Newsletter-banner-No-151

Copernicus fire danger forecast goes online

Francesca Di Giuseppe, Matthew Manoussakis, Blazej Krzeminski, Claudia Vitolo, Fredrik Wetterhall, Florian Pappenberger

 

Copernicus fire danger forecast products from the Global ECMWF Fire Forecast (GEFF) system are now available for download through the ECMWF public dataset web interface. The products have been developed at ECMWF over the last three years as part of the EU-funded Copernicus Emergency Management Service. They complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (CAMS) operated by ECMWF. The latter product is calculated daily by the CAMS-Global Fire Assimilation System (GFAS) based on active fires detected by the MODIS satellite instrument.

To access GEFF data, please follow the links below.

GEFF-reanalysis:

http://apps.ecmwf.int/datasets/

GEFF-realtime (link to follow - expected release time May 2017)

ECMWF public dataset documentation:

https://software.ecmwf.int/wiki/display/WEBAPI/Access+ECMWF+Public+Datasets

ECMWF Web-API FAQ:

https://software.ecmwf.int/wiki/display/WEBAPI/WebAPI+FAQ

Two fire danger datasets are available for download: GEFF-reanalysis and GEFF-realtime. GEFF-reanalysis provides historical records of global fire danger conditions from 1980 to today. It is updated as new ERA-Interim data become available. GEFF-realtime provides real-time high-resolution deterministic and lower-resolution probabilistic fire danger forecasts up to 15 days ahead using weather forcings from the latest model cycle of ECMWF’s Integrated Forecasting System (IFS). The real-time dataset is updated every day with a new set of forecasts. Forecasts for the past year are available for immediate retrieval using the web interface, while older forecasts are archived on ECMWF servers and can be accessed upon request. This allows users to assess the system’s performance for recent events.

The development of GEFF was funded through a third-party agreement with the European Commission’s Joint Research Centre (JRC). A subset of GEFF data is also feeding the European Forest Fire Information System (EFFIS) web portal, an operational platform which gives access to timely fire danger information at a pan-European scale. Thirty-eight local and national authorities across Europe are part of the EFFIS network and have been relying on GEFF output for the early identification of regions prone to fire events as a result of persistent drought conditions.

With the new data download service, users will be able to access the complete GEFF database, which comprises the Canadian Fire Weather index and the US and Australian fire danger models. The web interface enables users to navigate through the GEFF datasets in a dynamic and user-friendly way. Whenever a selection is made (e.g. of a particular model or parameter), the user interface is updated automatically in order to reflect data availability. In this way the system prevents users from submitting requests for data that do not exist and visualises the dataset layering. Once users have made their selections, they can submit the request to download the requested data. Alternatively they can view the corresponding Python script request and use the ECMWF Web-API service to download the data in a programmatic way.

The release of the GEFF dataset is in line with the data and information policy of the Copernicus programme, which provides users with free, full and open access to environmental data. No registration is required for discovery and exploratory services, but it is a prerequisite if users wish to download GEFF data. Registration is free of charge.

%3Cstrong%3EGEFF%20datasets%20on%20the%20web%3C/strong%3E.%20Users%20can%20specify%20a%20timeframe%20and%20select%20a%20number%20of%20parameters%20when%20accessing%20the%20GEFF-reanalysis%20and%20GEFF-realtime%20datasets.
GEFF datasets on the web. Users can specify a timeframe and select a number of parameters when accessing the GEFF-reanalysis and GEFF-realtime datasets.