Rainfall extremes observed by a weather radar in the northern tropical Andes
Bibliographic record
Abstract
We characterize seasonal and diurnal spatiotemporal features of extreme rainfall in a 45,000 km 2 area within the tropical Andes of northern South America. We validate radar-based quantitative precipitation estimates for extreme rainfall using in-situ observations, finding a strong spatiotemporal coherence between the datasets through methods based on correlations, timing, and probability mass functions. We then explore the seasonal and diurnal cycles of extreme rainfall, focusing on the local atmospheric environments associated with these extremes. Results show that rainfall extremes in the region (percentile 99.5) exhibit intensities of more than 27 times the average, while the strongest events may reach intensities of up to 85 times the average. Moreover, our findings reveal a consistent timing of extreme precipitation events, occurring between 15:00 and 22:00 LST, accounting at this time range for more than 3% of all seasonal rainfall accumulation. We show that the traditional approach of analyzing seasonal 10-year average rainfall might leave out the high spatiotemporal variability of extreme events. By focusing on a specific threshold and the most intense rainfall events, we identify two main spatiotemporal patterns of extreme rainfall based on the magnitude and the ratio between maxima and mean rain rate. Moreover, these patterns are driven by the interplay of regional atmospheric mechanisms and orographic features. This research improves our understanding about the spatiotemporal characteristics of rainfall extremes and their relationship with atmospheric and orographic factors. It uses high-resolution weather radar data to provide valuable insights for diagnosing, understanding, and modeling extreme rainfall in tropical regions.
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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".