Ground-based lidar measurement of liquid water content and effective droplet diameter in water clouds: instrumentation, retrieval method, and results
Bibliographic record
Abstract
Lidar probing of dense water clouds gives rise to significant multiple scattering contributions. Instead of trying to mitigate the effect, we have developed methods to collect and angularly resolve the multiple scattering returns. The proposed retrieval method combines these measurements with a rapid semi-empirical computation method of the lidar multiple scattering contributions. Solutions are calculated for the extinction coefficient, the effective droplet diameter, the liquid water content and the rain rate. The paper reviews the main measurement methods, discusses briefly the concept and implementation of the retrieval technique, presents validation results obtained from Monte Carlo simulations, and compares lidar solutions for liquid water content and effective droplet diameter with in-cloud aircraft measurements. Good correlation is demonstrated for both simulation and field data. We conclude that multiple-scattering lidar is a practical option for the remote sensing of water cloud microphysical parameters up to the lidar penetration depths of typically 200 m.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".