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Record W2011530129 · doi:10.1109/igarss.2014.6947027

A new semi-empirical sea surface microwave backscatter model coupled with the rain effect

2014· article· en· W2011530129 on OpenAlexaff
Biao Zhang, Guosheng Zhang, William Perrie, Yijun He

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of Oceanography
FundersNational Youth Foundation of ChinaNational Natural Science Foundation of China
KeywordsScatterometerEnvironmental scienceRemote sensingSynthetic aperture radarWind speedRadarBackscatter (email)SatelliteMeteorologyMicrowave radiometerPrecipitationBuoyEyeTyphoonGeologyRadiometerTropical cycloneGeographyComputer scienceOceanography

Abstract

fetched live from OpenAlex

The geophysical model function is generally employed to invert the surface wind speed using scatterometer or synthetic aperture radar (SAR) measurements over the ocean. The GMF relates the normalized radar cross-section (NRCS) to the incidence angle and wind vector. The rain effect on NRCS is not considered in the GMF. In raining areas, the NRCS of the ocean surface is altered by rain. Rain contamination introduces errors to wind speed retrieved by scatterometer, particularly at high incidence angles [1]. Moreover, under extreme weather conditions, significant differences exist between the SAR retrieved wind speed and those measured by Stepped-Frequency Microwave Radiometer (SFMR) measurements in hurricane eyewall regions [2], due to the intense rainfall there. The challenges in satellite retrievals of ocean winds related to precipitation effects has been elaborated in [3].

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.460

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 head0.208
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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