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Record W2028263228 · doi:10.1175/2007waf2006093.1

Impact of Satellite Winds on Marine Wind Simulations

2008· article· en· W2028263228 on OpenAlex
William Perrie, Weiqing Zhang, Mark A. Bourassa, Hui Shen, P.W. Vachon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWeather and Forecasting · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of OceanographyEnvironment and Climate Change CanadaFisheries and Oceans Canada
Fundersnot available
KeywordsBuoyScatterometerMeteorologyData assimilationEnvironmental scienceSatelliteMiddle latitudesWind speedMinificationRemote sensingComputer scienceGeologyGeographyPhysicsOceanography

Abstract

fetched live from OpenAlex

Abstract A variational data assimilation method is applied to remotely sensed wind data from Hurricanes Gustav (2002) and Isabel (2003) to produce enhanced marine wind estimates. The variational method utilizes constraints to ensure that an optimum combination of winds is determined, in the sense of minimization of a cost function measuring the misfit between observations and background input field data and constraining nongeophysical features in the spatial derivatives. Constraints are multiplied by weights, which are objectively determined by cross validation. Verification is obtained by comparison with available operational in situ buoy observations and analyses winds. It is shown that the newly constructed midlatitude wind fields represent an improvement relative to background wind field estimates and also relative to Quick Scatterometer–National Centers for Environmental Prediction reanalysis blended winds, and that the new winds have an impact on simulations of waves and upper-ocean currents.

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.

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 score0.195
Threshold uncertainty score0.338

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 head0.037
GPT teacher head0.233
Teacher spread0.196 · 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