Improving the Explicit Prediction of Freezing Rain in a Kilometer-Scale Numerical Weather Prediction Model
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
Abstract A freezing rain event, in which the Meteorological Centre of Canada’s 2.5-km numerical weather prediction system significantly underpredicted the quantity of freezing rain, is examined. The prediction system models precipitation types explicitly, directly from the Milbrandt–Yau microphysics scheme. It was determined that the freezing rain underprediction for this case was due primarily to excessive refreezing of rain, originating from melting snow and graupel, in and under the temperature inversion of the advancing warm front ultimately depleting the supply of rain reaching the surface. The refreezing was caused from excessive collisional freezing between rain and graupel. Sensitivity experiments were conducted to examine the effects of a temperature threshold for collisional freezing and on varying the values of the collection efficiencies between rain and ice-phase hydrometeors. It was shown that by reducing the rain–graupel collection efficiency and by imposing a temperature threshold of −5°C, above which collisional freezing is not permitted, excessive rain–graupel collection and graupel formation can be controlled in the microphysics scheme, leading to an improved simulation of freezing rain at the surface.
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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 | 0.001 | 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) | 0.000 | 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 it