Leveraging deterministic weather forecasts for in-situ probabilistic predictions via deep learning
Bibliographic record
Abstract
We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. The developed method is applicable to any gridded forecast including the recent machine learning weather prediction model outputs. To postprocess multiple lead times using a single model, we introduce a lead time embedding that encodes the shift in biases as the forecast progresses. We apply our approach to operational outputs from the Global Deterministic Prediction System up to ten-day lead times. The model is trained to predict METAR in-situ surface temperature observations in Canada and the United States. The resulting forecasts have a mean CRPS below 2.5 K at 10 days lead time while maintaining a spread-error ratio of approximately 0.9, suggesting appropriate calibration. For extreme temperatures, the model’s biases are comparable to that of the underlying deterministic forecast. Our approach increases the utility of a deterministic forecast by adding information about the uncertainty, without incurring the cost of simulating multiple trajectories. It requires no information regarding forecast spread and can be used to generate probabilistic predictions from any deterministic forecast.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".