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Record W4321488120 · doi:10.5194/egusphere-egu23-5358

Retrieval and validation of global tropospheric nitrogen dioxide (NO2) vertical profiles obtained via cloud-slicing TROPOMI partial columns

2023· preprint· en· W4321488120 on OpenAlexaboutno aff
Rebekah P. Horner, Eloïse A. Marais, Nana Wei

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsTroposphereAtmospheric sciencesEnvironmental scienceNOxAerosolMixing ratioChemical transport modelMeteorologyClimatologyChemistryGeologyGeography

Abstract

fetched live from OpenAlex

Observations of the vertical distribution of nitrogen oxides (NOx ≡ NO + NO2) in the troposphere are severely limited, despite its influence on ozone formation. Here, we derive vertical profiles of the NOx component NO2 by applying cloud-slicing to partial columns of NO2 from the space-based TROPOMI instrument. This yields seasonal means of NO2 volume mixing ratios at ~100 km resolution for multiple years (March 2018 to February 2022) on a global scale in the upper troposphere (180-320 hPa and 320-450 hPa), the middle troposphere (450-600 hPa and 600-800 hPa) and the boundary layer (800 hPa to the Earth’s surface). We evaluate our product against in situ NO2 measurements from NASA DC-8 aircraft campaigns over Canada (ARCTAS, ATom, INTEX-A), the Eastern US (ATom, SEAC4RS, INTEX-A), the North and South Atlantic (ATom), and the Central and South Pacific (ATom) and use our validated dataset to assess state-of-knowledge of global tropospheric NOx as simulated by GEOS-Chem. In the middle troposphere, cloud-sliced NO2 has a mean value of 20-40 pptv and deviates by < 5 pptv where NO2 from aircraft observations exceeds the instrument detection limit. The consistency between cloud-slicing results and aircraft observations here is due to high sampling frequency and ideal conditions for cloud-slicing. Differences with aircraft observations are larger (up to 120 pptv) in the upper troposphere between 320-180 hPa where aircraft observations may be susceptible to biases and where cloud-sliced NO2 data are relatively sparse. In the boundary layer, retrievals consistent with the aircraft observations are only possible over marine environments where NO2 concentrations differ by < 35 pptv compared to > 450 pptv over terrestrial regions. This is because large land-based NOx sources cause steep vertical NO2 gradients that are problematic for cloud-slicing which assumes NO2 is well mixed throughout the troposphere. We find that NO2 concentrations above the Eastern US differ by < 20 pptv when comparing cloud-sliced tropospheric vertical profiles to simulated vertical profiles from the GEOS-Chem chemical transport model. However, GEOS-Chem consistently underestimates concentrations of NO2 in the remote troposphere, simulating concentrations that are 50% less than the mean cloud-sliced NO2 observations. This is a result of the limited number of current NO2 observations used to validate models like GEOS-Chem which are limited in both time and space. By deriving tropospheric vertical profiles from cloud-slicing satellite observations there is an opportunity to obtain routine NO2 observations which can then be compared to aircraft measurements and simulations from the GEOS-Chem model. From this, we can determine the environmental factors that impact tropospheric NOx on a global scale and address long-standing uncertainties in our understanding of NOx in the troposphere.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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