Emphasizing Reliability in Member-by-Member Postprocessing of Temperature Forecasts
Bibliographic record
Abstract
Abstract Member-by-member postprocessing (MBMP), a nonparametric method that undertakes bias and dispersion correction on individual ensemble members, has emerged as a promising approach. Traditionally, MBMP variants have relied on regression for bias correction, a technique that does not take into account type-1 conditional bias, i.e., reliability. This study introduces novel approaches to implement MBMP that seek to improve forecast quality by focusing on ensemble reliability rather than accuracy during the bias-correction process. A new evaluation metric is proposed, and an innovative multiobjective combination of metrics is implemented during coefficient estimation. This is tested on daily air temperature forecasts with lead times of 2, 5, and 9 days over 44 watersheds in Quebec, Canada. Results demonstrate that higher ensemble forecast reliability is achieved when it is emphasized during the bias-correction step compared to other MBMP variants.
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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 | 0.001 |
| 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) | 0.001 | 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".