MétaCan
Menu
Back to cohort
Record W4416188349 · doi:10.5194/amt-19-2407-2026

TROPOMI/WFMD v2.0: Improved retrievals of XCH <sub>4</sub> and XCO with XGBoost-based quality filtering

2025· article· en· W4416188349 on OpenAlexaff
Oliver Schneising, H. Bovensmann, Michael Buchwitz, Matthias Buschmann, Nicholas M. Deutscher, David Griffith, Jonas Hachmeister, Frank Hase, Laura T. Iraci, Rigel Kivi, Isamu Morino, Hirofumi Ohyama, Christof Petri, Maximilian Reuter, John Robinson, Coleen M. Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Wei Wang, Thorsten Warneke, Damien Weidmann, Debra Wunch, Minqiang Zhou, Hartmut Boesch

Bibliographic record

VenueAtmospheric measurement techniques · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersBundesministerium für Bildung und ForschungEuropean Space Agency
KeywordsDifferential optical absorption spectroscopyRadianceAerosolHyperspectral imagingSatelliteRadiometerTroposphereShortwaveData quality

Abstract

fetched live from OpenAlex

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite provides daily global observations of atmospheric methane (CH4) and carbon monoxide (CO) at relatively high spatial resolution. The dense spatial and temporal coverage is achieved by the instrument’s wide swath, which permits detailed mapping of the worldwide distribution of these important atmospheric constituents. The adaptation and optimisation of the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) algorithm for the simultaneous retrieval of the column-averaged dry-air mole fractions XCH4 and XCO from TROPOMI’s shortwave infrared (SWIR) radiance measurements has proven to be a valuable complement and alternative to the operational TROPOMI products. The latest release of the TROPOMI/WFMD product (version 2.0) includes several improvements expanding its suitability for a wider range of scientific applications. Data yield at mid and high latitudes has increased, accompanied by improved accuracy and precision according to the validation with the ground-based Total Carbon Column Observing Network (TCCON). These advancements are primarily due to more refined quality filtering that has been accomplished by replacing the previous Random Forest Classifier with the more efficient and potentially higher performing Extreme Gradient Boosting (XGBoost) algorithm in conjunction with improved training data incorporating an updated cloud product from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the TROPOMI Aerosol Index. This enhanced training data set enables more reliable identification of cloudy scenes and mitigates issues related to specific aerosol events over bright surfaces. Importantly, as with previous product versions, the actual quality classification does not depend on the real-time availability of these external data products, which are only required during the training phase.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.223
Teacher spread0.212 · 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
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAtmospheric measurement techniquesSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207