Predictable Pattern of Precipitation Over Asian Summer Monsoon Regions
Bibliographic record
Abstract
Abstract Precipitation prediction has been a challenging issue. The predictable pattern of Asian summer monsoon (ASM) precipitation is identified by the average predictable time method and precipitation hindcasts from the European Centre For Medium Range Weather Forecasts. The leading predictable pattern is characterized by a dipole pattern with the first center spanning from the northeast Bay of Bengal eastward to the western North Pacific, and the opposite center mainly in the Maritime Continent. It provides skillful prediction up to 21–24 days, far exceeding the skill averaged over the ASM (less than 5 days). Empirical mode decomposition analysis shows that the intraseasonal component, contributing 55% of the total variance of predictable components, provides the most predictability for ASM precipitation. The intraseasonal component originates from the boreal summer intraseasonal oscillation, which is enhanced by local ocean‐atmosphere interactions. The interannual component originating from the El Niño‐Southern Oscillation strengthens the predictability.
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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 | 0.000 | 0.000 |
| 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".