Extending and Merging the Purple Crow Lidar Temperature Climatologies Using the Inversion Method
Bibliographic record
Abstract
Rayleigh and Raman scatter measurements from The University of Western Ontario Purple Crow Lidar (PCL) have been used to develop temperature climatologies for the stratosphere, mesosphere, and thermosphere using data from 1994 to 2013 (Rayleigh system) and from 1999 to 2013 (vibrational Raman system). Temperature retrievals from Rayleigh-scattering lidar measurements have been performed using the methods by Hauchecorne and Chanin (1980; henceforth HC) and Khanna et al. (2012). Argall and Sica (2007) used the HC method to compute a climatology of the PCL measurements from 1994 to 2004 for 35 to 110 km, while Iserhienrhien et al. (2013) applied the same technique from 1999 to 2007 for 10 to 35 km. Khanna et al. (2012) used the inversion technique to retrieve atmospheric temperature profiles and found that it had advantages over the HC method. This paper presents an extension of the PCL climatologies created by Argall and Sica (2007) and Iserhienrhien et al. (2013). Both the inversion and HC methods were used to form the Rayleigh climatology, while only the latter was adopted for the Raman climatology. Then, two different approaches were used to merge the climatologies from 10 to 110 km. Among four different functional identities, a trigonometric hyperbolic relation results in the best choice for merging temperature profiles between the Raman and Low level Rayleigh channels, with an estimated uncertainty of 0.9 K for merging temperatures. Also, error function produces best result with uncertainty of 0.7 K between the Low Level Rayleigh and High Level Rayleigh channels. The results show that the temperature climatologies produced by the HC method when using a seed pressure are comparable to the climatologies produced by the inversion method. The Rayleigh extended climatology is slightly warmer below 80 km and slightly colder above 80 km. There are no significant differences in temperature between the extended and the previous Raman channel climatologies. Through out this study, we discuss the data collected by PCL for a long term (1994–2013) up to 110 km. Moreover, different merging functions in various methods were used to merge the temperature climatologies for different channels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".