Learning uncertainty models from weather forecast performance databases using quantile regression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forecast uncertainty information is not available in the immediate output of Numerical weather prediction (NWP) models. Such important information is required for optimal decision making processes in many domains. Prediction intervals are a prominent form of reporting the forecast uncertainty. In this paper, a series of learning methods are investigated to obtain prediction interval models by a statistical post-processing procedure involving the historical performance of an NWP system. The article investigates the application of a number of different quantile regression algorithms, including kernel quantile regression, to compute prediction intervals for target weather attributes. These quantile regression methods along with a recently proposed fuzzy clustering-based distribution fitting model are practically benchmarked in a set of experiments involving a three years long database of hourly NWP forecast and observation records. The role of different feature sets and parameters in the models are studied as well. The forecast skills of the obtained prediction intervals are evaluated not only by means of classical cross fold validation test experiments, but also subject to a new sampling variation process to assess the uncertainty of skill score measurements. The results show also how the different methods compare in terms of various quality aspects of prediction interval forecasts such as sharpness and reliability.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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