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Record W4366411442 · doi:10.1175/mwr-d-22-0287.1

The Histories of Well-Documented Maritime Cyclones as Portrayed by an Automated Tracking Method

2023· article· en· W4366411442 on OpenAlexaff
Paul J. Roebber, Kevin M. Grise, John R. Gyakum

Bibliographic record

VenueMonthly Weather Review · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCyclogenesisCyclone (programming language)Tracking (education)MeteorologyExtratropical cycloneEnvironmental scienceSmoothingClimatologyTropical cycloneComputer scienceTyphoonGeologyGeographyComputer vision

Abstract

fetched live from OpenAlex

Abstract This study examines extratropical cyclone tracks, central pressure, and maximum intensification rates from a widely used automated cyclone tracking scheme and compares them with the manual tracking of five well-known North Atlantic cyclones whose histories are available in the refereed literature. The automated tracking scheme is applied to sea level pressure data from four different reanalyses of varying levels of sophistication to test the sensitivity of the results to input data resolution and quality. Further, we test the tracking scheme using lower-tropospheric vorticity obtained from the most recent reanalysis (ERA5) for four of these cyclone events. Substantial discrepancies in cyclone position, intensity, and maximum intensification rates exist between the manual tracking and the automated tracking and are not eliminated by using higher-resolution reanalyses or by “turning off” the spatial smoothing feature of the automated tracking scheme (needed to reduce spurious cyclone detections). The results point to a particular problem in detecting weaker and earlier stage cyclones and confirm findings from studies that have examined a broad range of cyclone tracking schemes for a range of reanalyses. Notably, this early cyclone stage often involves a smaller-scale secondary cyclogenesis or cyclone wave, which are detected by the automated scheme only after subsequent growth in the ensuing 6–12 h. It is known that these early stages are critical for a comprehensive understanding of rapid intensification events. A discussion of possible future solutions to this problem is presented. Significance Statement Because of the availability of large modern datasets portraying sea level pressure across the globe, meteorologists have turned to automated detection and tracking of midlatitude cyclones. Detection and tracking are of interest since these storm systems play an important role in weather and climate and potential changes in their location, frequency, and intensity are of considerable societal interest given climate change. This paper compares the results obtained from one commonly used automated tracking method with tracks obtained by human analysts. We find substantial discrepancies in cyclone position, intensity, and intensification rates and that these differences are not eliminated by using improved analyses. A discussion of possible future solutions is presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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.

Study designNot applicable
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

Citations11
Published2023
Admission routes1
Has abstractyes

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