Cloud and Precipitation Microphysical Retrievals from the EarthCARE Cloud Profiling Radar: The C-CLD Product
Bibliographic record
Abstract
This presentation delves into the C-CLD processor and its output product, both named the same, developed for the EarthCARE mission. The C-CLD processor has been designed to extract detailed microphysical properties of clouds and precipitation from the EarthCARE Cloud Profiling Radar data. The algorithm introduces a significant advancement by incorporating Doppler velocity information for the first time in space-borne radar retrievals. Our approach integrates an optimal estimation method to deduce vertical profiles of hydrometeor water content and particle characteristic size, employing reflectivity, mean Doppler velocity measurements, and path-integrated attenuation. The algorithm's robustness is further amplified by an ensemble-based method in the ice regions, ensuring both accuracy and consistency in the forward model relations.Emphasizing the algorithm's advancements, we present a comprehensive overview of its theoretical basis and development. This includes the validation process, performance sensitivity analysis and quantification of the information content. The presentation will demonstrate the retrieval efficacy in diverse atmospheric conditions, ranging from warm to cold rain and snow.In addition to algorithmic developments, our research also emphasizes the importance of iterative testing and refinement. Our approach combines model simulations with actual campaign datasets, which include both in-situ and remote sensing measurements, to validate and refine our methods. The rigorous analysis of data from campaigns like CADDIWA or IMPACTS, provided insights that allowed us to improve the C-CLD algorithm, ensuring its robustness and improving the reliability of its retrievals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".