Aircraft Observations of Orographic Cloud and Precipitation Features over Southern Baffin Island, Nunavut, Canada
Bibliographic record
Abstract
This study evaluates cloud and precipitation features over the orography of southern Baffin Island in the southeast Canadian Arctic during the Storm Studies in the Arctic (STAR) field project in autumn 2007. Three case studies provide the basis for a comparative analysis of how cloud and precipitation features from upstream ocean regions are modified by the orography, in addition to the variability of these features over diverse synoptic and sea-ice conditions. Using data collected by a research aircraft with an onboard W-band Doppler radar and microphysical instrumentation, multiple factors were found to play roles in enhancing and/or reducing cloud and precipitation over the orography of the region. Gravity waves, terrain shape, atmospheric stability, and atmosphere–ocean exchanges were all associated with precipitation enhancement. In addition, several factors that reduce precipitation were identified, including sublimation, high sea-ice extent, and low-level blocking in the upstream environment. Accretion and aggregation were identified as important particle growth mechanisms over the orography. By increasing particle density and/or mass, the probability of ice particles precipitating to the surface increased. These results indicate that the complexity of these critical features over terrain in high-latitude regions poses considerable challenges for modelling.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".