Explainable AI Applied to the Analysis of the Climatic Behavior of 11 Years of Meteosat Water Vapor Images
Bibliographic record
Abstract
Large stocks of meteorological satellite images contain important climatic information of the last decades. This paper explores the capabilities of three Explainable AI procedures (Occlusion, XRAI and SHAP) as tools to analyze and uncover patterns in the mid to upper troposphere Water Vapor dynamics, and their relationships with yearly seasons. The data consisted of 4140 daily Meteosat satellite images on the WV6.2 band between 2010 to 2020 and the base model was a convolutional neural network targeting season prediction. The results show that each explanation procedure highlights different aspects of the atmospheric processes and are appropriate for different purposes: Occlusion for studying the general traits of the 11 years time period covered by the images, XRAI for detecting and following dynamic patterns for short spans of time, and SHAP for detailed geographical expressions of atmospheric processes. All together Explainable AI exhibits an important potential for the study of atmospheric and climate change, which should be further investigated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".