Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.036 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".