Wet Days in the Dry Quarter of the Alcântara Launch Center Region: Observational Features
Bibliographic record
Abstract
ABSTRACT: Precipitation events are infrequent in the dry quarter (SON) of the Alcântara Launch Center (Centro de Lançamento de Alcântara, CLA), the main launch site of the Brazilian Space Program. However, their occurrence could be a risk for activities during launch missions. In this work, the observational features of wet days (daily precipitation total ≥ 1 mm/day) in the dry quarter of the CLA region were studied. Daily precipitation totals over the course of 37 years (1979-2016, except 2006), outgoing longwave radiation (OLR) data and ERA-Interim reanalysis data were used. On average, in the dry quarter, there were 9 wet days, which accumulated 32 mm. The number and quarterly precipitation total of wet days showed pronounced interannual variability. This variability was negatively and significantly correlated with the interhemispheric sea surface temperature anomalies gradient in the Atlantic Ocean and the wind speed at 925 hPa over the CLA region. Based on a theoretical distribution (log-normal), the probability of occurrence of heavy precipitation days (daily total ≥ 10 mm/day) was only 0.5%. For days with heavy precipitation and deep convection (OLR ≤ 230 W·m-2), over a large area along the northeastern coast of South America including the CLA region, negative OLR differences (from the mean) and the strengthening of favorable conditions for deep convection were found. The large-scale organization of the convective activity and atmospheric features for higher precipitation events obtained in this work could be helpful for nowcasting and short-range weather forecasting during launch missions at the CLA.
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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) | 0.000 | 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".