Measurement of specific surface area of fresh solid precipitation particles in heavy snowfall regions of Japan
Bibliographic record
Abstract
Abstract. In countries like Japan, particular solid precipitation particles (PPs), such as unrimed PPs and graupel, often form a weak layer in snow, which triggers slab avalanches. An understanding of weak PP layers is therefore essential for avalanche prevention authorities to design a predictive model for slab avalanches triggered by those layers. Specific surface area (SSA) is a parameter that could characterize the physical properties of PPs. The SSAs of solid PPs in Nagaoka – a city in Japan experiencing the heaviest snowfall in the country – were measured for four winters (from 2013/2014 to 2016/2017). More than 100 SSAs of PP were measured during the study period using the gas absorption method. The measured SSA values range from 42 to 153 m2 kg−1. Under melting conditions, PPs showed comparatively smaller values. Unrimed and slightly rimed PPs exhibited low SSA, whereas heavily rimed PPs and graupel exhibited high SSA. The degree of PP riming depends on the synoptic meteorological conditions. Based on the potential of weak PP layer formation with respect to the degree of riming of PPs, the results indicate that SSA is a useful parameter for describing the characteristics of PP, and consequently predicting avalanches triggered by weak PP layers. The study found that the values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. SSA increases with increase in WS and decreases with increase in Tw. An equation to empirically estimate the SSA of fresh PPs in Nagaoka using WS and Tw was established. The equation successfully reproduced the fluctuation of SSA. The SSA equation, along with the meteorological data, is an efficient first step toward describing the development of weak PP layers in the snow cover models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".