Analysis of tropical cyclone eye over the North Indian Ocean during 2013–2023
Bibliographic record
Abstract
This study examines the tropical cyclone (TC) eyes over the North Indian Ocean (NIO) from 2013 to 2023. TCs feature a warm and cloud-free region called the eye. In recent years, meteorologists have taken a keen interest in geometric and thermodynamic characteristics of TC’s eye as these attributes are useful for operational forecasting of TCs. In this study, we analyzed data from the INSAT-3D/R satellite, passive microwave imagery, and thermodynamic parameters over an 11-year period (2013–2023). Analysis showed that 37.73 % of the TCs developed an eye over the NIO, with 60 % of these occurring in the Arabian Sea (AS) and 40 % in the Bay of Bengal (BoB). The eye was observed most frequently approximately 36 h (1.5 days) after the storm's onset (>34 knots). The mean maximum sustained wind speed at which an eye formed was 66 knots, with a standard deviation of 14.26 over the NIO. The average estimated central pressure of the eye was 982.15 hPa. TCs' eyes formed at an average latitude of 13.60°N and longitude of 83.67°E in the BoB, with standard deviations of 2.33° and 5.93°, respectively. The average radius of a TC's eye was 22.3 km (with a diameter of 44.6 km) over the NIO. The calculated Eye Roundness Value (ERV) was 0.59, with a range from 0.5 to 0.8. The average intensity of TC's eyes over the NIO was classified as Dvorak’s T4.0 (64–89 knots). The dominant pattern observed before the formation of the TC's eye was the Curved Band Pattern. Our results indicated that as one moves poleward, both the size and number of eyes increase. The findings of this study are valuable for operational forecasters and disaster managers in mitigating socioeconomic impacts and preserving human lives.
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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 | 0.000 |
| 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".