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Record W3132151773 · doi:10.5194/amt-14-1239-2021

Characterization and potential for reducing optical resonances in Fourier transform infrared spectrometers of the Network for the Detection of Atmospheric Composition Change (NDACC)

2021· article· en· W3132151773 on OpenAlexafffund
Thomas Blumenstock, Frank Hase, A. Keens, Denis Czurlok, Orfeo Colebatch, Omaira García, David Griffith, Michel Grutter, James W. Hannigan, Pauli Heikkinen, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Erik Lutsch, Maria Makarova, Hamud Kh. Imhasin, J. Mellqvist, Isamu Morino, Tomoo Nagahama, Justus Notholt, Iván Ortega, Mathias Palm, Uwe Raffalski, Markus Rettinger, John Robinson, Matthias Schneider, Christian Servais, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco

Bibliographic record

VenueAtmospheric measurement techniques · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencySorbonne UniversitéEnvironment and Climate Change CanadaBundesministerium für Wirtschaft und EnergieCentre National de la Recherche ScientifiqueUniversity of TorontoMinistry of Business, Innovation and EmploymentSaint Petersburg State UniversityCentre National d’Etudes SpatialesFonds De La Recherche Scientifique - FNRSBundesministerium für Bildung und ForschungAntarctica New ZealandFédération Wallonie-BruxellesNational Aeronautics and Space AdministrationEurostarsRussian Foundation for Basic ResearchNational Center for Atmospheric ResearchMinisterio de Economía y CompetitividadConsejo Nacional de Ciencia y TecnologíaNational Science Foundation
KeywordsSpectrometerDetectorInfraredFourier transform infrared spectroscopyFourier transform spectroscopyFourier transformOpticsBeam splitterIndium antimonidePhysicsAnalytical Chemistry (journal)ChemistryOptoelectronicsLaser

Abstract

fetched live from OpenAlex

Abstract. Although optical components in Fourier transform infrared (FTIR) spectrometers are preferably wedged, in practice, infrared spectra typically suffer from the effects of optical resonances (“channeling”) affecting the retrieval of weakly absorbing gases. This study investigates the level of channeling of each FTIR spectrometer within the Network for the Detection of Atmospheric Composition Change (NDACC). Dedicated spectra were recorded by more than 20 NDACC FTIR spectrometers using a laboratory mid-infrared source and two detectors. In the indium antimonide (InSb) detector domain (1900–5000 cm−1), we found that the amplitude of the most pronounced channeling frequency amounts to 0.1 ‰ to 2.0 ‰ of the spectral background level, with a mean of (0.68±0.48) ‰ and a median of 0.60 ‰. In the mercury cadmium telluride (HgCdTe) detector domain (700–1300 cm−1), we find even stronger effects, with the largest amplitude ranging from 0.3 ‰ to 21 ‰ with a mean of (2.45±4.50) ‰ and a median of 1.2 ‰. For both detectors, the leading channeling frequencies are 0.9 and 0.11 or 0.23 cm−1 in most spectrometers. The observed spectral frequencies of 0.11 and 0.23 cm−1 correspond to the optical thickness of the beam splitter substrate. The 0.9 cm−1 channeling is caused by the air gap in between the beam splitter and compensator plate. Since the air gap is a significant source of channeling and the corresponding amplitude differs strongly between spectrometers, we propose new beam splitters with the wedge of the air gap increased to at least 0.8∘. We tested the insertion of spacers in a beam splitter's air gap to demonstrate that increasing the wedge of the air gap decreases the 0.9 cm−1 channeling amplitude significantly. A wedge of the air gap of 0.8∘ reduces the channeling amplitude by about 50 %, while a wedge of about 2∘ removes the 0.9 cm−1 channeling completely. This study shows the potential for reducing channeling in the FTIR spectrometers operated by the NDACC, thereby increasing the quality of recorded spectra across the network.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.486
Threshold uncertainty score0.474

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 communication0.0000.000
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 head0.015
GPT teacher head0.207
Teacher spread0.193 · 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 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

Citations39
Published2021
Admission routes2
Has abstractyes

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