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Record W2069269793 · doi:10.1002/qj.917

Comparison of cloud products within IASI footprints for the assimilation of cloudy radiances

2011· article· en· W2069269793 on OpenAlexaffabout
L. Lavanant, Nadia Fourrié, Antonia Gambacorta, Giuseppe Grieco, Sylvain Heilliette, F. Hilton, Mi‐Jeong Kim, A. P. McNally, H. Nishihata, E. Pavelin, Florence Rabier

Bibliographic record

VenueQuarterly Journal of the Royal Meteorological Society · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Oceanic and Atmospheric Administration
KeywordsRadianceOvercastEnvironmental scienceCloud computingRemote sensingFootprintMeteorologyCloud coverDepth soundingSatelliteData assimilationSkyComputer scienceGeographyCartographyEngineering

Abstract

fetched live from OpenAlex

Abstract This article compares different methods of deriving cloud properties in the footprint of the Infrared Atmospheric Sounding Interferometer (IASI), onboard the European MetOp satellite. Cloud properties produced by ten operational schemes are assessed and an intercomparison of the products for a 12 h global acquisition is presented. Clouds cover a large part of the Earth, contaminating most of the radiance data. The estimation of cloud top height and effective amount within the sounder footprint is an important step towards the direct assimilation of cloud‐affected radiances. This study first examines the capability of all the schemes to detect and characterize the clouds for all complex situations and provides some indications of confidence in the data. Then the dataset is restricted to thick overcast single layers and the comparison shows a significant agreement between all the schemes. The impact of the retrieved cloud properties on the residuals between calculated cloudy radiances and observations is estimated in the long‐wave part of the spectrum. Copyright © 2011 Royal Meteorological Society, Crown in the right of Canada, and British Crown copyright, the Met Office

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.252

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 studies0.0000.001
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 head0.049
GPT teacher head0.274
Teacher spread0.225 · 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

Citations32
Published2011
Admission routes2
Has abstractyes

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