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Record W4226334904 · doi:10.1038/s41612-022-00252-8

ENSO influence on Bay of Bengal cyclogenesis confined to low latitudes

2022· article· en· W4226334904 on OpenAlexfundno aff
Shinto Roose, R. S. Ajayamohan, Pallav Ray, P. Reshmi Mohan, K. Mohanakumar

Bibliographic record

Venuenpj Climate and Atmospheric Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
FundersTamkeenOffice of Naval Research GlobalMinistry of Earth SciencesYork UniversityOffice of Naval ResearchNew York University Abu Dhabi
KeywordsClimatologyEl Niño Southern OscillationTeleconnectionTropical cyclogenesisCyclogenesisBayBENGALTropical cycloneIndian Ocean DipoleLa NiñaOceanographyGeologyLatitudeSouthern oscillationCyclone (programming language)Atmospheric sciencesEnvironmental science

Abstract

fetched live from OpenAlex

Abstract The low-latitudinal cyclones (LLCs, originating between 5°N–10°N) constitute ≈40% of tropical cyclones (TCs) formed in the Bay of Bengal (BoB). We investigate the interannual variability of post-monsoonal (October to December) BoB LLCs and their teleconnection with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). It is found that the years with the fewer number of BoB LLCs are associated with anomalous equatorial easterlies that are largely connected with the El Niño and positive IOD. Likewise, equatorial westerly phases, often associated with the La Niña and negative IOD years, favour the LLC formation by providing the initial spin-up required for cyclogenesis. This teleconnection between ENSO/IOD and BoB TC frequency is confined in the low-latitudinal region with little influence for cyclogenesis north of 10°N during ENSO and IOD except during negative IOD. These results may help extend the lead time and improve the seasonal prediction of BoB TCs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.010
GPT teacher head0.237
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2022
Admission routes1
Has abstractyes

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Same venuenpj Climate and Atmospheric ScienceSame topicTropical and Extratropical Cyclones ResearchFrench-language works237,207