District-Level Seasonal Rainfall Characteristics over Andhra Pradesh and its Global Teleconnections in Changing Climate
Bibliographic record
Abstract
Investigating the trends and changes in rainfall over vulnerable regions is of huge importance in this global warming era. The present study intensively investigates the rainfall over the Indian state, Andhra Pradesh (AP), and its 13 districts using a high-resolution (0.25°×0.25°) gridded rainfall analysis dataset from India Meteorological Department (IMD) for the study period of 118 years (1901-2018). For this, normality, homogeneity, persistence, and change-point tests are performed and changes in the district-level rainfall in the present global warming period (1991-2018) as compared to the pre-global warming period (1901-1990) is also analyzed.
 The results suggest that the long-term average annual rainfall over AP is 882 mm and most of the rainfall is contributed by the monsoon (55.7%) and the post-monsoon rainfall (32.8%). The coefficient of variation is low (high) during monsoon (winter). The coastal region receives more rainfall than the inland districts. The post-monsoon rainfall over AP is more consistent than in other seasons, and the persistence is only during the southwest monsoon season. The southwest monsoon and post-monsoon rainfall have increased (by about 10%) over most of the districts in the recent period. The Nino3.4 region SST (South Oscillation Index; SOI) has a significant negative (positive) relationship with southwest summer monsoon rainfall in most of the districts. The relationship of Nino 3.4 SST and DMI is strikingly similar for post-monsoon and has significantly weakened in recent decades. This study is useful for proper planning and mitigation measures for the agricultural and water resources sector at the district level over AP in this global warming era.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".