Spatial spread‐skill relationship in terms of agreement scales for precipitation forecasts in a convection‐allowing ensemble
Bibliographic record
Abstract
Verification of precipitation is one of the major issues in evaluating numerical weather prediction. In this study, a recently developed neighbourhood‐based method in terms of agreement scales is applied to characterize the scale‐dependent spatial spread‐skill relationship of precipitation forecasts in a 3 km convection‐allowing ensemble prediction system (EPS) over the Yangtze‐Huaihe river basin of China. Thirty cases during the Meiyu season of 2013 are classified into two weather regimes, large coverage (LC) and small coverage (SC), based on the precipitation fractional coverage. Overall, precipitation distributions for these two weather regimes are reasonably forecast by the EPS. The results show that the spatial spread‐skill relationship depends highly on the weather regime. The spatial spread‐skill relationship under SC is poorer and shows more diurnal variation compared to that under LC. In addition, this article extends the neighbourhood‐based method to investigate the relative influence of precipitation intensity and placement on the spatial spread‐skill relationship. With increasing precipitation threshold, the relative impact of precipitation intensity on the relationship gradually decreases, and the influence of precipitation placement becomes dominant.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| 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".