Fire Risk Map (FRM)

NRT Product Available since Feb 2010


Product Documentation

This operational product is documented in the Algorithm Theoretical Basis Document (ATBD), Product User Manual document (PUM) and the Product Output Format document (POF) The validation results for this product are available in the (VR) document.
Data Policy
The use of LSA SAF products in publications is kindly requested to be duly acknowledged:
FRM was provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)

Trigo, I. F., C. C. DaCamara, P. Viterbo, J.-L. Roujean, F. Olesen, C. Barroso, F. Camacho-de Coca, D. Carrer, S. C. Freitas, J. García-Haro, B. Geiger, F. Gellens-Meulenberghs, N. Ghilain, J. Meliá, L. Pessanha, N. Siljamo, and A. Arboleda, 2011: The Satellite Application Facility on Land Surface Analysis. Int. J. Remote Sens., 32, 2725-2744, doi: 10.1080/01431161003743199
Rural fires are common events in ecosystems characterized by oscillations between rainy and drought periods, which inevitably lead to high levels of vegetation stress and to the accumulation of fuels during the dry phase. This is particularly true in Mediterranean region, where rainy and mild winters are followed by warm and dry summers. Meteorological factors play a crucial role in the setting and spreading of wildfire and are an important factor in the resulting fire severity. Fire risk indices may be based on single or combined use of meteorological observations, weather forecast model outputs and remote sensing estimations. The latter are particularly useful to identify pre-fire indicators (e.g. signals of vegetation stress), which merged with meteorological parameters may lead to the formulation of indicators of fire risk.
Product Description
FRM product from the LSA SAF combines information from Numerical Weather Prediction (NWP) models - in this case the operational forecasts from ECMWF - and vegetation state from SEVIRI to derive forecasts of the risk of fire for the European region. The rationale is to provide the user community with information on meteorological risk that will allow adopting the adequate measures to mitigate fire damage. The FRM algorithm computes the set of components of the Canadian Forest Fire Weather Index System (CFFWIS; van Wagner, 1987) for the following 24h, 48h and 72h. These indicate prognostic levels of fire danger over the European area.
Algorithm Description
The first step of the FRM product is to use gridded values of 24 h, 48 h and 72 h ECMWF forecasts of meteorological parameters (namely, temperature at 2 m, relative humidity, wind velocity at 10m and cumulated precipitation in 24 h) in order to compute the set of six fire indices that constitute CFFWIS. These values are computed on a pixel basis over the European window and are disseminated everyday at 12 UTC. Classes of fire danger are finally obtained by combining, at each MSG pixel, daily values of fire weather index (FWI) with vegetation classes as derived from GLC2000. Risks of fire occurrence (for specified levels of severity) are associated to each class of fire danger by crossing FWI and vegetation cover information with active fires as detected by the FD&M algorithm during July and August of 2008 and 2009. Calibration of fire danger classes will be extended to the period of June to September 2005-2009 as soon as data from the RFM product become available.
Data Characteristics
For each processed pixel in the European window the FRM algorithm computes all fire indices of CFFWIS: the Fuel Moisture Codes, i.e., FFMC, DMC and DC and the Fire Behaviour Indices, i.e., ISI, BUI, FWI and DSR. The FRM algorithm also computes classes of fire risk.
Product Uncertainties
van Wagner, C.E., 1987: Development and structure of the Canadian Forest Fire Index System. Canadian Forestry Service, Ottawa, Ontario, Forestry Technical Report 35, 37 pp.

Example of Product