Subseasonal Prediction Skill of Winter Quasi-Stationary Waves in the Northern Hemisphere
Bibliographic record
Abstract
Quasi-stationary Rossby waves modulate persistent (lasting days to weeks) atmospheric ridges and troughs, and can lead to extreme weather events, particularly in the midlatitudes. Due to their persistent nature, these quasi-stationary waves (QSWs) provide a unique opportunity to improve subseasonal forecasts of extreme events. Here, we evaluate the skill of the ECMWF dynamical subseasonal-to-seasonal (S2S) forecast model to forecast QSWs in Northern Hemisphere winter. The model shows spatially varying prediction skill, with the North Pacific region showing the highest skill across all lead times studied (7 to 35 days). We find very large inter-annual and intra-seasonal variability in the subseasonal skill of this North Pacific region. The inter-annual variability is statistically consistent across different S2S models, indicating that physical conditions varying from year to year influence the prediction skill. Further investigation shows improvements in the S2S skill under certain phases of the Madden-Julian Oscillation (MJO), under La Niña ocean conditions, during the westerly phase of the Quasi-Biennial Oscillation (QBO), and following the onset of Sudden Stratospheric Warmings (SSWs). These identified conditions may be windows of opportunity for better S2S QSW forecast skill. Our results indicate that although the S2S skill of QSWs is low, there is potential to utilize natural modes of variability to better capture uncertainty of model outputs, and identify times when skill is likely to be higher.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".