The EarthCARE cloud profiling radar (CPR) doppler measurements in deep convection: challenges, post-processing, and science applications
Bibliographic record
Abstract
The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite is a joint European Space Agency and Japanese Aerospace Exploration Agency mission scheduled to launch in 2021. EarthCARE (EC) will host the first Doppler cloud profiling radar (CPR) in space which, in addition to constraining microphysical retrievals in particle sedimentation regimes, is expected to provide the first ever global observations of convective vertical air motion and associated mass fluxes. Here, the potential of the EC-CPR velocity measurements in convection is evaluated using forward-simulations performed using a state-of-the-art EC-CPR Doppler simulator and output from high-resolution, bulk microphysics numerical models. Results indicate that the EC-CPR has the potential to measure Doppler velocities in the top 40 % of convective cores, the rest being not observed/contaminated by attenuation and multiple scattering. In these observable regions, non-uniform beam filling (NUBF) and velocity aliasing could affect the quality of the velocity measurements. We show how observed reflectivity gradient can be used to correct for NUBF effects on Doppler velocity to achieve an accuracy higher than 0.3-0.5 ms<sup>-1</sup> . Velocity aliasing remains an important challenge. Our results suggest that the current Nyquist velocity of the EC-CPR will enable it to document, with minimal need for de-aliasing correction, convective events with vertical velocity below 7-8 ms<sup>-1</sup> while the information collected about more vigorous events is expected to be more challenging to recover. Overall, despite it being affected by several limiting factors, the EC-CPR has the potential to collect valuable velocity observations in deep convection thus complementing the current sparse ground-based record.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".