Observing carbon monoxide and volatile organic compounds from Canadian wildfires in 2023 from FengYun-3E/HIRAS-II in a dawn-dusk sunsynchronous orbit
Bibliographic record
Abstract
This study presents the first attempt to observe wildfire enhancements of carbon monoxide (CO) and volatile organic compounds (VOCs) around sunrise and sunset from a hyperspectral infrared sounder in a dawn-dusk sun-synchronous orbit. The 2 nd generation of High Spectral Infrared Atmospheric Sounder (HIRAS-II) on board FengYun-3E (FY-3E), the world's first civilian dawn-dusk orbit meteorological satellite, provides global observations in the thermal infrared spectral range with equatorial overpass times of 5:30am/pm local solar time (LST). The spectral observations are used to retrieve CO, formic acid (HCOOH) and peroxyacetyl nitrate (PAN) emitted from three major Canadian wildfire events from June to August 2023. Extreme enhancements of CO, HCOOH and PAN were detected in the 2023 Canadian wildfires which are unprecedented in time and spatial scales and intensity. The HIRAS-II successfully captured the strong signals of CO, HCOOH, and PAN. The averaging kernel (AK) matrix, indicative of detection vertical sensitivity, peaks mostly in the free troposphere where extensive transport typically takes place. Comparison with the Infrared Atmospheric Sounding Interferometer (IASI) reveals that the spatial distribution patterns of the total columns extracted from HIRAS-II are in good agreement. Validation with the CAMS model and ground-based observations from TCCON and NDACC confirms that HIRAS-II retrievals are consistent. The HCOOH-to-CO and the PAN-to-CO column enhancement ratios derived from HIRAS-II are close to those derived from IASI. This paper exhibits the capability of FY-3E/HIRAS-II in observing wildfire emissions during dawn and dusk hours, which will potentially enhance the climatemonitoring capability of low-orbit meteorological satellites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".