Parameterization of incoming longwave radiation at glacier sites in the Canadian Rocky Mountains
Bibliographic record
Abstract
Abstract We examine longwave radiation fluxes in the Canadian Rocky Mountains based on multiyear observations at glaciers in the southern and northern Rockies. Our main objective is to develop improved parameterizations of incoming longwave radiation for surface energy balance and melt modeling in glaciological studies, in situations where minimal meteorological data are available. We concentrate on the summer melt season, June through August. We test several common parameterizations of mean daily incoming longwave radiation and also explore simple regression‐based models of atmospheric emissivity as a function of near‐surface vapor pressure, relative humidity, and a sky clearness index (i.e., a proxy for cloud cover). Multivariate regressions based on these three variables have the strongest performance at our two sites, with RMS errors of 9–13 W m−2 and biases 1–2 W m−2 when transferred to different time periods or between sites in our study region. We also find good results for all‐sky atmospheric emissivity with a bivariate relation based on vapor pressure and relative humidity. This parameterization requires only screen‐level temperature and humidity as input data, which has value for modeling of incoming longwave radiation and surface energy balance when observational radiation and cloud data are not available.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".