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Record W2041211931 · doi:10.1175/2011jas3658.1

The Influence of the Cloud Shell on Tracer Budget Measurements of LES Cloud Entrainment

2011· article· en· W2041211931 on OpenAlexaff
J. T. Dawe, Philip H. Austin

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEntrainment (biomusicology)TRACERCloud computingAtmospheric sciencesEnvironmental scienceLiquid water contentMomentum (technical analysis)Cloud heightMeteorologyPhysicsCloud coverNuclear physics

Abstract

fetched live from OpenAlex

Abstract Direct measurements of rates of entrainment into and detrainment from cumulus cloud cores obtained from LES model cloud fields produce values twice as large as those produced from tracer budget calculations. This difference can be explained by two effects: the presence of a shell of air around the cloud cores that is moister than the mean environment and air at the edge of the cloud core that is drier than the mean core, and the tendency for the mean tracer values of the entrained fluid to be greater than the mean tracer values of the cloud shell. Preferential entrainment of shell air that is moving upward faster than the mean shell creates strong vertical momentum fluxes into the cumulus cloud core, thereby making the assumption that cumulus cloud cores entrain fluid with zero vertical momentum incorrect. Variability in the properties of the moist cloud shell has strong impacts on entrainment values inferred from tracer budget calculations. These results indicate that the dynamics of the cloud shell should be included in parameterization of cumulus clouds used in general circulation models.

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)0.002
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 score0.254
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.237
Teacher spread0.178 · 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

Citations56
Published2011
Admission routes1
Has abstractyes

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