Bibliographic record
Abstract
Abstract. Several sun photometer networks worldwide include instruments for aerosol optical depth (AOD) observations, such as Global Atmospheric Atmosphere Watch-Precision Filter Radiometer (GAW-PFR) and Aerosol Robotic Network (AERONET). AERONET provides additional aerosol properties such as the detailed volume size distribution and the single scattering albedo through inversion modelling of sky radiance measurements. However, the data availability for such properties is limited due to the limited number of daily almucantar sky radiance scans and cloudiness. AOD measurements are significantly more frequent as they can be even every minute and are affected only by clouds being too close or covering the solar disk. The Generalized Retrieval of Atmosphere and Surface Properties (GRASP) is a flexible inversion model to retrieve aerosol properties from various observations. One of its capabilities is the retrieval of the volume concentration, the volume median radius and geometric standard deviation for each aerosol size distribution mode and the separation of AOD to each mode using only spectral AOD as an input parameter. Such properties are important for various applications, as the size of aerosols affects their interaction with solar radiation, clouds and radiative forcing modelling. Size also shows significant differences depending on the aerosol type such as dust or biomass burning. In this study, we selected four common stations of GAW-PFR and AERONET and used GRASP to retrieve the bimodal size distribution parameters from AOD measured by GAW-PFR instruments (PFRs). We assessed the homogeneity with the AERONET output parameters and investigated the effect of the spectral range and on such retrievals. We also assessed the performance for certain dust and biomass burning cases. Our results showed good agreement between PFR AOD-based and AERONET sky radiance inversions for AOD modal separation and volume concentrations. Significant improvement of the PFR-AERONET intercomparison was also possible for the fine mode volume and effective radius when restricting the datasets to AOD at 500 nm > 0.1 and Angström Exponent (AE) >1. Also, the results showed consistency with previous study regarding the validation of such retrievals using AERONET AOD. Focusing on conditions with high proportion of dust particles, we found consistent results with the general cases. Using AOD with a larger spectral range (from BTS spectroradiometer), we found that the wavelength selection may affect the results and that using longer wavelengths can increase the sensitivity of coarse mode volume median radius to AOD and improve the correlation of the GRASP BTS AOD-based and AERONET datasets. However, the available data were limited, so it is not clear under what conditions the inclusion of such wavelengths will result in more accurate retrievals. Finally, we were able to reproduce with GRASP the aerosol size characteristics of unusual biomass burning cases from the Canadian wildfires during 2023, but the results showed systematically increased fine mode radius and concentration compared to the AERONET output.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.186 | 0.159 |
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".