EarthCARE Cloud Profiling Radar Observations of the Vertical Structure of Marine Stratocumulus Clouds
Bibliographic record
Abstract
Abstract. Launched in May 2024, the EarthCARE Cloud Profiling Radar (EC-CPR) provides enhanced sensitivity, finer vertical and horizontal resolution, and greatly reduced surface clutter contamination compared to its predecessor, the CloudSat's CPR (CS-CPR). These improvements enable more accurate detection and characterization of the vertical structure of marine low-level clouds. This study presents the first year of EC-CPR observations of stratocumulus (Sc) clouds over the Southeast Pacific and Southeast Atlantic Oceans. The analysis of EC-CPR clear-sky profiles and comparisons with airborne radar data confirm that surface clutter is effectively suppressed above 0.5 km. Comparisons with CS-CPR data from 2007–2008 show that EC-CPR detects nearly double the Sc amount relative to CS-CPR in the regions of study. When a columnar maximum reflectivity (ZMAX) threshold of −15 dBZ is used to flag raining profiles, CS-CPR is found to underestimate rainfall occurrence by up to ~ 20 % relative to EC-CPR. Using a steady-state one-dimensional drizzle model, the impact of the point target response (PTR) on EC-CPR reflectivity profiles in Sc clouds is examined. PTR causes vertical stretching of radar-detected cloud boundaries, resulting in an overestimation of cloud thickness by approximately 0.4–0.5 km in drizzling clouds. Additionally, PTR induces parabolic shaping of reflectivity profiles regardless of drizzle presence, complicating the distinction between drizzle-free and drizzle-containing clouds. These findings underscore the need for cautious interpretation of radar reflectivity profiles and suggest the incorporation of additional constraints, such as Doppler velocity and path-integrated attenuation (PIA) to improve future drizzle detection strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".