Validation of the Aura Microwave Limb Sounder temperature and geopotential height measurements
Bibliographic record
Abstract
Global satellite observations of temperature and geopotential height (GPH) from the Microwave Limb Sounder (MLS) on the EOS Aura spacecraft are discussed. The precision, resolution, and accuracy of the data produced by the MLS version 2.2 processing algorithms are quantified, and recommendations for data screening are made. Temperature precision is 1 K or better from 316 hPa to 3.16 hPa, degrading to ∼3 K at 0.001 hPa. The vertical resolution is 3 km at 31.6 hPa, degrading to 6 km at 316 hPa and to ∼13 km at 0.001 hPa. Comparisons with analyses (Goddard Earth Observing System version 5.0.1 (GEOS‐5), European Centre for Medium‐range Weather Forecasts (ECMWF), Met Office (MetO)) and other observations (CHAllenging Minisatellite Payload (CHAMP), Atmospheric Infrared Sounder/Advanced Microwave Sounder Unit (AIRS/AMSU), Sounding of the Atmosphere using Broadband Radiometry (SABER), Halogen Occultation Experiment (HALOE), Atmospheric Chemistry Experiment (ACE), radiosondes) indicate that MLS temperature has persistent, pressure‐dependent biases which are between −2.5 K and +1 K between 316 hPa and 10 hPa. The 100‐hPa MLS v2.2 GPH surface has a bias of ∼150 m relative to the GEOS‐5 values. These biases are compared to modeled systematic uncertainties. GPH biases relative to correlative measurements generally increase with height owing to an overall cold bias in MLS temperature relative to correlative temperature measurements in the upper stratosphere and mesosphere.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".