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Record W1968135231 · doi:10.1002/qj.49712657016

Accounting for overlap of fractional cloud in infrared radiation

2000· article· en· W1968135231 on OpenAlexaff
J. Li

Bibliographic record

VenueQuarterly Journal of the Royal Meteorological Society · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEmissivityCloud computingWater vaporSkyRadiative transferFlux (metallurgy)Environmental scienceRadiative coolingCloud coverMeteorologyComputational physicsAtmospheric sciencesPhysicsMaterials scienceOpticsComputer science

Abstract

fetched live from OpenAlex

Abstract The cloud‐matrix method for describing the mutual cloud‐coverage relationship between any two levels is systematically discussed. A general method is devised for calculating the effective cloud emissivity for maximum‐random overlap clouds. For several cloud configurations with extreme variation in fractional cloud amounts, the errors are generally very small (<5%). The radiative‐transfer process that corresponds to the random‐overlap cloud scheme is discussed. Compared with the purely random clouds scheme, the maximum‐random overlap scheme always produces a smaller cooling rate in the lower layers of a cloud block and a smaller downward flux. The difference in cooling rate can be about 3 K d −1 and the difference in the downward flux near the surface can be as large as 20 W m −2 . The calculations show that the scheme of effective cloud emissivity commonly used in general‐circulation models could cause underestimation of cloud cooling rate. The clear‐sky and the cloudy‐sky radiation field can be obtained through a single calculation process but with different water‐vapour profiles. The results show that for the all‐sky case the separate treatment of the water‐vapour profile for clear and cloudy portions makes only a very small difference in the cooling rate and upward flux at the top of the atmosphere in comparison with the results of an averaged water‐vapour profile.

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 score0.385
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations17
Published2000
Admission routes1
Has abstractyes

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