Contrast in monsoon precipitation over oceanic region of north Bay of Bengal and east equatorial Indian Ocean
Bibliographic record
Abstract
ABSTRACT This study explores the possible causes of rainfall distribution over the two major oceanic raining regions of the north Bay of Bengal (nBoB) and the east equatorial Indian Ocean (eEIO). Despite 17% difference in vertically averaged humidity, there is almost 34% difference in mean rainfall over these two regions. The climatological seasonal [June–September (JJAS)] mean (standard deviation) rainfall over nBoB region is always higher (lower) than that over the eEIO region in all the independent data used. The eEIO region has a much larger percentage of low stratiform and convective rainfall (<5 mm day −1 ) distribution as compared to nBoB, which is totally opposite in case of moderate stratiform and convective rainfall (>5 mm day −1 ) distribution. This is further substantiated by a much lower values of outgoing long‐wave radiation (OLR) in nBoB (<200 W m −2 ) as compared to the eEIO (217 W m −2 ) region. Mean Hadley circulation along with relative vorticity/divergence profile supports more intense (gentle) updrafts over nBoB (eEIO) region. Latent heat (LH) is almost three times at the upper level (∼8 km) in case of nBoB as compared to eEIO; however, at the lower level (∼3 km) LH is marginally higher over eEIO region. Microphysical variables, namely cloud ice optical thickness and cloud ice water path, are in much larger quantities over nBoB as compared to eEIO. Furthermore, the cold (warm) rain processes dominate among other microphysical processes over nBoB (eEIO) region. Thus, the interplay among large‐scale dynamics, thermodynamics and microphysics is very crucial in the formation of deep clouds and convective rain over the nBoB region and similarly shallow clouds and stratiform rain over the eEIO region. This study will be very useful to guide present‐day coupled models for proper representation of different rain components over the nBoB and eEIO region.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".