Comment on egusphere-2025-1680 - Commonalities to Doppler wind lidar on Aeolus
Bibliographic record
Abstract
Abstract. The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) mission, a joint effort between the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA), aims to advance our understanding of aerosols, clouds, precipitation, and radiation using a comprehensive active and passive sensors payload. A key component of the payload is the 94-GHz Cloud Profiling Radar (CPR), which provides the first-ever Doppler velocity measurements collected from space. Accurate knowledge of the CPR antenna pointing is essential for ensuring high quality CPR Doppler velocity measurements. This study focuses on the geolocation assessment and antenna mispointing corrections during EarthCARE's commissioning phase and beyond, using Earth’s surface Doppler velocity measurements collected over the first nine months of the mission. While the instrument footprint is proven to be properly geolocated within about 100 meters, surface Doppler velocity observations reveal mispointing trends influenced by solar illumination cycles and thermoelastic distortions on the antenna. Correcting these effects significantly reduces biases, ensuring better Doppler velocity measurements, essential for understanding cloud microphysics and dynamics. The results, validated through the analysis of Doppler velocities in ice clouds, underline the critical role of pointing corrections for the success of the EarthCARE mission.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.024 |
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".