Retrieval and validation of global tropospheric nitrogen dioxide (NO2) vertical profiles obtained via cloud-slicing TROPOMI partial columns
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".