Dust events characterization from visibility, trends and Dust Adversity Index in the Canary Islands for the period 1980–2022
Bibliographic record
Abstract
Dust events in the Canary Islands have been documented since the late 19th century. However, during the past few years, several severe dust episodes have occurred in the Canary Islands, resulting in significant impacts on various sectors, such as aviation, air quality, and health, among others. These recent severe events have drawn the attention of both scientists and the general population, raising questions about whether these episodes are now more frequent and more severe. This study analyzes 483 dust events recorded in the Canary Islands over the last 40 years. Data analysis reveals that the average number of dust event days per year is approximately 24 days, and these events have an average duration of 1.8 days, both of which show a statistically significant decreasing trend over the series. Seasonal examination indicates that events occurring in the first and fourth quarters of the year have twice the duration of those in the other quarters. Furthermore, on an annual basis, events in the first quarter exhibit negative trends in both average and minimum visibilities. This suggests that dust events in the Canary Islands are becoming shorter in duration but more intense in terms of visibility. In this article, the Dust Adversity Index (DAI) is introduced to objectively compare the severity of events. Finally, anomalies in geopotential have been utilized to determine the prevailing synoptic patterns during dust events. It is evident that the dominant synoptic pattern during the first and fourth quarters of the year consists of a low cut-off system located to the west of the Canary Islands and a high-pressure system to the north of the Iberian Peninsula.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".