In-Cloud ICing and Large-drop Experiment (ICICLE) Part 2: airborne measurements
Bibliographic record
Abstract
The In-Cloud ICing and Large-drop Experiment (ICICLE) campaign was a multi-platform and multi-sensor flight program organized and funded by the Federal Aviation Administration (FAA) and conducted by the National Research Council of Canada (NRC), Environment and Climate Change Canada (ECCC), National Center for Atmospheric Research (NCAR) and other partners. This effort was in support of the pressing need for provision of weather tools that enable safe air travel and support the avoidance of hazardous icing conditions, required by FAA regulations. As such, one of ICICLE’s main objectives included the collection of data that will be used to evaluate and improve diagnostic and forecast icing weather tools. Other objectives included identification of remote sensing signatures, characterization of cloud microphysical properties and aerosol-cloud interactions, as well as, evaluation of the sensitivity of the instruments to hydrometeor size and morphology. In this study, the investigation and documentation of spatial and temporal variability, intensity and types of hydrometeors (i.e. snow, freezing drizzle, drizzle, freezing rain, rain and ice pellets), and ice accretion onboard the NRC Convair-580 aircraft, in the period of late January – early March, 2019 was conducted. Mission flights, departing the aircraft operational base in Rockford, IL, totalled over 120 flight hours, covering 9 states and 1 Canadian province. This operational base was selected because of the proximity to areas that climatologically experience high frequencies of icing environments. NRC’s Convair-580 is a twin-engine, turbo prop aircraft with wing-mounted pylons equipped with a comprehensive array of sensors, courtesy of NRC and ECCC, for measurements of aircraft and atmospheric state parameters as well as commonly used aerosol and cloud-microphysics probes for measurements of small, sub-micron size aerosol particles up to large, centimeter size, precipitation. In order to minimize data loss due to the harsh operational conditions, several redundant probes were installed. In addition to the in-situ measurements, the aircraft was equipped with remote sensing systems that included triple-frequency radars (X, W and Ka), 355 nm lidars, and 183 GHz radiometer. An overview of the airborne measurements for ICICLE will be presented, along with an analysis of selected cases. Details of the icing conditions sampled will be provided, along with the spatial and temporal icing condition variability from a few cases. This airborne component of the research was funded by the FAA, NRC, and ECCC. The views expressed are those of the authors and do not necessarily represent the official policy of the organizations that funded the airborne campaign.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".