Impact of Orographic Gravity Wave Drag Parameterization on Northeast China Cold Vortex Heavy Rainfall Simulated at a Convection-Allowing Resolution
Bibliographic record
Abstract
Abstract The Northeast China cold vortex (NECV) is a major weather system producing heavy rainfall in northern China, yet the influence of complex terrain, especially orographic gravity waves (OGWs), on such heavy rainfall remains poorly understood. This study investigates the impact of OGW drag (OGWD) parameterization on an NECV heavy rainfall event over the southern Yanshan Mountains on 6 July 2011, using the Weather Research and Forecasting Model at 3-km resolution. Results show that the OGWD parameterization can weaken the NECV circulation and diminish the orographic lifting and moisture transport over the southern slope of the Yanshan Mountains given the decelerated upslope flow. Therefore, the overestimation of the heavy rainfall intensity in the absence of OGWD parameterization was alleviated significantly, indicating the importance of OGWD parameterization even in high-resolution numerical models. However, the parameterization of OGWD introduced weak but widespread spurious rainfall ahead of the Taihang Mountains, as it decelerated the northwesterly downslope winds on the southeastern slope of the Taihang Mountains which enhanced the upslope moisture transport. This spurious rainfall was mitigated significantly when using a revised OGWD scheme accounting for the nonhydrostatic effect (NHE) on the surface momentum flux of vertically propagating OGWs. The NHE more notably attenuated the OGWD over the Taihang Mountains than over the Yanshan Mountains, which strengthened the NECV northwesterly flow downgliding the Taihang Mountains and inhibited the moisture transport.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".