Numerical Prediction of the Migrating Diurnal Tide Total Variability in the Mesosphere and Lower Thermosphere
Bibliographic record
Abstract
Abstract We present a forecast model for the total variability of DW1 and study its prediction accuracy against the actual variability from the extended Canadian Middle Atmospheric Model (eCMAM) simulations and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) observations in the mesosphere and lower thermosphere region. To account for the long‐term variability (periods >30 days), we fit the data with a multi‐linear regression model that contains the solar cycle, El Niño Southern Oscillation, quasi‐biennial oscillation, and the seasonal harmonics at 12, 6, 4, and 3 months. The fitting coefficients/amplitudes from each deterministic variability are examined between the eCMAM and SABER. To predict the short‐term tidal variability, we adopted an auto‐regression (AR) model from Vitharana et al. (2019, https://doi.org/10.1029/2019JD030573 ). The forecast model is a combination of the multi‐linear regression model and the AR model. The forecast model can predict the total tidal variability of DW1 with high accuracy. The prediction accuracy (correlation coefficient) ranges between 0.81 and 0.98 (average 0.93) for eCMAM and 0.76 and 0.92 (average 0.82) for SABER. The prediction accuracy for the short‐term tidal variability is very high for both eCMAM and SABER and shows little variation with correlation coefficients at ∼0.95. The prediction accuracy for the total tidal variability follows that for the long‐term tidal variability. The discrepancy between the forecast model and the actual total tidal variability is mainly because the forecast model cannot capture all the long‐term tidal variability (periods >30 days). Besides the well‐known periods for long‐term variability, the other periods change on a year‐to‐year basis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".