Section: 5. Discover the data: GEFF Fire Danger forecasts and reanalysis | User workshop and training on fire monitoring products (2020) | EUMETSAT

Main course page
  • Welcome to the User Workshop and Training on Fire Monitoring

    An online event through interactive data discovery and user practices on state-of-the-art operational datasets for detection of fire, related emissions and impacts

    Jointly organized by EUMETSAT, CAMS-ECMWFAC SAFLSA SAF

    with support from Copernicus

      Data Discovery Week 
              11-15 May - Daily webinars with experts on datasets with interactive handling (open access).

      Data Discovery Resources
              A collection of videos and presentations of datasets designed for the training workshop, but open to all.     

              18-19 May - A two day workshop on practical workflows and user needs (for registered participants only).

5. Discover the data: GEFF Fire Danger forecasts and reanalysis

  • 5. Discover the data: GEFF Fire Danger forecasts and reanalysis

    The European Centre for Medium-Range Weather Forecasts (ECMWF) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (GEFF) model. Reanalysis (and soon seasonal forecasts) is available through the Copernicus Climate Data Store (CDS) while the medium-range real-time forecast is available through the EFFIS and GWIS platforms, which give access to timely fire danger information at a pan-European and global scale, respectively. Thirty-eight local and national authorities across Europe are part of the EFFIS network and have been relying on GEFF outputs for the early identification of regions prone to fire events as a result of persistent drought conditions.

    • GEFF-reanalysis provides historical records of global fire danger conditions from 1980 to the present day and it is made of four types of products: (i) deterministic model outputs (called simply 'reanalysis' on the CDS), (ii) probabilistic model outputs (made of 10 ensemble members), (iii) ensemble mean and (iv) ensemble spread. It is updated as soon as new ERA-5 data becomes available (~2 months behind real-time). 
    • GEFF-realtime provides real-time high-resolution deterministic (~9 Km) and lower-resolution probabilistic (~18Km) fire danger forecasts up to 15 days ahead using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS). The real-time dataset is updated every day with a new set of forecasts. Forecast data can be requested to EFFIS using an online form.

    These products have been developed as part of the EU-funded Copernicus Emergency Management Services (CEMS) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (CAMS).  The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (JRC). 

    GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.

    For more information, please refer to the documentation on the CDS and on the EFFIS website.


    Figure 5.1  - Example fire danger forecast at day 10 (classified Fire Weather Index) in the Iberian Peninsula

    Software tools




    Journal papers

    • Vitolo, C., Di Giuseppe, F., Krzeminski, B. and San-Miguel-Ayanz, J., 2019. A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices. Scientific data, 6, p.190032.

    • Vitolo, Claudia, Francesca Di Giuseppe, and Mirko D’Andrea. Caliver: An r package for calibration and verification of forest fire gridded model outputs. PLOS ONE, 13(1):1–18, 01 2018

    • 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.

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