Estimation de la température de l'air et de la quantité de la vapeur d'eau atmosphérique à l'aide des données AVHRR de NOAA
Bibliographic record
Abstract
Detailed measurements of the space-time variability of environmental variables such as the air temperature and moisture are crucial for the study of biophysical and climate processes. However, on a regional and global scale, the spatial density and distribution of meteorological stations do not allow to meet these needs in spatial information. Satellite imagery represents an interesting source of meteorological data outside meteorological stations with an appreciable spatial extent. The objective of this article is to adapt published estimation methods for air temperature and precipitable water using National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) data and to apply them to the southwestern region of the province of Quebec (Canada) context to analyse their performance. Data from 15 AVHRR images and 16 meteorological stations were used for the calibration and the validation of the satellite estimation methods for the first decade of June and July 1997. Precipitable water was thus estimated with an R2 of 0.59 and a standard error of 3.11 mm. As for the air temperature, it was obtained with an R2 of 0.72 and a standard error of 2.1 °C. The sensitivity analysis showed that the precipitable water estimation method is more sensitive to surface temperature fluctuations. The air temperature estimation method is particularly sensitive to the maximum value of the vegetation index.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".