Trajectory model validation using newly developed altitude‐controlled balloons during the International Consortium for Atmospheric Research on Transport and Transformations 2004 campaign
Bibliographic record
Abstract
During the summer of 2004, five altitude‐controlled tracking balloons were flown as part of the International Consortium for Atmospheric Research on Transport and Transformations (ICARTT) campaign. These Controlled Meteorological (CMET) balloons, newly developed at the University of Massachusetts, are notable for their light weight (∼1 kg mass), efficient altitude control, ease of launch, long‐duration flight capability, and ability to perform repeated quasi‐Lagrangian soundings. The balloons were embedded in urban plumes from New York and Boston which they tracked over New England, eastern Canada, and the Atlantic Ocean while maintaining a nearly constant altitude. The flights ranged from 10 to 111 hours and covered a maximum distance of 3000 km. Balloon flight tracks are used here to assess the accuracy of trajectory models during intensive aircraft sampling periods. A new method is presented for increasing the number of available reference trajectories by dividing the balloon flights into shorter segments for statistical analysis. For trajectory durations between 2 and 12 hours, mean trajectory errors are found to be approximately 26% and 34% of the flight distance for ECMWF‐based and GFS‐based trajectories, respectively. Anomalously large model errors observed during three of the flights are found to be the result of a narrow low‐level jet (15 July) and synoptic‐scale flow patterns (9 and 10 August). The results from this study should be useful to researchers evaluating the performance of trajectory models and chemical transport models during the ICARTT campaign. Complete CMET balloon and model trajectory data sets are available as a supplement to this paper.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".