Using redundancy analysis to improve dynamical seasonal mean 500 hPa geopotential forecasts
Bibliographic record
Abstract
Abstract In this study, we evaluate and compare 500 hPa geopotential height hindcast skill in two large dynamical hindcast experiments performed with the Canadian Climate Centre second generation general circulation model (GCM). In one hindcast experiment, seasonal hindcasts are made from lagged initial conditions observed at the beginning of each season. The sea‐surface temperatures (SSTs) required by the model during each forecast period are forecast by persisting the SST anomalies observed during the month just prior to the forecast period. The second hindcast experiment consists of an ensemble of simulations in which continuously evolving observed SSTs are specified at the model's lower boundary. These hindcasts do not benefit from re‐specification of the initial state at the beginning of each season, but they do enjoy the benefit of ‘perfect’ SST forecasts. We also demonstrate the use of a regression technique, called redundancy analysis (RA), for statistically improving the skill of both types of dynamical hindcast. The results indicate that specification of the initial state at the beginning of each season adds skill to the seasonal hindcasts, even though SSTs at the lower boundary are imperfectly specified. We also find that the model can predict the mean state of the North Atlantic Oscillation (NAO) with some skill in boreal winter and spring when the initial state is specified at the beginning of each season. The results also indicate that statistical post‐processing with the RA technique improves the (cross‐validated) skill of both types of dynamical hindcast. Copyright © 2001 Royal Meteorological Society
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".