Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The prediction skill of precipitation at submonthly time scales during the boreal summer season is investigated based on hindcasts from three global ensemble prediction systems (EPSs). The results, analyzed for lead times up to 4 weeks, indicate encouraging correlation skill over some regions, particularly over the Maritime Continent and the equatorial Pacific and Atlantic Oceans. The hindcasts from all three models correspond to high prediction skill over the first week compared to the following three weeks. The ECMWF forecast system tends to yield higher prediction skill than the other two systems, in terms of both correlation and mean squared skill score. However, all three systems are found to exhibit large conditional biases in the tropics, highlighted using the mean squared skill score. The sources of submonthly predictability are examined in the ECMWF hindcasts over the Maritime Continent in three typical years of contrasting ENSO phase, with a focus on the combined impact of the intraseasonal MJO and interannual ENSO. Rainfall variations over Borneo in the ENSO-neutral year are found to correspond well with the dominant MJO phase. The contribution of ENSO becomes substantial in the two ENSO years, but the MJO impact can become dominant when the MJO occurs in phases 2–3 during El Niño or in phases 5–6 during the La Niña year. These results support the concept that “windows of opportunity” of high forecast skill exist as a function of ENSO and the MJO in certain locations and seasons, which may lead to subseasonal-to-seasonal forecasts of substantial societal value in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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) | 0.000 | 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 it