On using the relationship between Doppler velocity and radar reflectivity to identify microphysical processes in midlatitudinal ice clouds
Bibliographic record
Abstract
Abstract Ground‐based 35 GHz profiling Doppler cloud radar observations of ice clouds were used to derive the power law relation between Doppler velocity Vd and radar reflectivity Z (Vd = aZb). By removing the vertical air motion from Vd, the power law can be rewritten as Vt = aZb with Vt being the reflectivity‐weighted particle terminal fall velocity. Profiles of this relation are variable with height. An attempt was made to relate this variability to the dominant microphysical processes in different layers of the cloud. Based on that, the possibility of using profiles of the parameters a and b to distinguish different microphysical regimes was explored. The methodology was applied to long‐term measurements (January 1997 to December 2010) at the Atmospheric Radiation Measurement site in the Southern Great Plains. Principal component analysis was used to determine the modes of the profiles that explain most of the observed variance in the observations. Profile‐averaged means and standard deviations of parameters a and b amounted to 0.65 ± 0.42 and 0.03 ± 0.19, respectively. Furthermore, three commonly used microphysical relations related to bulk quantities were used to determine values of a and b. These results were found to compare reasonably well with the values obtained from the radar observations. Finally, microphysical considerations showed that radar‐derived values of parameter b can be explained in terms of particle size distribution moment changes.
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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 | 0.003 |
| 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 | 0.001 | 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".