Ammonia measurements from space with the Cross-track Infrared Sounder (CrIS): characteristics and applications
Bibliographic record
Abstract
Satellite data are helping to fill monitoring gaps in order to better inform decision makers and assess the impact of ammonia-related policies. Presented is an overview demonstrating the current capabilities of the ammonia (NH3) data product derived from the CrIS satellite instrument for monitoring, air quality forecast model evaluation, dry deposition estimates, and emissions estimates. This includes examples of daily, seasonal, and annual observations of CrIS ammonia that demonstrate the spatiotemporal variability of ammonia globally. These results further demonstrate the ability of CrIS to observe regional changes in ammonia concentrations, such as spring maximum values over agricultural regions from the fertilizing of crops. Also shown is the importance contribution of wildfires, especially in regions where there is little or no agriculture sources, such as the northern latitudes in North America during summer. Initial comparisons of CrIS NH3 satellite observations with air quality model simulations show that while there is general agreement on the spatial distribution of the anthropogenic hotspots, some areas are markedly different. Some key findings are that dry deposition estimates of NH3 and NO2 from CrIS and the Ozone Monitoring Instrament (OMI), respectively, indicate that the NH3 dominates over most regions across North America. Their 2013 annual ratio shows NH3 accounting for ~82% and ~55 % of the combined reactive nitrogen dry deposition from these two species over Canada and the U.S. Furthermore, we show the use of CrIS satellite observations to estimate annual and seasonal emissions over Concentrated Animal Feeding Operations (CAFOs). These results are used to evaluate the seasonal and temporal emissions profiles used in bottom-up inventories over an agriculture hotspot, which are often underreported
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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 | 0.000 |
| 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 | 0.000 | 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".