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Record W4405580470 · doi:10.1175/jas-d-23-0143.1

Mean Cold Pool Size of Quasi-Equilibrium Convection. Part I: Why Do Cold Pools Collide?

2024· article· en· W4405580470 on OpenAlexaff
Hao Fu, Morgan O’Neill

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversity of Toronto
FundersUniversity of Chicago
KeywordsConvectionRADIUSPrecipitationAtmospheric sciencesBuoyancyEnvironmental scienceEvaporationPhysicsRadiative transferMechanicsMeteorology

Abstract

fetched live from OpenAlex

Abstract Precipitation-driven cold pools play an important role in organizing tropical convection. Previous studies of tropical convection in the radiative–convective equilibrium (RCE) setup found that cold pools tend to collide with each other and trigger new convection. It remains unclear why most cold pools do not have enough space to dissipate without collision. We explain it as the smaller mean cold pool radius R eq compared to its maximum potential radius R max . The latter denotes the radius needed for a cold pool’s buoyancy deficit to be dissipated by surface heating. Applying an energy balance constraint leads to an analytical solution for their ratio R max / R eq , which depends on the Bowen ratio, surface precipitation–evaporation ratio, and rain sedimentation efficiency. The theory predicts that in the regime of marine tropical convection where the Bowen ratio is much smaller than one, R eq cannot reach R max , and cold pools must collide frequently. This prediction is supported by large-eddy simulations using varying rain evaporation rates. In Part II, we combine the energy balance constraint with a convective life cycle model to obtain a theory of the mean cold pool radius R eq .

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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