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Record W1988536071 · doi:10.1080/01431160500181754

Broadband solar radiances from visible band measurements: A method based on ScaRaB observations and model simulations

2005· article· en· W1988536071 on OpenAlexafffund
Jianying Feng, H. G. Leighton

Bibliographic record

VenueInternational Journal of Remote Sensing · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRadianceRemote sensingNarrowbandBroadbandZenithEnvironmental scienceSatelliteRadiometerSolar zenith anglePhysicsOpticsGeologyAstronomy

Abstract

fetched live from OpenAlex

Using simultaneous and co-located broadband and narrowband observations of solar radiation from the Scanner for Radiation Budget (ScaRaB) instrument on Meteor-3 during 1994-1995, empirical relationships between ScaRaB visible band and broadband solar radiances are derived for different surface types, cloud amounts, solar zenith angles and satellite viewing angles. Relationships between ScaRaB visible channel and National Ocean and Atmosphere Administration (NOAA) Advance Very High Resolution Radiometer (AVHRR) visible channel radiances are derived from radiation transfer model simulations for different surface types and cloud cover. Combining the narrow-to-broadband (NTB) and narrowband-to-narrowband (NTN) relationships, broadband solar radiance can be derived from narrowband radiances measured by the AVHRR on operational meteorological satellites. The derived NTB conversion coefficients are evaluated against independent data. Radiances are converted to fluxes by the application of angular distribution models. Typical differences between fluxes derived from ScaRaB narrowband and broadband channels are of the order of 1 W m-2 with standard deviations of the order 15 W m-2.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.168
Threshold uncertainty score0.391

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 head0.039
GPT teacher head0.293
Teacher spread0.254 · 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 designSimulation or modeling
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

Citations3
Published2005
Admission routes2
Has abstractyes

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