Measuring the specific surface area of wet snow using 1310 nm reflectance
Bibliographic record
Abstract
Abstract. The specific surface area (SSA) of snow can be used as an objective measurement of grain size and is therefore a central variable to describe snow physical properties such as albedo. Snow SSA can now be easily measured in the field using optical methods based on infrared reflectance. However, existing optical methods have only been validated for dry snow. Here we test the possibility to use the DUFISSS instrument, based on the measurement of the 1310 nm reflectance of snow with an integrating sphere, to measure the SSA of wet snow. We perform cold room experiments where we measure the SSA of a wet snow sample, freeze it and measure it again, to quantify the difference in reflectance between frozen and wet snow. We study snow samples in the SSA range 12–37 m2 kg−1 and in the mass liquid water content (LWC) range 5–32%. We conclude that the SSA of wet snow can be obtained from the measurement of its 1310 nm reflectance using three simple steps. In most cases, the SSA thus obtained is less than 10 {%} different from the value that would have been obtained if the sample had been considered dry, so that the three simple steps constitute a minor correction. We also run two optical models to interpret the results, but no model reproduces correctly the water–ice distribution in wet snow, so that their predictions of wet snow reflectance are imperfect. The correction on the determination of wet snow SSA using the DUFISSS instrument gives an overall uncertainty better than 11%, even if the LWC is unknown. If SSA is expressed as a surface to volume ratio (e.g., in mm−1), the uncertainty is then 13% because of additional uncertainties in the determination of the volume of ice and water when the LWC is unknown.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".