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Record W2955245659 · doi:10.1139/anc-2018-0019

Wind drag in oil spilled ocean surface and its impact on wind-driven circulation

2019· article· en· W2955245659 on OpenAlexafffund
Hui Shen, William Perrie, Yongsheng Wu

Bibliographic record

VenueAnthropocene Coasts · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersOffice of Energy Research and DevelopmentCanadian Space AgencyOcean Frontier Institute
KeywordsRacing slickEnvironmental scienceWind stressOcean currentWind speedDrag coefficientSurface roughnessDragMeteorologyCurrent (fluid)GeologyAtmospheric sciencesClimatologyOil spillOceanographyMechanicsGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract The drag coefficient is a key parameter to quantify the wind stress over the ocean surface, which depends on the ocean surface roughness. During oil spill events, oil slicks cover the ocean surface and thus change the surface roughness by suppressing multi-scale ocean surface waves, and the drag coefficient is changed. This change has not been included in the current ocean circulation models. In this study, such change in sea surface roughness is studied by satellite remote sensing via synthetic aperture radar (SAR) data to quantify the changes in the wind effect over the oil-covered ocean surface. The concept of effective wind speed is introduced to quantify the wind work on the ocean. We investigate its influence on the wind-driven Ekman current at the ocean surface. Using observations from the Deepwater Horizon oil spill (2010) as an example, we find that the presence of oil can result in an effective wind speed of 50%~100% less than the conventional wind speed, causing overestimates by 75%~100% in the wind driven Ekman current. The effect of such bias on oil trajectory predictions is also discussed. Our results suggest that it is important to consider the effect of changes in the drag coefficient over oil-contaminated areas, especially for large-scale oil spill situations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

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)0.0020.001

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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2019
Admission routes2
Has abstractyes

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