Recent cloud and aerosol observations from the NRC Convair-580 aircraft
Bibliographic record
Abstract
In the last decade, there has been significant progress in in-situ characterization of cloud structures, microphysical properties, and aerosol content, as a result of new instrumentation and focused field studies. However, the difficulties with data collection in adverse conditions and limited number of aircraft campaigns as well as monitoring stations have resulted in significant observational gaps and high measurement uncertainties. These issues are most evident in remote regions. This presentation focuses on recent field campaigns conducted using the National Research Council of Canada‘s (NRC) Convair-580 aircraft, instrumented jointly by NRC and Environment and Climate Change Canada (ECCC). This twin-engine aircraft with wing-mounted pylons, is equipped with an array of commonly used cloud microphysics probes and aerosol instruments to sample in-situ, at altitudes of up to 7 km. The sampling range is extended further from the aircraft with active and passive remote sensing systems (lidars, radars and radiometer), providing cloud and precipitation structures many kilometres away. In the past year alone, over 150 hours of cloud data were collected in diverse environments, including the arctic and mid-latitude weather systems, in a number of collaborative projects. We present selected observations (e.g. aerosol, hydrometeor, and water content) from arctic and mid latitude clouds, which are complemented with the airborne remote-sensing for spatial characterization of the clouds. Lastly, we discuss the common uncertainties in such measurements. This overall characterization allows us to gain a better understanding of atmospheric processes, which will allow improved weather forecasting and increase safety and cost-effectiveness of air transportation worldwide.
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 | 0.001 | 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 | 0.000 |
| 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".