Bibliographic record
Abstract
Although the quality of medium‐range forecasts has increased considerably over the decades since the start of operational forecasts at the European Centre for Medium‐Range Weather Forecasts (ECMWF), individual forecasts still occasionally experience very large errors. Often the phrasing ‘drop‐outs’ or ‘forecast busts’ is used for such episodes. The aim of this report is to use a combination of methods to track errors in three cases of extreme forecast errors between 2014 and 2016, to understand the error sources better. Manual error tracking and ensemble sensitivity are used to give a first guess for the source region and relaxation experiments are used to confirm the result. In the three cases investigated, the errors originated from the tropical eastern Pacific, western/central Canada and western Atlantic, respectively. The mechanisms behind the errors are discussed in the report. The results from this study can form a basis for further investigations of these cases and the methodology explained can be applied to understand future bust cases, to increase our knowledge of the origin and propagation of forecast errors.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".