Evaluation de la sensibilité de l’instrument FCI à bord du nouveau satellite Meteosat Troisième Génération imageur (MTG-I) aux variations de la quantité d’aérosols d’origine désertique dans l’atmosphère
Bibliographic record
Abstract
This thesis deals with a methodology to assess the capabilities of future spaceborne instruments. The case study is the Flexible Combined Imager (FCI) of the future Meteosat Third Generation Imaging mission (MTG - I), and in particular its ability to detect variations in load of desert aerosols in a realistically variable atmosphere. A better understanding of the behavior of these aerosols is part of regularly expressed needs for the study of the climate, weather forecast or assessment of the solar resource in arid areas such as the Sahara. This type of aerosols is abundant in the atmosphere. Their physical and chemical properties make them distinguishable from other types of aerosols such as those resulting from anthropogenic pollution, especially as they are emitted in areas protected from contamination by these other types. They therefore represent a simple case study to validate the methodology developed in this thesis.The methodology is to provide a simulator of the view of the instrument to perform a large number of simulations of the radiance measured under different atmospheric conditions and ground albedo, to analyze the results in order to quantify the influence of each variable in the variation of radiance, and then conclude on the capabilities of detection through a test of detectability taking into account the characteristics of the instrument.The developed simulator was validated by comparison against actual measurements of the SEVIRI instruments onboard Meteosat Second Generation satellites. The main innovation lies in the use of the global sensitivity analysis approach (GSA). The latter quantifies the influence of each variable separately as well as their crossed terms. Cumulative distribution functions were computed from actual observations and allow a realistic sensitivity analysis of the instrument. The GSA is also used to compute functional representation of the influence of one or more variables on the variability of the observed signal. The usefulness of such representations is discussed for various applications in remote sensing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".