The performance of the EarthCARE Cloud Profiling Radar in marine stratiform clouds
Bibliographic record
Abstract
Abstract Marine stratiform clouds are a challenging target for spaceborne radars due to their proximity to Earth's surface, limited vertical extent, and low radar reflectivity. The joint European‐Japanese Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) mission is scheduled for launch in 2019 and features the first atmospheric Cloud Profiling Radar (CPR) with Doppler capability in space. Here the performance of the CPR in (i) detecting these clouds and their boundaries and (ii) measuring the Doppler velocities of drizzle particles is evaluated. Extensive observations from the Atmospheric Radiation Measurement Mobile Facility in marine stratus regimes are used as input to an EarthCARE CPR simulator and to compare the resulting reflectivity factors, Doppler velocities, and cloud detections. Cloud detection of the CPR is 70–80% that of the ground‐based radars, depending upon integration length and feature mask configuration. For clouds entirely contained within the surface clutter, detection is limited but is predicted to be an order of magnitude greater for the EarthCARE CPR than for CloudSat due to the improved range sampling rate of the former. The EarthCARE‐CPR range resolution is found to introduce cloud top height and reflectivity biases of +100 m (equal to the range sampling rate) and +1.3 dB; by applying a constrained linear inversion to the range resolution, these are reduced to 30 m and 0.1 dB, respectively. The analysis indicates that a velocity uncertainty of 0.5 ms −1 is achievable through either a 5 km along‐track integration or a combination of matched spatial filters and 1 km along‐track integration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".