Effect of Spectral Variability of Aerosol Optical Properties on Direct Aerosol Radiative Effect
Bibliographic record
Abstract
Aerosol optical properties depend on wavelength as well as both mixing ratios and size distributions of components that make up a particular type of aerosol. This study examines impacts on direct aerosol radiative effect (DARE) for desert, clean maritime, and polluted maritime aerosol types over the ocean when their optical properties are determined by various combinations of observations made by active (i.e., lidar) and passive (e.g., shortwave spectrometer) satellite sensors. Spectral optical properties are perturbed by altering mixing ratios of components that define aerosol types with assumptions that components within an aerosol type are fixed and only one aerosol type is present in the atmosphere. When 532 nm depolarization ratio from the lidar is used to identify desert aerosol, the uncertainty in the mean DARE due to spectral optical property variabilities is 10%. When the 532 nm depolarization and lidar ratios are used to identify clean and polluted maritime aerosols, uncertainties in mean DARE are, respectively, 4 and 18%. When scattering optical thicknesses are also known to within ± 3% at four passive imager wavelengths (340 nm, 546 nm, 966 nm, and 1,657 nm), uncertainty in the polluted maritime DARE decreases to 8%. Uncertainties in the instantaneous top-of-atmosphere (TOA) reflected irradiances derived from observed broadband radiances and angular distribution models are also estimated. When TOA irradiances are derived solely from the nadir view, their uncertainties can be reduced if aerosol type can be identified and aerosol type dependence is considered in the radiance to irradiance conversion. This is especially so for aerosols with a large fraction of nonspherical particles, such as desert aerosols.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".