Examining the influence of mid-tropospheric conditions and surface wind changes on extremely large fires and fire growth days
Brian E. Potter A *A Pacific Wildland Fire Sciences Lab, USDA Forest Service, Seattle, WA, USA.
International Journal of Wildland Fire 32(5) 777-795 https://doi.org/10.1071/WF22187
Submitted: 20 August 2022 Accepted: 26 January 2023 Published: 6 March 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
Background: Previous work by the author and others has examined weather associated with growth of exceptionally large fires (‘Fires of Unusual Size’, or FOUS), looking at three of four factors associated with critical fire weather patterns: antecedent drying, high wind and low humidity. However, the authors did not examine atmospheric stability, the fourth factor.
Aims: This study examined the relationships of mid-tropospheric stability and dryness used in the Haines Index, and changes in surface wind speed or direction, to growth of FOUS.
Methods: Weather measures were paired with daily growth measures for FOUS, and for merely ‘large’ fires paired with each FOUS. Distributions of weather and growth were compared between the two fire sets graphically and statistically to determine which, if any, weather properties correspond to greater growth on FOUS than on large fires.
Key results: None of the factors showed a robust difference in fire growth response between FOUS and large fires.
Conclusions: The examined measures, chosen for their anecdotal or assumed association with increased fire growth, showed no indication of that association.
Implications: Focus on wind changes and mid-tropospheric properties may be counterproductive or distracting when one is concerned about major growth events on very large fires.
Keywords: atmospheric stability, extreme fire behaviour, fire growth, fire weather, wind shift.
References
Brotak EA (1976) A synoptic study of the meteorological conditions associated with major wildland fires. PhD dissertation, Yale University, New Haven, CT.Cade BS, Noon BR (2003) A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment 1, 412–420.
| A gentle introduction to quantile regression for ecologists.Crossref | GoogleScholarGoogle Scholar |
Cade BS, Terrell JW, Schroeder RL (1999) Estimating effects of limiting factors with regression quantiles. Ecology 80, 311–323.
| Estimating effects of limiting factors with regression quantiles.Crossref | GoogleScholarGoogle Scholar |
Crosby JS (1949) Vertical wind currents and fire behavior. Fire Control Notes 10, 12–14.
Foley JC (1947) ‘A study of meteorological conditions associated with bush and grass fires and fire protection strategy in Australia’. Commonwealth of Australia Bureau of Meteorology, Bulletin Number 38. (Commonwealth of Australia Bureau of Meteorology: Melbourne)
Haines DA (1988) A lower atmospheric severity index for wildland fires. National Weather Digest 13, 23–27.
Hayes GL (1947) Forest fires and sea breezes. Fire Control Notes 8, 30–33.
Koenker R (2020) quantreg: Quantile Regression. R package version 5.55. Available at https://CRAN.R-project.org/package=quantreg [accessed 28 March 2022]
Potter BE (2012) Atmospheric Interactions with wildland fire behaviour – I. Basic surface interactions, vertical profiles and synoptic structures. International Journal of Wildland Fire 21, 779–801.
| Atmospheric Interactions with wildland fire behaviour – I. Basic surface interactions, vertical profiles and synoptic structures.Crossref | GoogleScholarGoogle Scholar |
Potter BE (2018) Quantitative evaluation of the Haines Index’s ability to predict fire growth events. Atmosphere 9, 177
| Quantitative evaluation of the Haines Index’s ability to predict fire growth events.Crossref | GoogleScholarGoogle Scholar |
Potter BE, McEvoy D (2021) Weather factors associated with extremely large fires and fire growth days. Earth Interactions 25, 160–176.
| Weather factors associated with extremely large fires and fire growth days.Crossref | GoogleScholarGoogle Scholar |
Potter BE, McEvoy DJ (2022) ‘Fire growth and associated weather data for selected Fires of Unusual Size (FOUS) and other fires from 2004-2018.’ (Forest Service Research Data Archive: Fort Collins, CO)
| Crossref |
R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at https://www.R‐project.org/
Wasserstein RL, Lazar NA (2016) The ASA statement on p-values: context, process, and purpose. The American Statistician 70, 129–133.
| The ASA statement on p-values: context, process, and purpose.Crossref | GoogleScholarGoogle Scholar |
Werth PA, Potter BE, Alexander ME, et al. (2016) Synthesis of knowledge of extreme fire behavior: Volume 2 for fire behavior specialists, researchers, and meteorologists. General Technical Report PNW-GTR-891. 258 p. (USDA Forest Service, Pacific Northwest Research Station: Portland, OR)