Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3407-2023
https://doi.org/10.5194/gmd-16-3407-2023
Development and technical paper
 | 
19 Jun 2023
Development and technical paper |  | 19 Jun 2023

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States

Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine

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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Cited articles

Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a
Abatzoglou, J. T. and Kolden, C. A.: Relationships between climate and macroscale area burned in the western United States, Int. J. Wildland Fire, 22, 1003–1020, https://doi.org/10.1071/WF13019, 2013. a
Abatzoglou, J. T. and Williams, A. P.: Impact of anthropogenic climate change on wildfire across western US forests, P. Nl. Acad. Sci. USA, 113, 11770–11775, https://doi.org/10.1073/pnas.1607171113, 2016. a, b, c
Abatzoglou, J. T., Kolden, C. A., Williams, A. P., Lutz, J. A., and Smith, A. M. S.: Climatic influences on interannual variability in regional burn severity across western US forests, Int. J. Wildland Fire, 26, 269–275, https://doi.org/10.1071/WF16165, 2017. a, b
Abatzoglou, J. T., Battisti, D. S., Williams, A. P., Hansen, W. D., Harvey, B. J., and Kolden, C. A.: Projected increases in western US forest fire despite growing fuel constraints, Commun. Earth Environ., 2, 227, https://doi.org/10.1038/s43247-021-00299-0, 2021a. a
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We leverage machine learning techniques to construct a statistical model of grid-scale fire...
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