Bibliographic record
Abstract
Abstract. Tropospheric ozone, a key atmospheric pollutant and greenhouse gas, exhibits significant spatio-temporal variability on seasonal, inter-annual, and decadal scales, posing a challenge for satellite observation systems. Methods like the Convective Cloud Differential (CCD) and Cloud Slicing Algorithms (CSA) are standard for Tropospheric Column Ozone (TCO) retrievals but are limited to the tropical band (20° S–20° N). This study presents the first successful global application of CCD retrieval outside the tropical region. We introduce the CHORA-CCD (Cloud Height Ozone Reference Algorithm-CCD) for retrieving near-global TCO from TROPOMI. It utilises a local cloud reference sector (CLCD, CHORA Local Cloud Decision) to determine the stratospheric (above cloud) column ozone (ACCO). The ACCO is subtracted from the total column in clear-sky scenes to determine the TCO. The new approach presented here minimises the impact of stratospheric ozone variability, which is generally higher in the extratropics. An iterative approach is used to automatically select an optimal local cloud reference sector around each retrieval grid box, varying the radius from 60 to a maximum of 600 km, for which a mean TCO is determined until a sufficient number of ground pixels with nearly full cloud cover are found. Due to the prevalence of low-level clouds in mid-latitudes, the TCO calculation is constrained to the column from the surface up to the reference altitude of 450 hPa. There are two independent methods used: (I) CLCD-C, which uses an ozone climatology and (II) CLCD-T, an alternative method which estimates the ACCO at 450 hPa by linear regression (Theil-Sen) in cases where the cloud-top-heights in the local cloud sector vary sufficiently. The Theil-Sen approach is a combination of the CCD and CSA methods. The CLCD algorithm dynamically decides between the CLCD-C and CLCD-T to determine ACCO depending on the cloud characteristics. The CLCD algorithm is further refined by introducing a homogeneity criterion for total ozone to overcome inhomogeneities in stratospheric ozone. Monthly averaged CLCD TCOs have been determined over the tropics and mid-latitudes (60° S–60° N) using TROPOMI data from 2018 to 2022. The method’s accuracy was investigated by comparing spatially collocated SHADOZ/WOUDC/NDACC HEGIFTOM ozonesonde measurements from 36 stations. The validation results reveal that CLCD TCO retrievals are in good agreement with ozonesondes at most stations with an overall statistical bias of 0.6 DU and dispersion of 2.5 DU. Across all stations, the maximum bias and dispersion are around ∼5 DU and 4 DU, respectively. The CLCD approach effectively captures tropospheric ozone enhancements across diverse regions, including Northeast China and North America, with particular sensitivity to areas impacted by significant emission sources. Our results demonstrate the advantage of using the modified local cloud reference sector, providing an important basis for subsequent systematic applications in current and future missions, in particular, geostationary satellites with an emphasis on observing higher latitudes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.416 | 0.281 |
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".