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Record W3137338974 · doi:10.1175/jpo-d-20-0208.1

Impact of Sea Spray and Sea Surface Roughness on the Upper Ocean Response to Super Typhoon Haitang (2005)

2021· article· en· W3137338974 on OpenAlexafffund
Lianxin Zhang, Xuefeng Zhang, William Perrie, Changlong Guan, Bo Dan, Chunjian Sun, Xinrong Wu, Kexiu Liu, Li Dong

Bibliographic record

VenueJournal of Physical Oceanography · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersNational Key Research and Development Program of ChinaMarine Environmental Observation Prediction and Response NetworkOcean Frontier InstituteNatural Science Foundation of Tianjin CityDalhousie UniversityNational Natural Science Foundation of China
KeywordsSea sprayTyphoonGeologyWind waveSea stateClimatologySurface roughnessOcean heat contentRoughness lengthMeteorologyAtmospheric sciencesSea surface temperatureWind speedOceanographyAerosolMaterials scienceWind profile power lawPhysics

Abstract

fetched live from OpenAlex

Abstract A coupled ocean-wave-sea spray model system is used to investigate the impacts of sea spray and sea surface roughness on the response of the upper ocean to the passage of the super typhoon Haitang. Sea spray mediated heat and momentum fluxes are derived from an improved version of Fairall’s heat fluxes formulation (Zhang et al., 2017) and Andreas’s sea spray-mediated momentum flux models. For winds ranging from low to extremely high speeds, a new parameterization scheme for the sea surface roughness is developed, in which the effects of wave state and sea spray are introduced. In this formulation, the drag coefficient has minimal values over the right quadrant of the typhoon track, along which the typhoon-generated waves are longer, smoother, and older, compared to other quadrants. Using traditional interfacial air-sea turbulent (sensible, latent, and momentum) fluxes, the sea surface cooling response to typhoon Haitang is overestimated by 1 °C, which can be compensated by the effects of sea spray and ocean waves on the right side of the storm. Inclusion of sea spray-mediated turbulent fluxes and sea surface roughness, modulated by ocean waves, gives enhanced cooling along the left edges of the cooling area by 0.2 °C, consistent with the upper ocean temperature observations.

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 score0.006
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations28
Published2021
Admission routes2
Has abstractyes

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