Estimation of errors associated with the EarthCARE 3D scene construction algorithm
Bibliographic record
Abstract
The EarthCARE satellite mission plans to perform a continuous closure experiment to assess the quality of retrieved cloud and aerosol properties. It will do so by comparing top‐of‐atmosphere (TOA) broad‐band (BB) fluxes with simulated values produced by three‐dimensional (3D) radiative transfer models that act on the two‐dimensional (2D) retrieved cross‐section and a 3D atmosphere around it produced by a scene construction algorithm (SCA). This study proposes and tests a method for estimating errors in simulated TOA BB fluxes due to the SCA. Two methods for estimating SCA‐related errors for TOA fluxes are presented. The primary one relies on computation of errors for reconstructed narrow‐band imager nadir radiances. A‐train satellite data were used to show that for constructed domains measuring (11 km) 2 , approximately the size of the EarthCARE assessment domains, with total cloud fractions > 0.2, errors for reflected BB short‐wave fluxes due to the SCA are smaller than ±4.2 and ±11.5 W m −2 for 66 and 90% of the domains, respectively. Corresponding values for outgoing long‐wave fluxes are ±1.2 and ±3.0 W m −2 . The largest and smallest errors are associated with fields of broken convective cloud and overcast stratiform cloud, respectively. The SCA was applied to simulated measurements for a (153 km) 2 field of deep convective clouds produced by a cloud‐system‐resolving model. Actual and estimated TOA BB short‐wave flux errors due to the SCA agree well and are smaller than ±22 and ±40 W m −2 for 66 and 90% of the (11 km) 2 sampled subdomains. Assuming that errors due to the SCA are purely bias errors, they were subtracted from fluxes estimated for the constructed domains. This resulted in TOA BB short‐wave flux errors smaller than ±7 and ±25 W m −2 for 66 and 90% of the sampled subdomains. This suggests that estimated errors due to the SCA should be removed directly from simulated TOA BB fluxes before executing a closure assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".