“Designing Lagrangian experiments to measure regional‐scale trace gas fluxes”
Bibliographic record
Abstract
Knowledge of trace gas fluxes at the land surface is essential for understanding the impact of human activities on the composition and radiative balance of the atmosphere. An ability to derive fluxes at the regional scale (on the order of 102–104 km2), at the scale of ecosystems and political borders, is crucial for policy and management responses. Lagrangian (“air mass‐following”) aircraft experiments have potential for providing direct estimates of regional‐scale fluxes by measuring concentration changes in air parcels as they travel over the landscape. Successful Lagrangian experiments depend critically on forecasts of air parcel locations, rate of dispersion of air parcels, and proper assessment of forecast errors. We describe an operational tool to forecast air parcel locations and dispersion and to guide planning of flights for air mass‐following experiments using aircraft. The tool consists of a particle dispersion model driven by mesoscale model forecasts from operational centers. The particle model simulates time‐reversed motions of air parcels from specified locations, predicting the source regions which influence these locations. Forecast errors are incorporated into planning of Lagrangian experiments using statistics of wind errors derived by comparison with radiosonde data, as well as the model‐to‐model spread in forecast results. We illustrate the tool’s application in a project designed to infer regional CO2 fluxes—the CO2 Budget and Rectification Airborne study, discuss errors in the forecasts, and outline future steps for further improvement of the tool.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".