Comparative study between ground-based observations and NAVGEM-HA analysis data in the mesosphere and lower thermosphere region
Bibliographic record
Abstract
Abstract. Recent studies have shown that day-to-day variability of the migrating semidiurnal solar (SW2) tide within the mesosphere and lower thermosphere (MLT) is a key driver of anomalies in the thermosphere–ionosphere system. Here, we study the variability in both the amplitude and phase of SW2 using meteor radar wind and lidar temperature observations at altitudes of 75–110 km as well as wind and temperature output from the Navy Global Environmental Model – High Altitude (NAVGEM-HA), a high-altitude meteorological analysis system. Application of a new adaptive spectral filter technique to both local radar wind observations and global NAVGEM-HA analyses offers an important cross-validation of both data sets and makes it possible to distinguish between migrating and non-migrating tidal components, which is difficult using local measurements alone. Comparisons of NAVGEM-HA, meteor radar and lidar observations over a 12-month period show that the meteorological analyses consistently reproduce the seasonal as well as day-to-day variability in mean winds, mean temperatures and SW2 features from the ground-based observations. This study also examines in detail the day-to-day variability in SW2 during two sudden stratospheric warming, events that have been implicated in producing ionospheric anomalies. During this period, both meteor radar and NAVGEM-HA winds show a significant phase shift and amplitude modulation, but no signs of coupling to the lunar tide as previous studies have suggested. Overall, these findings demonstrate the benefit of combining global high-altitude meteorological analyses with ground-based observations of the MLT region to better understand the tidal variability in the atmosphere.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".