Non-parametric interval forecast models from fuzzy clustering of Numerical Weather Predictions
Why this work is in the frame
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Bibliographic record
Abstract
Clustering methods are proposed and evaluated as post-processing techniques that can model the uncertainty of forecasts provided by Numerical Weather Prediction (NWP) systems. These techniques try to discover relevant information about forecast uncertainty that is inherent in the performance records of the system. We investigate the application of Fuzzy C-means clustering as a powerful unsupervised learning method to discover fuzzy sets of weather forecast situations which represent different forecast uncertainty patterns. These patterns are then utilized by different distribution fitting methods to obtain statistical prediction intervals which can express the expected accuracy of the NWP system output. Three years of weather forecast records in two weather stations are used in a set of experiments to empirically study the application of the proposed approach. Skills of the probabilistic forecasts obtained by these post-processing methods are investigated by considering cross fold validation and sampling variations. Results demonstrate that the Prediction intervals generated by the proposed procedure have a higher skill compared to baseline methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.009 | 0.001 |
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 it