Composite Structure of Monsoon Low Pressure Systems and Its Relation to Indian Rainfall
Bibliographic record
Abstract
Abstract The tropical disturbances formed in the Bay of Bengal and the Arabian Sea and over land points in central India, known as low pressure systems (LPSs), are shown to contribute significantly to the seasonal monsoon rainfall over India. Analyses of daily rainfall over India and statistics of the LPSs for the period of 1901–2003 show that the rainfall pattern when the LPSs are present captures the most dominant daily rainfall pattern that represents the active monsoon phase. The rainfall pattern when the LPSs are absent is similar to the pattern representing the break monsoon phase. The location, number, and duration of the LPSs are found to be closely related to the phases and propagation of the dominant intraseasonal modes of the Indian rainfall. The LPSs are also associated with the strengthening of the monsoon trough and low-level monsoon winds. The number of LPSs and their total duration and the corresponding rainfall during July and August exceed those in June and September. The LPS tracks reach up to northwest India during flood years, whereas they are confined to central India during drought years. However, the contribution of rainfall during the LPSs to the total seasonal rainfall is same during flood or drought years. Although the LPSs seem to play an important role in the monsoon rainfall, they alone may not determine the interannual variability of the seasonal mean monsoon rainfall.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".