Subseasonal Prediction Skill of Winter Quasi-Stationary Waves in the Northern Hemisphere
Bibliographic record
Abstract
Quasi-stationary Rossby waves (QSWs) 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, QSWs provide a unique opportunity to improve subseasonal forecasts of extreme events. Here, we evaluate the forecast skill of weather models in the Northern Hemisphere (NH) winter QSWs in the ECMWF dynamical subseasonal-to-seasonal (S2S) forecast model against ERA5 reanalysis data. The model shows varying prediction skill, as expected, with skill declining as the lead time increases. The North Pacific region shows the highest skill across all lead times studied (7 to 35 days). Further investigation shows an effect of a La-Nina like sea surface temperature (SST) pattern on North Pacific QSWs, which the model is able to reproduce. We find very large inter-annual variability in the subseasonal skill of the North Pacific region. This inter-annual variability of skill is not captured by variability in the ensemble spread, i.e. more skillful years do not have more certain forecasts. The annual time-series of aggregated subseasonal skill shows consistency between different S2S models, indicating that the aggregated annual skill may be partially driven by a physical forcing. We find strong correlations between aggregated subseasonal skill and SSTs, upper troposphere zonal winds, and waveguides. Overall, the results indicate that although the S2S skill of QSWs is currently low in forecast models, there is potential to utilize natural modes of variability to better capture uncertainty of model outputs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".