Verification and application of the extended spectral deconvolution algorithm (SDA+) methodology to estimate aerosol fine and coarse mode extinction coefficients in the marine boundary layer
Bibliographic record
Abstract
Abstract. The spectral deconvolution algorithm (SDA) and SDA+ (extended SDA) methodologies can be employed to separate the fine and coarse mode extinction coefficients from measured total aerosol extinction coefficients, but their common use is currently limited to AERONET (AErosol RObotic NETwork) aerosol optical depth (AOD). Here we provide the verification of the SDA+ methodology on a non-AERONET aerosol product, by applying it to fine and coarse mode nephelometer and particle soot absorption photometer (PSAP) data sets collected in the marine boundary layer. Using data sets collected on research vessels by NOAA-PMEL(National Oceanic and Atmospheric Administration – Pacific Marine Environmental Laboratory), we demonstrate that with accurate input, SDA+ is able to predict the fine and coarse mode scattering and extinction coefficient partition in global data sets representing a range of aerosol regimes. However, in low-extinction regimes commonly found in the clean marine boundary layer, SDA+ output accuracy is sensitive to instrumental calibration errors. This work was extended to the calculation of coarse and fine mode scattering coefficients with similar success. This effort not only verifies the application of the SDA+ method to in situ data, but by inference verifies the method as a whole for a host of applications, including AERONET. Study results open the door to much more extensive use of nephelometers and PSAPs, with the ability to calculate fine and coarse mode scattering and extinction coefficients in field campaigns that do not have the resources to explicitly measure these values.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".