A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization
Bibliographic record
Abstract
We present a computationally simple, theoretically based parameterization for the broadband albedo of snow and ice that can accurately reproduce the theoretical broadband albedo under a wide range of snow, ice, and atmospheric conditions. Depending on its application, this parameterization requires between one and five input parameters. These parameters are specific surface area of snow/ice, concentration of light‐absorbing carbon, solar zenith angle, cloud optical thickness, and snow depth. The parameterization is derived by fitting equations to albedo estimates generated with a 16‐stream plane‐parallel, discrete ordinates radiative transfer model of snow and ice that is coupled to a similar model of the atmosphere. Output from this model is also used to establish the physical determinants of the spectral albedo of snow and ice and evaluate the characteristics of spectral irradiance over snow‐covered surfaces. Broadband albedo estimates determined from the radiative transfer model are compared with results from a selection of previously proposed parameterizations. Compared to these parameterizations, the newly proposed parameterization produces accurate results for a much wider range of snow, ice, and atmospheric conditions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".