Comparison of Model Forecast Skill of Sea Level Pressure along the East and West Coasts of the United States
Bibliographic record
Abstract
Abstract Despite recent advances in numerical weather prediction, major errors in short-range forecasts still occur. To gain insight into the origin and nature of model forecast errors, error frequencies and magnitudes need to be documented for different models and different regions. This study examines errors in sea level pressure for four operational forecast models at observation sites along the east and west coasts of the United States for three 5-month cold seasons. Considering several metrics of forecast accuracy, the European Centre for Medium-Range Weather Forecasts (ECMWF) model outperformed the other models, while the North American Mesoscale (NAM) model was least skillful. Sea level pressure errors on the West Coast are greater than those on the East Coast. The operational switch from the Eta to the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM) at the National Centers for Environmental Prediction (NCEP) did not improve forecasts of sea level pressure. The results also suggest that the accuracy of the Canadian Meteorological Centre’s Global Environmental Mesoscale model (CMC-GEM) improved between the first and second cold seasons, that the ECMWF experienced improvement on both coasts during the 3-yr period, and that the NCEP Global Forecast System (GFS) improved during the third cold season on the West Coast.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".