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Record W2121517225 · doi:10.2478/v10085-009-0028-1

How surface latent heat flux is related to lower-tropospheric stability in southern subtropica marine stratus and stratocumulus regions

2009· article· en· W2121517225 on OpenAlexaff
Yanping He

Bibliographic record

VenueOpen Geosciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Victoria
FundersUniversity of WashingtonNational Science Foundation
KeywordsLatent heatSensible heatAtmospheric sciencesFlux (metallurgy)Environmental scienceHumidityMarine stratocumulusEvaporationHeat fluxRadiative fluxShortwave radiationTroposphereWind speedClimatologyMeteorologyRadiationHeat transferChemistryAerosolThermodynamicsGeologyGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract The relationship between surface latent heat flux and the lower-tropospheric stability (LTS) is examined using ERA-40 reanalysis, NCEP reanalysis and COADS (Comprehensive Ocean-Atmosphere Data Set) ship data in two southern subtropical marine stratus and stratocumulus regions. The change of surface latent heat flux with LTS is determined by a comparison of the correlation of LTS with surface wind speed and with near surface humidity difference. At intermediate LTS (10 K-15 K), both surface evaporation and downward surface radiation flux amplify small LTS perturbations due to the surface wind-LTS relationship and cloud-radiation feedback. At high LTS, surface latent heat flux exceeds its peak value and becomes a regulating mechanism to keep LTS at its commonly observed equilibrium value. Surface radiation flux is seen to decrease at a smaller rate with LTS than surface latent heat flux. By applying the regulating effect of LTS on near surface humidity differences, monthly surface latent heat flux can be better represented.

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 score0.053
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.024
GPT teacher head0.252
Teacher spread0.228 · 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
Published2009
Admission routes1
Has abstractyes

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