Ice water content and precipitation rate as a function of equivalent radar reflectivity and temperature based on in situ observations
Bibliographic record
Abstract
Using ice particle spectra measured in stratiform ice clouds in midlatitude and Arctic regions, ice water content (IWC) and precipitation rate (Fm) retrieval algorithms as a function of temperature and radar reflectivity factor (Z) have been developed. These parameterizations were compared with (1) direct measurements of IWC using a Nevzorov probe, (2) precipitation retrieved using an X‐band Doppler scanning radar and a Precipitation Occurrence Sensor System (POSS), (3) the Canadian Global Environmental Multiscale (GEM) and High‐Resolution Model Application Project (HIMAP) models, and (4) derived IWC and precipitation from measured ice spectra during four field projects. The derived IWC and Fm from measured spectra have a correlation coefficient (r) better than 0.8. The IWC retrieved using the X‐band scanning radar during the First Alliance Icing Research Studies (AIRS I) agreed with IWC measured using the Nevzorov Probe much better than conventional IWC retrieval schemes. The retrieved precipitation rate based on the new algorithm using the POSS reflectivity measurements during the Second Alliance Icing Research Study (AIRS II) project agreed reasonably well with the precipitation rates predicted by the GEM and HIMAP models. This study clearly demonstrated that conventional IWC‐Ze relationships fail to replicate the IWC measured with Nevzorov probe. Furthermore, the Fm –Z relationship currently in use in the Canadian operational radar network appears to underestimate the ice precipitation rate. However, since there are some uncertainties in the direct measurements of IWC and no reliable direct measurements of precipitation, further studies are required to validate these parameterizations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".