Spatial Variability and Linkage Between Extreme Convections and Extreme Precipitation Revealed by 22‐Year Space‐Borne Precipitation Radar Data
Bibliographic record
Abstract
Abstract Based on unprecedented 22‐year records from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR), this study aims to investigate the global distribution and linkage between both convections and precipitation extremes quasi‐globally. Overall, the spatial distributions of extreme events show significant regional variability and land‐ocean contrast. Extreme precipitation is dominant over tropical ocean while extreme convections are exclusive over land from tropics to higher latitude. Besides, there are strong positive relationships between extreme convections and precipitation based on multiple threshold‐based definitions of extremes, which provides more consistent physical properties of extreme events globally. The correlation is significant over land but relatively weak over oceans. Seasonal occurrences of extreme events are also examined. Furthermore, extreme events exhibit notable diurnal characteristics over global land with most extreme convections occurring in the afternoon, while diurnal cycles are more homogeneous over ocean.
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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".