Improving Seasonal Forecast Skill of North American Surface Air Temperature in Fall Using a Postprocessing Method
Bibliographic record
Abstract
Abstract A statistical postprocessing approach is applied to seasonal forecasts of surface air temperatures (SAT) over North America in fall, when the original uncalibrated predictions have little skill. The data used are ensemble-mean seasonal forecasts from four atmospheric general circulation models (GCMs) in the Canadian Historical Forecasting Project (HFP2) during the period 1969–2001. The statistical postprocessing uses the relationship between the predicted 500-hPa geopotential height (Z500) and the observed SAT to calibrate the SAT forecasts. The dimensions of the predicted Z500 fields are reduced to three modes with fixed spatial structures but time-dependent amplitudes. The latter are obtained through a singular value decomposition (SVD) analysis linking the variability of the ensemble-mean predicted Z500 to the tropical Pacific sea surface temperatures (SSTs). Results show that the postprocessing significantly improves the predictive skill of North American SAT in fall. The distributions of the SAT temporal standard deviation and the skill of the postprocessed ensemble forecasts are consistent among the GCMs, indicating that the approach is effective in reducing the model-dependent part of the errors associated with GCMs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".