Fire Weather Index - ERA-Interim
- 1. European Centre for Medium-Range Weather Forecasts
- 2. Joint Research Centre
Description
The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread.
This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.
This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, Vitolo et al. 2018).
Details:
- File format: netcdf4
- Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).
- Longitude range: [-180, +180]
- Temporal resolution: 1 day
- Spatial resolution: 0.7 degrees (~80 Km)
- Spatial coverage: Global
- Time span: from 1980-01-01 to 2018-06-30
Notes
Files
Files
(7.4 GB)
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md5:af8a27caec260aa44db85de12abebb87
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Additional details
Related works
- Is new version of
- 10.5281/zenodo.1065401 (DOI)
- Is referenced by
- 10.1038/sdata.2019.32 (DOI)
- References
- 10.1175/JAMC-D-15-0297.1 (DOI)
- 10.5281/zenodo.582416 (DOI)
- 10.1371/journal.pone.0189419 (DOI)
References
- 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. DOI: https://doi.org/10.1175/JAMC-D-15-0297.1
- 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
- Vitolo C., Di Giuseppe F., D'Andrea M., 2017. caliver R package version 1.0. URL: https://github.com/ecmwf/caliver, DOI: https://doi.org/10.5281/zenodo.582416.