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Record W2033251820 · doi:10.1109/igarss.2012.6350495

Spectral dependence of the response time of sea state to local wind forcing

2012· article· en· W2033251820 on OpenAlexaff
David D. Chen, Scott Gleason, Christopher S. Ruf, Mounir Adjrad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsConcordia University
Fundersnot available
KeywordsSea stateRemote sensingWind speedWind waveRadiometerWavelengthEnvironmental scienceForcing (mathematics)Electromagnetic spectrumMeteorologyGeologyPhysicsAtmospheric sciencesOptics

Abstract

fetched live from OpenAlex

Bistatic remote sensing using L-band GPS signals has been proposed as an alternative to using microwave radiometers and monostatic radar scatterometers for spaceborne ocean surface windspeed measurements. L-band scattered signals are sensitive to waves with longer wavelengths than are the signals sensed by conventional radiometers and scatterometers, which typically operate at higher frequencies. It is known that longer surface waves take more time to respond to surface winds, propagate further before decaying, and are generally less directly coupled to the local wind field. These factors could affect the ability of scattered GPS L-band signals to retrieve local wind fields. In this work, we attempt to quantify the relationship between the longwave spectrum and local winds by examining windspeed and surface slope measurements by buoys. Specifically, by applying a lag-correlator, it is observed that the average lag time decreases monotonically as the ocean surface wavelength decreases. It is found that 1 hour serves as a conservative bound on the average response time of L-band waves to local wind forcing.

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 score0.125
Threshold uncertainty score0.502

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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