Constraining U.S. ammonia emissions using TES remote sensing observations and the GEOS‐Chem adjoint model
Bibliographic record
Abstract
Ammonia (NH 3 ) has significant impacts on biodiversity, eutrophication, and acidification. Widespread uncertainty in the magnitude and seasonality of NH 3 emissions hinders efforts to address these issues. In this work, we constrain U.S. NH 3 sources using observations from the TES satellite instrument with the GEOS‐Chem model and its adjoint. The inversion framework is first validated using simulated observations. We then assimilate TES observations for April, July, and October of 2006 through 2009. The adjoint‐based inversion allows emissions to be adjusted heterogeneously; they are found to increase in California throughout the year, increase in different regions of the West depending upon season, and exhibit smaller increases and occasional decreases in the Eastern U.S. Evaluations of the inversion using independent surface measurements show reduced model underestimates of surface NH 3 and wet deposited NH x in April and October; however, the constrained simulation in July leads to overestimates of these quantities, while TES observations are still under predicted. Modeled sulfate and nitrate aerosols concentrations do not change significantly, and persistent nitrate overestimation is noted, consistent with previous studies. Overall, while satellite‐based constraints on NH 3 emissions improve model simulations in several aspects, additional assessment at higher horizontal resolution of spatial sampling bias, nitric acid formation, and diurnal variability and bi‐directionality of NH 3 sources may be necessary to enhance year‐round model performance across the full range of gas and aerosol evaluations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".