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Record W1967099195 · doi:10.1080/07055900.2014.941323

Climatology of Lightning Activity over the Indian Seas

2014· article· en· W1967099195 on OpenAlexvenueno aff
M. I. R. Tinmaker, Mohammad Aslam, D. M. Chate

Bibliographic record

VenueATMOSPHERE-OCEAN · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsClimatologyMonsoonLightning (connector)BayBENGALEnvironmental scienceSea surface temperatureStormTropical cycloneFlash floodAtmospheric sciencesOceanographyGeologyGeographyFlood myth

Abstract

fetched live from OpenAlex

The climatology of lightning activity over the Indian seas (Arabian Sea (AS) and Bay of Bengal (BoB)) has been studied using monthly satellite-based lightning flash count grid (0.5° × 0.5°) data from 1998 to 2007. These data have been used to investigate the annual and seasonal variations in lightning activity over the Indian seas. It was found that annual variations in flash rate density and sea surface temperature (SST) show a bimodal pattern with the first peak occurring in May and the second in October. The correlation coefficients between flash rate density and SSTs are 0.76 and 0.65 for the AS and BoB, respectively. Further, the relationship between flash rate density and a low pressure system (LPS) over the BoB shows that the formation of severe tropical cyclonic storms starts during April with the maximum number of storms forming during August. The performance of monsoon on a seasonal and monthly basis depends on the total number of lows, the formation of a depression in the monsoon trough, and the number of days with an LPS. Secular decreases in the number of lows and monsoon depressions were observed in 2000, 2002, and 2004. Overall, results indicate that the peaks in SST during April and September/October over the AS and the BoB may be responsible for advancing the onset of the southwest and northeast monsoon by 30–40 days.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.139
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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 teacher head, 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

Citations18
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicLightning and Electromagnetic PhenomenaFrench-language works237,207