The Dependence of TOA Reflectance Anisotropy on Cloud Properties Inferred from ScaRaB Satellite Data
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Bibliographic record
Abstract
An angular dependence model (ADM) describes the anisotropy in the reflectance field. ADMs are a key element in determining the top-of-the-atmosphere (TOA) albedos and radiative fluxes. This study utilizes one-year satellite data from the Scanner for Radiation Budget (ScaRaB) for overcast scenes to examine the variation of ADMs with cloud properties. Using ScaRaB shortwave (SW) overcast radiance measurements, a SW mean overcast ADM, similar to the ERBE ADM, was generated. Differences between the ScaRaB and ERBE overcast ADMs lead to biases of ~0.01-0.04 in mean albedos inferred from specific angular bins. The largest biases are in the backward scattering direction. Overcast ADMs for the visible (VIS) wavelength were also generated using ScaRaB VIS measurements. They are very similar in general to, but a little smaller at large viewing angles and a little larger at nadir than, the SW overcast ADMs. To evaluate the impact of cloud properties on ADMs, ScaRaB overcast observations were further classified into thin, thick, warm, and cold cloud categories to generate four subsets of ADMs. The resulting ADMs for thin and thick clouds show opposite trends and they deviate significantly from the overall mean ADM by several to more than ten percents. Deviations from the mean ADM were also noted for the ADMs developed for warm water clouds and cold ice clouds. These deviations were attributed to the different scattering phase functions of water and ice particles and were compared to results from model simulations. Use of a single mean overcast ADM results in albedo biases of 0.01-0.04, relative to the use of specific ADMs for particular cloud types. The biases reduced to ~0.005 when averaged over all cloud types and viewing geometry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it