Cloud climatology and microphysics at Eureka using synergetic radar/lidar measurements
Bibliographic record
Abstract
Despite their importance in Earth's radiation budget and atmospheric models, Arctic clouds remain poorly documented and understood. The deployment of a cloud radar and a high spectral resolution lidar at Eureka (80°N) in August 2005 offers a unique data set for the study of Arctic clouds. In this project, synergetic retrievals were developed and applied to two years of data in order to provide a first climatology of the clouds and their microphysics at this remote location. Results show an annual cycle in cloud coverage. They are mostly detected in the low levels or in single-layer, especially in winter due to a temperature inversion and cloud top radiative cooling. An analysis of the winds also demonstrated that different wind directions relate to different cloudiness conditions, while a strong channelling from the topography is present in the low levels. Moreover, liquid phase particles were detected all year round, with a minimum occurrence in winter due to colder temperatures. Turbulence and high relative humidity seem to maintain supercooled liquid, especially when ice crystals were also present. Precipitation was mostly identified during summer months, often in the form of virga, although falling snow might have been missed due to the difficulty to distinguish it from glaciated clouds. Finally, results show that satellite validation is possible using Eureka's data, but only under homogeneous conditions and when the instruments characteristics (like the sampling and sensitivity) are taken into account.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".