Satellite observations of mean winds and tides in the lower thermosphere: 1. Aliasing and sampling issues
Bibliographic record
Abstract
The Canadian Middle Atmosphere Model, a general circulation model extending up to 200 km, is used to analyze the impact of aliasing and data gaps on satellite observations of zonal mean winds and migrating tides in the lower thermosphere. Focus is placed on measurements from the Wind Imaging Interferometer (WINDII). Mock data sets are constructed by sampling daily zonal mean and migrating tides from the model like the satellite. Previously unverified assumptions that satellite observations of the zonal mean zonal winds and the meridional component of the diurnal tide winds are largely unaffected by aliasing are confirmed. Of the two, the meridional component of the diurnal tide is more robust. The amplitude of the zonal component of the diurnal tide is not strongly affected by aliasing. Tidal phase is nearly unaffected. Gaps in the WINDII data do not adversely affect the results provided sufficient local time coverage is available. The analysis suggests that more subtle features in the WINDII observations, such as the tidally driven cell‐like structures in the zonal mean meridional wind at low latitudes, may be observable. Issues pertaining to the sampling interval and the use of daytime‐only data are also addressed. Sampling every second or third day provides nearly as good results as sampling every day, and that using daytime‐only data results in substantial biases in the zonal mean zonal winds in the lower thermosphere. The latter underscores the importance of the accurate removal of the migrating diurnal tide from climatologies generated from daytime‐only satellite data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".