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Record W3112083939 · doi:10.1109/jstars.2020.3043246

An Improved Asymmetric Hurricane Parametric Model Based on Cross-Polarization SAR Observations

2020· article· en· W3112083939 on OpenAlexfundno aff
Sheng Wang, Xiaofeng Yang, Haiyan Li, Kaijun Ren, Die Hu, Yanlei Du

Bibliographic record

VenueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsnot available
FundersCanadian Space AgencyNational Oceanic and Atmospheric AdministrationU.S. Air ForceState Key Laboratory of Remote Sensing ScienceNational Natural Science Foundation of China
KeywordsSynthetic aperture radarWind speedRemote sensingParametric statisticsEnvironmental scienceMean squared errorMeteorologyParametric modelGeologyComputer sciencePhysicsMathematics

Abstract

fetched live from OpenAlex

Synthetic aperture radar (SAR) has been proven to be a useful tool in monitoring hurricane structure and intensity. By far, SAR is the most promising spaceborne sensor to obtain high-resolution hurricane wind field on the ocean surface. In this article, an improved asymmetric hurricane parametric (IMAHP) model has been proposed to reconstruct the asymmetric wind speed, where the high-resolution cross-polarization SAR imagery is used to determine the value of model parameters. Compared with other models, the new model can better reconstruct hurricane wind speed with a more concise model function. For verification, taking SAR-retrieved wind speed as a reference, the root-mean-square error and bias of the wind speed estimated by the IMAHP model are 1.86 m/s, 1.89 m/s for Hurricane Arthur (2014), 2.01 m/s, 1.77 m/s for Iselle (2014), and 1.99 m/s, 1.74 m/s for Norbert (2014), respectively. Finally, comparisons with airborne stepped-frequency microwave radiometer and dropwindsondes measurements show that the wind speed simulated by the IMAHP model is close to these measurements.

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 score0.184
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.241
Teacher spread0.209 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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