References

Journal papers

  • Vitolo, C., Di Giuseppe, F., Barnard, C. et al. ERA5-based global meteorological wildfire danger maps. Sci Data 7, 216 (2020). https://doi.org/10.1038/s41597-020-0554-z

  • Vitolo, C., Di Giuseppe, F., Krzeminski, B. et al. A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices. Sci Data 6, 190032 (2019). https://doi.org/10.1038/sdata.2019.32

  • Vitolo C, Di Giuseppe F, D’Andrea M (2018) Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs. PLOS ONE 13(1): e0189419. https://doi.org/10.1371/journal.pone.0189419

  • Di Giuseppe, F., Pappenberger, F., Wetterhall, F., Krzeminski, B., Camia, A., Libertá, G. and San Miguel, J., 2016. The potential predictability of fire danger provided by numerical weather prediction. Journal of Applied Meteorology and Climatology, 55(11), pp.2469-2491.

Newsletter articles

Software tools

ECMWF have also developed a series of resources in R and Python to help users access and process these  data, the most notables are 

The name caliver stands for CALIbration and VERification of forest fire gridded model outputs. This is a package developed for the R programming language and available under an APACHE-2 license from a public repository. Complete documentation, including a vignette, is also available within the package.

Jupyter notebooks to explore, visualise and post-process fire danger reanalysis and forecast data from the GEFF modelling system.