Evaluation of fire emissions for HTAP3 with CAMS GFAS and IFS-COMPO
Bibliographic record
Abstract
The Copernicus Atmosphere Monitoring Service CAMS is using ECMWF's Integrated Forecasting System IFS-COMPO with fire emissions from its Global Fire Assimilations System GFAS to monitor and forecast the effect of smoke from vegetation fires, resp. biomass burning, on atmospheric composition. The simulated atmospheric composition fields are routinely validated against observations including from satellites, aircraft and ground stations.The emissions calculation by the operational GFAS version 1.2 have recently been updated for use in the upcoming HTAP3 multi-model, multi-pollutant study of fire impacts (Whaley et al. 2024), creating the dataset GFAS4HTAP. It is based on the dry matter burnt estimates of GFASv1.2, and uses an updated spurious signal mask, ESA CCI land cover data for 2018, a global peat map (Xu et al. 2018) and emission factors from NEIVA (Shahid et al. 2024) to calculate emission fluxes for various smoke constituents for 2003-2024. An additional GFAS-based dataset has been created by calibration against GFED5beta.Global comparisons of dry matter, resp. biomass, combustion rates of the three GFAS-based inventories with GFED4s, GFED5beta, and the two variants of FINN2.5 reveal that these inventories can be roughly classified into one group of "traditional" inventories with lower fire activity, resp. emissions, and another of "more recent" inventories with higher fire activity. The pyrogenic carbon monoxide emission estimates from an inversion of satellite observations of atmospheric composition (Zheng et al. 2019) lie between these two groups in terms of global annual values. However, at a global level, they are more consistent with the "more recent" inventories during the late boreal summer peak of the global fire activity and with the "traditional" inventories during periods of lower fire activity.In order to gain more insight from independent validation, we here present simulations with IFS-COMPO for 2019 based on the three GFAS-based inventories and compare these with atmospheric observations of carbon monoxide, nitrogen dioxide and aerosol optical depth. We find that the best agreement of simulation and observations is achieved by different inventories for different regions, seasons and smoke constituents. However, the emissions of the GFAS4HTAP dataset appears to lead to the overall most balanced atmospheric composition simulation. This supports the group of "traditional" inventories mentioned above.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".