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If you utilize the dataset, please acknowledge this service by properly referencing it through citation of the associated papers. This will enhance the visibility of the dataset's usefulness and support its continued availability. Thank you! |
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The description of this dataset and its verification has been documented in a data description paper published in submitted in Nature Scientific Report. Please cite this paper fi you use the dataset Di Giuseppe, F., Vitolo, C., Barnard, C. et al Fire Danger seasonal forecast: data and predictability, Scientific Data (2023)
. Global seasonal prediction of fire danger. Sci Data11, 128 (2024). https://doi.org/10.1038/s41597-024-02948-3 |
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In Brief
This dataset provides modelled offers modeled daily fire danger time series forced with seasonal meteorological reforecasts, driven by seasonal weather forecasts. It provides long-range prediction predictions of meteorological conditions favourable conducive to the startinitiation, spread, and sustainability persistence of fires. The fire danger metrics provided metrics included in this dataset are part of a vast an extensive dataset produced by the Copernicus Emergency Management Service (CEMS) for the European the European Forest Fire Information System (EFFIS) and the Global Wildfire Information System (GWIS). EFFIS incorporates the and GWIS are used for monitoring and forecasting fire danger at both European and global scales. The dataset incorporates fire danger indices for the from 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.
This dataset was produced generated by forcing the GEFF (GEFF, https://git.ecmwf.int/projects/CEMSF/repos/geff/browse) fire forecast model with seasonal meteorological ensemble reforecasts. The forcing meteorological data are seasonal reforecasts driving the Global ECMWF Fire Forecast (GEFF) model with seasonal weather ensemble forecasts from the European Centre of for Medium-range Range Weather Forecasts (ECMWF) System5 System 5 (SEAS5) , consisting prediction system.These forecasts initially consist of 25 ensemble members up until December 2016, and after that referred to as re-forecasts. After that period, they consist of seasonal forecasts with 51 members. The temporal resolution is daily forecasts initialised once a month with an horizon of 216 days (7 months) over the past period 1981-2022. The selected data records in this data set will be extended with time as SEAS5 forcing data become available. This is however not a real time service as real time forecasts are made available through the EFFIS/GWIS web services
Reforests are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. This dataset is produced by ECMWF in its role of the computational centre for fire danger forecast of the CEMS, on behalf of the Joint Research Centre which is the managing entity of the service.
Fire danger variables descriptions
The Canadian Fire Weather index
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It is important to note that the re-forecast dataset was initialized using ERA-Interim analysis data, while forecast simulations from 2016 onward are initialized using ECMWF operational analysis. Therefore, it is suggested that the period 1981-2016 be used as a reference period, while the period 2017-to present as a real time forecast.
For both the re-forecast (1981-2016) and forecast periods (2017-present), the temporal resolution is daily forecasts at 12:00 local time, available once a month, with a prediction horizon of 216 days (equivalent to 7 months). The data records in this dataset will be extended over time as seasonal forcing data becomes available. Once the SEAS5 operation ceases, the dataset will be updated with the next ECMWF seasonal system (SYS6). It is essential to note that this is not a real-time service, as real-time forecasts are accessible through the EFFIS web services.
These seasonal forecasts can be used to assess the performance of the forecasting system or to develop tools for statistically correcting forecast errors. ECMWF produces this dataset as the computational center for fire danger forecasting within the Copernicus Emergency Management Service (CEMS) on behalf of the Joint Research Centre, which serves as the managing entity for this service.
Fire danger variables descriptions
The Canadian Fire Weather index
Schematic of the FWI
The Canadian Fire Weather Index (FWI) is a system used in Canada to assess the potential risk and behavior of forest fires. It provides a numerical rating that indicates the relative ease of ignition and the potential intensity of fire spread in forest fuels. The FWI system incorporates various weather and fuel moisture measurements to generate indices that collectively describe the fire danger level. The purpose of the Canadian Fire Weather Index is to
Schematic of the FWI
The Canadian Fire Weather Index (FWI) is a system used in Canada to assess the potential risk and behavior of forest fires. It provides a numerical rating that indicates the relative ease of ignition and the potential intensity of fire spread in forest fuels. The FWI system incorporates various weather and fuel moisture measurements to generate indices that collectively describe the fire danger level. The purpose of the Canadian Fire Weather Index is to assist fire managers, fire behavior analysts, and meteorologists in making informed decisions regarding fire prevention, preparedness, and suppression strategies. It helps in allocating firefighting resources efficiently by identifying areas with high fire risk and potential fire behavior. There are various indices that are provided
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- Bureau of Meteorology (Australia). (n.d.). Fire Danger Rating and the McArthur Forest Fire Danger Index.
Available variables
We provide the following subset of variables
| MAIN VARIABLES |
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| Name | Units | Description |
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| Build-up index | Dimensionless | The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. |
| Burning index | Dimensionless | The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. |
| Drought code | Dimensionless | The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. |
| Drought factor | Dimensionless | The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum field capacity of 203.2 mm (i.e. 8 inches) and a minimum of 0 mm. |
| Duff moisture code | Dimensionless | The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. |
| Energy release component | J/m2 | The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. |
| Fine fuel moisture code | Dimensionless | The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. |
| Fire daily severity index | Dimensionless | Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. |
| Fire danger index | Dimensionless | The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation |
| Fire weather index | Dimensionless | The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. |
| Ignition component | % | The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. |
| Initial spread index | Dimensionless | The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. |
| Keetch-Byram drought index | Dimensionless | The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. |
| Spread component | Dimensionless | The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. |
Time Interpolation to local noon
A model integration at any nominal time will simulate the atmospheric conditions at a different local time depending on the location. A temporal and spatial collage of 24-h time model simulations is performed to produce a snapshot at 1200 local time. Thus, temperature and relative humidity fields are cut into, for example, 3-hourly time strips using the closest 3-h forecast output and then concatenated together so that the final field is representative of the conditions around the local noon within the 3-h resolution available. Using this method, the driving forcing are a composite of forecast outputs at different lead times in a 24-h interval and could therefore have different forecast accuracy. This inconsistency is assumed insignificant given the limited difference in forecast skills in a 24-h lead time range (Buizza et al. 1999)[1]. The most common way to achieve a 12 local time field is through a concatenation of fields. Adjacent stripes of forecasts can be sliced together as highlighted in Figure. The simplicity of the approach however implies that artefact can be introduced at the interface between two slices.
Concatenation of forecasts to derive a field at 12 local time everywhere. The discontinuity line represents the change of date. The stripes are taken from the forecast times specified at the bottom.
ECMWF has developed a new interpolation method performing a weighted average between the two closest timesteps. Below is a graphical explanation of the way the two methods are implemented is provided. If we extract the time series of two locations 1 and 2 near a change of time, the 24 hours forecast will provide the diurnal time for temperature (here chosen as an example) in those points. If we are interested in the value of temperature at 12 local time this will be the value of the prediction at 12 UTC for point 1 and 15 UTC for point 2 in the assumption of a 3-hour resolution forecast. The choice of the different forecast will create a discontinuity in the fields as depicted in the map. The new method instead will interpolate for both points between the value at 12 UTC and the value at 15UTC by weighting the two temperatures by their closeness to any available forecast. This new method provides a much closer agreement to the real diurnal cycle and removes the boundary artefacts that were presented above.
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Interpolation methods to obtain a 12 local time composite fields. The "stripes" method implements a nearest neighbour approach, the "interpolation" method adopts a weighted bilinear interpolation between successive time stamps. The left-hand side figure shows the collated temperature fields for one sample day. The right-hand side figure shows the diurnal cycle for temperature in the two points indicated with “1” and “2” and the corresponding interpolated value in the two cases | |
Usage notes
Download global fire danger forecast maps
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