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International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Objective identification of thunderstorm gust fronts in numerical weather prediction models for fire weather forecasting

James F. Bresch https://orcid.org/0000-0002-1442-8943 A C , Jordan G. Powers A , Craig S. Schwartz A , Ryan A. Sobash A and Janice L. Coen A B
+ Author Affiliations
- Author Affiliations

A National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Laboratory, PO Box 3000, Boulder, CO 80307, USA.

B University of San Francisco, 2130 Fulton St., San Francisco, CA 94117, USA.

C Corresponding author. E-mail: bresch@ucar.edu

International Journal of Wildland Fire 30(7) 513-535 https://doi.org/10.1071/WF20059
Submitted: 22 April 2020  Accepted: 8 April 2021   Published: 19 May 2021

Abstract

Abrupt changes in wind direction and speed can dramatically impact wildfire development and spread, endangering firefighters. A frequent cause of such wind shifts is outflow from thunderstorms and organised convective systems; thus, their identification and prediction present critical challenges for fire weather forecasters. Here, we develop a methodology and implement it in a software tool that can identify and depict convective outflow boundaries in high-resolution numerical weather prediction (NWP) models to provide guidance for fire weather forecasting. The tool can process model output, objectively identify gust fronts, and graphically display detected gust fronts and similar boundaries in NWP model forecasts. The tool is demonstrated with output from the Weather Research and Forecasting (WRF) model from the operational High-Resolution Rapid Refresh (HRRR) forecasting system and from a WRF ensemble run at the National Center for Atmospheric Research that can provide probabilistic information about model-predicted gust fronts. The tool can identify outflow boundaries in model forecasts of convective events occurring in both simple and complex terrain, both with and without concurrent wildfire activity. With accurate underlying model forecast output, the tool can reliably reveal areas of potential gust front activity and thus provide valuable guidance to incident meteorologists and command personnel.

Keywords: WRF, outflow boundary, gust front, HRRR, mesoscale model, ensemble forecasting, multiple directional non-maximum suppression, Yarnell Hill.


References

Benjamin SG, Weygandt SS, Brown JM, Hu M, Alexander C, Smirnova TG, Olson JB, James E, Dowell DC, Grell GA, Lin H, Peckham SE, Smith TL, Moniger WR, Kenyon J, 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 |

Bluestein HB (1993) ‘Synoptic–Dynamic Meteorology in Midlatitudes. Vol. II.’ (Oxford University Press: New York)

Chipilski HG, Wang X, Parsons DB (2018) An object-based algorithm for the identification and tracking of convective outflow boundaries in numerical models. Monthly Weather Review 146, 4179–4200.
An object-based algorithm for the identification and tracking of convective outflow boundaries in numerical models.Crossref | GoogleScholarGoogle Scholar |

Clark P, Roberts N, Lean H, Ballard SP, Charlton‐Perez C (2016) Convection‐permitting models: a step‐change in rainfall forecasting. Meteorological Applications 23, 165–181.
Convection‐permitting models: a step‐change in rainfall forecasting.Crossref | GoogleScholarGoogle Scholar |

Comissão Técnica Independente (2017) Análise e apuramento dos factos relativos aos incêndios que ocorreram em Pedrógão Grande, Castanheira de Pera, Ansião, Alvaiázere, Figueiró dos Vinhos, Arganil, Gois, Penela, Pampilhosa da Serra, Oleiros e Sertã, entre 17 e 24 de junho de 2017. Available at https://www.parlamento.pt/Documents/2017/Outubro/RelatórioCTI_VF%20.pdf

Draeger R (2016) Frog Fire fatality. USDA Forest Service Learning Review Report. Available at https://www.wildfirelessons.net/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=c197dde6-ced5-01a2-0b0d-2b735e4934ea&forceDialog=0

Goens DW, Andrews PL (1998) Weather and fire behavior factors related to the 1990 Dude Fire near Payson, Arizona. In ‘Proceedings, 2nd Symposium on Fire and Forest Meteorology’, 11–16 January 1998, Phoenix, AZ. pp. 153–158. (American Meteorological Society: Boston, MA)

Hanley DE, Cunningham P, Goodrick SL (2013) Interaction between a wildfire and the sea-breeze front. In ‘Remote sensing and modeling applications to wildland fires.’ (Eds JJ Qu, WT Sommers, R Yang, AR Riebau) pp. 81–98. (Beijing, China: Tsinghua University Press and New York: Springer)10.1007/978-3-642-32530-4_7

Houze RA (1993) ‘Cloud Dynamics.’ (Academic Press: San Diego)

Karels J (2014) Yarnell Hill Fire. Serious Accident Investigation Report. State of Arizona, Serious Accident Investigation Team. Available at https://www.wildfirelessons.net/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=4c98c51d-102c-4e04-86e0-b8370d2beb27&forceDialog=0

Knupp K (2006) Observational analysis of a gust front to bore to solitary wave transition within an evolving nocturnal boundary layer. Journal of the Atmospheric Sciences 63, 2016–2035.
Observational analysis of a gust front to bore to solitary wave transition within an evolving nocturnal boundary layer.Crossref | GoogleScholarGoogle Scholar |

Lindley TT, Speheger DA, Day MA, Murdoch GP, Smith BR, Nauslar NJ, Daily DC (2019) Megafires on the Southern Great Plains. Journal of Operational Meteorology 7, 164–179.
Megafires on the Southern Great Plains.Crossref | GoogleScholarGoogle Scholar |

Powers JG, Klemp JB, Skamarock WC, Davis CA, Dudhia J, Gill DO, Coen JL, Gochis DJ, Ahmadov R, Peckham SE, Grell GA, Michalakes J, Trahan S, Benjamin SG, Alexander CR, Dimego GJ, Wang W, Schwartz CS, Romine GS, Liu Z, Snyder C, Chen F, Barlage MJ, Yu W, Duda MG (2017) The weather research and forecasting model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society 98, 1717–1737.
The weather research and forecasting model: Overview, system efforts, and future directions.Crossref | GoogleScholarGoogle Scholar |

Schwartz CS, Sobash RA (2017) Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations. Monthly Weather Review 145, 3397–3418.
Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations.Crossref | GoogleScholarGoogle Scholar |

Schwartz CS, Sobash RA (2019) Revisiting sensitivity to horizontal grid spacing in convection-allowing models over the central and eastern United States. Monthly Weather Review 147, 4411–4435.
Revisiting sensitivity to horizontal grid spacing in convection-allowing models over the central and eastern United States.Crossref | GoogleScholarGoogle Scholar |

Schwartz CS, Romine GS, Smith KR, Weisman ML (2014) Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter. Weather and Forecasting 29, 1295–1318.
Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter.Crossref | GoogleScholarGoogle Scholar |

Schwartz CS, Romine GS, Sobash RA, Fossell KR, Weisman ML (2015) NCAR’s experimental real-time convection-allowing ensemble prediction system. Weather and Forecasting 30, 1645–1654.
NCAR’s experimental real-time convection-allowing ensemble prediction system.Crossref | GoogleScholarGoogle Scholar |

Schwartz CS, Romine GS, Sobash RA, Fossell KR, Weisman ML (2019) NCAR’s real-time convection-allowing ensemble project. Bulletin of the American Meteorological Society 100, 321–343.
NCAR’s real-time convection-allowing ensemble project.Crossref | GoogleScholarGoogle Scholar |

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang XY, Wang W, Powers JG (2008) A description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN–475+STR. (University Corporation for Atmospheric Research: Boulder, CO). 10.5065/D68S4MVH

Sun C, Vallotton P (2009) Fast linear feature detection using multiple directional non-maximum suppression. Journal of Microscopy 234, 147–157.
Fast linear feature detection using multiple directional non-maximum suppression.Crossref | GoogleScholarGoogle Scholar | 19397744PubMed |

Thomas CM, Schultz DM (2019) What are the best thermodynamic quantity and function to define a front in gridded model output? Bulletin of the American Meteorological Society 100, 873–895.
What are the best thermodynamic quantity and function to define a front in gridded model output?Crossref | GoogleScholarGoogle Scholar |

Wakimoto RM (1982) The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data. Monthly Weather Review 110, 1060–1082.
The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data.Crossref | GoogleScholarGoogle Scholar |

Weisman ML, Skamarock WC, Klemp JB (1997) The resolution dependence of explicitly modeled convective systems. Monthly Weather Review 125, 527–548.
The resolution dependence of explicitly modeled convective systems.Crossref | GoogleScholarGoogle Scholar |

Zachariassen J, Zeller K, Nikolov N, McClelland T (2003) A review of the Forest Service Remote Automated Weather Station (RAWS) network. USDA Forest Service, Rocky Mountains Research Station, General Technical Report RMRS-GTR-1119. (Fort Collins, CO)