Mid-Latitude Climatologies of Mesospheric Temperature and Geophysical Temperature Variability Determined with the Rayleigh-Scatter Lidar at ALO-USU
Bibliographic record
Abstract
From 1993-2004, 839 nights were observed with the Rayleigh-scatter lidar at Utah State University’s Atmospheric Lidar Observatory. They were reduced to obtain nighttime mesospheric temperatures between 45 and ~90 km, which were then combined to derive composite annual climatologies of mid-latitude temperatures and geophysical temperature variability. At 45 km, near the stratopause, there is a ~250 K temperature minimum in mid-winter and a 273 K maximum in mid-May. The variability behaves oppositely, being 7-10 K in winter and 2.5 K in summer. At 85 km, there is a 215 K temperature maximum at the end of December and a 170 K mesopause minimum in early June. In contrast, the variability is roughly constant at ~20 K. At both low and high altitudes, the temperatures change much more rapidly in spring than in fall. The transition between these opposite temperature behaviors is 65 km. Distinctive temperature structures occur in all regions. In mid-winter, between 45 and 50 km, a 6 K warm region appears, most likely from occasional sudden stratospheric warmings. Above that, a “cold valley” extends to 70 km, which may be related to the bottom side of intermittent inversion layers. Both regions have increased variability. Near 85 km, there is a very rapid heating event of 25 K/month in August with high variability. In October, a temperature minimum, a “cold island”, occurs from 78–86 km with low variability, indicating a regular feature. These USU results are compared extensively to those from other mid-latitude lidars in Canada and France.
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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 | 0.000 |
| 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) | 0.001 | 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".