Bibliographic record
Abstract
Abstract. To accurately estimate outgoing top-of-atmosphere (TOA) shortwave (SW) fluxes from measurements of broadband radiances, angular distribution models (ADMs) are necessary. ADMs rely on radiance-predicting models that are trained on hemispherically-resolved CERES TOA radiance observations. The estimation of SW fluxes is particularly challenging for cloudy skies due to clouds’ anisotropy, which substantially varies with their optical properties for any given sun-object-observer geometry. The aim of this study is to investigate, the influence of micro- and macrophysical properties of liquid clouds on SW fluxes estimated by ADMs that are based on a semi-physical model and compare to operational ADMs. We hypothesize that a microphysically-aware ADM performs better in observation angles influenced by single-scattering features. The semi-physical model relies on an optimized asymmetry parameter g∆ that depends on the cloud effective radius. To improve the radiance prediction, g∆ is adjusted for the different viewing geometries during the training of the model. In this work these adjustments are linked to single scattering features as the shift of cloud bow and glory with varying cloud droplet size. For the investigation synthetic 3D cloud scenes based on observations and theoretical assumptions are created. Using a Monte Carlo Model the TOA broad band SW radiances and fluxes of the synthetic cloud scenes are simulated for different scenarios with varying viewing angles (θv) along the principle plane and solar angles (θs). Analyzing the scenarios the sensitivity and accuracy of the two SW radiance-to-irradiance conversion approaches to cloud droplet size, spatial distribution of liquid water path, and mean optical thickness is quantified. The study emphasizes that the inclusion of liquid droplet effective radius in the generation of ADMs can result in more accurate SW flux estimates. Particularly for viewing geometries that exhibit single scattering phenomena, such as cloud glory and cloud bow, instantaneous flux estimates can benefit from microphysical-aware ADMs. For instantaneous flux estimates, we found that the error in the SW flux estimates could be reduced by up to 25 W /m2. For cases with very large or small droplets, the median error was reduced by 5 W /m2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.495 | 0.326 |
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".