Error Analysis for an Algorithm That Reduces Radiative Transfer Calculations in High‐Resolution Atmospheric Models
Bibliographic record
Abstract
Abstract This study assesses characteristics of spatial and temporal errors for net (shortwave + longwave) surface irradiances and atmospheric heating rates predicted by the Partitioned‐Gauss‐Legendre Quadrature (PGLQ) algorithm, whose aim is to economize computation of radiative transfer (RT) in high‐resolution cloud system‐resolving models (CSRMs). Most results reported here are for PGLQ having made 200 times fewer calls to the RT models than the Independent Column Approximation, which applies the RT models to each column in CSRM domains. Due to the nature of GLQ, PGLQ yields almost unbiased domain averages. This is shown for data pertaining to two convective cloud systems produced by a CSRM. Relative to the Independent Column Approximation, PGLQ works with much reduced amounts of information, and so it has the potential to produce rare, but sizable, localized errors. Two methods are employed to assess the “randomness” of spatial and temporal series of PGLQ flux and heating rate errors; one of which is developed here. Regarding spatial transects of net surface irradiance errors ΔFNET, ~30–60% of them are considered by both assessment techniques, simultaneously, to be indistinguishable, at the 95% confidence level, from fully uncorrelated sequences. Correspondingly, for time series of ΔFNET (at 8‐s time step), ~15–20% of 128‐step series, and ~10–70% of 30‐step series are considered by both methods, simultaneously, to be random. For time series of errors for net radiative heating rates, ~40% of cloudless series get classed, by both tests simultaneously, as random, compared to ~20% of those that contain some cloud.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".