Validation of the Aura Microwave Limb Sounder middle atmosphere water vapor and nitrous oxide measurements
Bibliographic record
Abstract
The quality of the version 2.2 (v2.2) middle atmosphere water vapor and nitrous oxide measurements from the Microwave Limb Sounder (MLS) on the Earth Observing System (EOS) Aura satellite is assessed. The impacts of the various sources of systematic error are estimated by a comprehensive set of retrieval simulations. Comparisons with correlative data sets from ground‐based, balloon and satellite platforms operating in the UV/visible, infrared and microwave regions of the spectrum are performed. Precision estimates are also validated, and recommendations are given on the data usage. The v2.2 H2O data have been improved over v1.5 by providing higher vertical resolution in the lower stratosphere and better precision above the stratopause. The single‐profile precision is ∼0.2–0.3 ppmv (4–9%), and the vertical resolution is ∼3–4 km in the stratosphere. The precision and vertical resolution become worse with increasing height above the stratopause. Over the pressure range 0.1–0.01 hPa the precision degrades from 0.4 to 1.1 ppmv (6–34%), and the vertical resolution degrades to ∼12–16 km. The accuracy is estimated to be 0.2–0.5 ppmv (4–11%) for the pressure range 68–0.01 hPa. The scientifically useful range of the H2O data is from 316 to 0.002 hPa, although only the 82–0.002 hPa pressure range is validated here. Substantial improvement has been achieved in the v2.2 N2O data over v1.5 by reducing a significant low bias in the stratosphere and eliminating unrealistically high biased mixing ratios in the polar regions. The single‐profile precision is ∼13–25 ppbv (7–38%), the vertical resolution is ∼4–6 km and the accuracy is estimated to be 3–70 ppbv (9–25%) for the pressure range 100–4.6 hPa. The scientifically useful range of the N2O data is from 100 to 1 hPa.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".