Monitoring wildfires from satellite, integration in Copernicus services and characterizing atmospheric impacts from the regional to the global scales
Bibliographic record
Abstract
Satellite-borne observations offer the possibility to monitor wildfires and their impact worldwide. In addition, satellite products are increasingly used in early warning and forecasting systems for fire management. Europe is implementing a long-term and reliable observational programme and, within this frame, EUMETSAT, the European meteorological satellite operator, provides numerous observational products ranging from near-real-time wildfire identification (e.g. fire radiative power) to atmospheric impacts (e.g. major pollutants and smoke). Our presentation will focus on the satellite data value chain, e.g. the integration in the Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS). To do that, we will firstly present datasets addressing wildfires (e.g. Fire Radiative Power, atmospheric composition, and smoke) currently generated at EUMETSAT and its Satellite Applications Facility (SAF). We will also introduce upcoming (based on the Flexible Combined Imager on-board the Meteosat Third Generation) and future products (Sentinel-4 and 5), with an example of potential joint use for a past intense fire case in the Mediterranean (Greece, August 2023). We will then show the entire value chain, including how the data is used in the Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS), with an example on the recent intense and anomalous fire season in Canada (spring to summer 2023). This will show how distinct phases of wildfires management – from early warnings up to the impacts on yearly emissions – can be monitored with the synergy of satellite data and Copernicus forecast and analysis. Finally, we will touch also on the user support activities within EUMETSAT in this area.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".