Intercomparisons Between Lidar and Satellite Instruments in the Middle Atmosphere
Bibliographic record
Abstract
The comparison of ground and ship-based lidar measurements of atmospheric temperature, ozone, and wind to similar measurements made from orbiting satellites is a unique challenge. In this talk we will discuss general challenges associated with (i) determining coincidence by compensating for geographic and temporal offsets, (ii) satellite-lidar sampling errors, and (iii) comparing results made by different techniques. We will show that comparisons of absolute temperature improve when the ground based measurements are compared to a composite satellite profile, created by a weighted average of multiple profiles from one overpass, instead of comparing to the single satellite profile from the closest approach. We discuss the importance of including the variation between consecutive satellite profiles for a given overpass in addition to the given satellite instrument uncertainty when calculating the error budget of the comparisons, even when comparing to single satellite profiles. We demonstrate how comparing lidar and satellite measurements of events such as small-scale fast moving gravity waves over a particular geographic region can be affected by instrument averaging kernels. Illustrative examples we will be showing include lidar measurements made during recent instrument validation campaigns at L’Observatoire de Haute Provence (OHP, 43.93 N, 5.71 E), La Réunion (21.17 S, 55.37 E), Hohenpeißenberg Meteorological Observatory (47.80 N, 11.00 E), and onboard the French Navy Research Ship Monge as well as satellite measurements from the Microwave Limb Sounder (MLS), the Sounding of the Atmosphere by Broadband Emission Radiometry instrument (SABER), Global Ozone Monitoring by Occultation of Stars (GOMOS), and Atmospheric Dynamics Mission Aeolus (Aeolus).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".