Tropospheric nitric acid columns from the IASI satellite instrument interpreted with a chemical transport model: Implications for parameterizations of nitric oxide production by lightning
Bibliographic record
Abstract
Abstract This paper interprets tropical tropospheric nitric acid columns from the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument with a global chemical transport model (GEOS‐Chem). GEOS‐Chem and IASI columns generally agree over the tropical ocean to within 10%. However, the GEOS‐Chem simulation underestimates IASI nitric acid over Southeast Asia by a factor of 2. The regional nitric acid bias is confirmed by comparing the GEOS‐Chem simulation with additional satellite (High Resolution Dynamics Limb Sounder, Atmospheric Chemistry Experiment Fourier Transform Spectrometer) and aircraft (Pacific Exploratory Mission (PEM)‐Tropics A and PEM‐West B) observations of the middle and upper troposphere. This bias appears to be driven by the lightning NOx parameterization, both in terms of the magnitude of the NOx source and the ozone production efficiency of concentrated lightning NOx plumes. We tested a subgrid lightning plume parameterization and found that an ozone production efficiency of 15 mol/mol in lightning plumes over Southeast Asia in conjunction with an additional 0.5 Tg N would reduce the regional nitric acid bias from 92% to 6% without perturbing the rest of the tropics. Other sensitivity studies such as modified NOx yield per flash, increased altitude of lightning NOx emissions, decreased convective mass flux, or increased scavenging of nitric acid required unrealistic changes to reduce the bias.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".