Evaluation of Probabilistic Medium-Range Temperature Forecasts from the North American Ensemble Forecast System
Bibliographic record
Abstract
Abstract Ensemble temperature forecasts from the North American Ensemble Forecast System were assessed for quality against observations for 10 cities in western North America, for a 7-month period beginning in February 2007. Medium-range probabilistic temperature forecasts can provide information for those economic sectors exposed to temperature-related business risk, such as agriculture, energy, transportation, and retail sales. The raw ensemble forecasts were postprocessed, incorporating a 14-day moving-average forecast–observation difference, for each ensemble member. This postprocessing reduced the mean error in the sample to 0.6°C or less. It is important to note that the North American Ensemble Forecast System available to the public provides bias-corrected maximum and minimum temperature forecasts. Root-mean-square-error and Pearson correlation skill scores, applied to the ensemble average forecast, indicate positive, but diminishing, forecast skill (compared to climatology) from 1 to 9 days into the future. The probabilistic forecasts were evaluated using the continuous ranked probability skill score, the relative operating characteristics skill score, and a value assessment incorporating cost–loss determination. The full suite of ensemble members provided skillful forecasts 10–12 days into the future. A rank histogram analysis was performed to test ensemble spread relative to the observations. Forecasts are underdispersive early in the forecast period, for forecast days 1 and 2. Dispersion improves rapidly but remains somewhat underdispersive through forecast day 6. The forecasts show little or no dispersion beyond forecast day 6. A new skill versus spread diagram is presented that shows the trade-off between higher skill but low spread early in the forecast period and lower skill but better spread later in the forecast period.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".