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
ECMWF forecasts support Portugal wildfire response (Number 153 - Autumn - October 2017) https://www.ecmwf.int/en/newsletter/153/news/ecmwf-forecasts-support-portugal-wildfire-response
Devastating wildfires in Chile in January 2017 (Number 151 - Spring - April 2017) https://www.ecmwf.int/en/newsletter/151/news/devastating-wildfires-chile-january-2017
Copernicus fire danger forecast goes online (Number 151 - Spring - April 2017) https://www.ecmwf.int/en/newsletter/151/news/copernicus-fire-danger-forecast-goes-online
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.