Seasonal prediction skill and predictability of the Northern Hemisphere storm track variability in Project Minerva
Bibliographic record
Abstract
The seasonal prediction skill and predictability of the Northern Hemisphere storm track anomalies in boreal winter (December–January–February, DJF) is examined using seasonal ensemble reforecasts for 1982–2009 from the ECMWF Integrated Forecast System at two different atmospheric resolutions in Project Minerva. It is found that the predictable signals of storm track variations are associated with the two leading EOF modes of ensemble-averaged DJF variances of the high-pass filtered daily meridional winds at 250-hPa level derived from each of the hindcast ensemble members. These two EOF modes are highly correlated both temporarily and spatially between two sets of reforecasts. The first mode (EOF1) mainly shows a latitudinal shift of the storm tracks over the central-eastern North Pacific and the North America continent. The second mode (EOF2) is primarily the pulsing signal exerting on the mean storm track background of the North Pacific. The model predictive skill is verified against observations. The first mode has higher prediction skills and larger skillful regions than the second one. In particular, the first predictable mode is generated by the ENSO-induced wave train, starting from tropical central Pacific and propagating to North America. The skillful region lies in the North Pacific to the west of California, corresponding to the southern lobe of EOF1. The second predictable mode is generated by the North Pacific Mode, which evokes a distinctive wave train, emanating from the tropical western Pacific and propagating northeastward. Its skillful region of the storm track prediction is confined to a small area of Canada western coastlines.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".