Seasonal AOD analysis based on AERONET observations in North and West Africa over 2010–2019
Bibliographic record
Abstract
The use of aerosol optical depth (AOD) properties Version 3 (level 2) of the surface-based AERONET was used to characterize aerosols at Eurafrican stations during the last decade of 2010–2019. The quality-assured AOD and Angstrom exponent (AE) data from Cairo_EMA_2 (30.081 N, 31.290E) and Tamanrasset_INM (22.790 N, 5.530E) are used to classify aerosols. Two additional stations from the west IER Cinzana (13.3 N, 5.9 W) and Cape Verde (16.7 N, 22.9 W) were compared as control to see the regional aerosols typing. The analyzed AOD data were first detrend to remove seasonal trend from the data and may provide difficulty in comparing relative AOD changes. Therefore, validated AOD and AE were employed to characterize the AOD type and determine the seasonal predominance. This method of analysis was derived by the deviation of the monthwise mean from the AOD data. The dominant aerosol types are coarsely absorbed due to dust from the Sahara. Saharan dust was observed in Tamanrasset_INM with AOD < 1 and AE < 1 and in Cairo_EMA_2 with AOD < 1 and AE < 1 over the spectral decadal trend. The west stations showed both AOD and AE > 1 for IER Cinzana and Cape Verde. The winter mean and standard deviation are − 0.18 ± 0.14 with AOD (− 0.009 ± 0.06) for the east. This indicates that the AOD dominance varies with the site and is heavily dependent on meteorological cycles. In the premonsoon season, the AE had AOD characteristics of 0.13 ± 0.15 (− 0.003 ± 0.03). The seasonal cycle indicates pure AOD characteristics, and the results have good confidence that the monsoon season is the major dust-driven season. The results of the study present aerosol characterization over Eurafrican stations and provide better insight into regional climate and local air pollution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".