Seasonal Forecasts of Canadian Winter Precipitation by Postprocessing GCM Integrations
Bibliographic record
Abstract
Abstract In the second phase of the Canadian Historical Forecasting Project (HFP2), four global atmospheric general circulation models (GCMs) were used to perform seasonal forecasts over the period of 1969–2003. Little predictive skill was found from the uncalibrated GCM ensemble seasonal predictions for the Canadian winter precipitation. This study is an effort to improve the precipitation forecasts through a postprocessing approach. Canadian winter precipitation is significantly influenced by two of the most important atmospheric large-scale patterns: the Pacific–North American pattern (PNA) and the North Atlantic Oscillation (NAO). The time variations of these two patterns were found to be significantly correlated with those of the leading singular value decomposition (SVD) modes that relate the ensemble mean forecast 500-mb geopotential height over the Northern Hemisphere and the tropical Pacific SST in the previous month (November). A statistical approach to correct the ensemble forecasts was formulated based on the regression of the model’s leading forced SVD patterns and the observed seasonal mean precipitation. The performance of the corrected forecasts was assessed by comparing its cross-validated skill with that of the original GCM ensemble mean forecasts. The results show that the corrected forecasts predict the Canadian winter precipitation with statistically significant skill over the southern prairies and a large area of Québec–Ontario.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".