Modeling the Downwelling Longwave Radiation over a Groomed Ski Run under Clear Skies
Bibliographic record
Abstract
Abstract The surface radiation budget of a groomed ski run is important to ski racing. Variables such as snow-surface temperature and liquid water content depend upon the surface radiation budget and are crucial to preparing fast skis. This case study focuses on downwelling longwave radiation, measurements of which were made at a point on a ski run on Whistler Mountain, British Columbia, Canada, throughout a 5-day clear-sky intensive observation period. Tall trees often dominate the horizon of a point on a ski run, and so contributions to total downwelling longwave radiation from trees and sky were treated separately. The “LWRAD” longwave radiative flux model estimated the total downwelling longwave radiation by first calculating thermal contributions from the trees, incorporating regressions for tree temperature that use routine meteorological measurements. Contributions from each azimuth direction were determined with horizon-elevation angles from a theodolite survey. Thermal emissions were weighted accordingly and summed. Sky contributions were estimated using the “libRadtran” radiative transfer model with input of local atmospheric profiles of temperature and humidity and were added to tree emissions. Two clear-sky emissivity parameterizations using screen-height measurements were tested for comparison. LWRAD total downwelling longwave radiation varies between 235 and 265 W m−2 and compares well to measurements, with correlation coefficient squared (r2) of 0.96. These results can be used to improve estimates of downwelling longwave radiation for a groomed ski run.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".