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Record W2118725015 · doi:10.5194/amt-4-547-2011

SCIAMACHY stratospheric aerosol extinction profile retrieval using the OMPS/LP algorithm

2011· article· en· W2118725015 on OpenAlexaff
Ghassan Taha, Didier Rault, Robert Loughman, Adam Bourassa, Christian von Savigny

Bibliographic record

VenueAtmospheric measurement techniques · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
FundersUniversität BremenLangley Research CenterNational Aeronautics and Space Administration
KeywordsSCIAMACHYAerosolOccultationEnvironmental scienceRemote sensingExtinction (optical mineralogy)Standard deviationMeteorologyAtmospheric sciencesAlgorithmMathematicsGeologyTropospherePhysicsOpticsStatistics

Abstract

fetched live from OpenAlex

Abstract. The Ozone Mapper and Profiler Suite, Limp Profiler (OMPS/LP) algorithm is used to retrieve ozone concentration and aerosol extinction profiles using a series of 120 SCIAMACHY limb measurements collocated with SAGE II solar occultation events. The primary goal of the study is to ascertain the capability of the OMPS/LP retrieval algorithm to accurately retrieve the vertical distribution of stratospheric aerosol extinction coefficient so as to better account for aerosol effects in the ozone profiling retrieval process. Using simulated radiances, we show that the aerosol extinction coefficient can be retrieved from limb scatter measurements within 5% and a standard deviation better than 15%, which is more than sufficient to improve the OMPS/LP ozone products to be used as Environmental Data Records. We also illustrate the ability of SCIAMACHY limb measurements to retrieve stratospheric aerosol extinction profiles with accuracy comparable to other instruments. The retrieved aerosol extinction profiles agree with collocated SAGE II measurements on average to within 25%, with a standard deviation of 35%.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.058
GPT teacher head0.237
Teacher spread0.180 · 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.

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

Citations49
Published2011
Admission routes1
Has abstractyes

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