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Record W2907182050

Upper stratospheric O 3 recovery as observed by IASI over 10 years of measurements

2018· article· en· W2907182050 on OpenAlexaboutno aff
Catherine Wespes, Daniel Hurtmans, Gaétane Ronsmans, Cathy Clerbaux, Pierre‐François Coheur

Bibliographic record

VenueEGUGA · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsStratosphereEnvironmental scienceOzone layerAtmospheric sciencesClimatologyLatitudeMeteorologyTotal Ozone Mapping SpectrometerGeologyGeographyGeodesy
DOInot available

Abstract

fetched live from OpenAlex

In this study, we assess how daily ozone (O3) measurements from the first ten years of the Infrared Atmospheric Sounding Interferometer (IASI/MetOp-A) operation can contribute to the determination of the processes driving O3 variability and to the monitoring of long-term trends in the stratosphere. To that end, we rely on the IASI ozone profiles retrieved with the Fast Optimal Retrievals on Layers for IASI (FORLI) software set up at ULB for near real time and large scale processing of IASI data. We present the global fingerprints of recent changes in the stratospheric O3 measured over January 2008 - December 2017. Using a dedicated regression model applied on gridded daily mean O3 time series, we discriminate anthropogenic trends from various modes of natural variability which are accounted for in the regression model by a series of geophysical parameters. The results show that the effectiveness of the Montreal Protocole is measureable by IASI in the upper stratosphere at a global scale and, more particularly, at mid-high latitudes of both hemispheres where the trend reaches +1.5 DU/decade. Nevertheless, we calculate that 5 years of additional measurements are required to detect an unequivocal upper stratospheric O3 recovery of |1.5| DU/decade at a global scale.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0500.002

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 head0.032
GPT teacher head0.231
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEGUGASame topicAtmospheric Ozone and ClimateFrench-language works237,207