AerGOM, an improved algorithm for stratospheric aerosol extinction retrieval from GOMOS observations – Part 2: Intercomparisons
Bibliographic record
Abstract
Abstract. AerGOM is a retrieval algorithm developed for the GOMOS instrument onboard Envisat as an alternative to the operational retrieval (IPF). AerGOM enhances the quality of the stratospheric aerosol extinction retrieval due to the extension of the spectral range used, refines the aerosol spectral parameterization, the simultaneous inversion of all atmospheric species as well as an improvement of the Rayleigh scattering correction. The retrieval algorithm allows for a good characterization of the stratospheric aerosol extinction for a wide range of wavelengths.In this work, we present the results of stratospheric aerosol extinction comparisons between AerGOM and various spaceborne instruments (SAGE II, SAGE III, POAM III, ACE-MAESTRO and OSIRIS) for different wavelengths. The aerosol extinction intercomparisons for λ < 700 nm and above 20 km show agreements with SAGE II version 7 and SAGE III version 4.0 within ±15 % and ±45 %, respectively. There is a strong positive bias below 20 km at λ < 700 nm, which suggests that cirrus clouds at these altitudes have a large impact on the extinction values. Comparisons performed with GOMOS IPF v6.01 alongside AerGOM show that at short wavelengths and altitudes below 20 km, IPF retrievals are more accurate when evaluated against SAGE II and SAGE III but are much less precise than AerGOM. A modified aerosol spectral parameterization can improve AerGOM in this spectral and altitude range and leads to results that have an accuracy similar to IPF retrievals. Comparisons of AerGOM aerosol extinction coefficients with OSIRIS and SAGE III measurements at wavelengths larger than 700 nm show a very large negative bias at altitudes above 25 km. Therefore, the use of AerGOM aerosol extinction data is not recommended for λ > 700 nm.Due to the unique observational technique of GOMOS, some of the results appear to be dependent on the star occultation parameters such as star apparent temperature and magnitude, solar zenith angle and latitude of observation. A systematic analysis is carried out to identify biases in the dataset, using the various spaceborne instruments as references. The quality of the aerosol retrieval is mainly influenced by the star magnitude, as well as star temperature to a lesser degree. To ensure good-quality profiles, we suggest to select occultations performed with star magnitude M < 2.5 and star temperature T > 6 × 103 K. Stray-light contamination is negligible for extinction coefficients below 700 nm using occultations performed with a solar zenith angle > 110° but becomes important at larger wavelengths. Comparison of AerGOM results in the tropics shows an enhanced bias below 20 km that seem to confirm cirrus clouds as its cause. There are also differences between mid-latitude and tropical observations that cannot yet be explained, with a bias difference of up to 25 %.This bias characterization is extremely important for data users and might prove valuable for the production of unbiased long-term merged dataset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".