Detection of supercooled liquid in mixed‐phase clouds using radar Doppler spectra
Bibliographic record
Abstract
Cloud phase identification from active remote sensors in the temperature range from 0 to −40°C, where both liquid and ice hydrometeor phases are sustainable, is challenging. Millimeter wavelength cloud radars (MMCR) are able to penetrate and detect multiple cloud layers. However, in mixed‐phase conditions, ice crystals dominate the radar signal, rendering the detection of liquid droplets from radar observables more difficult. The technique proposed here overcomes this fundamental limitation by using morphological features in MMCR Doppler spectra to detect supercooled liquid droplets in the radar sampling volume in the presence of ice particles. High lidar backscatter and near‐zero lidar depolarization measurements (good indicators of the presence of liquid droplets) from the Mixed‐Phase Arctic Clouds Experiment (MPACE) conducted in Barrow, Alaska, are used to train the technique and evaluate its potential for detecting mixed‐phase conditions. Ceilometer, microwave radiometer, and radiosonde measurements provide additional independent validation. Because of the ability of MMCRs to penetrate multiple liquid layers, this radar‐based technique does not suffer from the extinction limitations of lidars and is thus able to expand cloud phase identification methods to cloud regions beyond where lidars can penetrate, providing output at the native radar resolution. The technique is applicable to all profiling radars that have sufficient sensitivity to observe the small amount of liquid in mixed‐phase clouds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".