Estimating wildfire emissions of ammonia using Cross-track Infrared Sounder (CrIS) profile information
Bibliographic record
Abstract
Ammonia plays an important role for air, soil and water quality, as well as aerosol formation and plant growth. Accurate estimates of emission rates of ammonia from wildfires are crucial to understand the impact on human health and ecosystems. However, ground-based measurements of ammonia are sporadic. Satellite measurements can help address this monitoring gap. The Cross-track Infrared Sounder (CrIS) product provides a unique tool because some information on the vertical distribution of ammonia is derived from the profile retrievals in addition to vertical column densities (VCDs). Emission rates are retrieved by fitting measured vertical column densities (VCDs) to a three-dimensional function of the wind speed and spatial coordinates. This method requires VCDs to be rotated given the wind-direction to remove wind-direction as a fitting variable. The vertical information given by CrIS provides the potential for more accurate emission estimates as wind-direction and -speed at each profile level can be taken into account. The application of the vertical profile of wind also allows more accurate estimates of plume width, which can vary significantly in the traditional VCD rotation depending on the altitudes of wind used for the rotation. This approach was developed and validated using synthetic satellite measurements of plumes simulated by the FLEXPART (v10.0) model to better understand the impact of variability in the vertical profile of the wind. The methodology was then applied using CrIS satellite observations to estimate forest fire emissions of NH3. Preliminary results of this study will be presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".