On the quality of MIPAS kinetic temperature in the middle atmosphere
Bibliographic record
Abstract
Abstract. The kinetic temperature and line of sight elevation information are retrieved from the MIPAS Middle Atmosphere (MA), Upper Atmosphere (UA) and NoctiLucent-Cloud (NLC) modes of high spectral resolution limb observations of the CO2 15 μm emission using the dedicated IMK/IAA retrieval algorithm, which considers non-local thermodynamic equilibrium conditions. These variables are accurately derived from about 20 km (MA) and 40 km (UA and NLC) to 105 km globally and both at daytime and nighttime. Typical temperature random errors are smaller than 0.5 K below 50 km, 0.5–2 K at 50–70 km, and 2–7 K above. The systematic error is typically 1 K below 70 km, 1–3 K from 70 to 85 km and 3–11 K from 85 to 100 km. The average vertical resolution is typically 4 km below 35 km, 3 km at 35–50 km, 4–6 km at 50–90 km, and 6–10 km above. We compared our MIPAS temperature retrievals from 2005 to 2009 with co-located ground-based measurements from the lidars located at the Table Mountain Facility and Mauna Loa Observatory, the SATI spectrograph in Granada (Spain) and the Davis station spectrometer, and satellite observations from ACE-FTS, Aura-MLS and TIMED-SABER from 20 km to 100 km. We also compared MIPAS temperatures with the high latitudes climatology from falling sphere measurements. The comparisons show very good agreement, with differences smaller than 3 K below 85–90 km in mid-latitudes. Differences over the poles in this altitude range are larger but can be generally explained in terms of known biases of the other instruments. The comparisons above 90 km worsen and MIPAS retrieved temperatures are always larger than other instrument measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".