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Record W2147946819 · doi:10.5194/amt-3-523-2010

Water vapour profiles from SCIAMACHY solar occultation measurements derived with an onion peeling approach

2010· article· en· W2147946819 on OpenAlexfundno aff
Stefan Noël, K. Bramstedt, Alexei Rozanov, H. Bovensmann, John P. Burrows

Bibliographic record

VenueAtmospheric measurement techniques · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyUniversität Bremen
KeywordsSCIAMACHYOccultationWater vaporEnvironmental scienceSpectrometerDifferential optical absorption spectroscopyAltitude (triangle)Atmospheric sciencesRemote sensingAbsorption (acoustics)MeteorologyPhysicsOpticsGeologyMathematics

Abstract

fetched live from OpenAlex

Abstract. A new retrieval method has been developed to derive water vapour number density profiles from solar occultation measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). This method is intentionally kept simple and based on a combination of an onion peeling approach with a modified DOAS (Differential Optical Absorption Spectroscopy) fit in the wavelength region around 940 nm. Reasonable resulting water vapour profiles are currently obtained in the altitude range 15–45 km. Comparisons of the SCIAMACHY profiles with water vapour data provided by the Atmospheric Chemistry Explorer Fourier Transform Spectrometer (ACE-FTS) show an average agreement within about 5% between 20 and 45 km. SCIAMACHY water vapour data tend to be systematically higher than ACE-FTS. These results are in principal confirmed by comparisons with water vapour profiles derived from model data of the European Centre for Medium Range Weather Forecasts (ECMWF), although ECMWF concentrations are systematicly lower than both corresponding SCIAMACHY and ACE-FTS data at all altitudes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.225
Teacher spread0.192 · 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 designBench or experimental
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

Citations28
Published2010
Admission routes1
Has abstractyes

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