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Record W2787303367 · doi:10.5194/amt-11-5049-2018

NDACC harmonized formaldehyde time series from 21 FTIR stations covering a wide range of column abundances

2018· article· en· W2787303367 on OpenAlexafffund
Corinne Vigouroux, Carlos Augusto Bauer Aquino, Maïté Bauwens, Cornelis Becker, Thomas Blumenstock, Martine De Mazière, Omaira García, Michel Grutter, César A. Guarín, James W. Hannigan, Frank Hase, Nicholas Jones, Rigel Kivi, Dmitry Koshelev, Bavo Langerock, Erik Lutsch, Maria Makarova, Jean‐Marc Metzger, Jean‐François Müller, Justus Notholt, Iván Ortega, Mathias Palm, Clare Paton‐Walsh, А. В. Поберовский, Markus Rettinger, John Robinson, Dan Smale, Trissevgeni Stavrakou, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Geoffrey C. Toon

Bibliographic record

VenueAtmospheric measurement techniques · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersOffice of Polar ProgramsNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaEurostarsUniversity of TorontoCanadian Foundation for Climate and Atmospheric SciencesBelgian Federal Science Policy OfficeNational Oceanic and Atmospheric AdministrationNova Scotia Research Innovation TrustNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsFormaldehydeFourier transform infrared spectroscopyEnvironmental scienceSatelliteConsistency (knowledge bases)Column (typography)Remote sensingRange (aeronautics)MeteorologyChemistryAnalytical Chemistry (journal)Computer scienceMaterials scienceGeographyPhysicsEnvironmental chemistryOpticsArtificial intelligenceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract. Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12 % and 27 % and between 1 and 11×1014 molec cm−2, respectively. The median values among all stations are 13 % and 2.9×1014 molec cm−2 for the total systematic and random uncertainties.This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013 molec cm−2) to highly polluted levels (7×1016 molec cm−2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.204
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations87
Published2018
Admission routes2
Has abstractyes

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