This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS). The European Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada, United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from historical simulations provided by ECMWF ERA5 reanalysis. ERA5 by combining model data and a vast set of quality controlled observations provides a globally complete and consistent data-set and is regarded as a good proxy for observed atmospheric conditions. The selected data records in this data set are regularly extended with time as ERA5 forcing data become available. 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.

This dataset offers modeled daily fire danger time series, driven by seasonal weather forecasts. It provides long-range predictions of meteorological conditions conducive to the initiation, spread, and persistence of fires. The fire danger metrics included in this dataset are part of an extensive dataset produced by the Copernicus Emergency Management Service (CEMS) for the European Forest Fire Information System (EFFIS) and the Global Wildfire Information System (GWIS). EFFIS and GWIS are used for monitoring and forecasting fire danger at both European and global scales. The dataset incorporates fire danger indices 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 generated by driving the Global ECMWF Fire Forecast (GEFF) model with seasonal weather ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 5 (SEAS5) prediction system.These forecasts initially consist of 25 ensemble members until December 2016, referred to as re-forecasts. After that period, they consist of seasonal forecasts with 51 members. 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 (SEAS6). 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.

Past and future weather extremes across Europe

This repository contains the annual exceedance index data for past and future weather extremes across Europe on NUTS1 scale. The code and an accompanying paper analyzing the impact of this weather extremes on the European agricultural sector on subnational scale will be published during 2023. We use a percentile-based approach to assess the annual exceedance index of the four weather extremes heat waves, cold waves, fire-risk and droughts for the past (1981–2020) and future (2006–2100) [Zhang et al., 2005]. For the past, we used daily weather records on a grid level (around 11 km at the equator) from the ERA5-Land reanalysis dataset, and for future projections, we use modelled daily weather records from EURO-CORDEX [Christensen et al., 2020, Muñoz, 2019]. For past and future fire-risk we use precalculated fire weathernindex data from ERA5 and EURO-CORDEX, respectively [Giannakopoulos et al., 2020]. We used the model average of the following driving GCMs and RCMs for future projections: ICHECs Earth System Model (EC-Earth), MPI-Ms Earth System Model (MPI-ESM-LR), SMHIs Regional Climate Model (RCA4). The baseline period for the historical scenario is 1981–2010, and for future projections 1981–2005. Daily thresholds for heat waves, cold waves, and flash droughts are estimated from the 90th percentile of the daily minimum and maximum temperature, 10th percentile of the daily minimum and maximum temperature, and 30th percentile of the soil volumetric water content (0–28cm), respectively [**Sutanto** et al., 2020]. We use a five days centre data window for all three extreme events to estimate the thresholds from the previously listed baseline periods. The annual exceedance index for heat waves is calculated as the sum of days, at least for three consecutive days; the daily temperature values exceed the thresholds for June, July, and August. For cold waves, the annual exceedance index is the sum of days, at least for three consecutive days; the daily temperature values are below the thresholds for January, February, October, November, and December. In-base, exceedance is calculated using bootstrapping (1000x repetitions) for both extreme events. Heat and cold wave exceedance indices are rescaled to NUTS1 regions using a maximum resampling. We use sequent peak analysis to detect annual flash droughts, remove minor droughts, and pool interdependent droughts for the season from June to October [**Biggs** et al., 2004]. The annual exceedance index of droughts is rescaled to NUTS1 regions by using a mean resampling. Parameters for fire-risk are listed in the table below while.

Parameters of the analysis of the percentile-based extreme.

Type	Variable	Percentile	Window	Min duration	Rescaling	Months	Bootstrapping
Heat wave	tmin and tmax	90	5	3	max	6, 7, 8	yes
Cold wave	tmin and tmax	10	5	3	max	1, 2, 10, 11, 12	yes
Flash drought	swvl 0-28cm	30	5	5	mean	6, 7, 8, 9, 10	no
Fire risk	FWI	90	5	1	mean	3, 4, 5, 6, 7, 8, 9	yes

Xuebin Zhang, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. Avoiding inhomogeneity in percentile-based indices of temperature extremes. Journal of Climate, 18 (11):1641–1651, 2005. ISSN 08948755. doi: 10.1175/JCLI3366.1.

Samuel Jonson Sutanto, Claudia Vitolo, Claudia Di Napoli, Mirko D’Andrea, and Henny A.J. Van Lanen. Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134 (March 2019):105276, jan 2020. ISSN 01604120. doi: 10.1016/j.envint.2019.105276.

J. Sabater Muñoz. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2019.

O. B. Christensen, W. J. Gutowski, G. Nikulin, and S. Legutke. CORDEX Archive Design, 2020. URL https://is-enes-data.github.io/cordex_archive_specifications.pdf

Barry J. F. Biggs, Bente Clausen, Siegfried Demuth, Miriam Fendeková, Lars Gottschalk, Alan Gustard, Hege Hisdal, Matthew G. R. Holmes, Ian G. Jowett, Ladislav Kašpárek, Artur Kasprzyk, Elzbieta Kupczyk, Henny A.J. Van Lanen, Henrik Madsen, Terry J. Marsh, Bjarne Moeslund, Oldřich Novický, Elisabeth Peters, Wojciech Pokojski, Erik P. Querner, Gwyn Rees, Lars Roald, Kerstin Stahl, Lena M. Tallaksen, and Andrew R. Young. Hydrological Drought: Processes and Estimation Methods for Stream- flow and Groundwater. Elsevier, 1 edition, 2004. ISBN 0444517677.

Giannakopoulos, C., Karali, A., Cauchy, A. (2020): Fire danger indicators for Europe from 1970 to 2098 derived from climate projections, version 1.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.ca755de7

Funding
Tobias Seydewitz acknowledges funding from the German Federal Ministry of Education and Research for the [BIOCLIMAPATHS](https://www.pik-potsdam.de/en/output/projects/all/647) project (grant agreement No 01LS1906A) under the Axis-ERANET call. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.

The dataset presents projections of fire danger indicators for Europe based upon the Canadian Fire Weather Index System (FWI) under future climate conditions. The FWI is a meteorologically based index used worldwide to estimate the fire danger and is implemented in the Global ECMWF Fire Forecasting model (GEFF). In this dataset, daily FWI values, seasonal FWI values, and other FWI derived, threshold-specific, indicators were modelled using the GEFF model to estimate the fire danger in future climate scenarios. These indicators include the number of days with moderate, high, or very high fire danger conditions as classified by the European Forest Fire Information System (EFFIS) during the northern hemisphere's fire season (June-September):

very low: <5.2 low: 5.2 - 11.2 moderate: 11.2 - 21.3 high: 21.3 - 38.0 very high: 38.0 - 50 extreme: >=50.0

This dataset may serve to assess future fire danger conditions for regions across Europe and support the development of a long-term tourism strategy to reduce the risk of forest fires on nature-based tourism infrastructure. The FWI is a meteorologically based index that accounts for the effect of fuel moisture and weather conditions on fire behaviour. Daily noon values of air temperature, relative humidity, wind speed and 24-h accumulated precipitation are required for the calculation of the index. In order to attain the meteorological variables, projections from multiple global climate models downscaled to a regional climate model were used as input to the GEFF model. The climate models were developed within the EURO-CORDEX initiative, providing high-resolution and comparable model output centered on the European domain. In order to assess the impact of climate change, the GEFF model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios consistent with an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century (RCP8.5). Historical simulations, for the period 1970-2005, are included to provide a reference for the FWI projections. An estimate of the statistical uncertainty associated with climate projections may be derived with the use of multiple climate model outcomes. This may be achieved by the user both implicitly or explicitly by selecting from a choice of mean, best case, or worst case multi-model outcomes. It should be noted, however, that the multi-model approach may improve the robustness of the outcomes but does not take into account all possible aspects of uncertainty associated with modelling a future climate. This dataset was produced on behalf of the Copernicus Climate Change Service.

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.

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 (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. This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, Vitolo et al. 2018).

File format: netcdf4

Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).

Longitude range: [-180, +180]

Time span: from 1980-01-01 to 2017-12-31

Temporal resolution: 1 day

Spatial resolution: 0.7 degrees (~80 Km)

Spatial coverage: Global

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

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 is available through the Copernicus Climate Data Store (CDS) while the medium-range real-time forecast is available through the EFFIS and GWIS platforms.

This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:

ECMWF_EFFIS_20180723_1200_en.tar (ensemble forecasts issued on 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723_1200_hr.tar (deterministic forecasts issued on 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723-26_1200_hr_e5.tar (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723-26_1200_en_e5.tar (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)

ECMWF_EFFIS_20180723-26_e5.tar (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)

bbox.tar, containing 1 index (FWI) for the bounding box:

GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018

e5_hr, this folder contains deterministic model outputs

e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)

GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).

rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)

rt_en, this folder contains probabilistic forecasts (~18Km)

lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39

Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).

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.

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 is available through the Copernicus Climate Data Store (CDS) while the medium-range real-time forecast is available through the EFFIS and GWIS platforms.

This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018:

GEFF-reanalysis, which provides historical records of fire danger conditions

e5_hr, this folder contains deterministic model outputs

e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)

GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS).

rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)

rt_en, this folder contains probabilistic forecasts (~18Km)

Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69

Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).

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.

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 is available through the Copernicus Climate Data Store (CDS) while the medium-range real-time forecast is available through the EFFIS and GWIS platforms.

This repository provides FWI sample datasets for the assessment of the Portugal wildfires occurred on 25-27 July 2020:

GEFF-reanalysis, which provides historical records of fire danger conditions

e5_hr, this folder contains deterministic model outputs

e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)

GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 47r1 of the ECMWF’s Integrated Forecasting System (IFS).

rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)

rt_en, this folder contains probabilistic forecasts (~18Km)

Geographical bounding box: lon_min = 350.18, lon_max = 353.81, lat_min = 36.78, lat_max = 42.15

Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).

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.

Raw census population and housing data, and the EU Copernicus impervious and tree cover data is not suitable for use within the vulnerability assessment. Instead, all these raw data are processed as z-scores. These z-score indicator columns are described below. Two datasets were produced as outputs. The first dataset excludes the Copernicus impervious surface and tree cover indicators. The z-score indicators are: Indicator Description CCA,CPRO,CMUN,CDIS,CSEC Index related to each administrative area used by the census data. early_childhood_boy Male children aged under 5 (0 - 4 years old). early_childhood_girl Female children aged under 5 (0 - 4 years old). age_middle_to_oldest_old_male Male population aged 75 years and over (75+ years old). age_middle_to_oldest_old_female Female population aged 75 years and over (75+ years old). disability Population with a disability preventing work. one_parent_households One parent household with children. dependants Population who are dependants (under 16 years old). unemployment Unemployed population. attending_university Population attending university. no_higher_education Population with no higher education. foreign_nationals Foreign national population. rented Household population who are renting. primary_school_age Population who are not primary school aged. one_person_households Household population occupied by one person only. year_built Population who are living in dwellings built on or before 1970. age Age domain based on the young and old indicators. health Health domain based on the disability indicator. income Income domain based on the one_parent_households, dependants, and unemployment indicators. info_access_use Information access/use domain based on the no_higher_education indicator. local_knowledge Local knowledge domain linked to the foreign_nationals indicator. tenure Tenure domain based on the households renting indicator. social_network Social network domain based on the primary_school_age and one_person_households indicators. housing_characteristics Housing characteristics domain based on the year_built indicators. sensitivity Sensitivity dimension based on the age and health domains. prepare Ability to prepare dimension based on the income, info_access_use, local_knowledge and tenure domains. respond Ability to respond dimension based on the income, info_access_use, local_knowledge, and social_network domains. recover Ability to recover dimension based on the income, info_access_use, social_network, and part housing_characteristics domains. adaptive_capacity Adaptive capacity dimension based on the income, info_access_use, local_knowledge, social_network, and housing_characteristics domains. social_vulnerability Social Vulnerability Index based on the integration of all the indicators. The second dataset includes the above columns, plus the Copernicus impervious surface and tree cover densisty indicators. This adds the physical environment domain and enhanced exposure dimension based on these additional indicators. The updated Social Vulnerability Index is also recalculated to include these recalculated indicators. The new columns are, with the social_vulnerability being updated: Indicator Description tree_cover_density Indicator of tree cover. impervious Indicator of impervious surfaces. physical_environment Physical environment domain based on the tree_cover_density and impervious indicators. enhanced_exposure Enhanced exposure dimension based on the physical_environment domain and housing_characteristics domains. social_vulnerability Updated Social Vulnerability Index based on the integration of all the indicators.

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.

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.

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.

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.

Details:

• File format: netcdf4
• Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326)
• Longitude range: [-180, +180]
• Latitude range: [-90, +90]
• Temporal resolution: 1 day (at 12 local noon)
• Spatial resolution: 0.28 degrees (~31 Km)
• Spatial coverage: Global
• Time span: from 1980-01-01 to 2019-06-30
• Stream: Deterministic forecasts

The Fire Weather Index (FWI) is a numeric rating of fire intensity. It combines the Initial Spread Index (ISI) and the Build Up Index (BUI). It is suitable as a general index of fire danger.

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 (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. This dataset can be manipulated using the caliver R package (Vitolo et al. 2017a, b).

File format: netcdf4

Coordinate system: World Geodetic System 1984 (WGS84)

Longitude range: [-180, +180]

The Build Up Index (BUI) is a numeric rating of the total amount of fuel available for combustion. It combines the DMC and the DC.

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 (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. This dataset can be manipulated using the caliver R package (Vitolo et al. 2017a, b).

File format: netcdf4

Coordinate system: World Geodetic System 1984 (WGS84)

Longitude range: [-180, +180]

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.

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). 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.

This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, Vitolo et al. 2018).

Details:

• File format: netcdf4
• Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).
• Longitude range: [-180, +180]
• Temporal resolution: 1 day
• Spatial resolution: 0.7 degrees (~80 Km)
• Spatial coverage: Global
• Time span: from 1980-01-01 to 2018-06-30

The Initial Spread Index (ISI) is a numeric rating of the expected rate of fire spread. It combines the effects of wind and the FFMC on rate of spread without the influence of variable quantities of fuel.

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 (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. This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).

File format: netcdf4

Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).

Longitude range: [-180, +180]

Time span: from 1980-01-01 to 2017-12-31

Temporal resolution: 1 day

Spatial resolution: 0.7 degrees (~80 Km)

Spatial coverage: Global

This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).

References:

Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20^th 2019. https://cds.climate.copernicus.eu/cdsapp#!/home

Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.

Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.

This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the 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). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).

References:

Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20^th 2019. https://cds.climate.copernicus.eu/cdsapp#!/home

Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.

Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.

The Daily Severity Rating (DSR) is a numeric rating of the difficulty of controlling fires. It is based on the Fire Weather Index but more accurately reflects the expected efforts required for fire suppression.

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 (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. This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).

File format: netcdf4

Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).

Longitude range: [-180, +180]

Time span: from 1980-01-01 to 2017-12-31

Temporal resolution: 1 day

Spatial resolution: 0.7 degrees (~80 Km)

Spatial coverage: Global

The Drought Code (DC) is a numeric rating of the average moisture content of deep, compact organic layers. This code is a useful indicator of seasonal drought effects on forest fuels and the amount of smoldering in deep duff layers and large logs.

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 (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. This dataset can be manipulated using the caliver R package (Vitolo et al. 2017a, b).

File format: netcdf4

Coordinate system: World Geodetic System 1984 (WGS84)

Longitude range: [-180, +180]

The Duff Moisture Code (DMC) is a numeric rating of the average moisture content of loosely compacted organic layers of moderate depth. This code gives an indication of fuel consumption in moderate duff layers and medium-size woody material.

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 (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. This dataset can be manipulated using the caliver R package (Vitolo et al. 2017a, b).

File format: netcdf4

Coordinate system: World Geodetic System 1984 (WGS84)

Longitude range: [-180, +180]