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Record W4253191075 · doi:10.5194/amt-2018-185

Level 1b error budget for MIPAS on ENVISAT

2018· preprint· en· W4253191075 on OpenAlexaff
Anne Kleinert, Manfred Birk, Gaétan Perron, Georg Wagner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsABB (Canada)
FundersDeutsche Forschungsgemeinschaft
KeywordsRadianceRemote sensingAtmospheric soundingMichelson interferometerSatelliteEnvironmental scienceRadiometric calibrationSpectrometerDepth soundingLine-of-sightCalibrationInterferometryOpticsPhysicsGeologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is a Fourier Transform Spectrometer measuring the radiance emitted from the atmosphere in limb geometry in the thermal infrared spectral region. It was operated on-board the ENVISAT satellite from 2002 to 2012. Calibrated and geolocated spectra, the so-called level 1b data, are the basis for the retrieval of atmospheric parameters. In this paper we present the error budget for the level 1b data of the most recent data version 8 in terms of radiometric, spectral and line of sight accuracy. The major changes of version 8 compared to older versions are also described. The impact of the different error sources on the spectra is characterized in terms of spectral, vertical and temporal correlation, because these correlations have an impact on the quality of the retrieved quantities. The radiometric error is in the order of 1 to 2.4 %, the spectral accuracy is better than 0.3 ppm, and the line of sight accuracy at the tangent point is around 400 m. All errors are well within the requirements and the achieved accuracy allows to retrieve atmospheric parameters from the measurements with high quality.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.814

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 head0.132
GPT teacher head0.305
Teacher spread0.172 · 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 designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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