Cloud radar Doppler spectra in drizzling stratiform clouds: 2. Observations and microphysical modeling of drizzle evolution
Bibliographic record
Abstract
[1] In part I, the influence of cloud microphysics and dynamics on the shape of cloud radar Doppler spectra in warm stratiform clouds was discussed. The traditional analysis of radar Doppler moments was extended to include skewness and kurtosis as additional descriptors of the Doppler spectrum. Here, a short climatology of observed Doppler spectra moments as a function of the radar reflectivity at continental and maritime ARM sites is presented. The evolution of the Doppler spectra moments is consistent with the onset and growth of drizzle particles and can be used to assist modeling studies of drizzle onset and growth. Time-height radar observations are used to exhibit the coherency of the Doppler spectra shape parameters and demonstrate their potential to improve the interpretation and use of radar observations. In addition, a simplified microphysical approach to modeling the vertical evolution of the drizzle particle size distribution in warm stratiform clouds is described and used to analyze the observations. The formation rate of embryonic drizzle droplets due to the autoconversion process is not calculated explicitly; however, accretion and evaporation processes are explicitly modeled. The microphysical model is used as input to a radar Doppler spectrum forward model, and synthetic radar Doppler spectra moments are generated. Three areas of interest are studied in detail: early drizzle growth near the cloud top, growth by accretion of the well-developed drizzle, and drizzle depletion below the cloud base due to evaporation. The modeling results are in good agreement with the continental and maritime observations. This demonstrates that steady state one-dimensional explicit microphysical models coupled with a forward model and comprehensive radar Doppler spectra observations offer a powerful method to explore the vertical evolution of the drizzle particle size distribution.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".