Incoming longwave radiation to melting snow: observations, sensitivity and estimation in Northern environments
Bibliographic record
Abstract
Abstract At high latitudes, longwave radiation can provide similar, or higher, amounts of energy to snow than shortwave radiation due to the low solar elevation (cosine effect and increased scattering due to long atmospheric path lengths). This effect is magnified in mountains due to shading and longwave emissions from the complex topography. This study examines longwave irradiance at the snow surface in the Wolf Creek Research Basin, Yukon Territory, Canada (60° 36′N, 134° 57′W) during the springs of 2002 and 2004. Incoming longwave radiation was estimated from standard meteorological measurements by segregating radiation sources into clear sky, clouds and surrounding terrain. A sensitivity study was conducted to detect the atmospheric and topographic conditions under which emission from adjacent terrain significantly increases the longwave irradiance. The total incoming longwave radiation is more sensitive to sky view factor than to the temperature of the emitting terrain surfaces. Brutsaert's equation correctly simulates the clear‐sky irradiance for hourly time steps using temperature and humidity. Longwave emissions from clouds, which raised longwave radiation above that from clear skies by 16% on average, were best estimated using daily atmospheric shortwave transmissivity and hourly relative humidity. An independent test of the estimation procedure for a prairie site near Saskatoon, Saskatchewan, Canada, indicated that the calculations are robust in late winter and spring conditions. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".