Observational study for strong downslope wind event under fine weather condition during ICE-POP 2018
Bibliographic record
Abstract
Abstract. A strong downslope wind event under fine weather condition on 13–15 February 2018 was examined by various observational and high resolution reanalysis datasets during the 2018 Winter Olympic and Paralympic games in Pyeongchang, Korea. High spatio-temporal resolution of wind information was obtained by Doppler lidars, automatic weather stations (AWS), wind profiler, and sounding observations under the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This study aimed to understand the possible generation mechanisms of localized strong wind event across high mountainous areas and in the lee side of mountains associated with the underlying large-scale pattern of a low-pressure system (LPS). The spatial distribution of linear trends for surface wind shows different patterns, exhibiting increased trend in the lee side and a persistent one in mountainous areas with the approaching LPS. Surface wind speed was intensified dramatically from ~3 to ~12 m s−1 (gust was stronger than 20 m s−1 above ground) at a surface station in the lee side (named as GWW). However, the mountainous station at DGW site appeared to have a persistently strong wind (~10 m s−1) during the research period. Budget analysis of horizontal momentum equation and local reanalysis data suggests that the pressure gradient force (PGF) derived by adiabatic warming along the downslope and subsequent hydraulic jump in the lee side of mountains was a main factor in the acceleration of the surface wind at the GWW site. Detailed analysis of the retrieved 3D winds reveals that the PGF also dominate at the DGW site, which causes the persistent strong wind that is related to the channeling effect across the valley areas in the mountain range. The observational evidence presented here shows that the different mechanisms in local areas under the same synoptic condition with LPS are important references in determining the strength and persistence of the orographic-induced strong winds under fine weather condition.
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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) | 0.001 | 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 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".