On the use of simulated photon paths to co‐register top‐of‐atmosphere radiances in <i>EarthCARE</i> radiative closure experiments
Bibliographic record
Abstract
The Earth's Cloud, Aerosol and Radiation Explorer (EarthCARE) mission will retrieve vertical profiles of cloud and aerosol properties by combining data from active and passive instruments. The verisimilitude of retrievals will be assessed using data from its broad‐band radiometer (BBR), which measures top‐of‐atmosphere (TOA) short‐wave (SW) radiances at three along‐track viewing angles. BBR measurements will be compared with their modelled counterparts, simulated by a three‐dimensional (3D) Monte Carlo (MC) radiative transfer model acting on retrieved properties, thus defining a radiative closure experiment. Since cloud and aerosol microphysical and hence optical properties within each assessment domain vary horizontally and vertically, one challenge facing the closure process is selection of radiances that will foster the best assessments of retrievals. This study investigates whether co‐registration of radiances for closure assessment can be aided by information pertaining to photon paths from the MC model. Unlike methods that provide one effective reflecting layer (ERL), such as cloud‐top altitude, simulated photon paths can account for several reflecting layers. For this study, A‐Train satellite data provided cloud properties. The MC model was applied to this field to simulate BBR‐like measurements. Cloud properties were then perturbed randomly, to represent retrievals in approximate fashion, and the MC model reapplied to them. The resulting sets of radiances mimicked EarthCARE measured and modelled data, thus allowing a test of closure and co‐registration methodologies. Through the use of 3D photon path information, the rate of identification of inaccurate cloud retrievals improved over ERL approaches by ∼4% for cirrus clouds and ∼15% for broken clouds. For large‐scale deep convective clouds, however, inaccurate photon paths, ostensibly due to poor retrievals, reduced identification performance by 3%.
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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 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".