On the consistency of model, ground‐based, and satellite observations of tidal signatures: Initial results from the CAWSES tidal campaigns
Bibliographic record
Abstract
Comparisons between tidal wind signatures diagnosed from satellite and ground‐based observations and a general circulations model for two (September–October 2005, March–April 2007) of the four Climate and Weather of the Sun‐Earth System (CAWSES) Global Tidal Campaign observation periods are presented (CAWSES is an international program sponsored by Scientific Committee on Solar‐Terrestrial Physics). Specific comparisons are made between model (extended Canadian Middle Atmosphere Model), satellite (Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED)), meteor, MF and incoherent scatter radar (ISR), and lidar tidal signatures in the mesosphere and lower thermosphere. The satellite and ground‐based signatures are in good agreement and demonstrate for the first time that the tidal wind fields observed by both types of observations are consistent with each other. This is the first time that such agreement has been reported and effectively resolves the long‐standing issue between ground‐based radar and satellite optical measurements of winds. This level of agreement, which has proved elusive in the past, was accomplished by superposing the significant tidal components from the satellite analyses to reconstruct the fields observed by the ground stations. Particularly striking in these comparisons is the extent to which the superposed fields show strong geographic variability. This variability is also seen in the component superpositions generated from the extended Canadian Middle Atmosphere Model (eCMAM), although differences in the geographic patterns are evident.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".