The performance of modified CCCma RTM in representing the Global Clear-sky Downwelling Shortwave Flux
Bibliographic record
Abstract
Abstract The clear-sky total shortwave (SW, 0.3-5 μm), visible (VIS, 0.3-0.7 μm), and near-infrared (NIR, 0.7-5 μm) SW fluxes at the surface calculated by the low-spectral resolution version of the CCCma radiative transfer model (RTM) have been compared with the high-spectral resolution of MODTRAN6.0.2.5 (M6.0) calculations. The CCCma RTM was modified with four spectral bands: VIS (0.2– 0.69 μm), NIR1 (0.69–1.19 μm), NIR2 (1.19–2.38 μm), and NIR3 (2.38 – 5 μm), and used the same inputs of atmospheric profiles, AOD, surface albedo as M6.0. The computed total SW fluxes at the surface (SWDN sfc ) from these two RTMs are then compared with the NASA CERES SYN1deg product, computed by the NASA Langley modified broadband Fu-Liou RTM. The global mean SWDN sfc are 246.5 W m −2 for M6.0, 246.4 W m −2 for CCCma, and 242.3 W m −2 for CERES SYN1deg product. The differences in SWDN sfc between three RTMs are remarkably low for global average, but with relatively large differences over the heavy dust and polluted regions, presumably due to different aerosol optical properties used in these RTMs. The assumption of lower SSA values used in CCCma is valid, which are responsible for higher VIS and lower NIR1 fluxes reaching the surface. The modified CCCma shows an excellent performance compared to M6.0, with very small differences in SWDN sfc , as well as across all four spectral bands. The different signs in ΔVIS and ΔNIR1 bands in comparison between CCCma and M6.0 result in the small differences in global total SW flux due to the cancelation. In addition to its accuracy, the modified CCCma RTM is also significantly faster than M6.0. This makes it an ideal choice for large-scale simulations where computationally efficiency is crucial.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".