Stratospheric aerosol characteristics from space-borne observations: extinction coefficient and Ångström exponent
Bibliographic record
Abstract
Abstract. Stratospheric aerosols are of a great importance to the scientific community, predominantly because of their role in climate, but also because accurate knowledge of aerosol characteristics is relevant for trace gases retrievals from remote sensing instruments. There are several data sets published which provide aerosol extinction coefficients in the stratosphere. However, for the instruments measuring in the limb viewing geometry, the use of this parameter is associated with uncertainties resulting from the need to assume an aerosol particle size distribution (PSD) within the retrieval process. These uncertainties can be mitigated if PSD information is retrieved. While occultation instruments provide more accurate information on the aerosol extinction coefficient, in this study, it was shown that limb instruments have better potential for the PSD retrieval, especially during the background aerosol loading periods. A data set containing PSD information was recently retrieved from SCIAMACHY limb measurements and provides two parameters of the log-normal PSD for the SCIAMACHY operational period (2002–2012). In this study, the data set is expanded by aerosol extinction coefficients and Ångström exponents calculated from the retrieved PSD parameters. Errors in the Ångström exponents and aerosol extinction coefficients are assessed using synthetic retrievals. For the extinction coefficient the resulting accuracy is within ±25 %, and for the Ångström exponent, it is better than 10 %. The recalculated SCIAMACHY aerosol extinction coefficients are compared to those from SAGE II. The differences between the instruments vary from 0 to 25 % depending on the wavelength. Ångström exponent comparison with SAGE II shows differences between 10 % at 31 km and 40 % at 18 km. Comparisons with SAGE II, however, suffer from the low amount of collocated profiles. Furthermore, the Ångström exponents obtained from the limb viewing instrument OSIRIS are used for the comparison. This comparison shows an average difference within 7 %. The time series of these differences do not show signatures of any remarkable events. Besides, the temporal behavior of the Ångström exponent in the tropics is analyzed using the SCIAMACHY data set. It is shown, that there is no simple relation between the Ångström exponent and the PSD because the same value of Ångström exponent can be obtained from an infinite number of combinations of the PSD parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".