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Record W2808071844 · doi:10.5194/amt-11-5657-2018

Level 1b error budget for MIPAS on ENVISAT

2018· article· en· W2808071844 on OpenAlexaff
Anne Kleinert, Manfred Birk, Gaétan Perron, Georg Wagner

Bibliographic record

VenueAtmospheric measurement techniques · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsABB (Canada)
FundersDeutsche ForschungsgemeinschaftKarlsruhe Institute of Technology
KeywordsRadianceAtmospheric soundingRemote sensingRadiometric calibrationEnvironmental scienceMichelson interferometerSatelliteSpectrometerCalibrationInterferometryOpticsPhysicsGeology

Abstract

fetched live from OpenAlex

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 onboard 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 atmospheric parameters to be retrieved 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 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)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.082
GPT teacher head0.272
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations36
Published2018
Admission routes1
Has abstractyes

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