Statistical Modeling of Tidal Weather in the Mesosphere and Lower Thermosphere
Bibliographic record
Abstract
Abstract We study the statistical properties of tidal weather (variability period <30 days) of DW1 amplitude using the extended Canadian Middle Atmospheric Model (eCMAM) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). A hierarchy of statistical models, for example, the autoregressive (AR), vector AR, and parsimonious vector AR models, are built to predict tidal weather. The quasi 23‐day oscillation found in the tidal weather is a key parameter in the statistical models. Comparing to the more complex vector AR and parsimonious vector AR models, which consider the spatial correlations of tidal weather, the simplest AR model can predict one‐day tidal weather with an accuracy of 89% (R2: correlation coefficient squared). In the AR model, 23 coefficients at each latitude and height are obtained from seven years of eCMAM data. Tidal weather is predicted via a linear combination of 23 days of tidal weather data prior to the prediction day. Different sensitivity tests are performed to prove the robustness of these coefficients. These coefficients obtained from eCMAM are in very good agreement with those from SABER. SABER tidal weather is predicted with an accuracy of 86% and 87% at one day by the AR models with coefficients from eCMAM and SABER, respectively. The five‐day forecast accuracy is between 60 and 65%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".