Study of unusual atmospheric icing at Mount Zao, Japan, using the Weather Research and Forecasting model
Bibliographic record
Abstract
A mesoscale atmospheric model, the Weather Research and Forecasting model (WRF), was used for a case study that reconstructs mid‐spring episodes of rime formation at Mt. Zao, Japan. One particularly interesting and rare form of rime was observed. The formations were feathery, opaque aggregates of granular ice 15–30 cm long, called “shrimp tails” in Japanese. Based on an analysis of model‐generated results, we find good quantitative agreement of modeled and observed wind and temperature time series at Jizosancho ropeway station. We identified two icing events (lasting for 36 and 41 h respectively, with surface air temperatures between −6.3° and −0.1°C, relatively constant westerly winds up to 26 m s −1 , and maximum cloud liquid water contents (LWC) between 0.72 and 1.05 g m −3 ). We confirmed that high‐resolution modeling (1.1 km grid spacing) was much more accurate than simulations with coarser grids (10 and 3.3 km). The LWC during the formation period of this rare type of icing was estimated for the first time using the WRF model at Mt. Zao, and it was found to be up to several times higher than values previously used in experimental studies. We found that the joint wind speed‐air temperature distribution for this type of “tail” rime was more similar to that of a hard rime or glaze, than to a soft rime. We explain the formation of “shrimp tails” by wind impact angle and report previously made laboratory results on its effect on the droplet collision efficiency and the density of rime ice.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".