Challenges in retrieving stratospheric aerosol extinction and particle size from ground-based RMR-LIDAR observations
Bibliographic record
Abstract
Abstract. We report on the retrieval of stratospheric aerosol particle size and extinction coefficient profiles from multi-color backscatter measurements with the Rayleigh-Mie-Raman lidar operated at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in northern Norway. The retrievals are based on a two-step approach. In a first step the median radius of an assumed log-normal particle size distribution with fixed width is retrieved based on the color ratio formed from the measured backscatter ratios at wavelenghts of 1064 nm and 532 nm. An intrinsic ambiguity of the retrieved aerosol size information is discussed. In a second step, this particle size information is used to convert the measured lidar backscatter ratio to aerosol extinction coefficients. The retrieval is currently based on monthly-averaged lidar measurements covering the period from the year 2000 to present. A sensitivity study is presented that allows establishing an error budged for the aerosol retrievals. Assuming a log-normal aerosol particle size distribution with a geometric width of S = 1.3, median radii on the order of 100 nm are retrieved. The median radii are found to generally decrease with increasing altitude. The retrieved aerosol extinction profiles are compared to observations with various current and past satellite instruments.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".