MétaCan
Menu
← Back to cohort
Record W3008351357 · doi:10.5194/amt-2020-30

TROPOMI/S5P formaldehyde validation using an extensive network of ground-based FTIR stations

2020· article· en· W3008351357 on OpenAlexaff
Corinne Vigouroux, Bavo Langerock, Carlos Augusto Bauer Aquino, Thomas Blumenstock, Martine De Mazière, Isabelle De Smedt, Michel Grutter, James W. Hannigan, Nicholas Jones, Rigel Kivi, Erik Lutsch, Emmanuel Mahieu, Maria Makarova, Jean‐Marc Metzger, Isamu Morino, Isao Murata, Tomoo Nagahama, Justus Notholt, Iván Ortega, Mathias Palm, Gaïa Pinardi, Amelie N. Röhling, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Michel Van Roozendaël, Pucai Wang, Holger Winkler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTroposphereEnvironmental scienceFormaldehydeSatelliteRemote sensingAtmospheric sciencesMeteorologyChemistryGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract. TROPOMI (the TROPOspheric Monitoring Instrument), on-board the Sentinel-5 Precursor satellite, has been monitoring the Earth's atmosphere since October 2017, with an unprecedented horizontal resolution (initially 7×3.5 km2, upgraded to 5.5×3.5 km2 since August 2019). Monitoring air quality is one of the main objectives of TROPOMI, with the measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products (version 1.1.[5-7]), using ground-based solar-absorption FTIR (Fourier Transform Infrared) measurements of HCHO from twenty-five stations around the world, including high, mid, and low latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites. In this validation work, we first assess the accuracy of TROPOMI HCHO tropospheric columns, using the median of the relative differences between TROPOMI and FTIR ground-based data (BIAS). We observe that, at all sites, the TROPOMI accuracy is below the upper limit of the pre-launch requirements of 80 %, and below the lower limit of 40 % for 20 of the 25 stations. The provided TROPOMI systematic uncertainties are well in agreement with the observed biases at most of the stations, except for the highest HCHO levels site where it is found to be underestimated. We find that, while the BIAS has no latitudinal dependence, it is dependent on the HCHO concentration levels: an overestimation (+26 ± 5 %) of TROPOMI is observed for very small HCHO levels ( 8.0 × 1015 molec/cm2). This demonstrates the great value of such a harmonized network covering a wide range of concentration levels, the sites with high HCHO concentrations being crucial for the determination of the satellite bias at the regions of emissions, and the clean sites allowing a small TROPOMI offset to be determined. The wide range of sampled HCHO levels within the network allows the robust determination of the significant constant and proportional TROPOMI HCHO biases (TROPOMI=+ 1.10 (± 0.05) × 1015+ 0.64 (± 0.03) × FTIR, in molec/cm2). Second, the precision of TROPOMI HCHO data is estimated by the median absolute deviation (MAD) of the relative differences between TROPOMI and FTIR ground-based data. The clean sites are especially useful to minimize a possible additional collocation error. The precision requirement of 1.2 × 1016 molec/cm2 for a single pixel is reached at most of the clean sites, where it is found that the TROPOMI precision can even be twice better (0.5–0.8 × 1015 molec/cm2 for a single pixel). However, we find that the provided TROPOMI random uncertainties may be underestimated by a factor of 1.6 (for clean sites) to 2.3 (for high HCHO levels). The correlation is very good between TROPOMI and FTIR data (R = 0.88 for 3 hours-mean coincidences; R = 0.91 for monthly means coincidences). Using about 17 months of data (from May 2018 to September 2019), we show that the TROPOMI seasonal variability is in very good agreement at all of the FTIR sites. The FTIR network demonstrates the very good quality of the TROPOMI HCHO products which is well within the pre-launch requirements for both accuracy and precision. This paper advises for a refinement of the TROPOMI random uncertainty budget and of the TROPOMI quality assurance values for a better filtering of the remaining outliers.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAtmospheric and Environmental Gas Dynamics→French-language works237,207→