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Record W4388977925 · doi:10.1029/2023gl105723

Time Resolved Reflectivity Measurements of Convective Clouds

2023· article· en· W4388977925 on OpenAlexaff
Brenda Dolan, Pavlos Kollias, Susan C. van den Heever, Kristen L. Rasmussen, Mariko Oue, Edward Luke, Katia Lamer, Bernat P. Treserras, Ziad S. Haddad, Graeme L. Stephens, V. Chandrasekar

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill University
FundersScience Mission DirectorateLangley Research CenterNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyJet Propulsion LaboratoryU.S. Department of EnergyNational Science Foundation
KeywordsConvectionRadarRemote sensingDoppler effectSampling (signal processing)Doppler radarGeologyEnvironmental scienceMeteorologyPhysicsOpticsComputer scienceAstronomy

Abstract

fetched live from OpenAlex

Abstract National Aeronautics and Space Administration's Investigations of Convective Updrafts (INCUS) mission aims to document convective mass flux through changes in the radar reflectivity (Δ Z ) in convective cores captured by a constellation of three Ka‐band radars sampling the same convective cells over intervals of 30, 90, and 120 s. Here, high spatiotemporal resolution observations of convective cores from surface‐based radars that use agile sampling techniques are used to evaluate aspects of the INCUS measurement approach using real observations. Analysis of several convective cells confirms that large coherent Δ Z structure with measurable signal (>5 dB) can occur in less than 30 s and are correlated with underlying convective motions. The analysis indicates that the INCUS mission radar footprint and along track sampling are adequate to capture most of the desirable Δ Z signals. This unique demonstration of reflectivity time‐lapse provides the framework for estimating convective mass flux independent from Doppler techniques with future radar observations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.147
GPT teacher head0.347
Teacher spread0.200 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2023
Admission routes1
Has abstractyes

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