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Record W2019837465 · doi:10.1175/2007jamc1472.1

Time Series of Daily Averaged Cloud Fractions over Landfast First-Year Sea Ice from Multiple Data Sources

2007· article· en· W2019837465 on OpenAlexaffabout
Xin Jin, John Hanesiak, David G. Barber

Bibliographic record

VenueJournal of Applied Meteorology and Climatology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEnvironmental scienceSnowModerate-resolution imaging spectroradiometerSpectroradiometerClimatologyMeteorologyAtmospheric sciencesArcticCeilometerSolar zenith angleReflectivitySatelliteGeologyAerosolGeographyOceanography

Abstract

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Abstract The time series of daily averaged cloud fractions (CFs) collected from different platforms—two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on Terra and Aqua satellites, the National Centers for Environmental Prediction (NCEP) model, a Vaisala 25K laser ceilometer, and ground-based manual observations (manobs)—above the winter camp of the Canadian Arctic Shelf Exchange Study (CASES) field experiment are analyzed in this study. Taking the manobs as standard, the authors conclude that 1) the NCEP products considerably underestimated CFs in spring (e.g., from April to May) and 2) the performance of two MODIS products depends on the variation of solar zenith angle (SZA). Aqua MODIS misrepresents the snow-covered surface as clouds with almost randomly distributed CFs during the dark winter [cos(SZA) < 0], leading to the overestimation of CFs in winter while Terra MODIS has good agreement with manobs. When 0.1 < cos(SZA) < 0.4, both MODIS products regularly misrepresent the snow-covered background as clouds, leading to the significant overestimation of CFs in late winter (February) and early spring (March). When cos(SZA) > 0.4, both MODIS products have good performance in detecting cloud masks over snow backgrounds. If the sky is slightly cloudy, surface-based meteorological observers tend to underestimate cloud amounts when there is a lack of light. Comparing the CFs from Terra and manobs, the authors conclude that this bias can be over 10%. Power spectral analysis and wavelet analysis show three results: 1) High clouds more frequently appear in winter than in spring with periods between 8 and 16 days, indicating their close connection with synoptic events. Current NCEP products can predict this periodicity but have a phase lag. 2) Middle and low clouds are more local and are common in mid- and late spring (April and May) with periods between 2 and 4 days. At the CASES winter and spring field site, the periodicity of high clouds is dominant. 3) The time-scale-dependent correlation coefficients (CCs) between both MODIS products, NCEP and manobs, show that with high frequent CF sampling per day, the CCs are stable when the time scale varies between 1 and 4 days: with Terra MODIS and NCEP, the value is about 0.6; with Aqua MODIS, between 0.4 and 0.5. All CCs get smaller when the time scale increases beyond 8 days: with respect to both MODIS products, the CCs get closer with values between 0.3 and 0.4; with respect to NCEP, the CC dramatically decreases from positive values to negative values, indicating the lack of accuracy in current NCEP cloud schemes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.119
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2007
Admission routes2
Has abstractyes

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