A Diagnostic Verification of the Precipitation Forecasts Produced by the Canadian Ensemble Prediction System
Bibliographic record
Abstract
Abstract A comparatively long period of relative stability in the evolution of the Canadian Ensemble Forecast System was exploited to compile a large homogeneous set of precipitation forecasts. The probability of exceedance of a given threshold was computed as the fraction of ensemble member forecasts surpassing that threshold, and verified directly against observations from 36 stations across the country. These forecasts were stratified into warm and cool seasons and assessed against the observations through attributes diagrams, Brier skill scores, and areas under receiver operating characteristic curves. These measures were deemed sufficient to illuminate the salient features of a forecast system. Particular attention was paid to forecasts of 24-h accumulation, especially the exceedance of thresholds in the upper decile of station climates. The ability of the system to forecast extended dry periods was also explored. Warm season forecasts for the 90th percentile threshold were found to be competitive with, even superior to, those for the cool season when verifying across the sample lumping together all of the stations. The relative skill of the forecasts in the two seasons depends strongly on station location, however. Moreover, the skill of the warm season forecasts rapidly drops below cool season values as the thresholds become more extreme. The verification, particularly of the cool season, is sensitive to the calibration of the gauge reports, which is complicated by the inclusion of snow events in the observational record.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".