Multiple scattering identification in spaceborne W-band radar measurements of deep convective cores
Bibliographic record
Abstract
[1] CloudSat observations have indicated that multiple scattering affects 94 GHz spaceborne radar observations. The ESA EarthCARE explorer mission scheduled to launch in 2015 features also a spaceborne 94-GHz radar with Doppler capability for providing a global data set of convective motions and particle sedimentation rates. Vertical velocity measurements will be collected in all cloud conditions, including deep convection where multiple-scattering is expected to contaminate the signal. Thus, before the spaceborne Doppler radars are used for science application, it is imperative to develop a method to identify radar range gates contaminated by multiple scattering contributions. Based on simulations, a criterion to identify the onset of multiple scattering is presented in this paper; the cumulative integrated reflectivity from the top of the atmosphere is a proxy of the multiple scattering enhancement and can be confidently used to detect the onset of multiple scattering. Analysis of a limited (two months) CloudSat data set reveals that, for deep tropical convective cores, the onset of significant multiple scattering typically occurs in the region between 9–10 km and more than 35% of the range bins above the freezing level height and with reflectivity above −20 dBZ are not affected by multiple scattering. This assessment offers a conservative upper limit for EarthCARE 94-GHz radar multiple scattering effects due to the narrower field of view of the Doppler radar compared to CloudSat's radar. Identification of multiple scattering contamination in the CloudSat and EarthCARE radar observations facilitates the following objectives: (1) to constrain the region of validity of currently developed CloudSat products based on single scattering theory (e.g. 2B-CWC-RO, 2B-CWC-RVOD) and (2) to filter out multiple scattering affected range bins in any analysis aimed at the assessment of the feasibility and of the accuracy of the EarthCARE Doppler estimates within deep convective cores.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".