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Record W2072901822 · doi:10.1029/2003jd004486

Comparison of meteor radar and Na Doppler lidar measurements of winds in the mesopause region above Maui, Hawaii

2005· article· en· W2072901822 on OpenAlexaff
S. J. Franke, Xinzhao Chu, Alan Z. Liu, W. K. Hocking

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsLidarMesopauseRadarMeteor (satellite)Remote sensingGeologyDoppler effectEnvironmental scienceMesosphereMeteorologyAtmospheric sciencesStratospherePhysics

Abstract

fetched live from OpenAlex

Simultaneous sodium (Na) Doppler lidar and meteor radar measurements of horizontal winds in the mesopause region over Maui, Hawaii, were collected in July 2002 and October/November 2003. The coincident measurements span 96 hours and altitudes between 80 and 100 km. Statistical comparisons are carried out on radar/lidar winds with 1 hour and 4 km time and height resolution, respectively. The RMS radar/lidar wind component differences observed in this study are in the range 12–17 m/s at altitudes below 96 km. This is smaller than the RMS differences observed in a previous Na lidar and meteor radar comparison. Lidar wind component variances exceed radar variances, and radar/lidar covariance, is nearly equal to the radar variance. Excess variance observed by the lidar is consistent with the fact that the meteor radar cannot resolve wind perturbations with horizontal scales smaller than ∼200 km, whereas the lidar will respond to all horizontal scales. Close correspondence between the radar wind variance and radar/lidar covariance suggests that measurement errors associated with the radar winds are swamped by geophysical variation. Furthermore, the excess lidar variance exceeds lidar estimation errors by a large factor, indicating that the lidar measurement errors are also insignificant relative to geophysical variations. Together these observations suggest that the observed radar/lidar differences are a consequence of the different horizontal wave number filters associated with the techniques, and hence the differences are determined by the strength and shape of the horizontal wave number spectrum for wind perturbations at scales smaller than ∼200 km.

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)0.001
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 score0.374
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.056
GPT teacher head0.355
Teacher spread0.299 · 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

Citations115
Published2005
Admission routes1
Has abstractyes

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