MétaCan
Menu
Back to cohort
Record W2156349939 · doi:10.5589/m03-058

Relative merits of the 1.6 and 3.75 μm channels of the AVHRR/3 for cloud detection

2004· article· en· W2156349939 on OpenAlexvenueno aff
Andrew K. Heidinger, R. Frey, Michael J. Pavolonis

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNational Aeronautics and Space Administration
KeywordsAdvanced very-high-resolution radiometerRemote sensingCloud computingEnvironmental scienceModerate-resolution imaging spectroradiometerMeteorologyCloud topChannel (broadcasting)RadiometryCirrusCloud coverSatelliteComputer scienceGeographyTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

AbstractA study was performed to investigate the potential impacts of the cloud-masking capability of the advanced very high resolution radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites because of the addition of the 1.6 µm channel (ch3a) and the removal of the 3.75 µm channel (ch3b) during daylight operation. Both channels are measured by the AVHRR, but only one is available in the data stream. Because the AVHRR presents the longest time series of global imager data, this change could impact a critical source of climate data. Specifically, changes in its cloud-masking capability may introduce a discontinuity in its derived clear-sky data records. To study the relative cloud-detection capabilities of the AVHRR with ch3a or ch3b, data from the moderate resolution imaging spectroradiometer (MODIS) on the National Aeronautics and Space Administration (NASA) TERRA mission was used because it offers ch3a, ch3b, and the other AVHRR channels simultaneously. The MODIS data analysis indicated that ch3b offered more capability in separating cloud from snow. Cloud-masking results with ch3a and ch3b were comparable with respect to the separation of cloud from aerosol and the detection of cloud over a desert scene. Although these results are preliminary and based on a limited analysis, they do indicate that the switch from ch3b to ch3a may have significant impacts on the cloud-detection capability of the AVHRR.Une étude a été exécutée pour examiner les impacts potentiels du nuage masquer capacité du AVHRR à bord le NOAA les satellites orbitant polaires grâce à l'addition du 1,6 µm la chaîne (ch3a) et l'enlèvement du 3,75 µm la chaîne (ch3b) pendant l'opération de lumière. Les deux chaînes sont mesurées par le AVHRR mais seulement celui de les sont disponibles dans le données ruisseau. Parce que le AVHRR présente le feuilleton de temps le plus long de données de imager globales, ce changement pourrait influer une source critique de données de climat. En particulier, les changements dans son nuage masquer capacité peuvent introduire une discontinuité dans ses données disques de clair ciel dérivés. Pour étudier les capacités relatives de détection de nuage du AVHRR avec ch3a ou ch3b, les données de la MODIS TERRA ont été utilisées parce qu'il offre ch3a, ch3b et les autres chaînes de AVHRR simultanément. L'analyse de données de MODIS a indiqué que ch3b a offert plus de capacité dans séparer de nuage de la neige. Le nuage masque résulte avec ch3a et ch3b a exécuté comparablement dans sépare le nuage de l'aérosol et dans détecte s'obscurcit un déserte la scène. Pendant que ces résultats sont préliminaires et basés sur une analyse limitée, ils indiquent que le commutateur de ch3b à ch3a peut avoir des impacts significatifs sur la capacité de détection de nuage du AVHRR.[Traduit par la Rédaction]

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.997

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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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 designOther design
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

Citations19
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Remote SensingSame topicAtmospheric aerosols and cloudsFrench-language works237,207