PMC Turbo : Studying Gravity Wave and Instability Dynamics in the Summer Mesosphere Using Polar Mesospheric Cloud Imaging and Profiling From a Stratospheric Balloon
Bibliographic record
Abstract
The Polar Mesospheric Cloud Turbulence (PMC Turbo) experiment was designed to observe and quantify the dynamics of small-scale gravity waves (GWs) and instabilities leading to turbulence in the upper mesosphere during polar summer using instruments aboard a stratospheric balloon. The PMC Turbo scientific payload comprised seven high-resolution cameras and a Rayleigh lidar. Overlapping wide and narrow camera field of views from the balloon altitude of ~38 km enabled resolution of features extending from ~20 m to ~100 km at the PMC layer altitude of ~82 km. The Rayleigh lidar provided profiles of temperature below the PMC altitudes and of the PMCs throughout the flight. PMCs were imaged during an ~5.9-day flight from Esrange, Sweden, to Northern Canada in July 2018. These data reveal sensitivity of the PMCs and the dynamics driving their structure and variability to tropospheric weather and larger-scale GWs and tides at the PMC altitudes. Initial results reveal strong modulation of PMC presence and brightness by larger-scale waves, significant variability in the occurrence of GWs and instability dynamics on time scales of hours, and a diversity of small-scale dynamics leading to instabilities and turbulence at smaller scales. At multiple times, the overall field of view was dominated by extensive and nearly continuous GWs and instabilities at horizontal scales from ~2 to 100 km, suggesting sustained turbulence generation and persistence. At other times, GWs were less pronounced and instabilities were localized and/or weaker, but not absent. An overview of the PMC Turbo experiment motivations, scientific goals, and initial results is presented here. © 2019. The Authors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".