The Measured Relationship between Ice Water Content and Cloud Radar Reflectivity in Tropical Convective Clouds
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this paper, unprecedented bulk measurements of ice water content (IWC) up to approximately 5 g m −3 and 95-GHz radar reflectivities Z 95 are used to analyze the statistical relationship between these two quantities and its variability. The unique aspect of this study is that these IWC– Z 95 relationships do not use assumptions on cloud microphysics or backscattering calculations. IWCs greater than 2 g m −3 are also included for the first time in such an analysis, owing to improved bulk IWC probe technology and a flight program targeting high ice water content. Using a single IW– Z 95 relationship allows for the retrieval of IWC from radar reflectivities with less than 30% bias and 40%–70% rms difference. These errors can be reduced further, down to 10%–20% bias over the whole IWC range, using the temperature variability of this relationship. IWC errors largely increase for Z 95 > 16 dB Z , as a result of the distortion of the IWC– Z 95 relationship by non-Rayleigh scattering effects. A nonlinear relationship is proposed to reduce these errors down to 20% bias and 20%–35% rms differences. This nonlinear relationship also outperforms the temperature-dependent IWC– Z 95 relationship for convective profiles. The joint frequency distribution of IWC and temperature within and around deep tropical convective cores shows that at the −50° ± 5°C level, the cruise altitude of many commercial jet aircraft, IWCs greater than 1.5 g m −3 were found exclusively in convective profiles.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it