Analysis of CCCma Radiative Transfer Calculations for Low‐Level Overcast Liquid Clouds Over ARM SGP and ENA Sites
Bibliographic record
Abstract
Abstract This study uses the Canadian Centre for Climate Modeling and Analysis (CCCma) radiative transfer model to estimate shortwave flux for low‐level overcast liquid clouds. Calculations are evaluated against measurements at the Atmospheric Radiation Measurement Southern Great Plains (SGP, land) and Eastern North Atlantic (ENA, ocean) sites, as well as top of atmosphere (TOA) fluxes inferred from Clouds and Earth's Radiant Energy System (CERES) from 2014 to 2023. Mean observed surface (TOA) SW fluxes for the selected cases are 235.7 W m −2 (473.8 W m −2 ) at SGP and 348.7 W m −2 (356.4 W m −2 ) at ENA. Cloud microphysical properties retrieved from CERES MODIS are input into the CCCma using three assumed profiles: (a) cloud droplet effective radius ( r e ) and liquid water content (LWC) constant with height, (b) LWC and r e increasing linearly with height, and (c) LWC and r e increasing linearly from cloud base to ¾ height and then decreasing linearly up to cloud top. Overall, Method 3 produces the least error variance at both sites. At SGP, mean bias and root mean square error (RMSE) are −5.0 and 44.6 W m −2 at the surface and −4.6 and 25.4 W m −2 at TOA. At ENA, errors are +0.2 and 121.3 W m −2 at the surface and −8.0 and 26.1 W m −2 at TOA. Further screening cases with good agreement between satellite‐ and surface‐based cloud properties, RMSEs for surface fluxes decrease to 24.3 and 25.8 W m −2 at SGP and ENA. Comparisons with CERES Fu‐Liou calculations showed overall better performance by the CCCma, especially at ENA.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".