Effects of Mechanical and Thermal Forcing on the Enhancement and Ingredients of Orographic Rain Associated with the 2007-08 Madden-Julian Oscillation Passing the New Guinea Highlands
Bibliographic record
Abstract
In this study, the Advanced Research Weather Research and Forecasting (WRF) model was adopted to investigate the mechanical and thermal forcing effects associated with the New Guinea Highland (NGH) on Madden-Julian Oscillation (MJO) propagation and rainfall formation and enhancement mechanisms over the island of New Guinea. Our results show that both forces affect the propagation of the MJO07-08, resulting in orographic rainfall production. Even though each forcing helps produce orographic rainfall, the mechanical forcing of the NGH plays a much larger role in the orographic blocking than the thermal forcing. We also found two flow regimes associated with the propagation of MJO07-08 over the NGH. First, in the flow-around regime, the MJO and its associated convective system split around the NGH due to the strong orographic blocking. We can observe this splitting when looking at the splitting stage. Second, the flow-over regime could occur when the mountain is lower than its original height or the flow has a smaller Froude number. A series of numerical experiments indicate that the maximum orographic rainfall increases with increased mountain height; however, the maximum orographic rain decreases when the flow transitions to the flow-around regime. Finally, some common ingredients for orographic rainfall associated with the MJO07-08 passing over the NGH are consistent with those found for tropical cyclones passing over mountains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".