A Multiwavelength Retrieval Approach for Improved OSIRIS Aerosol Extinction Retrievals
Bibliographic record
Abstract
Abstract The Optical Spectrograph and InfraRed Imaging System (OSIRIS) on board the Odin satellite has been used to provide vertically resolved aerosol extinction since 2001. The OSIRIS version 5.07 aerosol product has been used in numerous studies and now provides a 17‐year record of global stratospheric aerosol. This work presents the new version 7 OSIRIS aerosol extinction retrieval. A multiwavelength aerosol extinction algorithm has been developed to reduce measurement geometry biases and improve extinction retrieval in the upper troposphere and lower stratosphere. The Chen et al. (2016, https://doi.org/10.5194/amt-9-1239-2016 ) cloud detection algorithm has been adapted for the OSIRIS wavelength range for improved cloud screening and polar stratospheric cloud detection, and comparisons after volcanic eruptions and with the CALIPSO‐GOCCP product show promising results. The version 7 product shows comparable agreement with version 5.07 when compared to coincident SAGE II and SAGE III measurements, and improved agreement with CALIPSO time series. The algorithm has been applied to the complete set of OSIRIS measurements, and the new data set is now publicly available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".