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Record W2093509809 · doi:10.1029/2006gl027087

A new hurricane wind retrieval algorithm for SAR images

2006· article· en· W2093509809 on OpenAlexaff
Hui Shen, William Perrie, Yijun He

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsWind speedEyeScatterometerRemote sensingMeteorologySynthetic aperture radarInterpolation (computer graphics)Wind directionGeologyMaximum sustained windRadarEnvironmental scienceTropical cycloneGeodesyComputer scienceWind gradientImage (mathematics)GeographyArtificial intelligence

Abstract

fetched live from OpenAlex

A new algorithm for hurricane wind retrieval is developed based on the primary characteristics of hurricane processes. By assuming constant wind speeds and equal‐distant wind directions in three neighboring sub‐image blocks of any specific concentric circle around the hurricane eye, both wind speed and wind direction are resolved without any external information. To evaluate the accuracy of the new algorithm, a Radarsat‐1 synthetic aperture radar image of hurricane Isabel is studied. The retrieved wind speed is compared with QuikSCAT scatterometer winds and in situ winds. Good consistency is shown. Since the new algorithm doesn't rely on any external wind information, fine resolution wind structures can be retrieved without adding errors due to linear interpolation of the externally‐derived coarse‐resolution wind direction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.277
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations49
Published2006
Admission routes1
Has abstractyes

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