity.Crossref | GoogleScholarGoogle Scholar |
Clements CB, Kochanski AK, Seto D, Davis B, Camacho C, Lareau NP, Contezac J, Restaino J, Heilman WE, Krueger SK, Butler B, Ottmar RD, Vihnanek R, Flynn J, Filippi J-B, Barboni T, Hall DE, Mandel J, Jenkins MA, O’Brien J, Hornsby B, Teske C (2019) The FireFlux II experiment: a model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread. International Journal of Wildland Fire 28, 308–326.
| The FireFlux II experiment: a model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread.Crossref | GoogleScholarGoogle Scholar |
Coen JL, Cameron M, Michalakes J, Patton EG, Riggan PJ, Yedinak KM (2013) WRF-Fire: Coupled Weather–Wildland Fire Modeling with the Weather Research and Forecasting Model. Journal of Applied Meteorology and Climatology 52, 16–38.
| WRF-Fire: Coupled Weather–Wildland Fire Modeling with the Weather Research and Forecasting Model.Crossref | GoogleScholarGoogle Scholar |
Deardorff JW (1972) Numerical Investigation of Neutral and Unstable Planetary Boundary Layers. Journal of the Atmospheric Sciences 29, 91–115.
| Numerical Investigation of Neutral and Unstable Planetary Boundary Layers.Crossref | GoogleScholarGoogle Scholar |
Deardorff JW (1980) Stratocumulus-capped mixed layers derived from a three-dimensional model. Boundary-Layer Meteorology 18, 495–527.
| Stratocumulus-capped mixed layers derived from a three-dimensional model.Crossref | GoogleScholarGoogle Scholar |
Dudhia J (1989) Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. Journal of the Atmospheric Sciences 46, 3077–3107.
| Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model.Crossref | GoogleScholarGoogle Scholar |
Finney MA (1998) FARSITE: Fire Area Simulator-model development and evaluation. Research Paper RMRS-RP-4, Revised 2004. 47 p. (Ogden, UT: US Department of Agriculture, Forest Service, Rocky Mountain Research Station)
| Crossref |
Forthofer JM, Butler BW, Wagenbrenner NS (2014) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements. International Journal of Wildland Fire 23, 969–981.
| A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements.Crossref | GoogleScholarGoogle Scholar |
Giannaros TM, Lagouvardos K, Kotroni V (2020) Performance Evaluation of an Operational Rapid Response Fire Spread Forecasting System in the Southeast Mediterranean (Greece). Atmosphere 11, 1264
| Performance Evaluation of an Operational Rapid Response Fire Spread Forecasting System in the Southeast Mediterranean (Greece).Crossref | GoogleScholarGoogle Scholar |
Haupt SE, Kosovic B, Shaw W, Berg LK, Churchfield M, Cline J, Draxl C, Ennis B, Koo E, Kotamarthi R, Mazzaro L, Mirocha J, Moriarty P, Muñoz-Esparza D, Quon E, Rai RK, Robinson M, Sever G (2019) On bridging a modeling scale gap: Mesoscale to microscale coupling for wind energy. Bulletin of the American Meteorological Society 100, 2533–2550.
| On bridging a modeling scale gap: Mesoscale to microscale coupling for wind energy.Crossref | GoogleScholarGoogle Scholar |
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Chiara GD, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan RJ, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, Rosnay Pd, Rozum I, Vamborg F, Villaume S, Thépaut J-N (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049.
| The ERA5 global reanalysis.Crossref | GoogleScholarGoogle Scholar |
Homer C, Dewitz J, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold N, Wickham J, Megown K (2015) Completion of the 2011 National Land Cover Database for the conterminous United States – representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing 81, 345–354. Available at http://www.ingentaconnect.com/content/asprs/pers/2015/00000081/00000005/art00002
Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly Weather Review 132, 103–120.
| A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation.Crossref | GoogleScholarGoogle Scholar |
Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres 113, D13103
| Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models.Crossref | GoogleScholarGoogle Scholar |
Jiménez PA, Dudhia J, González-Rouco JF, Navarro J, Montávez JP, García-Bustamante E (2012) A Revised Scheme for the WRF Surface Layer Formulation. Monthly Weather Review 140, 898–918.
| A Revised Scheme for the WRF Surface Layer Formulation.Crossref | GoogleScholarGoogle Scholar |
Jiménez PA, Muñoz-Esparza D, Kosović B (2018a) A High Resolution Coupled Fire–Atmosphere Forecasting System to Minimize the Impacts of Wildland Fires: Applications to the Chimney Tops II Wildland Event. Atmosphere 9, 197
| A High Resolution Coupled Fire–Atmosphere Forecasting System to Minimize the Impacts of Wildland Fires: Applications to the Chimney Tops II Wildland Event.Crossref | GoogleScholarGoogle Scholar |
Jimenez PA, Brown B, Kosovic B, Cowie J, Munoz-Esparza D, Anderson A, Petzke B, Boehnert J, Sampson KM, Mahoney WPI, Knievel JC (2018b) Description and evaluation of the Colorado Fire Prediction system (CO-FPS). AGUFM 2018, EP31B-03 Available at https://ui.adsabs.harvard.edu/abs/2018AGUFMEP31B..03J/abstract
Knievel JC, Bryan GH, Hacker JP (2007) Explicit Numerical Diffusion in the WRF Model. Monthly Weather Review 135, 3808–3824.
| Explicit Numerical Diffusion in the WRF Model.Crossref | GoogleScholarGoogle Scholar |
Knievel JC, Kosovic B, Cowie J, Anderson A, Boehnert J, Brown B, Brucker D, Chartier N, DeCastro A, Frediani ME, Hahn D, Haupt SE, Jimenez PA, Juliano TW, Mahoney WPI, Munoz-Esparza D, Petzke B, Sampson KM (2020) A Modeling System for Predicting the Behavior of Wildland Fires by Simulating Their Two-Way Interaction with the Atmosphere. AGUFM 2020, A143–0003. Available at https://ui.adsabs.harvard.edu/abs/2020AGUFMA143.0003K/abstract
Kochanski AK, Jenkins MA, Mandel J, Beezley JD, Clements CB, Krueger S (2012) Evaluation of WRF-Sfire Performance with Field Observations from the FireFlux experiment. Geoscientific Model Development Discussions 6, 121–169.
| Evaluation of WRF-Sfire Performance with Field Observations from the FireFlux experiment.Crossref | GoogleScholarGoogle Scholar |
Kochanski AK, Jenkins MA, Mandel J, Beezley JD, Krueger SK (2013) Real time simulation of 2007 Santa Ana fires. Forest Ecology and Management 294, 136–149.
| Real time simulation of 2007 Santa Ana fires.Crossref | GoogleScholarGoogle Scholar |
Ladwig W (2017) wrf-python (Version 1.3.4) [Software]. Boulder, Color. UCAR/NCAR. Available at
| Crossref |
Lai S, Chen H, He F, Wu W (2020) Sensitivity Experiments of the Local Wildland Fire with WRF-Fire Module. Asia-Pacific Journal of Atmospheric Sciences 56, 533–547.
| Sensitivity Experiments of the Local Wildland Fire with WRF-Fire Module.Crossref | GoogleScholarGoogle Scholar |
Lareau NP, Donohoe A, Roberts M, Ebrahimian H (2022) Tracking Wildfires with Weather Radars. Journal of Geophysical Research: Atmospheres 127, e2021JD036158
| Tracking Wildfires with Weather Radars.Crossref | GoogleScholarGoogle Scholar |
Lautenberger C (2013) Wildland fire modeling with an Eulerian level set method and automated calibration. Fire Safety Journal 62, 289–298.
| Wildland fire modeling with an Eulerian level set method and automated calibration.Crossref | GoogleScholarGoogle Scholar |
Li Y, Tong DQ, Ngan F, Cohen MD, Stein AF, Kondragunta S, Zhang X, Ichoku C, Hyer EJ, Kahn RA (2020) Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model. Journal of Geophysical Research: Atmospheres 125, e2020JD032768
| Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model.Crossref | GoogleScholarGoogle Scholar |
Linn RR (1997) ‘A transport model for prediction of wildfire behavior’. LA-13334-T. (Los Alamos National Laboratory: Los Alamos, NM)
| Crossref |
Littell JS, Mckenzie D, Peterson DL, Westerling AL (2009) Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecological Applications 19, 1003–1021.
| Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003.Crossref | GoogleScholarGoogle Scholar |
Mandel J, Beezley JD, Kochanski AK (2011) Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011. Geoscientific Model Development 4, 591–610.
| Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011.Crossref | GoogleScholarGoogle Scholar |
Maranghides A, Link E, Mell W, Hawks S, Wilson M, Brewer W, Brown C, Vihnaneck B, Walton WD (2021) A Case Study of the Camp Fire – Fire Progression Timeline. Technical Note (NIST TN) - 2135 (NIST: Gaithersburg, MD)
| Crossref |
Mass CF, Ovens D (2021) The Synoptic and Mesoscale Evolution Accompanying the 2018 Camp Fire of Northern California. Bulletin of the American Meteorological Society 102, E168–E192.
| The Synoptic and Mesoscale Evolution Accompanying the 2018 Camp Fire of Northern California.Crossref | GoogleScholarGoogle Scholar |
Mazzaro LJ, Muñoz‐Esparza D, Lundquist JK, Linn RR (2017) Nested mesoscale‐to‐LES modeling of the atmospheric boundary layer in the presence of under‐resolved convective structures. Journal of Advances in Modeling Earth Systems 9, 1795–1810.
| Nested mesoscale‐to‐LES modeling of the atmospheric boundary layer in the presence of under‐resolved convective structures.Crossref | GoogleScholarGoogle Scholar |
Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. International Journal of Wildland Fire 16, 1–22.
| A physics-based approach to modelling grassland fires.Crossref | GoogleScholarGoogle Scholar |
Miller DAB (1991) Huygens’s wave propagation principle corrected. Optics Letters 16, 1370
| Huygens’s wave propagation principle corrected.Crossref | GoogleScholarGoogle Scholar |
Muñoz-Esparza D, Kosović B, Mirocha J, van Beeck J (2014) Bridging the Transition from Mesoscale to Microscale Turbulence in Numerical Weather Prediction Models. Boundary-Layer Meteorology 153, 409–440.
| Bridging the Transition from Mesoscale to Microscale Turbulence in Numerical Weather Prediction Models.Crossref | GoogleScholarGoogle Scholar |
Muñoz-Esparza D, Lundquist JK, Sauer JA, Kosović B, Linn RR (2017) Coupled mesoscale-LES modeling of a diurnal cycle during the CWEX-13 field campaign: From weather to boundary-layer eddies. Journal of Advances in Modeling Earth Systems 9, 1572–1594.
| Coupled mesoscale-LES modeling of a diurnal cycle during the CWEX-13 field campaign: From weather to boundary-layer eddies.Crossref | GoogleScholarGoogle Scholar |
Muñoz-Esparza D, Kosović B, Jiménez PA, Coen JL (2018) An Accurate Fire-Spread Algorithm in the Weather Research and Forecasting Model Using the Level-Set Method. Journal of Advances in Modeling Earth Systems 10, 908–926.
| An Accurate Fire-Spread Algorithm in the Weather Research and Forecasting Model Using the Level-Set Method.Crossref | GoogleScholarGoogle Scholar |
Nakanishi M, Niino H (2006) An Improved Mellor–Yamada Level-3 Model: Its Numerical Stability and Application to a Regional Prediction of Advection Fog. Boundary-Layer Meteorology 119, 397–407.
| An Improved Mellor–Yamada Level-3 Model: Its Numerical Stability and Application to a Regional Prediction of Advection Fog.Crossref | GoogleScholarGoogle Scholar |
Papadopoulos GD, Pavlidou F-N (2011) A Comparative Review on Wildfire Simulators. IEEE Systems Journal 5, 233–243.
| A Comparative Review on Wildfire Simulators.Crossref | GoogleScholarGoogle Scholar |
Rooney B, Wang Y, Jiang JH, Zhao B, Zeng Z-C, Seinfeld JH (2020) Air quality impact of the Northern California Camp Fire of November 2018. Atmospheric Chemistry and Physics 20, 14597–14616.
| Air quality impact of the Northern California Camp Fire of November 2018.Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) ‘A mathematical model for predicting fire spread in wildland fuels.’ (Intermountain Forest & Range Experiment Station, Forest Service, U.S. Dept. of Agriculture) Available at https://books.google.pt/books?id=AfyMv5NBSjoC
Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. General Technical Reports RMRS-GTR-153. 72 p. (Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station)
| Crossref |
Shamsaei K, Juliano TW, Igrashkina N, Ebrahimian H, Kosovic B, Taciroglu E (2022) WRF-Fire Wikipage.
| Crossref |
Skamarock WC, Klemp JB, Dudhi J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A Description of the Advanced Research WRF Version 4.3.
| Crossref |
Sullivan AL (2009) Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models. International Journal of Wildland Fire 18, 349–368.
| Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models.Crossref | GoogleScholarGoogle Scholar |
Visualization & Analysis Systems Technologies (2022) ‘Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers (VAPOR version 3.6.1) [Software].’ (UCAR/NCAR - Comput. Inf. Syst. Lab.: Boulder, CO)
| Crossref |
Wang D, Guan D, Zhu S, Kinnon MM, Geng G, Zhang Q, Zheng H, Lei T, Shao S, Gong P, Davis SJ (2021) Economic footprint of California wildfires in 2018. Nature Sustainability 4, 252–260.
| Economic footprint of California wildfires in 2018.Crossref | GoogleScholarGoogle Scholar |
Westerling AL (2016) Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philosophical Transactions of the Royal Society B: Biological Sciences 371, 20150178
| Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring.Crossref | GoogleScholarGoogle Scholar |
Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase Western U.S. forest wildfire activity. Science 313, 940–943.
| Warming and earlier spring increase Western U.S. forest wildfire activity.Crossref | GoogleScholarGoogle Scholar |
WRF (2020) User’s Guide for the Advanced Research WRF (ARW) Modeling System Version 4.2. Available at https://www2.mmm.ucar.edu/wrf/users/docs/user_guide_v4/v4.2/contents.html [accessed 11 June 2022]
Wyngaard JC (2004) Toward Numerical Modeling in the “Terra Incognita”. Journal of the Atmospheric Sciences 61, 1816–1826.
| Toward Numerical Modeling in the “Terra Incognita”.Crossref | GoogleScholarGoogle Scholar |
Xu K-M, Randall DA (1996) A Semiempirical Cloudiness Parameterization for Use in Climate Models. Journal of the Atmospheric Sciences 53, 3084–3102.
| A Semiempirical Cloudiness Parameterization for Use in Climate Models.Crossref | GoogleScholarGoogle Scholar |
