Accounting for dust aerosol size distribution in radiative transfer
Bibliographic record
Abstract
Abstract The impact of size distribution of mineral dust aerosol on radiative transfer was investigated using the Aerosol Robotic Network‐retrieved aerosol size distributions. Three methods for determining the aerosol optical properties using size distributions were discussed. The first is referred to as a bin method in which the aerosol optical properties are determined for each bin of the size distribution. The second is named as an assembly mean method in which the aerosol optical properties are determined with an integration of the aerosol optical parameters over the observed size distribution. The third is a normal parameterization method based on an assumed size distribution. The bin method was used to generate the benchmark results in the radiation calculations against the methods of the assembly mean, and parameterizations based on two size distribution functions, namely, lognormal and gamma were examined. It is seen that the assembly mean method can produce aerosol radiative forcing with accuracy of better than 1%. The accuracies of the parameterizations based on lognormal and gamma size distributions are about 25% and 5%, respectively. Both the lognormal and gamma size distributions can be determined by two parameters, the effective radius and effective variance. The better results from the gamma size distribution can be explained by a third parameter of skewness which is found to be useful for judging how close the assumed distribution is to the observation result. The parameterizations based on the two assumed size distributions are also evaluated in a climate model. The results show that the reflected solar fluxes over the desert areas determined by the scheme based on the gamma size distribution are about 1 W m −2 less than those from the scheme based on the lognormal size distribution, bringing the model results closer to the observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".