Profiling Cloud Ice Mass and Particle Characteristic Size from Doppler Radar Measurements
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Bibliographic record
Abstract
A remote sensing method is proposed for the retrievals of vertical profiles of ice cloud microphysical parameters from ground-based measurements of radar reflectivity and Doppler velocity with a vertically pointed cloud radar. This method relates time-averaged Doppler velocities (which are used as a proxy for the reflectivity-weighted particle fall velocities) to particle characteristic sizes such as median or mean. With estimated profiles of particle characteristic size, profiles of cloud ice water content (IWC) are then calculated using reflectivity measurements. The method accounts for the intrinsic correlation between particle sizes and parameters of the fall velocity-size relations. It also accounts for changes of particle bulk density with size. The range of applicability of this method encompasses ice-phase clouds and also mixed-phase clouds that contain liquid drops, which are small compared to ice particles, so the radar signals are dominated by these larger particles. It is, however, limited to the observational situations without strong up-and downdrafts, so the residual of mean vertical air motions is small enough compared to the reflectivity-weighted cloud particle fall velocities. The Doppler-velocity reflectivity method was applied to the data obtained with an 8.6-mm wavelength radar when observing Arctic clouds. Typical retrieval uncertainties are about 35%-40% for particle characteristic size and 60%-70% for IWC, though in some cases IWC uncertainties can be as high as factor of 2 (i.e., 50%, 100%). Comparisons with in situ data for one observational case yielded 25% and 55% differences in retrieved and in situ estimates of characteristic size and IWC, respectively. The results of the microphysical retrievals obtained from the remote sensing method developed here were compared with data obtained from the multisensor technique that utilizes combined radar-IR radiometer measurements. For pure ice-phase layers unobstructed by liquid clouds (i.e., conditions where the multisensor approach is applicable), the relative standard deviations between the results of both remote sensing approaches were about 27% for mean particle size and 38% for IWC, with relative biases of only 5% and 20%, respectively.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it