Enhancement of the summer extreme precipitation over North China by interactions between moisture convergence and topographic settings
Bibliographic record
Abstract
Abstract This study investigates the roles of atmospheric moisture transport under the influence of topography for summer extreme precipitation over North China (NC) during 1979–2016. Based on rain gauge precipitation data and a reanalysis, 38 extreme precipitation days in NC during the 38 years were selected and associated moisture fluxes estimated. The results show that there is a dominant moisture influx of 311.8 kg m −1 s −1 into NC along its southern boundary from tropical oceans, and a secondary influx of 107.9 kg m −1 s −1 across its western boundary carried by mid-latitude westerlies. The outflux across the eastern boundary is 206.9 kg m −1 s −1 and across the northern boundary is 76.0 kg m −1 s −1 , giving a net moisture gain over NC of 136.8 kg m −1 s −1 . During extreme precipitation days, the moisture flux convergence (MFC) was much larger, exceeding 4 × 10 −5 kg m −1 s −1 . The MFC maximum core, the pronounced moisture transport, and the striking extreme precipitation zone over NC are all anchored to the east of the steep slopes of the surrounding topography. Moreover, a remarkably high humidity and strong upward motion also occur near steep slopes, indicating the critical role of the adjacent topography on the extreme precipitations. Simulations with and without the topography in NC using the Weather and Research Forecasting model for six selected out of the 38 extreme precipitation days demonstrate that the surrounding topography reinforces the MFC over NC by 16% relative to the case without terrain, primarily through enhanced wind convergence and higher moisture content, as well as stronger vertical motion induced by diabatic heating. The interactions between moisture convergence and topographic settings strengthen the extreme precipitation over NC.
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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) | 0.000 | 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".