Performance of a compact elastic 355 nm airborne lidar in tropical and mid-latitude clouds
Bibliographic record
Abstract
In 2014 a new AECL (Airborne Elastic Cloud Lidar) lidar system was installed on-board the NRC Convair-580. AECL is a single wavelength elastic lidar which operates at 355nm and can supply vertical profiles of clouds and aerosols at high vertical and temporal resolution (1.5m and 0.05s). AECL is also equipped with a polarization channel and can provide information on particle phase (i.e. liquid or glaciated). The NRC AECL lidar was flown briefly on March 28, 2014 near Ottawa, Canada. In May of 2015 it was also deployed during the multi-week international HAIC (High Altitude Ice Crystals) – HIWC (High Ice Water Content) campaign near Cayenne, French Guinea. During the midlatitude flight near Ottawa, a convective cloud with cloud top extending to 4000 m was sampled by AECL. The on board in-situ cloud microphysics probes showed that the aircraft climbed through rain below 2 km reaching a mixedphase cloud above the melting layer and finally going through a supercooled layer. The lidar depolarization data from the AECL clearly identified the shallow supercooled layer near the cloud top and ice crystals with high depolarization ratio between the melting layer and the supercooled layer. In the regions of HIWC near Cayenne, the AECL laser beam was generally completely extinguished within the first 200 m. The lidar extinction coefficient, estimated using the Klett inversion technique and taken at 50 m above the aircraft showed a very good qualitative agreement with the measured in-situ extinction at flight level. The lidar extinction values had to be scaled by a factor of 5.88 to match the in-situ data. The discrepancies between the lidar estimated extinction and the direct measurements were explained, in part, by insufficient overlap correction and/or the error in the initial parameters used for the Klett inversion. In general, AECL showed promising initial results and in conjunction with other instrumentation, supplied valuable insight into the cloud optical and microphysical properties.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".