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5 results for “fire weather index”
Fire Weather Index - ERA5 HRES
<p>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. </p> <p>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 Forecasts (ECMWF) ERA5 reanalysis dataset (Hersbach et al., 2019), and replaces the homonymous indices based on ERA-Interim (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. </p> <p>The 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 on Zenodo. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). The caliver R package (Vitolo et al. 2017, 2018) contains useful functions to process this dataset. </p> <p>Details: </p> <ul> <li>File format: netcdf4</li> <li>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326)</li> <li>Longitude range: [-180, +180]</li> <li>Latitude range: [-90, +90]</li> <li>Temporal resolution: 1 day (at 12 local noon)</li> <li>Spatial resolution: 0.28 degrees (~31 Km)</li> <li>Spatial coverage: Global</li> <li>Time span: from 1980-01-01 to 2019-06-30</li> <li>Stream: Deterministic forecasts</li> </ul>
Fire Weather Index - ERA-Interim
<p>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.</p> <p>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; Di Giuseppe et al., 2016). 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. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Structure of the Canadian Forest Fire Weather Index System: the model and its components
<p>This material is part of:</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br><strong>Structure of the Canadian Forest Fire Weather Index system: the model and its components</strong> — The <a href="../record/10806780#preview-iframe">figure below</a> (formats: <a href="../record/10806780/files/FWI-sys_simple_diagram.png?download=1">PNG</a> or <a href="../record/10806780/files/FWI-sys_simple_diagram.pdf?download=1">PDF</a>) shows the logical subdivision of the Canadian Forest Fire Weather Index system (FWI-sys) in components.</p> <p> </p> <p>The Canadian FWI-sys (De Groot,1987; Van Wagner,1987) is an index of fire danger by weather designed to consider the effects on vegetation fuels of the sequence of weather conditions. It is designed to estimate a uniform numerical rating for the relative fire potential accounting for the local sequence of temperature, wind speed, relative humidity, and precipitation, for the day in which the rating is estimated but also modelling the dynamics of the previous days. In addition, the variable amount of possible drying due to the varying solar irradiation in different seasons is taken into account by adjusting the parameters per each month of the year.<br><br>The system is standardised to consider the behaviour of a reference typology of vegetation fuel (mature pine stand) regardless of other non-weather factors which may locally influence the fire danger, such as the specific topography or the pattern, composition, and structure of vegetation assemblages. Therefore, FWI-sys is suitable to support the harmonised comparison among variable weather conditions, either spatially (comparing different spatial regions) or temporally (comparing the same region over time).<br><br>The FWI-sys components are organised in three layers, processing at the daily frequency weather information (either from observations, reanalysis, forecast, or climate scenarios) and estimating from it a final standard aggregated numerical rating of fire intensity.<br><br>The required input variables are</p> <ul> <li>Temperature T (nominally, FWI-sys requires T at noon)</li> <li>Wind speed W (nominally, FWI-sys requires T at noon)</li> <li>Relative humidity</li> <li>Precipitation (24-hour rainfall)</li> <li>Month of the year</li> </ul> <p>The FWI-sys was originally designed to fit the Candian conditions. Following its success, adaptations of the system were studied for different areas of the globe. This implies that the parameters used inside the FWI-sys globally also depend on the latitude (Alexander, 2008).</p> <p>The first layer of components (the <em>fuel moisture codes</em>: Fine Fuel Moisture Content, FFMC; Duff Moisture Code, DMC; Drought Code, DC) is composed by dynamic variables. This means that the value of each component for a given day depends also on the value of the same component the day before. The dynamic components with longer memory of their past history also approximate the seasonal changes in solar radiation, by considering the month of the year (see Figure, bottom left).</p> <ul> <li><strong>Fine Fuel Moisture Code (FFMC)</strong> : provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel.</li> <li><strong>Duff Moisture Code (DMC)</strong> : models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). This component of the FWI-sys represents wooden fuels of intermediate thickness.</li> <li><strong>Drought Code (DC)</strong> : models a standard moisture content of deeper, compact, organic layers. This component of the FWI-sys is able to track seasonal drought effects on coarse wooden fuels.</li> </ul> <p> </p> <p>The second layer of components (the<em> fire behaviour indices</em>: Initial Spread Index, ISI; Buildup Index, BUI; Fire Weather Index, FWI) mathematically is composed by stateless D-TM components. This means that these components do not have an internal memory of the past conditions, while instead they rely on the combined information offered by the different temporal inertia of the fuel moisture codes, which they process as input information.</p> <ul> <li><strong>Initial Spread Index (ISI)</strong> : represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of fuel moisture and availabity for the coarser wooden fuels.</li> <li><strong>Buildup Index (BUI)</strong> : combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire.</li> <li><strong>Fire Weather Index (FWI)</strong> : offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI.</li> </ul> <p><br>Given its structure, the model can also be interpreted as a recurrent neural network (RNN) where the input variables are transformed into the final aggregated numerical rating (FWI) by means of two hidden layers: the <em>fuel moisture codes</em> (three nodes/neurons); and the <em>fire behaviour indices</em> (two nodes/neurons).</p> <p>Note that this structure is not a simple feedforward network, as the first hidden layer is made by dynamic components (FFMC, DMC, DC, see highlighted feedack loops in <a href="../record/10806780/files/FWI-sys_simple_diagram_recurrent.png?download=1">PNG</a> format). The activation functions are complex, and the D-TM components (either dynamic or stateless) generally mix physically-based and empirical aspects. A consequence of the complexity of the FWI-sys activation functions is that a neural network with standard (e.g. sigmoidal) activation functions would need to exploit disproportionally many more additional neurons for the same FWI-sys D-TM complexity to be reasonably approximated.</p> <p> </p> <p>An additional FWI-sys component is a simple transfromation of the aggregated FWI values to better account for the nonlinear increase of fire control effort with increasing FWI values (Van Wagner, 1987):</p> <ul> <li><strong>Daily Severity Rating (DSR)</strong>: this transformation of FWI is meant to provide a measure of control difficulty:<br> DSR = 0.0272 ⋅ FWI <sup>1.77</sup><br>which easily invertible:<br> FWI = ( DSR / 0.0272 ) <sup>1 / 1.77</sup></li> </ul> <p><br><br>To cite the Figure, please refer to:<br><br>de Rigo, 2016. <strong>Structure of the Canadian Forest Fire Weather Index System: the model and its components</strong>. https://doi.org/10.5281/zenodo.6558576</p> <p>which is part of</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br> </p> <p> </p> <p><strong>References</strong></p> <p>De Groot, W.J., 1987. <strong>Interpreting the Canadian Forest Fire Weather Index (FWI) System</strong>. In: <em>Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar, Proceedings</em>. Winnipeg, Manitoba, Canada, pp. 3-14. <a href="https://purl.org/INRMM-MiD/c-14176512">https://purl.org/INRMM-MiD/c-14176512</a> </p> <p>Van Wagner, C.E., 1987. <strong>Development and structure of the Canadian Forest Fire Weather Index System</strong>. <em>Forestry Technical Report</em>. Canadian Forestry Service, Ottawa, Canada. <a href="https://purl.org/INRMM-MiD/c-14168337">https://purl.org/INRMM-MiD/c-14168337</a> </p> <p>Alexander, M.E., 2008. <strong>Latitude considerations in adapting the Canadian Forest Fire Weather Index System for use in other countries</strong>. In: Lawson, B.D., Armitage, O.B. (Eds.), <em>Weather Guide for the Canadian Forest Fire Danger Rating System</em>. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada, pp. 67–73. ISBN:978-1-100-11565-8 <a href="https://purl.org/INRMM-MiD/z-MBDA6A6I">https://purl.org/INRMM-MiD/z-MBDA6A6I</a></p> <p> </p>
Daily Fire Weather Index dataset over India - Current (2006-2015) and End Century (2091-2100)
<p>This is a gridded high-resolution fire weather index (FWI) dataset over India. This dataset is at 10km spatial and daily temporal resolution for two ten-year time slices i.e. Current (2006-2015) and Endcentury (2091-2100). FWI is calculated using the Canadian CFFDRS -FWI package implemented in MATLAB software (https://zenodo.org/records/10047237). The meteorological input to the system is taken from the 10km gridded bias-corrected and dynamically downscaled DSCESM dataset (https://www.wdc-climate.de/ui/entry?acronym=WRF10km_wbc_C5<i>forcoIndia). </i>The current file is named fwi-c-daily and the end-century file is named fwi-f-daily. The datasets are in MATLAB .mat format which is easily convertible in NetCDF format.</p>
Fire Weather Index for Europe from Downscaled and Bias-Corrected CMIP6 Model Outputs
<p>This dataset contains the Canadian Forest Fire Weather Index (FWI) calculated from six downscaled and bias-corrected CMIP6 model outputs. The models included are:</p> <ul> <li>ACCESS-CM2 (Ziehn et al. 2020)</li> <li>CanESM5 (Swart et al. 2019)</li> <li>CNRM-ESM2-1 (Séférian et al. 2019)</li> <li>EC-EARTH3 (EC-Earth Consortium 2019)</li> <li>MPI-ESM1-2-HR (von Storch et al. 2017)</li> <li>MRI-ESM2-0 (Yukimoto et al. 2019)</li> </ul> <p>The dataset encompasses four Shared Socio-economic Pathway (SSP) projections:</p> <ul> <li>SSP1-2.6</li> <li>SSP2-4.5</li> <li>SSP3-7.0</li> <li>SSP5-8.5</li> </ul> <p>Each model output has been downscaled to a resolution of 0.0703135°, corresponding to approximately 9km×9km grids before the FWI calculation. The data covers Europe spatially and temporally spans from 1950 to 2080, offering comprehensive insights into past, present, and future fire weather conditions.</p> <p>This dataset supports the manuscript titled <strong>"The fire weather in Europe: large-scale trends towards higher danger" </strong>by Hetzer et al., currently under review in ERL. Detailed instructions for accessing the data can be found in the included README file. </p> <p>Note: Downloads are password protected. Please use "FWI_2024" for access. </p> <p>Funding: The authors acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890.</p> <p> </p> <p> </p> <p> </p> <p> </p>
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