MétaCan
Menu
Back to cohort
Record W4412739353 · doi:10.1029/2025gl115872

Tropical Cyclone Center Estimates Purely From FY‐3E WindRAD Measurements in the Dawn‐Dusk Orbit

2025· article· en· W4412739353 on OpenAlexafffund
He Fang, William Perrie, Guosheng Zhang, Peng Zhang, Lin Chen

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersCanadian Space AgencyDalhousie UniversityNatural Science Foundation of Zhejiang ProvinceChina Meteorological AdministrationNational Natural Science Foundation of China
KeywordsDuskTropical cycloneMeteorologyCenter (category theory)Environmental scienceClimatologyOrbit (dynamics)GeodesyGeologyGeographyPhysicsAstronomyAerospace engineering

Abstract

fetched live from OpenAlex

Abstract The first C‐ and Ku‐band dual‐frequency scatterometer instrument (WindRAD) on board the world's first early‐morning‐orbiting meteorological satellite Fengyun‐3E (FY‐3E) has the capability to measure global ocean surface winds. However, WindRAD cannot determine the center locations of tropical cyclones (TCs) because it is limited by coarse spatial resolution. In this work, a relatively simple model is applied to identify the storm centers, using ocean wind measurements collected by the WindRAD scatterometer. The model—estimated storm centers are determined to minimize the errors between model—simulated winds and WindRAD measurements. Our data set consists of all WindRAD overpasses of TCs during 2022 and 2023 in the West Pacific, East Pacific, North Atlantic and Northern Indian Oceans. The average errors between the WindRAD model estimates and the reported best‐track storm center locations are 51, 40, and 25 km, for tropical storms, category 1–2 storms and major storms (>49 m s −1 ), respectively. Our model is both objective and automatic, thereby avoiding subjectivity and possible errors related to manual analysis.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.063
GPT teacher head0.322
Teacher spread0.259 · 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 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

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicTropical and Extratropical Cyclones ResearchFrench-language works237,207