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Record W2160893194 · doi:10.1029/2005gl023992

Can synthetic aperture radars be used to estimate hurricane force winds?

2005· article· en· W2160893194 on OpenAlexaff
Jochen Horstmann, D. R. Thompson, F. Monaldo, Steve Iris, Hans C. Graber

Bibliographic record

VenueGeophysical Research Letters · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsSynthetic aperture radarGeologyMeteorologyRemote sensingEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

We compare wind fields retrieved from a RADARSAT‐I synthetic aperture radar (SAR) image acquired over Hurricane Ivan on September 10, 2004 using the C‐band geophysical model functions Cmod4 and its newest version Cmod5. Cmod4 has previously been shown to yield very good wind field estimates under low and moderate wind conditions. Wind directions obtained from streaks imaged by the SAR, that are well aligned with the mean surface wind direction are used to invert both algorithms to obtain estimates of the wind speed on scales of 1 km. These estimates are compared with predictions from a high‐resolution tropical cyclone model as well as local in situ data. It is found that the SAR wind speeds using Cmod5 agree reasonably well, while those from Cmod4 significantly under predict the measured wind speeds near the hurricane eye wall that reach values as high as 60 m s−1.

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)0.002
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.303
Teacher spread0.272 · 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

Citations88
Published2005
Admission routes1
Has abstractyes

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