Analysis of scale dependence of quantitative precipitation forecast verification: A case‐study over the Mackenzie river basin
Bibliographic record
Abstract
Abstract Six‐hour rainfall accumulations derived from radar observations collected during a 3‐day summertime precipitation event over central Alberta (Canada) are used to assess the performance of a regional Canadian numerical weather prediction system for quantitative precipitation forecast verification. We show that radar data provide a simple and efficient way to significantly reduce model phase errors associated with misplacement of predicted precipitation patterns. Using wavelet analysis, we determine that the limiting spatial scale of predictability of the model is about six times its grid resolution for 6 h accumulated fields. The use of longer accumulation periods is shown to smooth out forecast errors that may have resulted from slight phase or time shift errors but does not change the limiting scale of predictability. The scale decomposition of the mean‐square forecast error also reveals that scales which cannot be accurately reproduced by the model account for about 20% of the total error. Using classical continuous and categorical scores, we show that significantly better model performance can be achieved by smoothing out wavelengths that cannot be predicted. Copyright © 2006 Royal Meteorological Society
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".