Satellite-pointing retrieval from atmospheric limb-scattering of solar UV-B radiation
Bibliographic record
Abstract
We present a new algorithm for tangent height retrievals from limb-scattering observations. These observations are performed by satellite-based spectrometers operating in the UV, visible, and short-wave IR spectral ranges. They record the solar radiation scattered in Earth's atmosphere in limb-viewing geometry and aim at vertically resolved retrievals of the atmospheric composition with global coverage. Inaccuracies in the knowledge of the instrument's pointing frequently dominate the error budgets of the atmospheric composition products. Therefore, additional information on satellite pointing is crucial for the quality of all products derived from limb-scattering observations. The information is commonly expressed in terms of the tangent heights. The presented algorithm determines the tangent heights directly from the observed limb radiances by analyzing the shapes of the so-called knee in several UV-B radiance profiles. All vertical pointing information contained in a UV-B fit window is exploited by simultaneously retrieving the ozone profile. The algorithm has been implemented in the toolbox SCIARAYS and named Tangent height Retrieval by UV-B Exploitation (TRUE) knee method. We have applied it to five orbits of SCIAMACHY's limb observations. It achieves a precision of about 200 m when applied to an individual limb scan. The broadband structure of the observations can be reproduced within 1% RMS. A comparison of the retrieved tangent heights with the engineering ones delivered by ESA reveals that the engineering tangent heights exhibit a systematic error, which varies with an amplitude of about 3 km. Its origin is traced to the on-board orbit model of Envisat. PACS Nos.: 94.80.+g, 42.68.Ay, 92.60.Ta, 42.68.Wt, 07.87.+v, 92.70.Cp, and 07.60.Rd
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".