Use of EarthCARE products within the EUMETSAT validation facility for Level 2 Cloud products  
Bibliographic record
Abstract
The EUMETSAT central facility generates and disseminates several cloud products from both geostationary and low-Earth orbit passive sensors, which serve a variety of applications, spanning from nowcasting, to numerical weather prediction to climate monitoring. The retrieved cloud parameters include cloud/dust/ash detection, cloud top height and microphysics (particle effective radius and optical thickness). All EUMETSAT products are validated and continuously quality monitored against independent reference data to ensure state-of-the-art algorithm performance, product quality/accuracy compliant with user and operational service requirements, and stability and continuity/consistency over time (i.e., coping with instrument degradation, algorithm evolutions, updated calibration, etc.).This contribution provides an overview of the tools developed at EUMETSAT to perform the monitoring and validation of cloud products against lidar/radar measurements, which have established themselves as a trustworthy source for the detection of cloud layers and superior to any other validation data source when it comes to estimate the cloud height, particle microphysical and optical properties. We focus on the status of these tools and the plans for their further development and release to users. The tools are fully automated and handle the validation of products from both geostationary and polar-orbiting satellites, including data download and organisation, instrument co-location and the development of comparison metrics. The toolkit includes:A tool performing the validation of EUMETSAT against space-based radar and lidar measurements. For almost two decades (since 2006), the CloudSat and CALIPSO observations have been the prime reference source for this validation. EarthCARE will provide the natural continuation to the observations provided by these two instruments, which reached their end of life in autumn 2023. We discuss the use of EarthCARE products as envisaged in the validation activities with a particular focus on the retrieval of cloud properties based on the synergistic use of lidar, radar and multi-spectral imager data. Furthermore, the higher sensitivity measurements expected from HSRL and CPR on board EarthCARE with respect to CALIPSO and CloudSat will require careful investigations in order to transfer the current experience in the use of A-Train products as a validation reference to the new EarthCARE products. A tool performing the validation of EUMETSAT cloud products against ground-based radar and lidar measurements from ACTRIS (the European Research Infrastructure for the observation of Aerosol, Cloud and Trace Gases), specifically using the cloud products generated by the ACTRIS-Cloudnet processing facility maintained by the Finnish Meteorological Institute (FMI). This validation activity fills in the gap between CloudSat/CALIPSO end of life and EarthCARE launch. METIS-Clouds (Monitoring and Evaluation of Thematic Information from Space), a web application tool providing access to the collection of monitoring and validation results of EUMETSAT cloud products, on a global and regional level. This collection is exploited by both the in-house algorithm developers (to identify and fix issues, bugs, etc.) and the users (to assess the product accuracy).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".