Best Practice Protocol for the validation of Aerosol, Cloud, and Precipitation Profiles (ACPPV)
Bibliographic record
Abstract
Validation activities are critical to ensure the quality, credibility, and integrity of Earth observation data. With the deployment of advanced active remote sensors in space, a clear need arises for establishing best practices in the field of cloud, aerosol and precipitation profile validation. This publication fulfills this need, by proposing common practices, capturing lessons learned from earlier missions. The ACPPV publication is a result of an international collaboration of 97 scientists from 58 Institutions and Space Agencies, reviewed and accepted by the CEOS Working Group for Calibration and Validation (action item CV-22-01). The approaches and recommendations included, cover a range of issues, including correlative site and instrument selection, data processing and quality control, campaign criteria, configurations, scenarios, collocation methods, suggestions on issues concerning scene representativeness, and intercomparison methodologies. Moreover, guidance on the statistical validation through intercomparison between satellite-based remote sensing observations, and on the near-real time validation through monitoring in an NWP data assimilation system are included. To this end, the scientific communities involved in past missions have reviewed lessons learned and identified areas where convergence on similar approaches is beneficial. Finally, existing gaps in our Cal/Val knowledge are summarized. Given the complexity and diversity of geophysical scenarios and retrievals of aerosol, cloud, and precipitation regimes, the ACPPV document is aimed at knowledge exchange and conveying lessons learned, rather than definitions on single and strict protocols that have been agreed upon in some other domains with fewer degrees of freedom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".