Effects of absorbing aerosols on the determination of the surface solar radiation
Bibliographic record
Abstract
Coincident and collocated measurements of solar radiation from the Scanner for Radiation Budget (ScaRaB) on Meteor‐3 and from towers in a boreal forest region during the Boreal Ecosystems and Atmosphere Study (BOREAS) period are used to evaluate an algorithm of Li et al. [1993a] which was developed to derive the net surface solar radiation flux from satellite measurements. The analysis shows that after correcting for mismatching between the footprints of the tower measurements and the satellite pixels, there is a substantial bias that is due to the presence of absorbing aerosols. Application of the aerosol correction term of Masuda et al. [1995] reduces the mean bias to about 5 W m−2. Cloud radiation forcing ratio R has been used in many studies to address the issue of a cloud absorption anomaly. Three methods, each with a different approach to the consideration of the effect on radiative fluxes by absorbing aerosols, are used to calculate R. Methods that account inadequately for aerosol effects when applied to the coincident and collocated tower and satellite measurements give large values of R (1.28–1.42) under conditions of heavy aerosol loading, which could be interpreted as an indication of a cloud absorption anomaly. However, application of the method that accounts best for aerosol effects gives values of R within the range 1.08∼1.18 and so does not support the idea of anomalous cloud absorption.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".