Improved Airborne Hot-Wire Measurements of Ice Water Content in Clouds
Bibliographic record
Abstract
Abstract Airborne measurements of ice water content (IWC) in both ice and mixed-phase clouds remain one of the long-standing problems in experimental cloud physics. For nearly three decades, IWC has been measured with the help of the Nevzorov hot-wire total water content (TWC) sensor, which had an inverted cone shape. It was assumed that ice particles would be captured inside the cone and then completely melt and evaporate. However, wind tunnel experiments conducted with the help of high-speed video recordings showed that ice particles may bounce out of the TWC cone, resulting in the underestimation of the measured IWC. The TWC sensor was modified to improve the capture efficiency of ice particles. The modified sensor was mounted on the National Research Council (NRC) Convair-580 and its measurements in ice clouds were compared with the measurements of the original Nevzorov TWC sensor, a Droplet Measurement Technologies (DMT) counterflow virtual impactor (CVI), and IWC calculated from the particle size distribution measured by optical array probes (OAPs). Results indicated that the IWC measured by the modified TWC hot-wire sensor as well as the CVI and that deduced from the OAP size distributions agreed reasonably well when the maximum size of ice particles did not exceed 4 mm. However, IWC measured by the original TWC sensor was approximately 3 times lower than that measured by the other three techniques. This result can be used for the retrieval of the past IWC measurements obtained with this TWC sensor. For clouds with ice particles larger than 4 mm, the IWC measured by the modified TWC sensor and CVI exhibited diverging measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".