Statistical Assessment and Augmentation of European Centre for Medium‐Range Weather Forecasts Monthly Precipitation Forecast (SEASonal Prediction of Precipitation)
Bibliographic record
Abstract
ABSTRACT Accurate prediction of precipitation is of paramount importance for effective planning of future water resources. In this study, we focused on the improvement and evaluation of the European Centre for Medium‐Range Weather Forecasts (ECMWF) fifth‐generation ensemble‐based seasonal precipitation prediction product, designated (SEASonal prediction of precipitation (SEAS5)). Three selected linear regression methods, namely ordinary least squares (OLS), flexible least squares (FLS) and the quantile‐quantile (Q‐Q) methods, were used to develop a correction procedure. The watershed of Lake Urmia was selected as a case study. The application of these augmentation methods has yielded encouraging results, demonstrating an improvement in the statistical metrics of SEAS5 precipitation forecasts for the first and second‐coming months. However, all linear projection methods improve the performance of the SEAS5 products. The Q‐Q method has shown the highest efficiency among the methods, playing a significant role in improving the accuracy of the hindcast precipitation. A variety of statistics (deterministic, forecast skill and uncertainty scores) were used to evaluate the effectiveness of both the raw and enhanced SEAS5 products. These analyses provide a comprehensive understanding of the performance of the SEAS5 product in its original form and after augmentation. The results highlight the potential of the linear projection method (specifically Q‐Q method) to improve the accuracy of hindcast precipitation and provide valuable insights for water resource planning in the study area.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".