Extra predictability from a seamless approach for Asian summer monsoon precipitation from days to weeks
Bibliographic record
Abstract
<!--!introduction!--> The multiple scale atmospheric or oceanic prediction has been a challenging issue due to different dynamical and thermodynamic processes involved and the uncertainty inherent in highly nonlinear and stochastic forcing, such as the prediction of Asian summer monsoon (ASM) precipitation. The seamless prediction was proposed to address this issue. The idea behind this approach is that temporal averaging reduces the spread of the prediction ensemble but retains the approximate first moment, which effectively increases the signal-to-noise ratio of the prediction. In this study, the superiority of the seamless prediction was assessed for the ASM precipitation at timescales from days to weeks. The hindcasts from the European Center for Medium-Range Weather Forecasts in the Subseasonal to Seasonal ensemble dataset were used for the detailed assessment. Results show significant advantages by the seamless approach in evaluating the ASM precipitation prediction on timescales ranging from two days to three weeks. Furthermore, the source of the average window predictability was analyzed for two lead times with significantly superior prediction accuracy: lead times of 4d4d and 3w3w. For 4d4d, the Madden–Julian Oscillation is dominant, while the role of the El Niño–Southern Oscillation phase becomes substantial for 3w3w. These results offer a detailed analysis for the prediction of ASM precipitation within a framework of seamless prediction, benefiting further interpretation of seamless prediction of ASM precipitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".