The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction

Abstract

A global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitative skill evaluation was performed using the extremal dependency index, which is a skill score specifically designed for rare events. It revealed that the three indices were more skillful than the random forecast to detect large fires on a global scale. The performance peaks in the boreal forests, the Mediterranean region, the Amazon rain forests, and Southeast Asia. The skill scores were then aggregated at the country level to reveal which nations could potentially benefit from the system information to aid decision-making and fire control support. Overall it was found that fire danger modeling based on weather forecasts can provide reasonable predictability over large parts of the global landmass.

The forest fire regime in Latvia during 1922–2014

“…The FWI has been successfully used for fire prediction in Finland (Tanskanen and Venäläinen 2008) as well as in the Eastern Mediterranean region (Papakosta and Straub 2016). In addition, the FWI has been successfully applied in studies assessing the potential influence of climate change on fire activity Flannigan et al 2016) and it has been implemented in the European Forest Fire Information System (Di Giuseppe et al 2016). We used arbitrarily adjusted FWI classes of Alexander and De Groot (1988) and Vajda et al (2014) as follows: very low (0.0-5.0), low (5.1-10.0), moderate (10.1-17.0), high (17.1-30.0), extremely high (≥30.1).…”

Global Emergence of Anthropogenic Climate Change in Fire Weather Indices

“…The FWI from the Canadian Forest Fire Weather Index System is considered in this study given its widespread usage globally (Di Giuseppe et al, 2016;Field et al, 2016;Groot et al, 2014); its reflection of potential fire intensity tied to fuel aridity and fire weather irrespective of land cover and biomass; its sensitivities to temperature, precipitation, humidity, and wind speed (e.g., Flannigan et al, 2016); and its established empirical relationships to burned area across broad regions of the globe (e.g., Abatzoglou et al, 2018). The FWI from the Canadian Forest Fire Weather Index System is considered in this study given its widespread usage globally (Di Giuseppe et al, 2016;Field et al, 2016;Groot et al, 2014); its reflection of potential fire intensity tied to fuel aridity and fire weather irrespective of land cover and biomass; its sensitivities to temperature, precipitation, humidity, and wind speed (e.g., Flannigan et al, 2016); and its established empirical relationships to burned area across broad regions of the globe (e.g., Abatzoglou et al, 2018).…”

An empirical prediction approach for seasonal fire risk in the boreal forests

“…Specifically, the fourth version (GFED4), which is fully described by Giglio et al (2003), combines 500 m MODIS maps of burned area with active fire data from the Along-Track Scanning Radiometer (ATSR) World Fire Atlas (Arino and Rosaz, 1999) and the Visible and Infrared Scanner (VIRS) (Giglio et al, 1999). GFED4 has been used regularly to link fire activity with large-scale modes of atmospheric-oceanic variability (e.g., Chen et al, 2016) and to verify forecasts of fire danger (Di Giuseppe et al, 2016). Secondly, we focus on specific large fires using data from the boreal burned area (BBA) dataset, a satellitebased fire scar product developed and described by Lehsten et al (2014) that identifies spatiotemporal fire occurrence at daily timescales for the period 2001-2011.…”

“…In practice, the precise location of a fire event may not be so crucial to planning procedures and resource allocation. Rather, a forecast of anomalous fire risk within the proximity of an observed fire event may still constitute valuable forecast information (Di Giuseppe et al, 2016). Here, our forecast-fire activity comparison at a given point is made between spatial means of MDC and burned area within a 7 × 7 domain centred on the point of interest.…”