Longitudinal Variation of the Lunar Tide in the Equatorial Electrojet
Bibliographic record
Abstract
Abstract The atmospheric lunar tide is one known source of ionospheric variability. The subject received renewed attention as recent studies found a link between stratospheric sudden warmings and amplified lunar tidal perturbations in the equatorial ionosphere. There is increasing evidence from ground observations that the lunar tidal influence on the ionosphere depends on longitude. We use magnetic field measurements from the CHAMP satellite during July 2000 to September 2010 and from the two Swarm satellites during November 2013 to February 2017 to determine, for the first time, the complete seasonal‐longitudinal climatology of the semidiurnal lunar tidal variation in the equatorial electrojet intensity. Significant longitudinal variability is found in the amplitude of the lunar tidal variation, while the longitudinal variability in the phase is small. The amplitude peaks in the Peruvian sector (∼285°E) during the Northern Hemisphere winter and equinoxes, and in the Brazilian sector (∼325°E) during the Northern Hemisphere summer. There are also local amplitude maxima at ∼55°E and ∼120°E. The longitudinal variation is partly due to the modulation of ionospheric conductivities by the inhomogeneous geomagnetic field. Another possible cause of the longitudinal variability is neutral wind forcing by nonmigrating lunar tides. A tidal spectrum analysis of the semidiurnal lunar tidal variation in the equatorial electrojet reveals the dominance of the westward propagating mode with zonal wave number 2 (SW2), with secondary contributions by westward propagating modes with zonal wave numbers 3 (SW3) and 4 (SW4). Eastward propagating waves are largely absent from the tidal spectrum. Further study will be required for the relative importance of ionospheric conductivities and nonmigrating lunar tides.
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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 | 0.001 | 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 | 0.001 | 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".