Extreme Precipitation over the Greater Antilles: Relation with Large-Scale Circulation and Humidity
Bibliographic record
Abstract
This study examines extreme precipitation over the Greater Antilles and its relationship with large-scale circulation and specific humidity over the period 1985-2015. The data used in this study were derived from the Reanalyses ERA5(specific humidity, wind) at 925hPa and the CHIRPS satellite product with a resolution of 31 km and 5km respectively. The characteristics of the synoptic environment for the six weather types (WTs) determined by the K-means algorithm and the Elbow method were described, the associated extreme precipitation events were defined and their frequency anomalies were calculated using the contingency table. The influence of ENSO was also analyzed using the Mann-Whitney Wilcoxon. The results show that the WTs [3-6] weather types are associated with extreme precipitation in Jamaica, Puerto Rico, and the eastern Dominican Republic. WTs weather types [3-6] also lead to an increase in the frequency of extreme days in Puerto Rico, Jamaica, the eastern Dominican Republic, and northeastern Cuba. During the dry season(DJF) , this increased frequency was observed in southern Haiti and eastern Dominican Republic. Conversely, for WTs weather types [2-4], a decrease in extreme day frequencies was observed on all islands, except northwest Cuba, where an increase in frequency was observed. Furthermore, for Wts [3-6], the results reveal that winters in cold years (negative ENSO phase) are associated with a significant increase in extreme precipitation in northern Haiti, eastern Dominican Republic, and northeastern Cuba. On the other hand, in summer, during ENSO's positive phase, the increase in extreme precipitation observed over certain regions is not significant.
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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 | 0.000 |
| 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".