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Record W4392760660 · doi:10.5194/egusphere-egu24-13604

Radiative closure assessment using A-Train satellite data for the EarthCARE mission

2024· preprint· en· W4392760660 on OpenAlexaff
Zhipeng Qu, Jason N. S. Cole, Howard W. Barker, Meriem Kacimi, Shannon Mason, Robin J. Hogan, Ben Courtier

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsEnvironment and Climate Change Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsSatelliteClosure (psychology)Remote sensingRadiative transferEnvironmental scienceMeteorologyComputer scienceAerospace engineeringGeographyEngineeringPolitical sciencePhysicsOptics

Abstract

fetched live from OpenAlex

The EarthCARE mission will perform continuous radiative closure assessment utilizing both 1D and 3D broadband (BB) radiative transfer (RT) models. The radiance and flux calculations from these models will be compared to observations obtained through EarthCARE's Broadband Radiometer (BBR). The inputs for the RT models will be derived from synergistic retrievals of cloud and aerosol properties, facilitated by the Clouds, Aerosol and Precipitation from Multiple Instruments using a Variational Technique (CAPTIVATE) algorithm. In preparation for the EarthCARE launch, this study involves the application of CAPTIVATE to A-Train data, with the resultant cloud, aerosol, and precipitation properties serving as inputs for the RT models. The outcomes of these models will be utilized in a radiative closure assessment, incorporating measurements from the Clouds and the Earth's Radiant Energy System (CERES). The analyses center on discerning differences between 1D and 3D RT calculations, as well as differences between RT calculations and measurements obtained from the CERES.

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)0.004
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.356
Teacher spread0.256 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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