Increased dependency of regional drought termination on landfalling tropical cyclones
Bibliographic record
Abstract
Landfalling tropical cyclones frequently occur with strong winds and heavy rainfall, providing substantial water resources. The positive impacts of tropical cyclones on drought alleviation and termination remain unknown. Here, we estimate the dependency of drought terminations on tropical cyclone rainfall through frequency analyses and event coincidence analysis. Results show that ~3% of the tropical cyclone-affected areas have experienced multiple drought events terminated by tropical cyclone rainfall from 1999 to 2021. Globally, tropical cyclone-terminated drought events averaged 1.12 annually and increased significantly by 1.92% per year. Increases were especially significant in the Arabian Peninsula, South Asia, and the northwest of Australia. Rainfall produced by tropical cyclones alleviates drought events more drastically than that by other weather systems, such as fronts, mesoscale convective systems, and atmospheric rivers. These findings underscore the adaptation of precipitation changes due to shifts in the timing and weather systems under a warming climate. Tropical cyclone rainfall terminated drought events on average of 1.1 times per year globally from 1999 to 2021, with a significant annual increase of 1.9%, particularly in the Arabian Peninsula, South Asia, and northwest of Australia, according to the frequency and event coincidence analyses
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".