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Record W2139988539 · doi:10.5194/acp-12-6157-2012

Aerosol observations and growth rates downwind of the anvil of a deep tropical thunderstorm

2012· article· en· W2139988539 on OpenAlexaff
D. Waddicor, G. Vaughan, T. W. Choularton, Keith Bower, Hugh Coe, M. W. Gallagher, P. I. Williams, Michael Flynn, A. Volz‐Thomas, H.‐W. Pätz, Peter Isaac, Jörg Hacker, Frances H. Arnold, Hans Schlager, J. A. Whiteway

Bibliographic record

VenueAtmospheric chemistry and physics · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsYork University
FundersNatural Environment Research Council
KeywordsAerosolAtmospheric sciencesCloud condensation nucleiThunderstormEnvironmental scienceParticle (ecology)ClimatologyMeteorologyPhysicsGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. We present a case study of Aitken and accumulation mode aerosol observed downwind of the anvil of a deep tropical thunderstorm. The measurements were made by condensation nuclei counters flown on the Egrett high-altitude aircraft from Darwin during the ACTIVE campaign, in monsoon conditions producing widespread convection over land and ocean. Maximum measured concentrations of aerosol with diameter greater than 10 nm were 25 000 cm−3 (STP). By calculating back-trajectories from the observations, and projecting onto infrared satellite images, the time since the air exited cloud was estimated. In this way a time scale of about 3 hours was derived for the Aitken aerosol concentration to reach its peak. We examine the hypothesis that the growth in aerosol concentrations can be explained by production of sulphuric acid from SO2 followed by particle nucleation and coagulation. Estimates of the sulphuric acid production rate show that the observations are only consistent with this hypothesis if the particles coagulate to sizes >10 nm much more quickly than is suggested by current theory. Alternatively, other condensible gases (possibly organic) drive the growth of aerosol particles in the TTL.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

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