The role of local topography and sea surface temperature on summer monsoon precipitation over Bangladesh and n<scp>ortheast</scp> India
Bibliographic record
Abstract
Abstract Bangladesh receives most of its precipitation from June to September in the form of rainfall as a part of the Asian summer monsoon system. Bangladesh is a relatively flat region, surrounded by the southern Himalayas and Meghalaya Plateau in the north, Arakan Mountains in the east, and the Bay of Bengal (BOB) in the south. Although several studies have investigated the mechanisms that drive the Asian monsoon precipitation, very few studies have focused on the monsoon precipitation in Bangladesh. This study investigated the influence of the topography of the surrounding regions and sea surface temperature on the summer monsoon precipitation of Bangladesh and the surrounding regions. Using observed data, we showed that moisture convergence near the mountains contributes to the precipitation of Bangladesh, whereas the BOB acts as a source of moisture. A strong low‐level jet carries the moisture inland as the land–sea thermal contrast intensifies the wind circulation during the summer. Three differently forced simulations of the Euro‐Mediterranean Centre on Climate Change coupled climate model (CMCC CM2) were analysed to investigate the influence of the surrounding region's topography and sea surface temperature on the summer monsoon precipitation. The low‐resolution simulation showed no spatial variability of precipitation and dry bias due to the overly smooth topographical representation of mountains. The high‐resolution coupled simulation, with a better representation of topography, improved the moisture convergence at the foothills and precipitation bias. The high‐resolution prescribed sea surface temperature further improved the precipitation bias by intensifying the low‐level jet that transports moisture over Bangladesh.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".