HL‐TWiM Empirical Model of High‐Latitude Upper Thermospheric Winds
Bibliographic record
Abstract
Abstract We present an empirical model of thermospheric winds (High‐latitude Thermospheric Wind Model [HL‐TWiM]) that specifies F region high‐latitude horizontal neutral winds as a function of day of year, latitude, longitude, local time, and geomagnetic activity. HL‐TWiM represents the large‐scale neutral wind circulation, in geomagnetic coordinates, for the given input conditions. The model synthesizes the most extensive collection to date of historical high‐latitude wind measurements; it is based on statistical analyses of several decades of F region thermospheric wind measurements from 21 ground‐based stations (Fabry‐Perot Interferometers and Scanning Doppler Imaging Fabry‐Perot Interferometers) located at various northern and southern high latitudes and two space‐based instruments (UARS WINDII and GOCE). The geomagnetic latitude and local time dependences in HL‐TWiM are represented using vector spherical harmonics, day of year and longitude variations are represented using simple harmonic functions, and the geomagnetic activity dependence is represented using quadratic B splines. In this paper, we describe the HL‐TWiM formulation and fitting procedures, and we verify the model against the neutral wind databases used in its formulation. HL‐TWiM provides a necessary benchmark for validating new wind observations and tuning our physical understanding of complex wind behaviors. Results show stronger Universal Time variation in winds at southern than northern high latitudes. Model‐data intra‐annual comparisons in this study show semiannual oscillation‐like behavior of GOCE winds, rarely observed before in wind 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".