Calibrating probabilistic forecasts from an NWP ensemble
Bibliographic record
Abstract
A post-processing method for calibrating probabilistic forecasts of continuous weather variables is presented. The method takes an existing probability distribution and adjusts it such that it becomes calibrated in the long run. The original probability distributions can be ones such as are generated from a numerical weather prediction (NWP) ensemble combined with a description of how uncertainty is represented by this ensemble. The method uses a calibration function to relabel raw cumulative probabilities into calibrated cumulative probabilities based on where past observations verified on past raw probability forecasts. Applying the calibration method to existing probabilistic forecasts can be beneficial in cases where the underlying assumptions used to construct the probabilistic forecast are not in line with nature’s generating process of the ensemble and corresponding observation. The method was tested on a forecast data set with five different forecast variables and was verified against the corresponding analyses. The calibration method reduced the calibration deficiency of the forecasts down to the level expected for perfectly calibrated forecasts. When the raw forecasts exhibited calibration deficiencies, the calibration method improved the ignorance score significantly. It was also found that the ensemble-uncertainty model used to create the original probability distribution affected the ignorance score.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".