On the aerosol and cloud phase function expansion moments for radiative transfer simulations
Bibliographic record
Abstract
Abstract The accuracy of the Henyey‐Greenstein (HG) approximation in radiative transfer process is systematically investigated for aerosol and cloud particles. For small‐size aerosols, the phase function moment by the HG approximation is close to that of the true phase function; therefore, the differences in four‐stream radiative transfer calculations are very small whether using the HG approximation or the true phase function moments. However, for large‐size aerosols, liquid cloud droplets and ice crystals, the HG approximation produces very different phase function moment result compared to the true phase function. In case of small optical depth, the single layer four‐stream radiative transfer calculations show that the HG approximation can produce relative errors larger than 20% while the errors are generally less than 5% by using true phase function moments. By applying the four‐stream radiative transfer scheme to a multilayer atmosphere with aerosol and cloud, the differences in flux error are generally less than 2 Wm −2 by using either the HG approximation or true phase function moments, but can be over 10 Wm −2 for ice cloud case. The aerosol/cloud instantaneous radiative forcing has been analyzed in climate simulation. Compared to the four‐stream radiative transfer scheme, the two‐stream radiative transfer scheme overestimates the aerosol radiative flux in low‐latitude regions and underestimates the upward flux for the high‐latitude regions. By using the HG approximation, the difference in aerosol radiative forcing between the four‐stream and the two‐stream radiative transfer schemes is enhanced; the enhancement can be up to 0.5 Wm −2 . The parameterization schemes for aerosol and cloud optical properties must be extended to contain higher‐order phase function moments, if higher‐order stream radiative transfer algorithms are to be used.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".