Summertime impact of convective transport and lightning NO <sub>x</sub> production over North America: modeling dependence on meteorological simulations
Bibliographic record
Abstract
Abstract. A 3-D regional chemical transport model (REAM) is applied to examine the uncertainties in modeling the effects of convective transport and lightning NOx production on upper tropospheric chemical tracer distributions. To assess the model uncertainties, two different cumulus convective parameterizations, KF-eta and Grell, are adopted in REAM from the respective meteorological models, WRF and MM5. The model simulations are evaluated using INTEX-NA aircraft measurements and satellite measurements of NO2 columns and cloud top pressure, and we find that mid and upper tropospheric trace gas concentrations are affected strongly by convection and lightning NOx productions. A major improvement of the KF-eta scheme is its inclusion of cloud entrainment and detrainment processes. KF-eta scheme simulates larger convective updraft mass fluxes below 150 hPa than the Grell scheme, resulting in more outflow of pollutants in the mid troposphere. The ratio of C2H6/C3H8 is found to be a sensitive parameter to convective outflow; the simulation by WRF-REAM is in closer agreement with INTEX-NA measurements than MM5-REAM, implying that convective mass fluxes by KF-eta scheme are more realistic. The inclusion of entrainment and detrainment processes in the KF-eta scheme also leads to lower cloud top heights (10–12 km) than the Grell scheme (up to 16 km), and hence smaller amounts of estimated (intra-cloud) lightning NOx and lower emission altitudes. WRF simulated cloud top heights are in better agreement with GOES satellite measurements than MM5. The model divergence on lightning NOx mostly is above 12 km. As a result, both models suggest that lightning NOx production enhances the concentrations of upper tropospheric NO2 by a factor of >5 (~100 pptv) and increases O3 by up to ~20 ppbv at 8–12 km.
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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".