Optimizing Airborne Emission Rate Retrievals with Sub-Hectometre Resolution Numerical Modelling
Bibliographic record
Abstract
A comprehensive model-based study is designed to provide optimal flight paths for airborne top-down emission rate retrieval methodologies. The meteorology and plume dispersion were modelled using the Weather Research and Forecasting (WRF) modelling platform with the Advanced Research WRF (ARW) dynamical core at 50-m resolution. Multiple flight path designs and parameters were investigated to determine emission rate retrieval accuracy for emissions of a trace gas as a function of downwind distance and transect spacing, which are ultimately related to flight time and cost. Three unique source types (multiple smokestack plumes, small area sources, and a large area source) were investigated for 4 summer afternoon flight cases over 2 d. The results demonstrate that emissions estimate uncertainty is primarily due to storage and release, with uncertainties as high as 30 % at optimal downwind distances (which vary by source type). Interpolation of the sparse measurements can be a significant source of error close to the source, but the uncertainty is ≤17 % for D ≥6 km. The average advective flux estimates are within 12 % of the known emissions for downwind distance of D ≥4 km. Variability between flights decreases with D . For stack sources the variability near D =10 km is approximately half that at D =4 km. For small area sources, there is less reduction with D , and for the large area source, variability reaches a minimum at D =8 km. For stack sources, vertical spacing of transects is optimized at 100 m, while for area sources, a spacing of 50 m reduces uncertainty. Error due to extrapolation below the lowest flight path is less than 20 % for stack sources and less than 30 % for area sources for non-dimensionalized downwind distance of D′≥3. Results demonstrate the need for surface sampling coincident with the flights to reduce extrapolation error, and the use of modeling with reanalysis data to account for storage and release effects.
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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 | 0.000 |
| 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 | 0.000 | 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".