Radiative closure assessment of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-DF product
Bibliographic record
Abstract
Measurements made by three instruments aboard the EarthCARE satellite, plus data from auxiliary sources, will be used synergistically to retrieve estimates of cloud and aerosol properties. The ACMB-DF processor consists of a continuous radiative closure assessment of these retrievals and is both described and demonstrated in this study. The closure procedure begins with 3D radiative transfer models (RTMs) acting on retrieved and auxiliary data. These models yield upwelling shortwave and longwave broadband radiances commensurate with measurements made by EarthCARE's multi-angle broadband radiometer (BBR). Measured and modelled radiances are averaged up to “assessment domains” that measure ∼ 21 km along-track by no more than 5 km across-track, centred on the retrieved cross-section of ∼ 1 km profiles, and are then combined, by angular distributions models (ADMs), to produce “effective” upwelling fluxes at top-of-atmosphere, denoted as F BBR and F RTM , respectively. Last, the probability pΔF^ of | F RTM − F BBR | being less than ΔF^ W m −2 is estimated recognizing as many sources of, assumed normally distributed, uncertainties as possible. For historical/programmatic reasons, ΔF^ is set to 10 W m −2 , but that might change during EarthCARE's commissioning phase and with Sun angle. The closure process is demonstrated up to calculation of pΔF^ using four 400 km long portions of one of EarthCARE's test frames for which simulated passive measurements were computed by 3D RTMs. Note that this study, like the ACMB-DF process with real EarthCARE observations, does not comment explicitly on performance of retrieval algorithms.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".